Source risks as constraints to future metal supply | Environmental

Subscriber access provided by Nottingham Trent University

Policy Analysis

Source risks as constraints to future metal supply Eleonore Lebre, John R. Owen, Glen D Corder, Deanna Kemp, Martin Stringer, and Rick K Valenta Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b02808 • Publication Date (Web): 21 Aug 2019 Downloaded from pubs.acs.org on August 27, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 31

Environmental Science & Technology

Manuscript: Source risks as constraints to future metal supply Authors: Éléonore Lèbre* Postdoctoral Research Fellow, Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland, QLD 4072, Australia. Email: [email protected] John R. Owen Professor, Deputy Director, Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland, QLD 4072, Australia. Glen D. Corder Associate Professor, Acting Director, Centre for Mined-Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, QLD 4072, Australia. Deanna Kemp Professor, Director, Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland, QLD 4072, Australia. Martin Stringer Postdoctoral Research Fellow, Dow Centre for Sustainable Engineering Innovation, School of Chemical Engineering, The University of Queensland, QLD 4072, Australia. Rick K. Valenta Professor, Director, W.H.Bryan Mining & Geology Research Centre, Sustainable Minerals Institute, The University of Queensland, QLD 4072, Australia.

1 ACS Paragon Plus Environment


TOC art


Page 2 of 31

Page 3 of 31


1

Rising consumer demand is driving concerns around the ‘availability’ and ‘criticality’ of

2

metals. Methodologies have emerged to assess the risks related to global metal supply. None

3

have specifically examined the initial supply source – the mine site where primary ore is

4

extracted. Environmental, social and governance (“ESG”) risks are critical to the

5

development of new mining projects and the conversion of resources to mine production. In

6

this paper, we offer a methodology that assesses the inherent complexities surrounding

7

extractives projects. It includes 8 ESG risk categories that overlay the locations of

8

undeveloped iron, copper and aluminium orebodies that will be critical to future supply. The

9

percentage of global reserves and resources that are located in complex ESG contexts (i.e.

10

with four or more concurrent medium-to-high risks) is 47% for iron, 63% for copper, and

11

88% for aluminium. This work contributes to research by providing a more complete

12

understanding of source level constraints and risks to supply.

13

Introduction

14

Human development, as an objective, involves enhancing people's freedoms and

15

opportunities, and improving their well-being. This objective relies on the viability of the

16

education, health care, telecommunications, agriculture, transportation, construction, water

17

and energy sectors. Technology is a fundamental enabler across these sectors, and requires

18

metals for manufacture or application. As technologies advance, the number of metals in use

19

has increased to 60 out of 91 known metals.1 Future demand for the most widely used metals

20

– iron, aluminium, manganese, copper, zinc, lead and nickel – is predicted to at least double,

21

and possibly triple, by mid-century1,84 with a potential eightfold increase in aluminium

22

demand.2-4 A doubling or tripling of demand is likewise anticipated for speciality metals such

23

as lithium, rhenium and some rare earths.2 Two concurrent drivers for this demand include

24

the continued increase in global population and human development measured in per-capita 3 ACS Paragon Plus Environment


25

wealth.5,84 A third driver is the rise in metal demand to support the decarbonisation of

26

economies to mitigate climate change. Renewable energy generation, transmission and

27

storage systems have considerably higher metal requirements on a per kWh basis than their

28

fossil fuels counterparts.6, 7

29 30

Such radical increases in demand can only be satisfied if there is sufficient global supply of

31

the appropriate metals. Presently, these metals are primarily sourced from mining, as

32

recycling can only supply a fraction of the demand in the foreseeable future.8 Even for steel

33

and aluminium, which have substantial recycling programs in place, predictive modelling

34

indicates that the majority of these metals will be from primary sources for at least another 30

35

years.9, 85

36 37

Several publications, including Vidal et al.,10 Kleijn et al.,11 Graedel et al.,12 Northey et al.13

38

and the reports of the International Panel on Climate Change (IPCC),14 acknowledge

39

potential material constraints in the global transition to renewable energy sources.

40

Methodologies are emerging to assess the supply risk of metals across the value chain,

41

according to reviews by Northey et al.,15 Achzet et al.16 and Erdmann and Graedel.17 The

42

methodology on metal “criticality” developed by Graedel et al.18 includes 16 macro-level

43

indicators that aggregate either national or global supply chain data, and 3 types of users –

44

global analysts, national governments and corporations. None of the abovementioned

45

methodologies have conducted a detailed examination of the initial supply “source” – the

46

mine site where primary ore is extracted.

47 48

Mines are the gateway through which metals enter the economy. There is a wide range of

49

geological, technological, economic, political, social and environmental factors that can


Page 4 of 31

Page 5 of 31


50

constrain the development of new mining projects. Recent research indicates that supply risk

51

assessment should extend to the source of supply, and include factors that influence the

52

production of metals from mineral resources.19 In recent years, supply chain standards and

53

certification schemes have sought to include risks at the source of extraction.20, 21

54

Environmental, social and governance (“ESG”) risks are increasingly acknowledged by

55

investors as factors that are material to the development of new mining projects and the

56

extraction of metallic minerals.22-24 Management of ESG risks is key for mineral resource

57

rich nations that seek to transform their natural capital into economic growth and human

58

development.86 We argue that ESG risks are becoming more pertinent in assessing the

59

inherent complexities of extractive projects and the extent to which supply might be

60

constrained as a result.

61 62

In this paper, we propose a methodology that assesses ESG risks at the source of supply. This

63

methodology is applied to a large sample of “undeveloped orebodies” and associated mining

64

projects in pre-production phase (i.e. projects for which a resource has been defined, but

65

which have not yet been fully permitted and moved to construction phase). The results

66

characterise the possible future of global metals supply based on a representative sample of

67

the world’s largest undeveloped copper, iron and aluminium orebodies. These may come into

68

production in the near future, or be held up and remain unexploited for decades to come.

69

Copper, iron and aluminium have the widest application among known metals. This work

70

contributes to research on criticality and supply risk assessment, and has major implications

71

for the mining industry and mineral resource rich countries.

72 73

Our methodology builds on an initial framework for analysing the co-occurrence of ESG

74

risks in undeveloped copper orebodies (Valenta et al., ref 19). Valenta et al. conclude that the



75

presence of multiple concurrent technical and ESG risks in the vast majority of the world’s

76

300 largest undeveloped copper orebodies has the potential to restrict global supply. We

77

advance this initial framework by: improving the ESG risk categories; overlaying these

78

categories to the locations of future mines; and extending the commodities of interest. This

79

approach characterises the local context of future mines, and quantifies the risks at the source

80

of metal supply chains.

81 82

In the next section, we provide an overview of issues related to supply risk, and situate our

83

approach within the literature. We then present our methodology, including the selection and

84

definition of eight ESG risk categories comprising eleven spatial variables. In the following

85

section, we apply our methodology. Finally, we discuss the current and future implications of

86

the results of our work for the global mining industry.

87

88 89

Research context: gaps in the availability and criticality literature

90 91

Geological availability. The literature on metal supply risk ranges from the issue of

92

“availability”, including “geological availability” and “accessibility”, to the complex multi-

93

factor definition of “criticality”.25-27 These issues are reviewed in turn. The literature on

94

geological availability analyses the data on global mineral resources provided by geological

95

surveys for geographical distribution, grade and tonnage estimates. Copper is commonly

96

singled out as the main metal of concern (e.g. ref 13), alongside zinc, lead (e.g. ref 28) and

97

silver (e.g. ref 29). Concerns around geological availability are based on the finite nature of

98

mineral deposits from which metals are produced, and primarily arise from the observed

99

global decline in ore grades.15 In other words, the increasing scarcity of high grade, easily 6 ACS Paragon Plus Environment

Page 6 of 31

Page 7 of 31


100

accessible deposits implies new mines will be larger, deeper and more complex. The

101

technical and economic challenges associated with accessing and extracting future mineral

102

resources could constrain global supply.

103 104

Accessibility. Concerns about availability are often inclusive of non-geological

105

considerations around the issue of access.13, 25, 30-32 Arndt et al.33 and Mudd and Jowitt34 argue

106

that resource depletion is overstated because reporting codes represent conservative estimates

107

of available resources. Such estimates are based on economic considerations and are bound to

108

evolve as metal prices and available technologies influence which portion of the orebody is

109

considered to be extractable at a profit. Declining ore grades raise technical and economic

110

challenges that can be and have been addressed through technological innovation. Greater

111

project footprint area, larger material movements, greater quantities of waste rock, and

112

increased water and energy requirements - all consequences of lower grades - can partially be

113

offset through enhanced selectivity, e.g. underground block caving, ore sorting or in-situ

114

leaching. ESG factors, however, are not easily overcome by technological innovation, can

115

restrict access to the orebody, and affect the longer term feasibility of mineral extraction.15, 19,

116

25

117

factors remain an unresolved gap for researchers conducting assessments on metal

118

availability,15 as well for asset managers undertaking due diligence for the acquisition of

119

mining properties.24

ESG factors tend to accumulate, and are exacerbated by geological scarcity. Local ESG

120 121

Criticality. The work on metal criticality considers global commodity markets and assesses

122

supply risk and its implications. While availability focuses on mineral resources, the

123

criticality approach extends to the resource supply chain, covering factors that represent

124

macro-scale supply and demand dynamics. Graedel et al’s.18 methodology uses a three-



125

dimensional definition of metal criticality, consisting of “supply risk” (i.e. the likelihood of

126

supply disruption), “vulnerability to supply restrictions” (i.e. the severity of the consequences

127

of a disruption for societal needs) and the “environmental implications” (i.e. the

128

environmental impacts embedded in metal supply chains). The supply risk dimension

129

includes several geological, technological, economic, social and political factors that echo the

130

literature on availability, and acknowledge the relevance of ESG risks. Numerous other

131

methodologies propose a wide variety of factors to include in the assessment of metal

132

criticality.16, 35

133 134

Erdmann and Graedel17 and Hatayama and Tahara36 warn that methodological choices

135

significantly influence the results of criticality assessments. For instance, criticality

136

methodologies issued by governments (e.g. the European Union and the United States) focus

137

on identifying the materials that are crucial to national or regional development and

138

emphasize security of supply.37, 38 Graedel et al.18 base their approach on corporations and

139

nations that utilise metals (both manufacturers and consumers). Studies that apply criticality

140

methodologies tend to identify solutions based on risks identified on the user side of the

141

supply chain, e.g. the dematerialisation of consumption or the reduction of dissipative uses.39,

142

40

143

these impacts are not localised, as they are generated throughout the supply chain. By design,

144

criticality methodologies do not consider source factors that affect mineral resource

145

extraction, as do availability studies.

Graedel’s methodology captures the environmental impacts of supplying metals, however,

146 147

Source level assessment of risk. Both availability and criticality literatures note the

148

importance of ESG factors in supply risk. The availability literature acknowledges the ability

149

of local ESG risks to constrain mining development.15 Valenta et al.19 represent the first


Page 8 of 31

Page 9 of 31


150

commodity-scale attempt to characterise these risks. As the source of supply, metal mines are

151

subject to local risks that differ from the supply risks defined in criticality methodologies.

152

The extensive work dedicated to understanding the risks for users of metal requires parallel

153

work that characterises the risk for source countries, regions, corporations, and project sites.

154

This parallel work is important considering the expected reliance on primary mining to

155

supply future demand for metals. For this, we return to availability and accessibility,

156

positioned at the source of supply, and expand these notions to encompass the ESG context.

157 158

The following section presents our methodology. It is applicable to a global sample of mines,

159

and relies on precise spatial coordinates to provide a geographically localised assessment.

160

Our methodology provides a global overview of “source risk”.

161 162

Methodology: Source risk and ESG risks

163 164

As the first link in metal supply chains, mines are influenced by global metal demand and

165

market prices, which incentivise new mine development41 and closures.42 At the same time,

166

mines are influenced by local factors that do not depend exclusively on macro-economic

167

dynamics. Local factors are the focus of our methodology, constituting the context – the

168

source risk – in which mines develop and operate. How miners respond to these factors, is not

169

encompassed by our methodology. The industry’s conceptualisation and engagement with

170

risk in different operating contexts influences whether these risks are exacerbated or reduced.

171 172

The local context of mining development is characterised by a range of ESG risks. In the

173

private sector, these risks are defined by the UNEP- Finance Initiative and the World



174

Business Council for Sustainable Development.24 Previously considered to be “externalities”

175

not well captured by market mechanisms, ESG risks are now being viewed as financially

176

material.43 The investor community is increasingly aware of the financial consequences of the

177

mining industry’s ESG failures.19, 23 Numerous mining projects have been stalled or

178

abandoned due to materialised ESG risks. The Pebble project in Alaska,44 Reko Diq in

179

Pakistan,45 or the Benga project in Mozambique,46 are examples, amongst others. Franks et

180

al.47 reported that 15 out of a sample of 50 mine-community conflicts resulted in project

181

suspension or abandonment, and that the majority of abandonment cases occurred during the

182

early stages of development, prior to construction or production.

183 184

Our framework for the assessment of source-based risk is presented in the figure below. It

185

overlays two types of spatial data, a selection of public indexes, variables representing

186

particular ESG risks categories, and the subscription based S&P Global Market Intelligence

187

database (herein the “S&P database”), the latter of which provides source information about

188

the size and spatial location of orebodies.59 By overlaying ESG risks at specific source

189

locations, we can determine the presence of concurrent ESG risk categories across the sample

190

of undeveloped orebodies. For more details on data collection and risk calculation, refer to

191

the Supporting Information SI-1 and SI-2.

192 193


Page 10 of 31

Page 11 of 31


194 195 196 197

Figure 1: Methodological framework – Spatial coincidence between the set of ESG risk categories and the orebodies sample

198

Our methodology uses spatial data related to the location of undeveloped orebodies. This

199

distinguishes our source-focused approach from criticality methodologies that rely on

200

country-level geological surveys. The ESG risk categories overlap to some extent with

201

Graedel’s criticality methodology,18 which include social and governance country level

202

indexes. Three of the risk categories – Social Vulnerability, Political Fragility and Approval

203

& Permitting – are also based on country level indexes, and represent the influence of

204

regulatory institutions and wider societal dynamics. These risk factors can constrain mining

205

and users downstream in the supply chain. Our remaining five Social and Environmental risk

206

categories, are built from seven high resolution variables, and provide site-specific data that

207

uses the precise location of the orebodies.

208 209

Our methodological framework incorporates three risk dimensions, which encompass eight

210

risk categories, built from eleven indexes (four country-level and seven local-level). All 11 ACS Paragon Plus Environment


211

eleven indexes were selected for their completeness, quality and level of detail, enabling a

212

comprehensive overview of the ESG risk context for each orebody. The orebody is positioned

213

at the centre of the framework (see Figure 1).

214 215

The orebodies sample (SI-1). Our methodology involves sourcing information from the S&P

216

database on a sample of mining projects in the early stages of development, prior to

217

construction and production. The S&P database is one of the most comprehensive and up-to-

218

date databases for the mining sector.60, 61 It comprises data on mining properties from across

219

the globe, for a wide range of commodities at all stages of development, from exploration to

220

closure. Spatial coordinates for each orebody were extracted from the S&P database, and

221

assembled into a global map, which was then overlayed with the ESG datasets.

222 223

Information on the “grade” (i.e. the average metal concentration in the orebody) and the

224

“reserves and resources” (i.e. the estimated metal content) was also extracted to provide an

225

approximation of the size of the mineral orebody. Reserves and resources are materials

226

considered for extraction - the terms correspond to varying levels of certainty as to their

227

economic extraction and recovery. Resources are converted to reserves by factoring in the

228

specified mining and processing methods (see SI-1.1 for a more extensive definition). A

229

sample of mining projects will exhibit particular grade and reserves and resources

230

distributions. Risk assessment results (see next section) are plotted as a function of these two

231

variables.

232 233

The ESG risk set. The following paragraphs present the ESG risk categories. For additional

234

information on the ESG risk categories and corresponding indexes, see Supporting

235

Information SI-2.


Page 12 of 31

Page 13 of 31


236 237

Waste. Mining produces large volumes of waste, requiring some of the largest waste facilities

238

ever built.62 These include tailings dams and waste rock dumps, both of which can contain

239

large volumes of potentially hazardous material. The design of mine waste and tailings

240

storage facilities, their location and their structural integrity, is central to the long-term

241

containment of polluting substances. Mine waste can potentially leave environmental, social

242

and economic legacies that last for thousands of years.63, 64 There have been 40 recorded

243

tailings dam failures over the last decade65 and the number of severe failures appears to be

244

increasing.66 The Waste category includes three spatial variables: the Terrain Ruggedness

245

Index,49 which conveys topographic challenges to waste storage, the global map for seismic

246

risk,48 a key factor to take into account when building tailings dams,67 and the Flood

247

Occurrence indicator,50 noting that floods can compromise the containment of waste.68

248 249

Water. Mines commonly have high freshwater requirements.69 To some extent, mines are

250

able to adapt their operations to the local hydrological context. This increasingly involves –

251

in case of limited freshwater resources – an investment in desalination infrastructure. In some

252

cases, however, water access issues can severely constrain mining developments.70 Similarly,

253

water abundance, or high seasonal variations, can also pose challenges in managing mine

254

voids, heap leaches and waste deposits.70 Mining activities can impact water resources, which

255

can in turn affect surrounding ecosystems and communities. When water is scarce,

256

withdrawals can adversely affect other water users. In addition, leaks from mine waste

257

impoundments can contaminate surface and groundwater.71 For this category, we use the

258

Aqueduct Water Risk Atlas by Reig et al.,72 a global, high-resolution database comprising 12

259

indicators relevant to mining, including groundwater and baseline water stress, seasonal



260

variability, drought severity. The Aqueduct Water Risk Atlas also measures regulatory and

261

reputational risk.

262 263

Biodiversity. Metal mines are physically destructive of natural habitats, not only within the

264

mining lease but also through project corridors used for transportation and power (e.g. access

265

roads, rail networks, pipelines, and power stations).73 Previous work60, 61 studied the

266

proximity between mines and critical biodiversity preservation areas. Duran et al.60 estimate

267

that 7% of mines directly overlap with a protected area as defined by the World Database for

268

Protected Areas (WDPA),52 and a further 27% lie within 10km. Oakleaf et al’s.74 calculation

269

indicates that only 5% of the Earth’s at-risk natural lands are under strict legal protection. We

270

represent biodiversity risk with two datasets: the above-mentioned WDPA,52 and the Key

271

Biodiversity Areas (KBA).51 This category considers both the proximity to strictly protected

272

areas (i.e. areas that apply legal restrictions to mining) and the adverse impact mines can have

273

on natural habitat.

274 275

Land Uses. The Mining Minerals and Sustainable Development project identified the

276

“control, use and management of land” as one of the main challenges faced by the mining

277

industry (p.6).62 The potential conflict between mining and natural conservation lands is part

278

of this challenge, as is the competition between mining and human land uses, which is

279

anticipated to increase alongside population growth, urbanisation, and the expansion of

280

agriculture and other industries.74 Land use changes that occur throughout the life of mining

281

projects can directly stimulate the movement of people, such as displacement and

282

resettlement,75 and project-induced in-migration.76 These movements can become sources of

283

tension amongst land users in areas affected by mining activities. In the context of mining,

284

conflicts are common77 and can be costly.47 The Land Uses risk category applies four


Page 14 of 31

Page 15 of 31


285

indicators of the global terrestrial Human Footprint maps developed by Venter et al.,53 to

286

capture the presence of built environments, croplands, pasturelands and the density of human

287

population.

288 289

Indigenous Peoples. The social and environmental impacts caused by mining activities affect

290

some social groups more than others. Indigenous and tribal peoples often experience higher

291

levels of poverty, marginalization, dispossession and discrimination.78 These peoples also

292

tend to have “deep spiritual and cultural ties to their land”, and “frequently retain de facto

293

influence over their ancestral lands” (p.369), regardless of state recognition of collective

294

rights.54 The presence of Indigenous or tribal peoples on or near a mining area may involve

295

additional processes before access to land for mining purposes can proceed. In certain

296

regions, mining is a major employer of indigenous people, which adds further complexity to

297

these relationships.87 The dataset used for this category was compiled by Garnett et al.,54 who

298

gathered information from 127 data sources to generate a global map of terrestrial lands

299

managed or owned by Indigenous Peoples.

300 301

Social Vulnerability. Added to the Land Uses and Indigenous Peoples categories, nation-wide

302

social vulnerability exacerbates the project’s risk profile. The Social Vulnerability risk

303

category is represented by the Fund for Peace’s Fragile States Index.55 This index includes

304

population inflows (e.g. refugees) and outflows (e.g. human flight), as well as intra-country

305

displacements, as indicators of state-wide instability. The index also includes economic

306

measures of poverty and inequalities, and records the presence of group-level grievances and

307

discontent. Each project stakeholder, be they an employee, a contractor, a host community

308

member, an artisanal miner, or a citizen of a country relying on mining revenues, has the

309

potential to both experience and generate social risks.19



310 311

Political Fragility. Political fragility can place constraints on mining development.30

312

Indicators of political fragility and instability include state illegitimacy, fragmentation of state

313

institutions and poor public services.55 In these settings, the national or state level political

314

context provide a permissive environment for sub-optimal social and environmental

315

performance from the operator and the regulator.79 For a sample of 448 significant

316

disruptions in mining production, Hatayama et al.36 estimate that 11% were due to political

317

and policy issues. A robust governance framework is a key factor determining the fair

318

distribution of resource revenues.80 One-quarter of known copper resources are in countries

319

with “less than satisfactory governance” (ref 81, p.368). The Political Fragility category

320

encompasses the political indicators of the Fragile States Index55 and the Resource

321

Governance Index.56

322 323

Approval and Permitting. Large-scale mines typically follow a defined permitting and

324

approval process. While variations are observable across jurisdictions, Ali et al.81 evaluate

325

that, on average, 13 to 23 years can elapse between mineral discovery and construction of a

326

mine. Unexpected delays in project approvals can compromise mining projects that can

327

generate revenue only once production starts. The efficiency and quality of a country’s

328

mining-related regulatory framework ensures that mining activities are not unnecessarily

329

constrained by complicated procedures, while complying with minimum social and

330

environmental standards.82 This category applies two indexes: the Policy Potential Index57

331

and Ease of Doing Business index,58 which characterise how a country’s rules affect or are

332

perceived to affect mining development.

333

334

Application 16 ACS Paragon Plus Environment

Page 16 of 31

Page 17 of 31


335

We apply the methodology to undeveloped iron, aluminium and copper projects, with the aim

336

of assessing and comparing the ESG risk context for the three metals. Iron, aluminium and

337

copper are the three most widely used metals and represent 95% by mass of all industrial

338

metals produced annually.83 Iron ore alone totals 90% of global metal mine production and is

339

the base metal for steel making, a primary material for the construction and manufacturing

340

sectors. Aluminium’s lightweight and malleability makes it a popular material for power

341

transmission, packaging and a wide range of other applications. Copper is valued for its high

342

electrical and thermal conductivity and increasingly for its antibacterial properties..

343 344

The three metals present contrasting profiles: an outcome of being mined in different areas of

345

the globe and in different orebody types. These metals present distinct technical challenges,

346

varying in their crustal abundance and extraction processes. Because iron is an abundant

347

metal, iron mines usually produce iron ore, a concentrate ready for metallurgical processing.

348

For aluminium, mines extract bauxite, the primary ore of aluminium, which is then converted

349

into alumina, and later aluminium. Copper is less abundant than either iron or aluminium, and

350

usually requires an on-site concentration process.

351 352

For each metal, we selected a sample of undeveloped orebodies and their associated mining

353

projects. By applying the methodology to these samples, we indicate the magnitude and

354

characteristics of source risk. The selected samples include the largest orebodies of copper,

355

iron ore or bauxite, and comprise approximately 50% of global copper reserves and

356

resources, 92% of iron ore reserves and resources, and 72% of bauxite reserves and resources

357

reported in the S&P database.59 On this basis, we consider the sample of the orebodies for

358

each metal to be a representative sample of the global orebody for that metal.

359



360

The samples are represented in the global map below (Figure 2). They include 296 copper

361

orebodies, 324 iron ore orebodies, and 50 bauxite orebodies. For additional information on

362

the sample selection process, see SI-1.1.

363

364 365

Figure 2: Global distribution of iron ore, bauxite and copper orebodies samples considered in the

366

analysis (source: S&P database 2019)

367 368

Results are presented in Figures 3 and 4 below. It should be noted that as there are data

369

uncertainties inherent to global-scale multi-factor analyses, results should be regarded with

370

caution. Uncertainties are reduced here by considering global commodity trends rather than

371

orebody-by-orebody results.

372 373

What is most notable from the analysis of the three samples is the high co-occurrence of ESG

374

risks (see Figure 3). Co-occurrence is present when more than one risk category has a value 18 ACS Paragon Plus Environment

Page 18 of 31

Page 19 of 31


375

above a defined medium risk threshold. The number of categories for which an orebody has

376

risk values above the threshold gives it an overall co-occurrence number between 0 and 8 (for

377

definitions on thresholds, see SI 1.3).

378 379

This co-occurrence number serves as an estimator of the complexity associated with mining

380

an orebody. In turn, the fraction of global reserves and resources that lie in orebodies with a

381

high co-occurrence score provides insight into the complexities associated with mining that

382

commodity at a future point in time. For example, the complexity of mining a commodity can

383

be defined as the percentage of global reserves and resources that are located in orebodies

384

with a co-occurrence number of four or more. For iron, this percentage is 47%. For copper,

385

this increases to 63%, and for bauxite, 88%. Based on this calculation, bauxite has the

386

highest source risk of the three metals.



387 388 389 390 391 392

Figure 3: Cumulative reserves and resources for iron ore, copper and bauxite, ordered by risk cooccurrence. Colour shades correspond to the average grades of individual orebodies, expressed in percentages. Dashed lines highlight the portion of the sample that is located in high risk co-occurrence contexts (i.e. four or more concurrent ESG risks).

393

Figure 4 shows that some of the highest grades are found in orebodies with six or more

394

concurrent ESG risks. This trend appears significant for copper orebodies, for which the

395

highest average grades also correspond to a high copper tonnage, and have a co-occurrence 20 ACS Paragon Plus Environment

Page 20 of 31

Page 21 of 31


396

number of 6. Some of the most technically feasible copper orebodies are situated in complex

397

ESG contexts.



398 22 ACS Paragon Plus Environment

Page 22 of 31

Page 23 of 31


399 400 401 402

Figure 4: Distribution of tonnage and average grade by medium-to-high risk co-occurrence for iron ore (top), bauxite (center) and copper (bottom). Proportion of specific ESG risk categories represented by different pattern and shading scale. Numbers above bars correspond to the number of orebodies.

403

A more in-depth analysis characterises the ESG risks across the three samples. The iron ore

404

sample is characterised by disparities between low co-occurrence (three or less risks) and

405

high co-occurrence (four or more risks) orebodies. Water, Waste, Biodiversity and

406

Indigenous Peoples risks are mostly present in low co-occurrence orebodies, whereas Social

407

Vulnerability, Political Fragility and Approval and Permitting are present in high co-

408

occurrence orebodies. Social Vulnerability, Political Fragility and Approval and Permitting

409

tend to be found together in all three samples because they are closely correlated (see SI-4 for

410

more correlation results). The bauxite sample exhibits an overall imbalance due to its small

411

size and because it is dominated in tonnage by six large orebodies, all located in the four to

412

six risk co-occurrence contexts. Land Uses, Social Vulnerability, Political Fragility, Approval

413

and Permitting and to a lesser extent Biodiversity are predominant in the bauxite sample. The

414

copper sample is characterised by a more evenly distributed profile, and by relatively strong

415

Water and Waste risks compared to the iron ore and bauxite samples. 186 orebodies out of

416

296, or 65% of the contained copper, are located in medium to extremely high water risk

417

regions. 42% of contained copper faces medium-to-high Waste risk. For more results,

418

including individual risk graphs and orebody-by-orebody result tables, see SI-3 and SI-5.

419

420

Discussion

421

Research on metal criticality has predominately assessed the supply risk for metals at a

422

macro-scale. Our methodology expands current thinking about resource criticality by

423

including source-based risks. Criticality studies focus on the likelihood of supply disruption



424

and its consequences for importing nations. Scholars have called for a restructuring of global

425

supply and demand networks, and propose strategies of supply diversification, subsidies for

426

national production, and development of strategic stockpiles. Our methodology assesses

427

source risks for the supplying regions of the globe. Without this, understandings of metal

428

criticality are incomplete.

429 430

This research has major implications for the mining industry, investors, governments and

431

downstream users of metals. The results indicate the presence of multiple concurrent risks

432

and raise concerns about the ability of the mining industry to meet demand, which has been

433

projected to grow significantly for copper and iron1 as well as for aluminium.85 To address

434

the complexity associated with these factors, major innovations are required in the design and

435

development of resource projects. Innovations will not only need to “cut across” disciplines

436

but also stakeholder groups to ensure that the responsibility for solutions extends beyond

437

governments and individual companies.

438 439

Our methodology identifies critical issues associated with the future supply of metals. This is

440

best highlighted in the case of Water, which rated as medium to high risk for two-thirds of the

441

undeveloped world copper orebodies. By building a global picture of the ESG risks

442

surrounding current undeveloped orebodies, we draw attention to the feasibility and potential

443

consequences of taking these projects forward into production. This information can be

444

utilised by a range of stakeholders, such as governments at the approval stage of new mining

445

projects, and by investors and /or multinational mining companies in managing their

446

portfolios.

447 448

The work opens avenues for further assessments. Future applications can expand the analysis

449

to other commodities, and compare them based on the assemblage of risks. Risk contexts 24 ACS Paragon Plus Environment

Page 24 of 31

Page 25 of 31


450

should also be evaluated and compared between geographic regions where large reserves and

451

resources are located. Case study research focusing on the influence of ESG factors and

452

project development costs would provide additional insight into the interplay between

453

external risk and the effect of company controls. Projects that face multiple complex ESG

454

risks and advance through to production should remain a point of focus given their potential

455

for disruption and delay.

456

457

Supporting Information

458

1. Data collection, risk calculation, determination of medium risk thresholds and risk co-

459

occurrence number 2. ESG risk categories and associated global datasets 3. Additional

460

results, iron ore, bauxite and copper sample graphs for each ESG risk category. 4. Correlation

461

graphs for each of the three samples. 5. Orebody-by-orebody result tables.

462

463

Acknowledgements

464

The authors are grateful for the strategic funds received from The University of Queensland (UQ) in

465

support of the Sustainable Minerals Institute’s (SMI) cross-disciplinary research on “complex

466

orebodies”. We acknowledge the organisations and people that have produced the datasets we used in

467

our analysis: G. Amatulli and colleagues, S. Garnett and colleagues, O. Venter and colleagues, BirdLife

468

International, World Resources Institute, Fraser Institute, Fund for Peace, Natural Resources

469

Governance Institute, International Union for Conservation of Nature, the U.S. Geological Survey, the 25 ACS Paragon Plus Environment


470

World Bank, and S&P Global Market Intelligence. Particular thanks to S. Garnett and colleagues for

471

sharing data from their work and their feedback on the draft.


Page 26 of 31

Page 27 of 31


References 1. Elshkaki, A.; Graedel, T. E.; Ciacci, L.; Reck, B. K., Resource Demand Scenarios for the Major Metals. Environmental Science & Technology 2018, 52, (5), 2491-2497. 2. Christmann, P., Towards a More Equitable Use of Mineral Resources. Natural Resources Research 2018, 27, (2), 159-177. 3. Liu, G.; Bangs, C. E.; Müller, D. B., Stock dynamics and emission pathways of the global aluminium cycle. Nature Climate Change 2013, 3, (4), 338. 4. Cullen, J. M.; Allwood, J. M., Mapping the global flow of aluminum: From liquid aluminum to end-use goods. Environmental science & technology 2013, 47, (7), 3057-3064. 5. Graedel, T. E.; Harper, E. M.; Nassar, N. T.; Reck, B. K., On the materials basis of modern society. Proceedings of the National Academy of Sciences 2015, 112, (20), 6295-6300. 6. Hertwich, E. G.; Gibon, T.; Bouman, E. A.; Arvesen, A.; Suh, S.; Heath, G. A.; Bergesen, J. D.; Ramirez, A.; Vega, M. I.; Shi, L., Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proceedings of the National Academy of Sciences 2015, 112, (20), 6277-6282. 7. Allwood, J. M.; Cullen, J. M.; Milford, R. L., Options for achieving a 50% cut in industrial carbon emissions by 2050. Environmental science & technology 2010, 44, (6), 1888-1894. 8. Graedel, T. E.; Allwood, J.; Birat, J.-P.; Buchert, M.; Hagelüken, C.; Reck, B. K.; Sibley, S. F.; Sonnemann, G., What Do We Know About Metal Recycling Rates? Journal of Industrial Ecology 2011, 15, (3), 355-366. 9. Van der Voet, E.; Van Oers, L.; Verboon, M.; Kuipers, K. J. J. o. I. E., Environmental implications of future demand scenarios for metals: methodology and application to the case of seven major metals. 2019, 23, (1), 141-155. 10. Vidal, O.; Goffé, B.; Arndt, N., Metals for a low-carbon society. Nature Geoscience 2013, 6, 894. 11. Kleijn, R.; van der Voet, E.; Kramer, G. J.; van Oers, L.; van der Giesen, C., Metal requirements of low-carbon power generation. Energy 2011, 36, (9), 5640-5648. 12. Graedel, T. E., On the future availability of the energy metals. Annual Review of Materials Research 2011, 41, 323-335. 13. Northey, S.; Mohr, S.; Mudd, G. M.; Weng, Z.; Giurco, D., Modelling future copper ore grade decline based on a detailed assessment of copper resources and mining. Resources, Conservation and Recycling 2014, 83, 190-201. 14. Bruckner, T.; Bashmakov, I. A.; Mulugetta, Y.; Chum, H.; de la Vega Navarro, A.; Edmonds, J.; Faaij, A.; Fungtammasan, B.; Garg, A.; Hertwich, E. G.; Honnery, D.; Infield, D.; Kainuma, M.; Khennas, S.; Kim, S.; Nimir, H. B.; Riahi, K.; Strachan, N.; Wiser, R.; Zhang, X., Energy Systems. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Edenhofer, O.; PichsMadruga, R.; Sokona, Y.; Farahani, E.; Kadner, S.; Seyboth, K.; Adler, A.; Baum, I.; Brunner, S.; Eickemeier, P.; Kriemann, B.; Savolainen, J.; Schlömer, S.; von Stechow, C.; Zwickel, T.; Minx, J. C., Eds. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2014. 15. Northey, S. A.; Mudd, G. M.; Werner, T., Unresolved complexity in assessments of mineral resource depletion and availability. Natural Resources Research 2018, 27, (2), 241-255. 16. Achzet, B.; Helbig, C., How to evaluate raw material supply risks—an overview. Resources Policy 2013, 38, (4), 435-447. 17. Erdmann, L.; Graedel, T. E., Criticality of non-fuel minerals: a review of major approaches and analyses. Environmental science & technology 2011, 45, (18), 7620-7630.



18. Graedel, T. E.; Barr, R.; Chandler, C.; Chase, T.; Choi, J.; Christoffersen, L.; Friedlander, E.; Henly, C.; Jun, C.; Nassar, N. T., Methodology of metal criticality determination. Environmental science & technology 2012, 46, (2), 1063-1070. 19. Valenta, R.; Kemp, D.; Owen, J.; Corder, G.; Lèbre, É., Re-thinking complex orebodies: Consequences for the future world supply of copper. Journal of Cleaner Production 2019, 220, 816826. 20. ASI, ASI Chain of Custody (CoC) Standard V1. In Aluminium Stewardship Initiative, Aluminium Stewardship Initiative: VIC, Australia, 2017; p 28. 21. RJC, Responsible Jewellery Council - Code of Practices. In London, UK, 2019. 22. LME, LME launches consultation on the introduction of responsible sourcing standards across all listed brands. In Exchange, L. M., Ed. London Metal Exchange: 2019. 23. Sanderson, H.; Hume, N., Global miners count the cost of their failings. Environmental, social and governance metrics steer investors away from sector making news for all the wrong reasons. Financial Times 16 February, 2019. 24. UNEP-FI; WBCSD Translating ESG into sustainable business value - Key insights for companies and investors United Nations Environment Programme. UNEP Finance Initiative. World Business Council for Sustainable Development: 2010. 25. Prior, T.; Giurco, D.; Mudd, G. M.; Mason, L.; Behrisch, J., Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change 2012, 22, (3), 577-587. 26. Elshkaki, A.; Graedel, T. E.; Ciacci, L.; Reck, B. K., Copper demand, supply, and associated energy use to 2050. Global Environmental Change 2016, 39, 305-315. 27. European Commission On the 2017 list of Critical Raw Materials for the EU; Brussels, Belgium, September 13, 2017. 28. Mudd, G. M.; Jowitt, S. M.; Werner, T. T., The world's lead-zinc mineral resources: Scarcity, data, issues and opportunities. Ore Geology Reviews 2017, 80, 1160-1190. 29. Hatayama, H.; Tahara, K.; Daigo, I., Worth of metal gleaning in mining and recycling for mineral conservation. Minerals Engineering 2015, 76, 58–64. 30. Meinert, L.; Robinson, G.; Nassar, N., Mineral Resources: Reserves, Peak Production and the Future. Resources 2016, 5, (1), 14. 31. West, J., Decreasing metal ore grades - Are They Really Being Driven by the Depletion of High-Grade Deposits? Journal of Industrial Ecology 2011, 15, (2), 165-168. 32. Tilton, J. E.; Lagos, G., Assessing the long-run availability of copper. Resources Policy 2007, 32, (1), 19-23. 33. Arndt, N. T.; Fontboté, L.; Hedenquist, J. W.; Kesler, S. E.; Thompson, J. F.; Wood, D. G., Future global mineral resources. Geochemical Perspectives 2017, 6, (1), 1-171. 34. Mudd, G. M.; Jowitt, S. M., Growing Global Copper Resources, Reserves and Production: Discovery Is Not the Only Control on Supply. Economic Geology 2018, 113, (6), 1235-1267. 35. Helbig, C.; Wietschel, L.; Thorenz, A.; Tuma, A., How to evaluate raw material vulnerability An overview. Resources Policy 2016, 48, 13-24. 36. Hatayama, H.; Tahara, K., Adopting an objective approach to criticality assessment: Learning from the past. Resources Policy 2018, 55, 96-102. 37. Blengini, G. A.; Blagoeva, D.; Dewulf, J.; Torres de Matos, C.; Nita, V.; Vidal-Legaz, B.; Latunussa, C. E. L.; Kayam, Y.; Talens Peirò, L.; Baranzelli, C.; Manfredi, S.; Mancini, L.; Nuss, P.; Marmier, A.; Alves-Dias, P.; Pavel, C.; Tzimas, E.; Mathieux, F.; Pennington, D.; Ciupagea, C. Assessment of the Methodology for Establishing the EU List of Critical Raw Materials; European Commission: Brussels, Belgium, 2017. 38. National Research Council Minerals, Critical Minerals, and the U.S. Economy; 500 Fifth Street, NW, Washington, D.C, 2008.


Page 28 of 31

Page 29 of 31


39. Sverdrup, H. U.; Ragnarsdottir, K. V.; Koca, D., An assessment of metal supply sustainability as an input to policy: security of supply extraction rates, stocks-in-use, recycling, and risk of scarcity. Journal of Cleaner Production 2017, 140, 359-372. 40. Gordon, R. B.; Bertram, M.; Graedel, T. E., Metal stocks and sustainability. Proceedings of the National Academy of Sciences 2006, 103, (5), 1209-1214. 41. Connolly, E.; Orsmond, D., The mining industry: from bust to boom. Citeseerx: The Pennsylvania State University, PA, United States, 2011. 42. Laurence, D., Establishing a sustainable mining operation: an overview. Journal of Cleaner Production 2011, 19, (2-3), 278-284. 43. van Duuren, E.; Plantinga, A.; Scholtens, B., ESG Integration and the Investment Management Process: Fundamental Investing Reinvented. Journal of Business Ethics 2016, 138, (3), 525-533. 44. Holley, E. A.; Mitcham, C., The Pebble Mine Dialogue: A case study in public engagement and the social license to operate. Resources Policy 2016, 47, 18-27. 45. Barrick Gold Driven by return - Barrick Gold Corporation Annual Report 2012; Toronto, Canada, 2012. 46. Ker, P., How Rio Tinto's Mozambique mess unfolded. Australian Financial Review 18 Oct 2017, 2017. 47. Franks, D. M.; Davis, R.; Bebbington, A. J.; Ali, S. H.; Kemp, D.; Scurrah, M., Conflict translates environmental and social risk into business costs. Proceedings of the National Academy of Sciences 2014, 111, (21), 7576-7581. 48. SED, Global Seismic Hazard Assessment Programme (GSHAP). In Swiss Seismological Service (SED), E. Z., Ed. USGS Earthquake Hazard Programme: 2018. 49. Amatulli, G.; Domisch, S.; Tuanmu, M.-N.; Parmentier, B.; Ranipeta, A.; Malczyk, J.; Jetz, W., A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific data 2018, 5, 180040. 50. Gassert, F.; Landis, M.; Luck, M.; Reig, P.; Shiao, T. Aqueduct Metadata Document - Aqueduct Global Maps 2.0; World Resources Institute: Washington DC, United States, 2013. 51. BirdLife International, Digital boundaries of Important Bird and Biodiversity Areas from the World Database of Key Biodiversity Areas. In February 2018 ed.; 2019. 52. UNEP-WCMC; IUCN, World Database on Protected Areas. In (IUCN), U. E. W. C. M. C. U.-W. a. t. I. U. f. C. o. N., Ed. Protected Planet Initiative: 2014. 53. Venter, O.; Sanderson, E. W.; Magrach, A.; Allan, J. R.; Beher, J.; Jones, K. R.; Possingham, H. P.; Laurance, W. F.; Wood, P.; Fekete, B. M.; Levy, M. A.; Watson, J. E. M., Global terrestrial Human Footprint maps for 1993 and 2009. In Dryad Digital Repository: 2016. 54. Garnett, S. T.; Burgess, N. D.; Fa, J. E.; Fernández-Llamazares, Á.; Molnár, Z.; Robinson, C. J.; Watson, J. E. M.; Zander, K. K.; Austin, B.; Brondizio, E. S.; Collier, N. F.; Duncan, T.; Ellis, E.; Geyle, H.; Jackson, M. V.; Jonas, H.; Malmer, P.; McGowan, B.; Sivongxay, A.; Leiper, I., A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability 2018, 1, (7), 369374. 55. Haken, N.; Fiertz, C.; Messner, J. J.; Taft, P.; Blyth, H.; Maglo, M.; Murphy, C.; Quinn, A.; Carlson, T.; Chandler, O.; Horwitz, M.; Jesch, L.; Mathias, B.; Wilson, W., Fragile States Index. In Peace, T. F. F., Ed. Washington, DC, USA, 2018. 56. NRGI 2017 Resource Governance Index; Natural Resource Governance Institute: 2017. 57. Stedman, A.; Green, K. P., Annual survey of mining companies: 2017. In Institute, F., Ed. Fraser Institute: 2018. 58. Djankov, S., Ease of doing business index. World Development Indicators. In The World Bank Group, D. B. p., Ed. Washington, DC, USA, 2018. 59. S&P, S&P Global Market Intelligence. In Thomson Reuters: New York, 2019. 60. Durán, A. P.; Rauch, J.; Gaston, K. J., Global spatial coincidence between protected areas and metal mining activities. Biological Conservation 2013, 160, 272-278. 29 ACS Paragon Plus Environment


61. Murguía, D. I.; Bringezu, S.; Schaldach, R., Global direct pressures on biodiversity by largescale metal mining: Spatial distribution and implications for conservation. Journal of Environmental Management 2016, 180, 409-420. 62. MMSD Breaking New Ground: Mining Minerals and Sustainable Development; Mining, Minerals and Sustainable Development project: London, United Kingdom, 2002; p 476. 63. Franks, D. M.; Boger, D. V.; Côte, C. M.; Mulligan, D. R., Sustainable development principles for the disposal of mining and mineral processing wastes. Resources Policy 2011, 36, (2), 114-122. 64. Owen, J. R.; Kemp, D., Displaced by mine waste: The social consequences of industrial risktaking. The Extractive Industries and Society 2019, 6, (2), 424-427. 65. UNEP; GRID-Arendal Mine Tailings Storage: Safety Is No Accident; United Nations Environment Programme and GRID-Arendal: Geneva, Switzerland, October 2017, 2017. 66. Bowker, L. N.; Chambers, D. M. The risk, public liability, and economics of tailings storage facility failures; Bowker Associates Science & Research In The Public Interest: Stonington, Maine, July 21, 2015. 67. LPSDP Tailings Management - Leading Practice Sustainable Development Program for the Mining Industry; Australian Government: Canberra, Australia, 2016. 68. WISE Chronology of major tailings dam failures. http://www.wise-uranium.org/mdaf.html (3rd February 2019), 69. Gunson, A. J.; Klein, B.; Veiga, M.; Dunbar, S., Reducing mine water requirements. Journal of Cleaner Production 2012, 21, (1), 71-82. 70. Northey, S. A.; Mudd, G. M.; Werner, T. T.; Jowitt, S. M.; Haque, N.; Yellishetty, M.; Weng, Z., The exposure of global base metal resources to water criticality, scarcity and climate change. Global Environmental Change 2017, 44, 109-124. 71. Huang, X.; Sillanpää, M.; Gjessing, E. T.; Peräniemi, S.; Vogt, R. D., Environmental impact of mining activities on the surface water quality in Tibet: Gyama valley. Science of The Total Environment 2010, 408, (19), 4177-4184. 72. Reig, P.; Shiao, T.; Gassert, F. Aqueduct water risk framework - WRI Working Paper; World Resources Institute: Washington DC, United States, 2013. 73. Bebbington, A. J.; Humphreys Bebbington, D.; Sauls, L. A.; Rogan, J.; Agrawal, S.; Gamboa, C.; Imhof, A.; Johnson, K.; Rosa, H.; Royo, A.; Toumbourou, T.; Verdum, R., Resource extraction and infrastructure threaten forest cover and community rights. Proceedings of the National Academy of Sciences 2018, 201812505. 74. Oakleaf, J. R.; Kennedy, C. M.; Baruch-Mordo, S.; West, P. C.; Gerber, J. S.; Jarvis, L.; Kiesecker, J., A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion. PLOS ONE 2015, 10, (10), e0138334. 75. Owen, J. R.; Kemp, D., Mining-induced displacement and resettlement: a critical appraisal. Journal of Cleaner Production 2015, 87, 478-488. 76. Bainton, N.; Vivoda, V.; Kemp, D.; Owen, J.; Keenan, J. Project-Induced In-Migration and Large-Scale Mining: A Scoping Study; St Lucia, Queensland, Australia, 2017. 77. Andrews, T.; Elizalde, B.; Le Billon, P.; Hoon Oh, C.; Reyes, D.; Thomson, I. The Rise in Conflict Associated with Mining Operations: What Lies Beneath?; Canadian International Resources and Development Institute (CIRDI): 2017. 78. FAO Policy on Indigenous and Tribal Peoples; Food and Agriculture Organisation of the United Nations: Rome, Italy, March 2015, 2015. 79. Ballard, C.; Banks, G., Resource wars: the anthropology of mining. Annual review of anthropology 2003, 32, (1), 287-313. 80. Robinson, J. A.; Torvik, R.; Verdier, T., Political foundations of the resource curse. Journal of Development Economics 2006, 79, (2), 447-468. 81. Ali, S. H.; Giurco, D.; Arndt, N.; Nickless, E.; Brown, G.; Demetriades, A.; Durrheim, R.; Enriquez, M. A.; Kinnaird, J.; Littleboy, A., Mineral supply for sustainable development requires resource governance. Nature 2017, 543, (7645), 367-372. 30 ACS Paragon Plus Environment

Page 30 of 31

Page 31 of 31


82. World Bank, Ease of Doing Business Score and Ease of Doing Business Ranking. 2019. 83. USGS Mineral Commodity Summaries 2018; U.S. Department of the Interior. U.S. Geological Survey: Reston, VA, United States, 2018. 84. OECD Global Material Resources Outlook to 2060 - Economic drivers and environmental consequences; Organisation for Economic Cooperation and Development: Paris, France, October, 2018. 85. Bertram, M.; Ramkumar, S.; Rechberger, H.; Rombach, G.; Bayliss, C.; Martchek, K. J.; Müller, D. B.; Liu, G., Global Aluminium Cycle 2017. In International Aluminium Institute: London, UK, 2017. 86. Ayuk, E. T.; Pedro, A. M.; Ekins, P.; Gatune, J.; Milligan, B.; B., O.; Christmann, P.; Ali, S.; Kumar, S. V.; Bringezu, S.; Acquatella, J.; Bernaudat, L.; Bodouroglou, C.; Brooks, S.; Burgii Bonanomi, E.; Clement, J.; Collins, N.; Davis, K.; Davy, A.; Dawkins, K.; Dom, A.; Eslamishoar, F.; Franks, D.; Hamor, T.; Jensen, D.; Lahiri-Dutt, K.; Petersen, I.; Sanders, A. R. D. Mineral resource governance in the 21st Century - Gearing extractive industries towards sustainable development; International Resource Panel: Nairobi, Kenya, 2019. 87. Brereton, D.; Parmenter, J., Indigenous Employment in the Australian Mining Industry. Journal of Energy & Natural Resources Law 2008, 26, (1), 66-90.


Source risks as constraints to future metal supply | Environmental

Recommend Documents