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Enregistrement W7143894265 · doi:10.20569/00003666

Develoment of Metal Supply Risk Assessment Method

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

Notice bibliographique

RevueInstitutional Repositories DataBase (IRDB) · 2018
Typedissertation
Langueen
DomaineEngineering
ThématiqueExtraction and Separation Processes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVolatility (finance)Profitability indexSustainabilityRisk assessmentRisk managementRisk governanceMarket riskSupply and demandWeighting

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,465
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,318
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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