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Comparative Analysis of Supply Risk Mitigation Strategies for Critical Byproduct Minerals – A Case Study of Tellurium Michele Bustamante, Gabrielle Gaustad, and Elisa Alonso Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03963 • Publication Date (Web): 07 Nov 2017 Downloaded from http://pubs.acs.org on November 12, 2017
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Environmental Science & Technology
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Comparative Analysis of Supply Risk Mitigation Strategies for Critical Byproduct
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Minerals – A Case Study of Tellurium
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Michele L. Bustamantea, Gabrielle Gaustadb,*, Elisa Alonsoc
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Dr. Michele Bustamante, Post-doctoral Associate aMaterials Systems Lab, Massachusetts
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Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
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Dr. Gabrielle Gaustad, Associate Professor bGolisano Institute for Sustainability, 190 Lomb
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Memorial Drive, Rochester Institute of Technology, Rochester, NY 14623 USA
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Dr. Elisa Alonso, Consultant cElisa Alonso LLC, Towson, MD 21022 USA
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*Corresponding Author:
[email protected]
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Abstract
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Materials criticality assessment is an analytical framework increasingly applied to identify
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materials of importance to stakeholders who face scarcity risks. Although criticality assessment
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studies highlight materials for the implicit purpose of informing future action, the aggregated
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nature of the studies’ results make them poorly suited for use as guidance in nuanced strategy
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development and implementation. As a first step in the selection of mitigation strategies, the
16
present work proposes a modeling framework and accompanying set of metrics to directly
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compare strategies by measuring effectiveness of risk reduction as a function of features of
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projected supply-demand balance over time. The work focuses on byproduct materials whose
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criticality is particularly important to understand because their supplies are less responsive to
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market balancing forces, i.e. price feedbacks. Tellurium, a byproduct of copper refining, critical
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to solar photovoltaics, is chosen as a case study and three commonly discussed byproduct-
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relevant strategies are selected: dematerialization of end-use product, byproduct yield
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improvement, and end-of-life recycling rate improvement. Results suggest that dematerialization
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will be nearly twice as effective at reducing supply risk as the next best option, yield
25
improvement. Finally, due to its infrequent current use and dependence on long product
26
lifespans, recycling end-of-life products is expected to be the least effective option, despite
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offering potentially other benefits (e.g. cost savings, environmental impact reduction).
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1.0 Introduction
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1.1 Critical Materials & Criticality
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Meeting the needs of a growing global population with limited shared resources is one of
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the most fundamental challenges of sustainability. Especially in cases where those resources are
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non-renewable, such as fossil fuels, metals, and other minerals, ensuring their continued supply
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at reasonable costs in the future may require intentional management of consumption over time.
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This is because rapid increase in demand, such as those resulting from technological paradigm
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shifts, or unexpected restrictions in supply, for example due to regional conflicts, natural
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disasters, or regulatory action, can lead to extreme price spikes and market disruptions, all of
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which can have damaging impacts on affected stakeholders. A number of historical examples
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have been studied showing the challenges that can be created for manufacturing firms and the
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national security concerns that can be raised when such conditions occur1–3.
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In order to better predict and prevent similar future supply shortages, several efforts have
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been made at the company4,5, regional6,7, and international8 levels to identify materials that are
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particularly vulnerable. These materials, often referred to as critical or strategic materials, are
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characterized as such because they are essential to key economic sectors but also have significant
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scarcity concerns. Metrics used in these studies include a wide variety of factors, such as limited
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natural abundance, international trade restrictions, recycling rates, national import reliance and
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per capita consumption. Commonly recognized critical materials include the rare earth
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elements6,9, particularly neodymium and dysprosium10,11, platinum group metals12,13, as well as
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indium and tellurium, particularly in the context of solar clean energy technologies9,14.
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1.2 Byproduct Mining: Obstacles to Market Balancing Feedbacks
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Conventional economic wisdom dictates that resource scarcity can be managed
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independently by inherent feedback mechanisms of the free market. That is, increased scarcity
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drives up price, which in turn may encourage use of less expensive substitutes by consumers,
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lowering demand. Simultaneously, higher prices also incentivize exploitation of previously sub-
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economic resources by producers, therefore reducing scarcity through increased supply. This
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mechanism is generally quite reliable for major metal markets, such as copper, iron, and
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aluminum, where new mines are prepared to open because they form relatively abundant ore
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deposits and are produced independently or as the main product of mining operations. However,
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the same cannot always be assumed for more minor metals, particularly those produced via
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byproduct mining15. 2 ACS Paragon Plus Environment
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Environmental Science & Technology
Byproduct mining is a general term for the supply of metals and minerals recovered
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predominantly as minor coproducts of major metal mining and refining operations. Byproduct
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minerals, like tellurium, selenium, indium, and gallium, are not found in adequately high
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concentrations to generate sufficient revenue to cover their full mining costs. Rather, their
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separation occurs as a natural consequence of the main product’s processing into a purer form.
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They do not command independent costs to mine from ore (only to refine further, which is
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typically performed by a separate agent) and instead are treated as a credit to mining costs,
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recovered in the quantities yielded naturally for sale to refiners. Despite the relatively limited
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attention paid to byproduct supply risk in the existing criticality literature, byproduct supply is
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quite common; one quarter of all naturally occurring elements are supplied over 50% from
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byproduct mining16,17.
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Because the quantity of byproducts are typically small compared to the main product,
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even large price increases in the byproduct market are seldom sufficient to justify the additional
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costs of processing more ore. Further, these materials are supplied via byproduct mining to
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begin with because they largely lack viable or economic alternatives for direct mining. This
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means that strong market signals, even sustained for a few years, may not be able to affect
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change from a supply perspective in the same way for byproduct metals as for major metals. As a
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result, it may become necessary to intervene or support changes in these markets to mitigate risk
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for highly critical byproduct materials if their continued use is considered desirable.
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Unfortunately, criticality assessment studies, while effective at communicating relative risk for
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different materials, are poorly suited to inform decision making regarding mitigation strategy
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selection to apply to those materials markets.
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1.3 Limitations to Criticality Assessment: Need for Supplemental Analyses to Respond
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Strategically
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Review of the criticality assessment literature reveals that results are often presented in a
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highly aggregated fashion. While this format is quite effective as a method of simple
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comparison among many materials, they provide little direct insight about how to resolve the
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risk being identified6,8,10,11. In many ways, this is a practical limitation to the scope of criticality
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studies, which already require significant time and effort to collect data for comparison across
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many materials; data that are often uncertain18. This limitation however can become 3 ACS Paragon Plus Environment
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problematic when there is no guidance for actors looking to interpret these results into
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meaningful action for reducing risk.
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Different agents, including policy makers and firm-level strategists, are faced with
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virtually endless potential approaches to resolve identified risk; some of these strategies are
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listed in Table 1 organized by key leverage area. Currently decision makers can only look to the
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risk indicator scores in published criticality studies, if available, and then try to compare them to
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one another to determine which lever to pull. However, if supply risk for a particular material is
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scored very highly because recycling rate is zero, byproduct portion of supply is 100%, and
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supplier geographical concentration is high, what should be done? Should decision-makers
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develop recycling technology or infrastructure? Should they reduce import reliance by levying
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tariffs to allow domestic suppliers to compete? Should they stop using the material altogether
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and develop novel substitutes? The best response is not immediately clear from the raw scores
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alone because it is not possible to directly compare the three disparate sources of risk driving this
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criticality. Each approach will have its own costs and benefits, so further analysis is needed to
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quantify these tradeoffs in pursuit of equitable comparison.
108 109
Table 1. Critical material risk mitigation strategies (non-comprehensive)19–21 Primary Supply
Secondary Supply
Demand Efficiency
General
trade, diplomacy
regulating collection and
government purchase
fundamental R&D
use of waste
quotas
development financing
import/export policies for
R&D for technologies and
government information
for mines
waste
innovation
sharing (e.g. USGS)
R&D in mining and
funding recycling
substitution
transparency regulations
extraction technology
infrastructure
(e.g. increase yield)
development
stockpile purchase
incentives to secondary
guarantees
processors
subsidize domestic
restrict landfilling
for firms in supply chain
dematerialization
reporting standards for trade (HS codes)
product lifetime extension
international collaboration
mining
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Additionally, the byproduct nature of critical materials is not often considered when
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evaluating mitigation response feasibility. For example, the potential effectiveness of
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byproducts or coproducts of the same restricted material system. Examples of this cited in
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previous literature include fellow copper byproduct selenium as a substitute for tellurium in solar
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cells22, fellow rare earth terbium as a substitute for dysprosium as a dopant in neodymium
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magnets23, and fellow platinum group metal palladium for platinum in autocatalysts24. Further,
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other common strategies applicable to major metals are often not equally applicable to
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byproducts. For example, existing mines’ production volumes cannot directly be increased in
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response to greater demand for the byproduct because of the additional costs cannot be justified
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in most cases. However, byproduct yield can be improved by refiners through the use of higher
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efficiency extraction processes in a somewhat analogous way, allowing for greater extraction of
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byproduct from what becomes available through mining wastes.
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The present work aims to expand upon the existing criticality literature to address
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challenges of evaluating strategies for supply risk mitigation of critical byproduct materials. The
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proposed framework draws upon previous work by the authors in byproduct supply and demand
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modeling25 to systematically evaluate and compare the effectiveness of different potential
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mitigation strategies using a small set of key metrics. This approach seeks to strike a balance
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between conflicting objectives, representing a mid-level modeling solution that is more
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informative than raw criticality results but much less complex and resource-intensive than
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detailed, agent-based or general equilibrium market models. Further, by grounding the analysis
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in dynamic material flow analysis, temporal aspects of different mitigation options’ strengths and
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weaknesses can be considered. Three commonly discussed firm-level strategies will be used to
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test the framework with the goal of identifying which response is most effective overall at
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reducing supply risk for case study material, tellurium. Tellurium is a byproduct of copper
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production considered to be critical to cadmium telluride solar photovoltaics.
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2.0 Methods To evaluate how supply risk can be reduced through different mitigation strategies, a set
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of scenarios is developed. First, a scenario representing the baseline supply and demand in the
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absence of intervention is created to establish risk associated with existing and expected future
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production and consumption trends. Then, additional scenarios representing alternate supply
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and/or demand expected to result from implementation of each mitigation strategy are created for
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comparison to the baseline as a basis for measuring supply risk reduction potential. Finally, a set 5 ACS Paragon Plus Environment
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of novel metrics are proposed to describe the different ways strategies may reduce risk using
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features of the modified supply and demand projections and how they change upon
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implementation. Benefits of different strategies relative to one another are then assessed using
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sensitivity of those change metrics to different levels of mitigation possible.
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2.1. Creating a baseline market scenario
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Independently derived models of future byproduct supply and demand are constructed
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using updated versions of dynamic material flow analysis methods previously described by the
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authors25. These methods use carrier metal production data to estimate byproduct supply in light
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of limited data availability and the crucial role of carrier metal supply-chain dynamics in driving
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byproduct availability. The authors were inspired to develop this method by emerging trends in
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copper production with negative implications for tellurium supply; i.e. increasing use of non-
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tellurium-yielding production techniques, i.e. hydrometallurgical solvent extraction and
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electrowinning (SX-EW) and steady use of secondary copper as a share of total production. For
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a more detailed description of this approach and its implications for tellurium, refer to the
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previous publication. A basic overview is provided in the following paragraphs with additional
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data and discussion of certain parameters included in the supplemental information files
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(Appendix A). Because approach of using carrier metal proxy data to estimate future supply is
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one that can be useful beyond this case study of tellurium production, generalized description of
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parameters is used when possible and then further clarified for its specific manifestation in this
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case.
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In this approach, primary supply of the byproduct is modeled as a function of its
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associated major carrier metal production rate, C [i.e. tonnes of copper], the share of total carrier
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metal production yielding the byproduct, α [i.e. % of copper produced via electrolytic refining
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rather than SX-EW or secondary] , byproduct ore grade associated with the carrier metal, x [i.e.
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kg tellurium available in ore relative to each tonne of copper produced], and byproduct yield per
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unit of major metal produced, γ [i.e. % of available tellurium ultimately extracted and refined].
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All of this is then offset by a factor, f, representing the share of total byproduct production
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associated with the predominant carrier metal as opposed to other means [i.e. tellurium produced
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from copper anode slimes as opposed to lead slag skimmings]. Each of these parameters may
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have dynamic character in reality, however, only total copper production, C, and tellurium6 ACS Paragon Plus Environment
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yielding share, α, are evolved over time in the base case scenario due to their crucial role and
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superior available knowledge of historical trends to project forward for these parameters.
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Alternate assumptions regarding these static values are tested for sensitivity and reported upon in
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the supplemental (Appendix B).
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For the case of tellurium, the predominant carrier metal is copper, estimated to supply
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over 90% of tellurium26. The byproduct-yielding share of copper production, α, refers to
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electrolytic refining of copper concentrates, which has been observed to be falling steadily as
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share of total copper production for several decades from over 80% before 1987 to below 65%
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by 201427. In the baseline scenario, it is assumed to continue to decline according to historically
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observed trends of about 0.4% share per year. This decline is driven by increasing use of
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hydrometallurgical (SX-EW) copper mining and recycling of copper scrap, neither of which
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offer meaningful potential for tellurium by-production28. Conversely, although yield is a
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parameter that may evolve in the future with direct implications for tellurium supply, in the
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baseline scenario it is assumed to be constant over time and remain at a conservative estimate of
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35% 29,30. Rather than evolve this parameter over time and assert how that happens within the
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base scenario, this fact became a major motivation for selecting yield improvement as a
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mitigation strategy for evaluation by the framework being developed. Total copper refinery
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production is assumed to continue growing at a historical rate of about 3% per year in the base
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case. Given rapid consumption in the face of declining ore grades, some recent studies suggest
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this may be an optimistic estimate beyond mid-century where primary mining may peak31,32. As
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a result, alternate growth rates are explored and presented in the supplemental (Appendix B). It
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is possible that this assumption is too strong, which would result in the underestimation of
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criticality risk for tellurium in the base case, however, in a major commodity market such as
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copper techno-optimists would argue there is potential for self-correction by miners beyond what
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is currently used to characterize these peak forecasts (e.g. scarcity drives prices up, high prices
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create incentive for more mines to open, development of better extraction technology, and
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increased scrap recovery, substitution). In fact, the relative weakness of these feedback
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mechanisms in byproduct material production systems is the reason they require the additional
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strategizing to manage criticality at the heart of this work.
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Secondary supply of the byproduct is also included in the supply model. The current approach focuses on old scrap from recycling of end-of-life PV since it is the most materially 7 ACS Paragon Plus Environment
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intensive tellurium-consuming end use product. Secondary supply from PV is modeled as a
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function of recycling rate, R [e.g. % of Te in CdTe PV panels in stock collected and recycled]
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and previous year’s demand, DPV(t-T) [e.g. kg Te in CdTe PV panel stock], which is assumed to
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become available according to a normally distributed product lifetime, where L is the average
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with standard deviation σ in years. Other distributions, such as Weibull, have been used
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elsewhere in the literature on solar PV lifetime but with limited empirical justification as to its
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applicability for CdTe PV specifically; for simplicity, a normal distribution is used to establish
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the base case as in Eq. 1. Alternate treatment using a Weibull distribution is included in the
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supplemental to test sensitivity to this assumption, but it was shown to have negligible impact on
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the overall conclusions (Appendix B). PV recycling rate is set to a low, but non-zero, initial
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value of 0.1% in the base scenario to represent the fact that there is currently no secondary
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supply of tellurium26 without introducing potential calculation errors when relative change in this
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parameter is used later to help describe risk reduction effectiveness. Secondary supply from
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non-PV sources, such as thermoelectric devices, could also be included (third term in numerator
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of Eq. 2), but in this base case, is assumed to be equal to zero for all time periods. Total supply
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is the sum of primary and secondary supply (Eq. 2).
223 t D (t −T) (T −L)2 EOL PV = ∑ PV exp 4σ 2 T=1 σ 2π
224 225
S(t) =
α(t)⋅γ(t)⋅x⋅C(t)
+
R(t)⋅EOLPV + R'(t)⋅EOLnon-PV f
(1) (2)
226 227
As the predominant and critical end-use of tellurium demand is modelled explicitly as a
228
function demand for cadmium telluride solar panels (CdTe PV). Demand from solar is predicted
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from external projections of future solar energy demand, estimation of CdTe’s share of the solar
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energy market, the material intensity of CdTe panels, and replacement needs as described in Eqs
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3 and 4. Annual demand for CdTe solar power is modelled using a logistic growth form to
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predict installed capacity (stock) because there is significant competition in the solar market and
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exponential growth like that observed may not continue for this particular technology (Eq 3).
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The eventual plateau in demand characteristic of these “S-shaped” functions is controlled
235
by parameter, K, set at 150 GW in the baseline scenario. Fitting parameters β0 and β1 are used to 8 ACS Paragon Plus Environment
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match the observed historical growth, approximated using installations reported by First
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Solar33,34, the major manufacturer or these panels, to reach the steady state demand plateau
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previously described by 2035. This base scenario assumes moderate levels of solar growth
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predicted by the US Energy Information Administration’s International Energy Outlook 2016
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under the reference scenario35, paired with an aggressive short-term outlook for the technology to
241
grow to represent up to one third of installed solar generation capacity by 2030. This assumption
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leads to 2020 sales of 8 GWp/y in line with the range of 10-36 GWp/y reported in the literature
243
for the entire thin film market (including CIGS and amorphous silicon PV)36 and cumulative
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installed CdTe capacity in line with a more modest share (5-10%) of the total solar market as
245
predicted by optimistic groups, like the International Renewable Energy Agency (IRENA) and
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the International Energy Agency’s Photovoltaic Power Systems Program (IEA-PVPS)37. The
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aggressive ramp up scenario evaluated here is followed by an assumed transition to a more
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diverse renewable energy future. CdTe sourced power demand eventually plateaus as other gen-
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II and gen-III PV technologies, like CIGS, multijunction III-V devices, dye-sensitized organic
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PV, and inorganic perovskites, currently in development and early stages of market penetration,
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begin to compete commercially and ramp up in market share as well. However, the base
252
scenario assumes some share of the market will remain best served by CdTe for its combination
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of moderately low levelized cost, strong high-temperature performance, and quick energy-
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payback time. Key candidates for this technology to persist include lower-income Subsaharan
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African and South Asian nations where high-temperature yield advantages are maximized and
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users would benefit from relatively low cost, yet mature technology38,39.”
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Material intensity of the panels [e.g. tellurium mass per unit power] is modelled as a
258
function of panel area, A, cadmium telluride active layer film thickness, and density, τ and ρ,
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tellurium mass fraction, n, and panel power rating, Π. Inherent within panel power rating is a
260
measure of panel efficiency; for the baseline scenario, 160 W/m2 rated panels are assumed,
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reflecting the average 16% efficiency for newly produced modules from leading CdTe
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manufacturer First Solar as of 201540,41. Finally, total demand for tellurium from PV is
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calculated considering extra capacity installation needed to replace end-of-life panels as in Eq 4.
264 265
StockPV (t) =
A τρnK Π1 + exp( −β0 −β1 t )
(3)
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DPV (t) = Stock(t) -Stock(t-1) + EOLPV (t) D(t) = DPV (t) + Dnon-PV (t)
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(4) (5)
268 269
However, because solar is not the only end-use of tellurium, estimation of total market
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balance for the commodity must include some estimation of non-PV demand as well (Eq 5).
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Other applications include thermo-electrics, ferrous, and non-ferrous alloying42, although it is
272
said that solar is the predominant use consuming 40% of tellurium demand43. Less is published
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about non-PV tellurium demand growth expectations, so this component was treated
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simplistically in this framework demonstration by assuming all other sectors grow
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proportionately to the solar sector. Although this approach represents a significant simplifying
276
assumption, more detailed modeling is beyond the scope of the current work and alternate
277
assumptions are tested in the Appendix for reference. Ideally, where available for other
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markets, similarly detailed approximations of non-critical application demand should be added to
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this formulation of total demand. In the present form, non-PV demand is represented using the
280
following approximation: Dnon-PV (t) = (1-η) η ⋅ DPV (t) , where η is share of tellurium demand from
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solar, fixed at 40% in the base case.
282
Note that market clearing price-related feedback mechanisms are intentionally excluded
283
from this framework because the goal is not to predict the actual future supply and demand, but
284
rather to frame a discussion of risk reduction in terms of deviation from the current path.
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Although this choice more than likely prevents the modeled supply and demand from reflecting
286
realistic future conditions, it allows for the communication of powerful insights regarding supply
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risk while also eliminating significant computational complexity and data collection
288
requirements necessary to fully model price dynamics. In the case of a byproduct material like
289
tellurium, this data collection and market modeling burden is doubled because both the carrier
290
metal market and byproduct market would need to be described, making the trade-off with this
291
approaches limitations more appealing than it might be otherwise for other more simply
292
structured commodity markets.
293 294 295 296
2.2. Measuring baseline supply risk Once baseline supply and demand scenarios are established, it becomes possible to identify features that communicate different information regarding risk; in this case, of potential 10 ACS Paragon Plus Environment
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market imbalance. Market imbalance is of concern at any point in time where demand outpaces
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supply. As a result, it becomes clearest to assess this risk by mapping the difference between
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demand and supply over time. When the market imbalance is positive, conditions for supply gap
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formation are in place and supply risk is high. When market imbalance is negative, supply risk
301
is low.
302
However, more detail is made available regarding supply risk than just whether or not
303
supply gap conditions emerge. Specifically, the following three features show promise: year at
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onset of supply gap conditions, degree of peak market imbalance, and duration of supply gap
305
condition persistence. Mathematical descriptions of each of these metrics is shown in Eqs 6-8.
306
Onset : t where S(t) = D(t) and
307
d D(t) - S(t) ) > 0 dt (
(6)
308
Imbalance: max {D(t)−S(t)} subject to t > Onset and D(t) − S(t) > 0
(7)
309
Duration : t − Onset where S(t) = D(t) and
d D(t) − S(t) ) < 0 dt (
(8)
t
310
Onset of supply gap conditions, or rather the number of years from model start until the onset of
311
supply gap conditions, is useful to characterize supply risk because it communicates the time
312
frame available for development of mitigation strategies (e.g. alternate supply resources or for
313
product innovations to reduce demand while still allowing demand to grow at present rate); the
314
sooner the onset, the faster the action is needed if maintaining the current course is desired.
315
Similarly, the peak market imbalance is useful to characterize supply risk because it can
316
communicate the quantitative deviation necessary to avoid supply gaps (e.g. additional supply
317
capacity needed to open or the amount of material that needs to be stockpiled for future use); the
318
greater the market imbalance, the larger the change in supply and/or demand needed to avoid gap
319
conditions. Finally, the duration of the supply gap condition is also useful to characterize supply
320
risk because it can communicate the degree of permanence needed from a solution (e.g. is a
321
temporary ramp up in marginal production sufficient, if possible, or is investment in developing
322
totally new direct mining resources required?) Together these metrics can communicate a great
323
deal of information directly useful for the development of mitigation strategies designed to
324
reduce supply risk.
325 326
2.3 Selecting Strategic Response Mechanisms 11 ACS Paragon Plus Environment
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Comprehensive analysis of all mitigation strategies outlined in Table 1 would be ideal to
328
produce a hierarchy of potential solutions in order of effectiveness, however this is outside the
329
scope of this work. Therefore, three mitigation strategies were selected to serve as commonly
330
discussed, illustrative examples suited for byproduct materials in this analysis: byproduct yield
331
improvement, dematerialization via film thickness reduction, and recycling collection rate
332
improvement. These represent one from each of the three key mitigation categories: primary
333
supply, secondary supply, and demand efficiency. Further, these approaches were strategically
334
selected to focus on responses available at the firm level and not requiring broader legislation or
335
diplomacy efforts that may be out of the purview of a company. In each instance, the associated
336
parameter values in the supply or demand models – yield, film thickness, and recycling rate,
337
respectively – are varied through a range dictated by the previous literature to create multiple
338
scenarios. A unique supply or demand scenario is generated for several degrees of improvement
339
for each strategy and used to determine risk reduction effectiveness as described in the following
340
section.
341 342
2.4. Measuring Risk Reduction Effectiveness
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Effectiveness of individual mitigation strategies is probed by, first, identifying the model
344
parameters which are affected by each approach, then using it to perturb the model in a way that
345
represents the effect these strategies would have on the system. The results are supply and/or
346
demand scenarios for the byproduct that differ from the baseline, creating shifts in the previously
347
identified risk metrics – (1) onset of supply gap conditions, (2) degree of market imbalance, and
348
(3) duration of supply gap – as well. Risk reduction efficacy of the strategies are then assessed
349
using three companion metrics: (1) delay of supply gap onset, (2) reduction in peak market
350
imbalance, and (3) reduction in supply gap duration; all calculated as percentage change of the
351
new scenario value from its baseline value. However, there are also at least 3 different ways to
352
describe effectiveness of each risk reduction strategy (equations listed in SI Table 2).
353
First, there is the maximum benefit, which is the largest reduction observable using the
354
strategy. This represents an upper bound on risk mitigation potential and can be communicated
355
as a raw result (e.g. x years delay of supply gap onset) or relative to the baseline risk level (e.g. y
356
more years delay of supply gap onset than in baseline scenario). Another potential measure is
357
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delay in supply gap onset, over the percentage change in the relevant model input parameter,
359
such as yield, from its baseline. This allows for comparison across the different risk reduction
360
strategies, each of which is based upon different parameters with different units and ranges.
361
Finally, the marginal benefit from implementing a strategy, can be calculated as the percentage
362
change in the risk reduction measure over the percentage change in the input parameter.
363
Marginal benefit results in a unitless measure, which allows for comparison between the risk
364
metrics for a given risk reduction strategy, which have different units, as well as across the
365
different strategies. Ultimately this enables the calculation of a single measure of risk reduction
366
effectiveness for each strategy that encompasses the impact to each of the three risk metrics.
367 368
3.0 Results & Discussion
369
3.1. Updated Baseline
370
Figure 1 shows the baseline scenario against which all strategic response scenarios will
371
be compared. Onset of supply gap conditions is expected in 2020 with imbalance peaking at 999
372
tonnes in 2024 before declining after total of 8 years. The periodic form observed for the market
373
imbalance is caused by the interaction of a plateauing logistic PV stock, lagging replacement
374
needs, and relatively slow-growing supply potential (see Appendix Fig S1 for underlying supply
375
and demand series). It is important to note that this result is not intended to be a prediction of
376
actual future market conditions because it lacks key market clearing feedback mechanisms.
377
However, it serves as an effective basis for this analysis because it communicates the level of
378
risk implied by the current path of supply and demand behavior, which is valuable to know for
379
those looking to reduce risk and understand the relative difficulties of doing so.
380
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381 382
Figure 1. Baseline tellurium market balance scenario showing onset of supply gap conditions,
383
peak market imbalance, and gap duration.
384 385 386
3.2. Dematerialization Dematerialization was modeled as a reduction of the thin film layer of CdTe. In reality,
387
efficiency improvement may also play a large role. The baseline scenario assumes layer
388
thickness of 3 microns, but here a range from 5 microns to 0.67 microns is considered for
389
potential scenarios. Although it is traditionally considered that 1 micron is the limitation of film
390
thickness without deterioration of PV cell electrical performance, reduction to 0.67 micron was
391
proposed as a goal for supply risk reduction44. Figure 2 shows market balance scenarios
392
representing the full range of film thicknesses found in the literature as compared to the baseline
393
in black. Interestingly, the results indicate that for film thicknesses less than 2 micron, supply
394
gap conditions are prevented from emerging altogether, even in the face of slowing growth of
395
electrolytic copper production and corresponding tellurium supply. This possibly provides
396
further motivation to push toward what has traditionally been considered the limitation of film
397
thickness reduction. Further, note that the dematerialization scenarios diverge during years with
398
growing demand but converge again during years with shrinking demand. This is due to the fact
399
that demand is directly proportional the film thickness.
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401 402
Figure 2. Market scenarios for CdTe PV dematerialization as an approach to tellurium risk
403
reduction.
404 405
To quantify risk reduction potential, impact of changing strategy relevant input
406
parameters – in this case film thickness – on risk metrics must be calculated. Figure 3a shows
407
results for delay of supply gap onset relative to the baseline scenario of 3 micron film thickness,
408
i.e. how effective dematerialization would be to delaying supply issues. To the right, where there
409
is higher film thickness, supply gap is projected to emerge earlier, as soon as 2018 for fleet
410
average film thickness of 5 microns and 2019 for thickness of 4 microns. When film thickness is
411
reduced below 3 microns assumed in the baseline, it continues a through a linear period of
412
improvement before approaching infinity below 2 micron thickness. Therefore, if such film
413
thickness reduction can be achieved without tradeoffs due to efficiency loss, even aggressive PV
414
growth like what has been modeled in the baseline scenario can proceed without inducing supply
415
gap conditions at all.
416
Next, the reduction in peak market imbalance is shown in Figure 3b. A maximum
417
decrease in peak market imbalance of about 1700 tonnes can be achieved, which is actually 75%
418
larger than the baseline imbalance value. This is because in the film thickness scenarios below 2
419
microns, the entire baseline imbalance is eliminated and a supply surplus is projected. By
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420
contrast with gap onset delay, market imbalance reduction maintains a linear form throughout the
421
entire range of scenarios.
422
Finally, reduction in gap duration is shown in Figure 3c. Results show a slightly less
423
linear trend than for reduction of market imbalance due to this metric’s greater dependence upon
424
the non-linear shape of the entire market balance curve, creating reductions in the gap from both
425
directions through later onset and swifter recovery. A maximum reduction in gap duration of 8
426
years is achievable, which corresponds to the length of the entire baseline gap, again because of
427
the complete avoidance of gap conditions below 2 micron film thickness. The difference in
428
linearity of effects underscores the need to examine a range of risk metrics.
429
430
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431 432
433 434
Figure 3. (a) Effectiveness of dematerialization as a means to delay supply gap onset. (b)
435
Effectiveness of dematerialization to reduce peak market imbalance. (c) Effectiveness of
436
dematerialization to reduce duration of supply gap conditions.
437 438
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Similarly, yield improvement was modeled by creating scenarios that represent a
440
reasonable range of values for fraction of tellurium that gets recovered from initial content in
441
copper ore. Because this definition of yield is broader than the efficiency of a single extraction
442
process, it is not reasonable to expect near perfect recovery could be possible. The concept here
443
is intended to capture losses across all stages of mining, refining, and byproduct extraction, and
444
the carrier metal processing that precedes tellurium extraction from the anode slime is not
445
optimized to reduce tellurium losses. A reasonable range of 35 to 80% discussed in previous
446
literature45 was ultimately selected (see Appendix A4 for more detail) (Figure 4).
447
Unlike how, in the dematerialization scenario set, the alternate film thickness cases scaled
448
proportionally in all time periods, yield improvement benefits amplify over time because this
449
parameter scales with growing supply and downward trending periodic demand. Notably, if
450
overall yield of 55% or above can be achieved as is sometimes reported in the literature, supply
451
gap conditions can be avoided by this technique as well and can lead to even more favorable
452
availability conditions, where relatively more excess supply capacity is expected.
453
454 455
Figure 4. Effectiveness of byproduct yield improvement for reducing supply risk, showing all
456
scenarios considered.
457 458
3.4. Recycling Collection Rate Improvement
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Finally, the introduction of mandates or at least goals for recycling rate of solar panels are
460
modeled (Figure 5). Because CdTe solar panels represent the highest tellurium density end-use
461
product for recovery, and are already modeled to calculate demand, the recycling program is
462
intended to apply to only this application. Currently, Austria, as part of broader e-waste
463
legislation, has set goals for future solar cell recycling rates between 70-80% and making it the
464
producers’ responsibility to collect and bear the costs of recycling46. First Solar also has a
465
recycling program in place that currently recycles the company’s fabrication scrap. However,
466
for the baseline, a near zero global average recycling rate of 0.1% is assumed to reflect the
467
current reality that most panels are still in their useful life and currently appear to lack a take-
468
back infrastructure47. The range for scenarios was developed all the way through 100% to reflect
469
a full recycling mandate; in reality, some losses in collection and material recovery are
470
inevitable.
471
The most striking difference between the recycling scenario evolution and the previous
472
strategies discussed is the lack of any benefit to delay and duration of gap onset. This is due to
473
the early emergence of the gap conditions relative to the long product lifetime before secondary
474
material becomes available. End-of-life recycling does impact the degree of market imbalance
475
expected; although in a very minor way, reducing the measure by a maximum of 1 tonne as
476
compared to several thousand by the other approaches reviewed. These are important results
477
because, for as much discussion as surrounds recycling of PV, the approach will have little value
478
in the short to mid-term future in terms of stabilizing tellurium supply risk. However, greater
479
impact could be expected through utilization of prompt scrap recycling from PV manufacturing
480
inefficiencies because this would have a much shorter residence time before it could be used to
481
supplement virgin supply.
482
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483 484
Figure 5. Effectiveness of CdTe recycling rate for reducing supply risk for tellurium, showing all
485
scenarios considered.
486 487 488
3.5. Comparing Strategies A key goal of performing this assessment is to compare supply risk mitigation strategies
489
to one another utilizing the metrics proposed. However, it is necessary first to determine, if,
490
how, and why the choice of evaluation metric matters to decision making. For example, does use
491
of average benefit lead to the different conclusions about strategy effectiveness as marginal
492
benefit does? In this case the answer is no as indicated by the consistent ranking of
493
dematerialization over yield improvement over recycling rate improvement regardless of risk
494
metric using each effectiveness measurement approach, as shown in Figure 6. However, it is
495
important to emphasize that despite having the same units, results across risk metric categories in
496
terms of average benefit cannot be compared due to their differing baseline and/or units, so the
497
normalized marginal benefit metric should be used if comparison is the goal.
498
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499 500
Figure 6. Comparison of results across the proposed measures of effectiveness: a) maximum
501
benefit, b) average benefit, and c) marginal benefit (graph), including detail on each contributing
502
risk reduction factor (column cluster) and mitigation strategy (column color).
503 504
An additional interesting result is the ability to generate a single measure of risk
505
reduction effectiveness for each mitigation strategy by summing the normalized metrics (Figure
506
7). The results confirm the greater effectiveness of dematerialization under current conditions,
507
followed by yield improvement, and then recycling. Further, they communicate in a single
508
measure of how much better it is in terms of supply risk reduction potential: in this case,
509
dematerialization provides on average 65% greater benefit than yield improvement for the same
510
relative degree of mitigation parameter change. These results provide a foundation for strategic
511
stakeholders to determine where they will get the greatest marginal benefit in terms of supply
512
risk reduction, suggesting which interventions could be most effective.
513
Despite already suggesting the greatest effectiveness among the strategies considered in
514
this study, the effect of dematerialization and yield improvement on supply risk reduction are
515
both still likely understated using the single score approach alone. They not only delay onset of
516
supply gap conditions and reduce gap duration but completely eliminate it in several of their
517
alternate value cases considered. Marginal benefit is calculated excluding points corresponding
518
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519
this. Instead, a comparison of the maximum benefit metric to the baseline risk metrics could be
520
used to reveal this advantage (Figure 6a); however, this would suggest no difference between
521
dematerialization and yield.
522
523 524 525
Figure 7. Total risk reduction potential (measured as marginal benefit) for each of the mitigation
526
strategies: dematerialization, yield improvement, and recycling.
527 528 529
4.0 Discussion This work presents an approach to quantitatively evaluate potential effectiveness of
530
supply risk mitigation strategies informed by previously published scenario modeling techniques
531
[25]. The approach proposes three metrics to measure risk, three metrics to measure risk
532
reduction, and three metrics to measure effectiveness of risk reduction by each strategy from
533
comparison of possible supply-demand imbalance scenarios. This method is one step toward a
534
better design of specific strategies in response to criticality assessment by focusing on
535
parameters that can be leveraged by firms and broader industries. Because the present approach
536
requires relatively in-depth, dynamic modeling of individual materials’ supply chains, it is not
537
recommended that this method be incorporated directly into traditional criticality assessment
538
frameworks. Rather, it could be thought of as a supplemental tool for use once the most critical
539
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540
goal is to standardize the ways in which decisions are made regarding criticality mitigation by
541
creating a tractable, quantitative basis for comparison between different mitigation strategies.
542
In the case of tellurium, the results consistently present a strong case for pursuing both
543
dematerialization and yield improvement strategies because each is shown to be able to prevent
544
gap conditions from emerging, suggesting significant lowering of supply risk for the otherwise
545
short-term highly critical material. However, results suggest that incremental progress toward
546
dematerialization (via film thickness reduction) might be significantly more effective than the
547
same degree of incremental improvement in yield: about 65% more so as indicated by comparing
548
the marginal benefit scores for these techniques. Finally, despite much research on recycling as a
549
solution to critical materials supply, in the case of materials like tellurium where gap short-term
550
criticality is the key problem and the major application has a long product lifetime with no
551
existing recycling infrastructure, the benefits of end of life recycling is not expected to be
552
effective at preventing or mitigating supply gap conditions.
553
The model presented here has several limitations. A strategy’s effectiveness is logically
554
quite sensitive to when it is implemented, but the current approach does not assume a slow
555
dynamic transition, rather a discrete jump to the alternate scenarios beginning in a particular year
556
(model year 1 for the baseline). This may not necessarily be the most realistic scenario but it can
557
be thought to set expectations and creates goals by presenting case an upper bound of
558
effectiveness via an immediate change in the system as compared to a longer-term phase in
559
solution. Several other simplifying assumptions exist in the current approach to modeling supply
560
and the demand. For example, in the specific case study assessed, tellurium demand is assumed
561
to increase at the same rate for other end-uses as for PV and similarly supply is assumed to grow
562
at the same rate for non-copper byproduction streams of tellurium as for copper. Some
563
assumptions skew the presented analysis in a conservative manner while others may be overly
564
optimistic. A less optimistic total copper production growth rate and a higher CdTe PV adoption
565
rate (both of which may be likely) will exacerbate the supply issue studied here, whereas
566
assuming a continuing gradual shift toward non-tellurium-yielding SX-EW copper production as
567
share of total and the continuation of rapid adoption of CdTe PV would alleviate baseline
568
risk. These assumptions could be adjusted if more detailed data is obtained. However, in the
569
interest of keeping data collection burden moderately low, sensitivity of the case study findings
570
to alternate assumptions are explored instead in the supplemental (Appendix B). The authors 23 ACS Paragon Plus Environment
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571
believe the results represent a reasonable basis for demonstration of the proposed mitigation
572
strategy evaluation methods and provide heightened level of insights into key levers for the
573
tellurium market to respond to criticality risk.
574
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Whether at the firm level or national level, translating information into action, even at the
575
level presented here, will still be challenging. This framework is intended to inform one
576
component of a more complex, multifaceted evaluation of candidate strategies’ benefits and
577
weaknesses likely to be necessary for institutional decision-making. For example, future work
578
could include additional analysis to evaluate complex tradeoffs in terms of other practical
579
considerations for each technique (e.g. marginal cost of improvement, environmental benefit of
580
improvement). Overall, the exploration of multiple effectiveness indicators for a diverse
581
sampling of mitigation strategies should serve as a reminder that there will be no universal
582
solution for all materials nor for a given materials critical applications. Instead, optimal results
583
should be pursued through targeted and temporally-relevant strategic development. As such,
584
these findings on strategy effectiveness should be regarded as specific to the tellurium market
585
and not a general conclusion on their merit for other commodity systems.
586 587
Acknowledgements
588
This work has been funded by the National Science Foundation (NSF) (CBET #1454166), the
589
Golisano Institute for Sustainability (GIS), and Rochester Institute of Technology (RIT). Special
590
thanks to Dr. Callie Babbitt for her helpful feedback and guidance in developing this work.
591 592
Supporting Information
593
The supporting information contains: A1) additional literature review, A2) model parameters,
594
A3) risk reduction metrics, A4) mitigation strategy parameters, B1) baseline sensitivity analysis,
595
and B2) alternate risk reduction sensitivity analysis.
596 597
TOC Art
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