MétaCan
Menu
Back to cohort
Record W7143894265 · doi:10.20569/00003666

Develoment of Metal Supply Risk Assessment Method

2018· dissertation· en· W7143894265 on OpenAlexaboutno aff
李文華

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2018
Typedissertation
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)Profitability indexSustainabilityRisk assessmentRisk managementRisk governanceMarket riskSupply and demandWeighting

Abstract

fetched live from OpenAlex

Measuring the supply risk of metals is of great importance for corporations to control market risks, for nations to make strategical investments or trading plans, and for humanity to achieve sustainability in the long run. Academically, supply risk of metals was discussed in metals’criticality studies, where,“supply risk”represented one aspect of criticality together with“importance to economy”and“environmental implication”. In these studies,“supply risk”was mainly measured by the weighted average of a series of arbitrarily selected risk indicators. Due to the subjective selection of the risk indicators and invalid weighting methods used in the studies, the results of those studies are too ambiguous to obtain practical significance. In view of these shortages, this doctoral study is aimed at finding solutions to evaluate supply risk of metals at different periods, and thereby, help relevant stakeholders take informed decisions. The sources of supply risk of metals vary according to the time periods. In the short term (one year or lesser), market risk should be considered a priority. It could come from the price volatility of metal commodities, which dominates the profitability of a project. In the medium term (one to five years), risks related to international investments should be considered a priority. This is because no country can stand alone in terms of natural resources; countries heavily depend on each other. For foreign direct investments, institutional conditions of countries with resource sovereignty are the primary consideration, especially in an era where mining is increasingly concentrated in developing countries. In the long term (ten years or more), human society, as a community with a shared future, will have to face physical depletion of natural resources, since resources buried in the earth's crust are limited, and recycling rates had hardly reached 100%. Moreover, long before encountering the physical depletion of ores, increased prices of resources may compel some minerals unavailable economically, leading to economic depletion. This dissertation mainly contains three parallel but independent studies regarding three periods, and an aggregated assessment based on the results of each period's study. Specifically, in the short term, the objects are copper, nickel, zinc, lead, tin, silver, gold, platinum, and palladium. A new method called Spline-Generalized Autoregressive Conditional Heteroskedasticity was applied to generate the low frequency price volatility series from the original price volatility series. Using this low frequency price volatility, empirical evidence of the impact of macroeconomic variables on price volatility was confirmed. Also, the impact of world unemployment rate; inflation rate of the U.S. dollar; Treasury-EuroDollar spread; Standard & Poor's 500 index; residential property prices in the USA; and exchange rate of the South African rand, the Russian ruble, and the Canadian dollar to the U.S. dollar were found to be significant. Moreover, based on world economic performance in 2017, we found that the riskiest metal in 2018 would be copper with a volatility of 48%, followed by lead (36%) and silver (33%). In the medium term, the objects are the same nine metals as that studied in the short term. Considering the lack of a quantitative analysis on the common causes of resource nationalism due to data limitation, this study started with a data survey in which the occurrence of resource nationalism in 83 metal-supplying countries from 2000 to 2013 was summarized into a binary panel. Then, an empirical analysis was conducted using the binary choice logit method. As a result, several factors such as high technology export, ores and metals exports, rule of law, natural resource rent, and trade openness were found to be dominating the risk of resource nationalism in high-and-upper-middle-income countries. In lower-middle-and-low-income-countries, changes in mineral rent, government effectiveness, high technology export, and policy perception index were determined to be relevant. Finally, a prediction of the risk of resource nationalism of countries and commodities was made. In 2015, North Korea (100%), Panama (100%), Lao PDR (92%), Mongolia (87%), Kazakhstan (84%), Vietnam (81%), Cuba (78%), Guatemala (72%), Peru (71%), Iran (68%), Venezuela (68%), Papua New Guinea (67%), Russian Federation (63%), Chile (60%), Suriname (54%), Congo (DRC) (54%), and Sierra Leone (51%) were predicted to be risky. The top three risky metals were found to be copper (49%), tin (48%), and silver (46%). In the long term, to estimate the supply shortage of silver, technological progress in the crystalline silicone (c-Si) photovoltaic (PV) industry was investigated using scenarios including PV lifetime prolongation, technology shift, efficiency improvement, silver demand rate reduction, PV recycling, and total effects. Classic curve fittings and theories (logistic curve, Weibull distribution, intensity of use) were introduced to complete the task. Mining supply was estimated based on parent metal sources, such as copper, zinc, lead, gold and silver. Recycling supply was estimated using a product of silver weighted life-time and end-of-recycling-rate. Demand for silver was divided according to usage, including those for jewelry and silverware, electronics and batteries, photography, c-Si PV, and others. The result shows that silver supply shortage for manufacturing demand will occur from 2030. Technology improvements in the PV industry could delay when the shortage begins, but they will not prevent it. Silver supply shortage for c-Si PV could be eliminated under the total effects scenario. Finally, we aggregated the results of the three periods into four risk ratings: low, marginal, risky, and crucial. For price volatility, the market Volatility Index (VIX) published by the Chicago Board Options Exchange was used as the risk scale because this index represents implied volatility of the options market. For resource nationalism, Value at Risk was used to divide relative risk levels among countries. For supply shortage, historical supply deficit (real-time balance of supply and demand) was considered as a measurement. As a result, a route map of the supply risk of metals was produced. Taking silver as an example, the result shows that the supply risk of silver would reduce from risky to marginal but rebound to crucial in the long term.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.318
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInstitutional Repositories DataBase (IRDB)Same topicExtraction and Separation ProcessesFrench-language works237,207