Resourcing the fairytale country with wind power: a dynamic material

8 mins ago - Wind energy is key to addressing the global climate challenge, but its development is subject to potential constraint of finite primary m...
0 downloads 0 Views 1MB Size
Subscriber access provided by Nottingham Trent University

Environmental Modeling

Resourcing the fairytale country with wind power: a dynamic material flow analysis Zhi Cao, Christopher O’Sullivan, Juan Tan, Per Kalvig, Luca Ciacci, Wei-Qiang Chen, Junbeum Kim, and Gang Liu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b03765 • Publication Date (Web): 28 Aug 2019 Downloaded from pubs.acs.org on August 29, 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 30

Environmental Science & Technology

1

Resourcing the fairytale country with wind power: a dynamic material flow

2

analysis

3

Zhi Cao,† Christopher O’Sullivan, † Juan Tan,‡ Per Kalvig,‡ Luca Ciacci,§ Weiqiang Chen,∥ Junbeum

4

Kim,⊥ Gang Liu†,*

5

† SDU Life Cycle Engineering, Department of Chemical Engineering, Biotechnology, and

6

Environmental Technology, University of Southern Denmark, 5230 Odense, Denmark

7

‡ Centre for Minerals and Materials (MiMa), Geological Survey of Denmark and Greenland (GEUS),

8

1350 Copenhagen, Denmark

9

§ Department of Industrial Chemistry “Toso Montanari”, Alma Mater Studiorum–University of

10

Bologna, 40136 Bologna, Italy

11

∥ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of

12

Sciences, Xiamen, Fujian 361021, China

13

⊥CREIDD Research Center on Environmental Studies & Sustainability, Department of Humanities,

14

Environment & Information Technology (HETIC), University of Technology of Troyes, 10300 Troyes,

15

France

16 17

ABSTRACT

18

Wind energy is key to addressing the global climate challenge, but its development is subject to

19

potential constraints of finite primary materials. Prior studies on material demand forecasting of wind

20

power development are often limited to a few materials and with low technological resolution, thus

21

hindering a comprehensive understanding of the impact of micro-engineering parameters on the

1 ACS Paragon Plus Environment

Environmental Science & Technology

22

resource implications of wind energy. In this study, we developed a component-by-component and

23

stock-driven prospective material flow analysis model, and used bottom-up data on engineering

24

parameters and wind power capacities to characterize the materials demand and secondary supply

25

potentials of wind energy development in Denmark, a pioneering and leading country in wind power.

26

We also explicitly addressed the uncertainties in the prospective modeling by the means of statistical

27

estimation and sensitivity analysis methods. Our results reveal increasing challenges of materials

28

provision and end-of-life (EoL) management in Denmark’s ambitious transition towards 100%

29

renewable energy in the next decades. Harnessing potential secondary resource supply from EoL and

30

extending lifetime could curtail the primary material demand, but they could not fully alleviate the

31

material supply risk. Such a model framework that considers bottom-up engineering parameters with

32

increased precision could be applied to other emerging technologies and help reveal synergies and

33

trade-offs of relevant resource, energy, and climate strategies in the future renewable energy and

34

climate transition.

35 36

GRAPHICAL ABSTRACT

37

38

2 ACS Paragon Plus Environment

Page 2 of 30

Page 3 of 30

Environmental Science & Technology

39

1. INTRODUCTION

40

Wind energy technologies are often regarded as an important enabler in many low-carbon scenarios,

41

such as the International Energy Agency (IEA)’s Sustainable Development scenario1 and the global

42

emission mitigation pathways in the Intergovernmental Panel on Climate Change (IPCC)’s 1.5°C

43

special report2. However, transitioning towards a low-carbon society, where large amounts of

44

renewable energy infrastructure are urgently needed, requires vast amounts of metals and minerals3.

45

Such resource implications4–7 of energy transition and consequent supply security8–11 and embodied

46

environmental impacts12,13 have gained increasing attention in recent years.

47 48

For example, Denmark, a pioneer in developing commercial wind power since the 1970s’ oil crisis, has

49

built up an energy system of which already about 48% of electricity is from wind in 201714,15. The

50

intermittent yet abundant wind energy in Denmark will continue to play a major role for achieving the

51

Danish government’s ambition to have a ‘100% renewable’ energy system by 205016,17. Understanding

52

potential resource supply bottlenecks, reliance on foreign mineral resources, and secondary materials

53

provision is therefore an important and timely topic for both the Danish wind energy sector and

54

Denmark’s energy and climate policy.

55 56

Construction and maintenance of wind power systems needs large quantities of raw materials mainly

57

due to large-scale deployment of wind turbines and infrastructure on land or at sea18. In particular, two

58

rare earth elements (neodymium and dysprosium) mainly used in permanent magnets have raised

59

special concerns in the wind energy sector10,19,20 due to over-concentration of rare earth’s supply in

60

China21, sustainability of upstream mining and production processes22, and complexity of wind

3 ACS Paragon Plus Environment

Environmental Science & Technology

61

turbines’ supply chain23. Moreover, the wind energy sector also faces increasing challenges in both

62

meeting future demands for several base metals (e.g., copper used in transmission18) and managing

63

mounting end-of-life (EoL) materials (e.g., glass fiber in blades24–26) arising from decommissioned

64

wind turbines.

Page 4 of 30

65 66

A variety of methods have been used to translate wind energy scenarios into material demand. If the

67

annual newly installed capacity of wind turbines is given, its associated material demand is often

68

directly determined by material intensity per capacity unit5,8,9,27–30. If annual installed capacity is not

69

given, its associated material demand can be derived from a Life Cycle Assessment (LCA) based input-

70

output method31, an economic model32, or a dynamic material flow analysis (MFA) model6,11,20,25,30,33–

71

36.

72

energy provisioning on a global scale6,11,30,33,34, a country scale (e.g., the US20, France25, and

73

Germany35), or a country scale with a regional resolution36. The principle of mass balance constitutes

74

the foundation of any MFA, so that the annual newly installed capacity (‘inflow’) and annual

75

decommissioned capacity (‘outflow’) of wind turbines are driven by their lifetime and the expansion

76

and replacement of the installed wind power capacity (‘stock’)20,36, which has also been widely used in

77

other anthropogenic stock studies37.

The dynamic MFA model has been increasingly used to explore material requirements of wind

78 79

However, the current practice of modeling raw material requirement or secondary material availability

80

in different wind energy technologies generally overlooks the hierarchical, layered characteristics of

81

wind power systems. This is important because materials embedded in a technology system are usually

82

distributed in its subsystem or subcomponents with varying compositions and recycling potentials38,39.

83

In the case of wind power systems, materials employed in a wind turbine are distributed in its 4 ACS Paragon Plus Environment

Page 5 of 30

Environmental Science & Technology

84

subcomponents such as rotor, tower, and nacelle, and their mass is largely determined by turbine size

85

(e.g., rotor diameter or hub height) and capacity13,40. These constraining factors and their leverages on

86

the sustainability and resilience of the wind energy provisioning should be fully examined. Such

87

information would enable wind turbine manufacturers, material suppliers, recyclers, end users, and

88

policy makers to plan their material-related policies with a comprehensive understanding on a range of

89

important aspects related to wind energy provisioning, such as secondary material supply,

90

technological development, and material efficiency.

91 92

Here, we developed a component-by-component and stock-driven prospective MFA model to

93

characterize material requirements and secondary material potentials of different Danish wind energy

94

development scenarios. Based on two datasets that cover a range of micro-engineering parameters (e.g.,

95

capacity, rotor diameter, hub height, rotor weight, nacelle weight, and tower weight) of wind turbines

96

installed in Denmark and worldwide, we established empirical regressions among these parameters in

97

order to address the size scaling effects of wind turbines. We considered important drivers such as

98

growing average capacity, material efficiency improvements, changing market share, and lifetime

99

extension, and aimed to address the following specific questions:

100 101

1. How much materials would be needed for Denmark’s wind energy systems in its different ‘100% renewable’ transition pathways?

102

2. What are the potentials of secondary materials supply from future Danish wind energy systems?

103

3. How would the above-mentioned key drivers affect material requirements and secondary

104

material supply?

105

5 ACS Paragon Plus Environment

Environmental Science & Technology

106

2. MATERIALS AND METHODS

107

2.1. System definition and modeling framework

108

The system definition and modeling framework for translating energy scenarios into material

109

requirements of Denmark’s wind energy system are delineated in Figure 1. The subsystems considered

110

cover wind turbines, foundations, transformer stations, and internal cables (connecting individual wind

111

turbines to the transformer station). Onshore and offshore wind turbines are differentiated. Six bulk

112

materials (steel, cast iron, nonferrous metals, polymer materials, fiberglass, and concrete) and two

113

critical materials (neodymium and dysprosium) which represent the majority of materials used in

114

different components are included in our model.

115 116

We employed a stock-driven prospective MFA approach41,42 to simulating future flows (i.e., new

117

installation and decommission) of onshore and offshore wind power capacities from 2018 to 2050. For

118

bulk materials, we developed a component-level modeling approach to converting wind power

119

capacities into material requirements, taking into account relationships among the engineering

120

parameters of wind turbines. For critical materials, we took into account the impacts of adopting wind

121

turbine technologies that use a permanent magnet generator, as well as the improvement of its material

122

intensity, on material demand and availability of secondary materials.

6 ACS Paragon Plus Environment

Page 6 of 30

Page 7 of 30

Environmental Science & Technology

123 124

Figure 1. Stock-driven modeling framework for material demand of the Danish wind energy system at

125

the component level. Elec. & Ca.: electronics and cables. EoL: end-of-life. MW: megawatt. PM:

126

permanent magnet.

127 128

2.2. Energy scenarios and stock-driven modeling

129

The Danish government has the ambition to achieve a ‘100% renewable’ energy system by 205016. The

130

Danish Energy Agency (DEA) has accordingly developed four fossil fuel free scenarios, plus a

131

reference fossil fuel scenario where all policy targets are disregarded16. In parallel, the Danish Society

132

of Engineers (IDA) developed a ‘Smart Energy System’ strategy for the same ‘100% renewable’ target,

7 ACS Paragon Plus Environment

Environmental Science & Technology

133

taking into account the cross-sectoral interaction of electricity, heat, gas and transport sectors17. These

134

five ‘100% renewable’ scenarios all imply a high reliance on wind energy or bioenergy.

135 136

We extracted future in-use capacities of onshore and offshore wind power systems from the energy

137

scenarios developed by the DEA and the IDA. Figure 2b demonstrates the smooth transitions of the

138

future Danish wind energy systems under six different scenarios. These energy scenarios are mainly

139

characterized by Denmark’s limited biomass resource and abundant but intermittent wind power

140

generation.

141 142

The DEA has outlined four potential fossil fuel free scenarios (i.e., Biomass, Biomass+, Wind, and

143

Hydrogen) for Denmark’s ‘100% renewable’ ambition16. Plus, a Fossil scenario has been developed in

144

parallel, neglecting all national targets and therefore continuing the consumption of fossil fuels. The

145

four fossil fuel free scenarios are constructed from a biomass perspective, and assume that a certain

146

portion of onshore capacities will be replaced by offshore capacities, as summarized below.

147



148 149

bioenergy. 

150 151

The Biomass scenario assumes a moderate electrification but an increased reliance on imported

The Biomass+ scenario assumes a higher reliance on imported bioenergy compared to the Biomass scenario.



The Wind scenario assumes a massive electrification in the transportation sector and the heating

152

sectors, thereby expecting a constant increase in wind power capacities and a limited biomass

153

demand.

8 ACS Paragon Plus Environment

Page 8 of 30

Page 9 of 30

Environmental Science & Technology



154 155

The Hydrogen scenario assumes that Denmark will heavily rely on hydrogen technologies to convert wind energy into hydrogen that is further used for hydrogenation of carbon sources.

156 157

The IDA developed an alternative scenario (hereafter named as the IDA scenario) which is between the

158

Wind scenario and the Hydrogen scenario17. The IDA scenario assumes that more efficient electrolysis

159

technologies will be adopted and thus less wind power will be needed compared to the Hydrogen

160

scenario. While the onshore capacities in the five DEA scenarios will decrease to 3.5 GW, the IDA

161

scenario assumes that the onshore capacities will expand to 5 GW by 2050. The rationale for this

162

assumption is buying up buildings to create more space for onshore wind turbines is socio-

163

economically more attractive than building offshore wind power capacities. 6

5

20

a) In-use capacities [Unit: GW] Onshore 2MW & 3MW

4

Offshore 2MW & 3MW

8

b) Scenarios [Unit: GW] Wind (onshore) Biomass (onshore) Biomass+ (onshore) Hydrogen (onshore) Fossil (onshore) IDA (onshore) Wind (offshore) Biomass (offshore) Biomass+ (offshore) Hydrogen (offshore) Fossil (offshore) IDA (offshore)

2 4

1

0

164

0 1977 1982 1987 1992 1997 2002 2007 2012 2017

165

Figure 2. In-use capacities of Danish wind power systems (onshore and offshore) a) from 1977 to 2017

166

and b) from 2018 to 2050 in the Hydrogen, IDA, Wind, Fossil, Biomass, and Biomass+ scenarios.

2018 2023 2028 2033 2038 2043 2048

9 ACS Paragon Plus Environment

Environmental Science & Technology

Page 10 of 30

167

Note: for onshore capacity scenarios, the lines of the Wind, Biomass, Biomass+, Hydrogen, and IDA

168

scenarios are overlaid by the line of Fossil scenario, because they use the same target value.

169 170

A stock-driven model was used to determine the annual new and decommissioned capacities of wind

171

turbines, based on a stock-flow modeling framework41–43 and assumed lifetime. Mathematically, the

172

relationship between the new and decommissioned capacities can be expressed as a convolution,

173

respecting the mass-balance principle. Therefore, the new and decommissioned capacities are driven by

174

the assumed development of in-use capacities and lifetime, according to the equations below.

175

𝐼𝑛𝑓𝑙𝑜𝑤𝑡 = 𝑆𝑡𝑜𝑐𝑘𝑡 ― 𝑆𝑡𝑜𝑐𝑘𝑡 ― 1 + 𝑂𝑢𝑡𝑓𝑙𝑜𝑤𝑡 (1) 𝑡′ = 𝑡 ― 1

176

𝑂𝑢𝑡𝑓𝑙𝑜𝑤𝑡 =



𝐼𝑛𝑓𝑙𝑜𝑤𝑡′ × (1 ― 𝑆𝑡 ― 𝑡′)

(2)

𝑡′ = 𝑡0

177

where 𝐼𝑛𝑓𝑙𝑜𝑤𝑡 or 𝐼𝑛𝑓𝑙𝑜𝑤𝑡′ refers to the new capacities at year 𝑡 or 𝑡′; 𝑆𝑡𝑜𝑐𝑘𝑡 or 𝑆𝑡𝑜𝑐𝑘𝑡 ― 1 refers to the

178

in-use capacities of wind turbines at year 𝑡 or 𝑡 ― 1; 𝑂𝑢𝑡𝑓𝑙𝑜𝑤𝑡 refers to the decommissioned capacities

179

at year 𝑡; and 𝑆𝑡 ― 𝑡′ refers to the probability that the previously installed capacities reach their end-of-

180

life after 𝑡 ― 𝑡′ years.

181 182

A Weibull distribution was used to determine the lifetime distribution of wind turbines and the

183

corresponding survival function (see Figure S1 and Table S1 in the Supporting Information). The

184

average lifetime of wind turbines in the Danish energy system is 17.8 years, which is slightly lower

185

than assumptions used in prior studies6,35, due mainly to the fact that as a pioneering country in wind

186

energy development, certain amounts of wind turbines installed in Denmark are pilot projects with a

187

shorter lifetime.

10 ACS Paragon Plus Environment

Page 11 of 30

Environmental Science & Technology

188 189

2.3. Empirical regressions of engineering parameters

190

The current trend shows that the size of wind turbines is continuously scaling up44, which has

191

considerable impacts on the mass of wind turbines. The technological trend of turbine size was taken

192

from the prediction made by the DEA45. The average capacity of onshore wind turbines will increase to

193

4 MW by 2030 and 5 MW by 2050; meanwhile, the average capacity of offshore wind turbines will

194

increase to 12 MW by 2030 and 15 MW by 2050. We used two large sample datasets to derive

195

empirical relationships among engineering parameters of wind turbines, as detailed below.

196

1. Size determination based on the Danish Master Data Register of Wind Turbines46: we

197

determined the empirical relationships between the capacity (C) and the hub height (H), and the

198

capacity (C) and the rotor diameter (D);

199

2. Mass determination based on The Wind Power database47: we determined the empirical

200

relationships between the rotor diameter (D) and the rotor weight, the rotor diameter (D) and the

201

nacelle weight, and the square of rotor diameter (D^2) multiplied by hub height (H) and the

202

tower weight.

203 204

Figure 3 demonstrates the size scaling effects on wind turbine’s mass. For example, a scaling factor

205

(the exponent of a power function) smaller than 1 is observed in the empirical regressions between

206

capacity and rotor diameter for both onshore wind turbines and offshore wind turbines. This means a

207

1% increase in average capacity will result in a 0.49% increase in onshore wind turbines’ rotor

208

diameter and a 0.59% increase in offshore wind turbines’ rotor diameter, respectively. Similar size

209

scaling effects can be observed in the empirical regressions between capacity and hub height, but its

11 ACS Paragon Plus Environment

Environmental Science & Technology

Page 12 of 30

210

scaling factor for onshore wind turbines (0.39) is larger than offshore wind turbines (0.34). However, a

211

scaling factor larger than 2 is observed in the empirical regression between rotor diameter and rotor

212

weight, as well as the empirical regression between rotor meter and nacelle weight. This means, for

213

example, a 1% increase in rotor diameter will result in a 2.01% increase in rotor weight for onshore

214

wind turbines and a 2.14% increase in rotor weight for offshore wind turbines. The scaling factor of the

215

empirical regression regarding tower weight is less than 1. In general, the empirical regression

216

equations in our study are in good agreement with a prior study40 which is based on 12 wind turbines.

12 ACS Paragon Plus Environment

Page 13 of 30

Environmental Science & Technology

217 218

Figure 3. Empirical regressions among engineering parameters of wind turbines, a) between capacity

219

and rotor diameter; b) between capacity and hub height; c) between rotor diameter and rotor weight; d)

220

between rotor diameter and nacelle weight; and e) between the square of rotor diameter multiplied by

221

hub height and the tower weight. D: rotor diameter; H: hub height. Sample size: Danish Master Data

222

Register of Wind Turbines (n=9450) and The Wind Power (n=1451).

223

13 ACS Paragon Plus Environment

Environmental Science & Technology

Page 14 of 30

224

2.4. Material composition specification

225

Material composition data were extracted from Life Cycle Inventory (LCI) data documented in various

226

Life Cycle Assessment (LCA) studies. Sources of LCI data and bulk material intensities are detailed in

227

Section 4 of the Supporting Information. Materials used in foundation, internal cables, and transformers

228

were computed based on the ratio of their mass to wind turbines’ mass.

229 230

For the two critical materials (i.e., neodymium and dysprosium), we took the averages of material

231

intensities used in prior studies (see Table S3 in the Supporting Information) according to the

232

assumptions from a prior study36. In general, the hybrid-drive generators that are mainly adopted in

233

onshore wind system, use about one-third the mass of their direct-drive counterparts that are mainly

234

adopted in offshore wind system20; therefore, we assumed that the intensity of neodymium and

235

dysprosium in onshore wind turbines is one-third the intensity of offshore wind turbines. In addition,

236

we assumed that the neodymium intensity and dysprosium intensity will decrease by 30% up to 205036.

237

Due to the availability of national data, the market shares of wind turbines in Denmark based on

238

permanent magnet generators are assumed to be the same as the entire European market (17% of

239

onshore and 6% of offshore), according to the JRC Wind Energy Status Report48.

240 241

2.5. Uncertainties and sensitivity analysis

242

Our model results depend on assumptions on a few key parameters, such as the market share of

243

permanent magnet based generators and the lifetime of wind turbines. Therefore, we assessed the

244

impacts of permanent magnet based generators’ market share (increased to 50%) and lifetime extension

245

(extended to 20 or 25 years) on simulation results. We also examined the impacts of statistical

14 ACS Paragon Plus Environment

Page 15 of 30

Environmental Science & Technology

246

uncertainties in the empirical regressions and the lifetime distribution on the simulation outputs. Details

247

on statistical uncertainties in engineering parameters of wind turbines are tabulated in Table S1 in the

248

Supporting Information. Based on the variation of the statistical uncertainties, a one-factor-at-a-time

249

sensitivity analysis was conducted to assess the effect of each individual parameter on simulation

250

outputs. In other words, one individual parameter was changed while the other parameters remained the

251

same.

252 253

3. RESULTS AND DISCUSSION

254

3.1. New capacities, decommissioned capacities, and size scaling effects

255

Figure 4 shows newly installed wind power capacities (for expansion and replacement) and

256

decommissioned capacities from 2018 to 2050 under the six scenarios. Overall, the newly installed

257

capacity under the six scenarios will reach its bottom in approximately 2027 and then start picking up.

258

The newly installed capacity under the Biomass, Biomass+, and Fossil scenarios will remain largely

259

unchanged at 0.61, 0.35, and 0.54 GW in 2050, respectively; while the newly installed capacity under

260

the Wind, Hydrogen, and IDA scenarios will grow to 1.57, 2.04, and 1.74 GW in 2050, respectively.

261

Under the Wind, Hydrogen, and IDA scenarios, the expansion of wind power systems drives the

262

demand for newly installed capacity, while under the Biomass, Biomass+, and Fossil scenarios, the

263

replacement of obsolete wind energy capacities is the main driver. Concurrently, the decommissioned

264

capacity under the Hydrogen, IDA, and Wind scenarios will slightly increase, but it keeps relatively

265

stable under the Fossil, Biomass, and Biomass+ scenarios after approximately 2027.

15 ACS Paragon Plus Environment

Environmental Science & Technology

Page 16 of 30

266 267

Figure 4. Newly installed wind power capacity (for expansion and replacement) and decommissioned

268

capacity from 2018 to 2050 in the Hydrogen, IDA, Wind, Fossil, Biomass, and Biomass+ scenarios.

269 270

Although the increases in wind turbine capacity will marginally lead to less increases in wind turbine

271

dimensions (i.e., rotor dimeter and hub height), this effect is canceled out by the exponential increases

272

in wind turbine mass resulting from the growing dimensions (see Figure 3). The empirical regressions

273

derived from engineering parameters of wind turbines add up to varying size scaling effects. As shown

274

in Figure S2 in the Supporting Information, the size scaling effects of onshore wind turbines will give

275

rise to a small material efficiency improvement, as their average capacity increases from 3.6 MW to 5

276

MW. During 2018-2050, the mass per MW of onshore wind turbines will slightly decrease from 104.87

16 ACS Paragon Plus Environment

Page 17 of 30

Environmental Science & Technology

277

t/MW to 102.14 t/MW. On the contrary, the size scaling effects of offshore wind turbines will lead to a

278

modest increase in material intensities. The mass per MW of offshore wind turbines will marginally

279

increase from 149.95 t/MW to 161.64 t/MW during the same period. The ~12 t/MW growth in the mass

280

per MW of offshore wind turbines is mainly attributed to size increases in rotor and nacelle.

281 282

The expected effects of upscaling wind turbines indicate that their material requirements will increase

283

if more offshore ones are to be erected, which has been well-considered in the prospective modeling in

284

our study. These effects have been recognized in several LCA studies related to wind energy13,40.

285

Although one of the two datasets in our study is Denmark-specific, the observed size scaling effects

286

could be applied to investigate material uses of wind energy provisioning in other regions or countries,

287

because wind turbines installed in Denmark cover a wide range of models and manufacturers. More

288

importantly, incorporating the micro-engineering parameters in the prospective modeling can help

289

understand the impacts of design or technological progress on the material demand and secondary

290

supply of wind energy provisioning.

291 292

3.2. Material requirements and potential secondary materials supply

293

Figure 5 assembles the results of material requirements (inflows) and potential secondary materials

294

supply (outflows) during 2018-2050 under the six scenarios. Several key observations on the trends of

295

inflows and outflows are detailed as below.

296



The inflows of bulk materials (concrete, steel, cast iron, nonferrous metals, polymer materials,

297

and fiberglass) under the Hydrogen, IDA, and Wind scenarios will increase by 413.31%,

298

211.91%, and 328.83% respectively. Meanwhile, the outflows of bulk materials will increase by

17 ACS Paragon Plus Environment

Environmental Science & Technology

299

52.90%, 49.86%, and 33.15% respectively. On the contrary, the inflows of bulk materials will

300

increase at a slower rate under the Fossil and Biomass scenarios, or fall slightly under the

301

Biomass+ scenario. Meanwhile, the outflows of bulk materials will decrease by 23.71%,

302

15.98%, and 37.76% respectively.

303



The inflow of neodymium under the Hydrogen, IDA, and Wind scenarios will climb to 14.50

304

tonne yr-1, 12.36 tonne yr-1, and 11.15 tonne yr-1 respectively. Meanwhile, the outflow of

305

neodymium will swell to 5.64 tonne yr-1, 5.71 tonne yr-1, and 4.98 tonne yr-1 respectively. On

306

the contrary, the inflow of neodymium will decrease at first and increase to 3.78 tonne yr-1 and

307

4.28 tonne yr-1 under the Fossil and Biomass scenarios respectively, or decrease to 2.46 tonne

308

yr-1 under the Biomass+ scenario; meanwhile, the outflow of neodymium will climb up and

309

stabilize at a certain level under the Fossil (3.07 tonne yr-1), Biomass (3.34 tonne yr-1), and

310

Biomass+ (2.60 tonne yr-1) scenarios.

311



Page 18 of 30

A similar trend is observed in the inflow and outflow of dysprosium. The inflow of dysprosium

312

under the Hydrogen, IDA, and Wind scenarios will eventually climb to 1.73 tonne yr-1, 1.48

313

tonne yr-1, and 1.33 tonne yr-1 respectively. Meanwhile, the outflow of dysprosium will

314

simultaneously grow to 0.67 tonne yr-1, 0.68 tonne yr-1, and 0.59 tonne yr-1 respectively. On the

315

contrary, the inflow of dysprosium will decrease at first and increase to 0.45 tonne yr-1 and 0.51

316

tonne yr-1 under the Fossil and Biomass scenarios respectively, or decrease to 0.29 tonne yr-1

317

under the Biomass+ scenario; meanwhile, the outflow of dysprosium will climb up and stabilize

318

at a certain level under the Fossil (0.37 tonne yr-1), Biomass (0.40 tonne yr-1), and Biomass+

319

(0.31 tonne yr-1) scenarios.

18 ACS Paragon Plus Environment

Page 19 of 30

320

Environmental Science & Technology



The aforementioned observations indicate that, in the case of both bulk materials and critical

321

materials, the gap between their inflow and outflow will be enlarged under the Hydrogen, IDA,

322

and Wind scenarios, and it will still be enlarged but to a lesser degree under the Fossil, Biomass,

323

and Biomass+ scenarios.

324

325 326

Figure 5. Material requirements (inflows) for newly installed capacity and potential secondary

327

materials supply (outflows) from decommissioned capacity from 2018 to 2050 in the Hydrogen, IDA,

328

Wind, Fossil, Biomass, and Biomass+ scenarios. Note: positive numbers represent inflows and

329

negatives represent outflows. Nd: neodymium. Dy: dysprosium.

330 19 ACS Paragon Plus Environment

Environmental Science & Technology

Page 20 of 30

331

Evidently, Denmark’s wind energy sector would be exposed to high supply risk if the country is

332

transitioning towards a wind powered economy in all 100% renewable energy scenarios. To

333

demonstrate the imbalance between material requirements and potential secondary material supply, as

334

well as its dynamics over time, we propose an indicator ‘circularity potential’, which is defined by the

335

ratio of outflow to inflow. This indicator measures not only the material supply risk that the wind

336

energy sector is exposed to, but also to what extent the secondary material supply can potentially

337

mitigate the material supply risk. We could observe that the ‘circularity potential’ of both bulk

338

materials and critical materials in the Fossil, Biomass, and Biomass+ scenarios is consistently higher

339

than that in the Hydrogen, IDA, and Wind scenarios, because in-use capacities in the former scenarios

340

will remain stable or only slightly increase and decommissioned capacities will gradually rise. It can be

341

observed that the ‘circularity potential’ of critical materials (neodymium and dysprosium) under the

342

Hydrogen, IDA, and Wind scenarios will increase from 0.24%, 0.17%, and 0.26%, peak at 45.5%,

343

54.30%, and 51.31%, and fall to 38.91%, 46.23%, and 44.68%, respectively. On the contrary, the

344

‘circularity potential’ of critical materials under the Fossil, Biomass, and Biomass+ scenarios will

345

climb from 0.34%, 0.23%, and 0.28% to 81.27%, 77.89%, and 105.55%, respectively. The consistently

346

higher ‘circularity potential’ of critical materials is explained by two factors: mounting secondary

347

supply from decommissioned wind turbines and less material intensities of new turbines (see Table S3

348

in the Supporting Information). Although Denmark is sending its wastes abroad (e.g., Germany,

349

Turkey, Sweden, Spain, or China)49, the ‘circularity potential’ can help understand to what extent

350

circular economy strategies reduce raw material requirement in Denmark if the country is to stipulate

351

extended producer responsibility (EPR) policies expanded across national borders50. The consistently

352

higher ‘circularity potential’ of critical materials also indicates that the supply risk of wind power

353

provision can be substantially reduced by implementing relevant circular economy strategies. 20 ACS Paragon Plus Environment

Page 21 of 30

Environmental Science & Technology

354 355

It should be noted that harnessing secondary materials in decommissioned wind turbines, as identified

356

in the ‘circularity potential’, depends on many other socioeconomic and technological factors as well.

357

For example, a wide range of circular economy measures on fiberglass were identified in a prior

358

study51, such as reuse, resize, recycle, recovery, and conversion. However, commercial applications of

359

secondary fiberglass are extremely limited, due to its low-value and complex composition, the lack of

360

material composition documentation, the long transportation distance, as well as the underdevelopment

361

of extended producer responsibility regulations. Another typical example is the currently negligible

362

recycling of neodymium and dysprosium52, because their recycling technologies are still in their

363

infancy. Reuse of permanent magnet seems to be a better option, but the size and materials

364

specifications of the permanent magnets available from decommissioned wind turbines might not fit

365

future wind turbine design. Therefore, to deliver reliable secondary material supply, several framework

366

conditions (e.g., regulations, logistics management, recycling infrastructure, appropriate design for

367

reuse, and enough economic incentives) need to be considered and improved, and different EoL options

368

for decommissioned wind turbines should be scrutinized and optimized53,54. Since Denmark is one of

369

the pioneers in developing wind power55, the resource implications derived from our results can be

370

transplanted to other countries who have the same ambition to develop large-scale wind power systems.

371 372

3.3. Impacts of increasing market share and lifetime extension

373

The market share of wind turbines that use permanent magnet generators would systematically alter the

374

landscape of neodymium demand (inflows) and potential secondary neodymium supply (outflows)

375

(Figure 6a; and results on dysprosium in the Supporting Information). Figure 6a shows that, if the

21 ACS Paragon Plus Environment

Environmental Science & Technology

Page 22 of 30

376

market share gradually increases to 50% by 2050, annual neodymium inflows will accordingly grow to

377

114.28 tonnes yr-1 (Hydrogen scenario; 788.14% compared to market share unchanged and the same

378

comparison hereafter), 93.27 tonnes yr-1 (IDA scenario; 754.61%), 86.36 tonnes yr-1 (Wind scenario;

379

774.53%), 24.93 tonnes yr-1 (Fossil scenario; 659.52%), 29.09 tonnes yr-1 (Biomass scenario;

380

679.67%), and 13.92 tonnes yr-1 (Biomass+ scenario; 565.85%), respectively. In particular, the impacts

381

of increasing market share on neodymium inflows under the Hydrogen, IDA, and Wind scenarios are

382

augmented by the expanding capacities, with the inflows in offshore wind turbines contributing the

383

most. Annual neodymium outflows will grow from negligible amounts (less than 1 tonne) to 7.36-

384

21.41 tonnes under the six scenarios. Under the Hydrogen, IDA, and Wind scenarios, the increasing

385

market share will consistently reduce the ‘circularity potential’ of neodymium, reaching 18.74%,

386

21.67%, and 21.20% by 2050, respectively. These results suggest that the penetration of wind

387

technologies that use a permanent magnet generator will aggravate the supply gaps of neodymium and

388

dysprosium.

389 390

On the contrary, lifetime extension will universally scale down the inflows and outflows of neodymium

391

(see Figure 6b) across the six scenarios. Results on the other seven materials (i.e., dysprosium,

392

concrete, steel, cast iron, nonferrous metals, polymer materials, and fiberglass) are documented in the

393

Supporting Information. If the average lifetime is extended to 20 years, the cumulative neodymium

394

inflows will decrease to 222 tonnes (Hydrogen scenario; 93.48% compared to lifetime unchanged and

395

the same comparison hereafter), 217 tonnes (IDA scenario; 93.08%), 187 tonnes (Wind scenario;

396

92.84%), 97 tonnes (Fossil scenario; 89.68%), 108 tonnes (Biomass scenario; 90.19%), and 79 tonnes

397

(Biomass+ scenario; 88.32%), respectively; if the average lifetime is extended to 25 years, the

398

cumulative neodymium demands will further decrease to 195 tonnes (Hydrogen scenario; 82.31%), 188 22 ACS Paragon Plus Environment

Page 23 of 30

Environmental Science & Technology

399

tonnes (IDA scenario; 81.01%), 162 tonnes (Wind scenario; 80.47%), 77 tonnes (Fossil scenario;

400

71.42%), 87 tonnes (Biomass scenario; 72.84%), and 60 tonnes (Biomass+ scenario; 67.53%),

401

respectively. However, the cumulative neodymium outflows will scale down simultaneously if the

402

average lifetime is extended, which makes the difference between the cumulative inflows and the

403

cumulative outflows almost unchanged. The results suggest that lifetime extension will not alleviate the

404

overall balance between material requirements and potential secondary supplies, but it could mitigate

405

the potential supply bottlenecks and the needs for recycling infrastructure by reducing throughputs of

406

materials.

407 408

The model was rerun for each parameter of lifetime function and each coefficient of empirical

409

regressions, where one input was moved while keeping others fixed. Results regarding fiberglass flows

410

under the Wind Scenario are selected to demonstrate the impacts of uncertainties in each parameter on

411

simulation outputs. Results on other scenarios and other materials are documented in the Supporting

412

Information. Figure 6c shows that the uncertainties in the exponent of the regression relationship

413

between offshore wind turbines’ rotor diameter and nacelle weight affect the simulation outputs the

414

most. If the exponent varies by ±76.00% (see statistical uncertainties in the Supporting Information),

415

the inflow of fiberglass in 2050 will accordingly vary from 12.99 kt yr-1 to 23.56 kt yr-1, which is

416

equivalent to [-19.37%, +48.68%]. Meanwhile, the outflow of fiberglass in 2050 will accordingly vary

417

from 4.69 kt yr-1 to 7.50 kt yr-1, which is equivalent to [-15.70%, +38.75%].

418 419

These model and uncertainty analysis results confirm that considering bottom-up engineering data of

420

technologies (e.g., the size scaling effect) in MFA could improve our understanding of materials

421

requirement and implications with improved precision. Such a modeling framework could be applied to 23 ACS Paragon Plus Environment

Environmental Science & Technology

Page 24 of 30

422

wind energy development in other countries or regions and other emerging technologies (e.g., electrical

423

vehicles, solar panels, and energy storage devices) that bear similar size scaling effects. Although

424

incorporating size scaling effects is already one step forward, understanding the materials distributed in

425

each individual component (e.g., fiberglass employed in blade or nacelle) could provide more nuanced

426

information regarding component-level supply risks or secondary material availability, which requires

427

future work on more detailed LCI data at the component level.

428 429

Using Denmark as an example, we presented a prospective model that incorporates the micro-

430

engineering parameters, delivering a comprehensive assessment of the materials demand and secondary

431

supply potentials of wind energy development. Our results signaled that Denmark’s ambitious

432

transition towards 100% renewable energy will be facing increasing challenges of materials provision

433

and end-of-life management in the next decades. We believe unlocking the material-energy-emission

434

nexus, as we show in this study, can eventually help understand the synergies and trade-offs of relevant

435

resource, energy, and climate strategies and inform governmental and industry policy making in future

436

renewable energy and climate transition.

437

24 ACS Paragon Plus Environment

Page 25 of 30

438

Environmental Science & Technology

25 ACS Paragon Plus Environment

Environmental Science & Technology

Page 26 of 30

439

Figure 6. a) Impacts of increasing market share on annual neodymium flows from 2018 to 2050 in the

440

Hydrogen, IDA, Wind, Fossil, Biomass, and Biomass+ scenarios; b) Impacts of lifetime extension on

441

cumulative neodymium flows from 2018 to 2050 in the Hydrogen, IDA, Wind, Fossil, Biomass, and

442

Biomass+ scenarios; and c) Impacts of uncertainties in parameters of lifetime function and coefficients

443

of empirical regressions on fiberglass flows under the Wind scenario. Dashed lines represent the

444

baseline values of inflows and outflows; solid lines represent the simulation outputs of the one-factor-

445

at-a-time sensitivity analysis on 22 parameters or coefficients.

446 447

ASSOCIATED CONTENT

448

The Supporting Information is available free of charge on the ACS Publications website at DOI: XXX.

449

Supporting Information 1 includes additional figures and tables that support the modeling and the result

450

interpretation.

451

Supporting Information 2 includes results by onshore and offshore, additional results of the sensitivity

452

analysis, and a prototype model that can be further adapted.

453 454

AUTHOR INFORMATION

455

Corresponding Author

456

*Phone: + 45 65509441; e-mail: [email protected] and [email protected]

457 458

ACKNOWLEDGMENTS

459

This work is partly financed by MinFuture (Global material flows and demand-supply forecasting for

460

mineral strategies), under the Horizon 2020 Framework Programme of the European Union (Grant

26 ACS Paragon Plus Environment

Page 27 of 30

Environmental Science & Technology

461

Agreement no. 730330). The views and opinions expressed in this manuscript are purely those of the

462

authors and may not in any circumstances be regarded as stating an official position of the funding

463

agency.

464 465

REFERENCES

466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498

(1) International Energy Agency. World Energy Outlook 2018; World Energy Outlook; OECD, 2018. https://doi.org/10.1787/weo-2018-en. (2) Rogelj, J.; Shindell, D.; Jiang, K.; Fifita, S.; Forster, P.; Ginzburg, V.; Handa, C.; Kheshgi, H.; Kobayashi, S.; Kriegler, E.; et al. Chapter 2 - Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development; In Press, 2018. (3) Ali, S. H.; Giurco, D.; Arndt, N.; Nickless, E.; Brown, G.; Demetriades, A.; Durrheim, R.; Enriquez, M. A.; Kinnaird, J.; Littleboy, A.; et al. Mineral Supply for Sustainable Development Requires Resource Governance. Nature 2017, 543 (7645), 367–372. https://doi.org/10.1038/nature21359. (4) 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. https://doi.org/10.1016/j.energy.2011.07.003. (5) Alonso, E.; Sherman, A. M.; Wallington, T. J.; Everson, M. P.; Field, F. R.; Roth, R.; Kirchain, R. E. Evaluating Rare Earth Element Availability: A Case with Revolutionary Demand from Clean Technologies. Environ. Sci. Technol. 2012, 46 (6), 3406–3414. https://doi.org/10.1021/es203518d. (6) Elshkaki, A.; Graedel, T. E. Dynamic Analysis of the Global Metals Flows and Stocks in Electricity Generation Technologies. J. Cleaner Prod. 2013, 59, 260–273. https://doi.org/10.1016/j.jclepro.2013.07.003. (7) Elshkaki, A.; Shen, L. Energy-Material Nexus: The Impacts of National and International Energy Scenarios on Critical Metals Use in China up to 2050 and Their Global Implications. Energy 2019, 180, 903–917. https://doi.org/10.1016/j.energy.2019.05.156. (8) Harmsen, J. H. M.; Roes, A. L.; Patel, M. K. The Impact of Copper Scarcity on the Efficiency of 2050 Global Renewable Energy Scenarios. Energy 2013, 50, 62–73. https://doi.org/10.1016/j.energy.2012.12.006. (9) Hoenderdaal, S.; Tercero Espinoza, L.; Marscheider-Weidemann, F.; Graus, W. Can a Dysprosium Shortage Threaten Green Energy Technologies? Energy 2013, 49, 344–355. https://doi.org/10.1016/j.energy.2012.10.043. (10) Elshkaki, A.; Graedel, T. E. Dysprosium, the Balance Problem, and Wind Power Technology. Appl. Energy 2014, 136, 548–559. https://doi.org/10.1016/j.apenergy.2014.09.064. (11) Habib, K.; Wenzel, H. Exploring Rare Earths Supply Constraints for the Emerging Clean Energy Technologies and the Role of Recycling. J. Cleaner Prod. 2014, 84, 348–359. https://doi.org/10.1016/j.jclepro.2014.04.035.

27 ACS Paragon Plus Environment

Environmental Science & Technology

499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542

Page 28 of 30

(12) Sprecher, B.; Xiao, Y.; Walton, A.; Speight, J.; Harris, R.; Kleijn, R.; Visser, G.; Kramer, G. J. Life Cycle Inventory of the Production of Rare Earths and the Subsequent Production of NdFeB Rare Earth Permanent Magnets. Environ. Sci. Technol. 2014, 48 (7), 3951–3958. https://doi.org/10.1021/es404596q. (13) Sacchi, R.; Besseau, R.; Pérez-López, P.; Blanc, I. Exploring Technologically, Temporally and Geographically-Sensitive Life Cycle Inventories for Wind Turbines: A Parameterized Model for Denmark. Renew. Energy 2019, 132, 1238–1250. https://doi.org/10.1016/j.renene.2018.09.020. (14) Annual and monthly statistics https://ens.dk/en/our-services/statistics-data-key-figures-andenergy-maps/annual-and-monthly-statistics (accessed Jun 13, 2019). (15) Danish Energy Agency. Denmark’s Energy and Climate Outlook 2018: Baseline Scenario Projection Towards 2030 With Existing Measures (Frozen Policy); 2018. (16) Danish Energy Agency. Energy Scenarios for 2020, 2035 and 2050; 2014. (17) Mathiesen, B. V.; Lund, H.; Hansen, K.; Ridjan, I.; Djørup, S. R.; Nielsen, S.; es, P. S.; Thellufsen, J. Z.; Grundahl, L.; Lund, R. S.; et al. IDA’s Energy Vision 2050: A Smart Energy System Strategy for 100% Renewable Denmark; Department of Development and Planning, Aalborg University, 2015. (18) Vidal, O.; Goffé, B.; Arndt, N. Metals for a Low-Carbon Society. Nat. Geosci. 2013, 6 (11), 894– 896. https://doi.org/10.1038/ngeo1993. (19) Habib, K.; Wenzel, H. Reviewing Resource Criticality Assessment from a Dynamic and Technology Specific Perspective – Using the Case of Direct-Drive Wind Turbines. J. Cleaner Prod. 2016, 112, Part 5, 3852–3863. https://doi.org/10.1016/j.jclepro.2015.07.064. (20) Nassar, N. T.; Wilburn, D. R.; Goonan, T. G. Byproduct Metal Requirements for U.S. Wind and Solar Photovoltaic Electricity Generation up to the Year 2040 under Various Clean Power Plan Scenarios. Appl. Energy 2016, 183, 1209–1226. https://doi.org/10.1016/j.apenergy.2016.08.062. (21) Chen, J.; Zhu, X.; Liu, G.; Chen, W.; Yang, D. China’s Rare Earth Dominance: The Myths and the Truths from an Industrial Ecology Perspective. Resour., Conserv. Recycl. 2018, 132, 139–140. https://doi.org/10.1016/j.resconrec.2018.01.011. (22) Lee, J. C. K.; Wen, Z. Pathways for Greening the Supply of Rare Earth Elements in China. Nat. Sustain. 2018, 1 (10), 598–605. https://doi.org/10.1038/s41893-018-0154-5. (23) Nansai, K.; Nakajima, K.; Kagawa, S.; Kondo, Y.; Suh, S.; Shigetomi, Y.; Oshita, Y. Global Flows of Critical Metals Necessary for Low-Carbon Technologies: The Case of Neodymium, Cobalt, and Platinum. Environ. Sci. Technol. 2014, 48 (3), 1391–1400. https://doi.org/10.1021/es4033452. (24) Andersen, N.; Eriksson, O.; Hillman, K.; Wallhagen, M. Wind Turbines’ End-of-Life: Quantification and Characterisation of Future Waste Materials on a National Level. Energies 2016, 9 (12), 999. https://doi.org/10.3390/en9120999. (25) Tazi, N.; Kim, J.; Bouzidi, Y.; Chatelet, E.; Liu, G. Waste and Material Flow Analysis in the Endof-Life Wind Energy System. Resour., Conserv. Recycl. 2019, 145, 199–207. https://doi.org/10.1016/j.resconrec.2019.02.039. (26) Liu, P.; Barlow, C. Y. Wind Turbine Blade Waste in 2050. Waste Manag. 2017, 62, 229–240. https://doi.org/10.1016/j.wasman.2017.02.007. (27) Wilburn, D. R. Wind Energy in the United States and Materials Required for the Land-Based Wind Turbine Industry From 2010 Through 2030; U.S. Geological Survey: Reston, Virginia, 2011.

28 ACS Paragon Plus Environment

Page 29 of 30

543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586

Environmental Science & Technology

(28) Kim, J.; Guillaume, B.; Chung, J.; Hwang, Y. Critical and Precious Materials Consumption and Requirement in Wind Energy System in the EU 27. Appl. Energy 2015, 139, 327–334. https://doi.org/10.1016/j.apenergy.2014.11.003. (29) Pavel, C. C.; Lacal-Arántegui, R.; Marmier, A.; Schüler, D.; Tzimas, E.; Buchert, M.; Jenseit, W.; Blagoeva, D. Substitution Strategies for Reducing the Use of Rare Earths in Wind Turbines. Resour. Policy 2017, 52, 349–357. https://doi.org/10.1016/j.resourpol.2017.04.010. (30) Moreau, V.; Dos Reis, P. C.; Vuille, F. Enough Metals? Resource Constraints to Supply a Fully Renewable Energy System. Resources 2019, 8 (1), 29. https://doi.org/10.3390/resources8010029. (31) 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. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (20), 6277–6282. https://doi.org/10.1073/pnas.1312753111. (32) Imholte, D. D.; Nguyen, R. T.; Vedantam, A.; Brown, M.; Iyer, A.; Smith, B. J.; Collins, J. W.; Anderson, C. G.; O’Kelley, B. An Assessment of U.S. Rare Earth Availability for Supporting U.S. Wind Energy Growth Targets. Energy Policy 2018, 113, 294–305. https://doi.org/10.1016/j.enpol.2017.11.001. (33) Davidsson, S.; Grandell, L.; Wachtmeister, H.; Höök, M. Growth Curves and Sustained Commissioning Modelling of Renewable Energy: Investigating Resource Constraints for Wind Energy. Energy Policy 2014, 73, 767–776. https://doi.org/10.1016/j.enpol.2014.05.003. (34) Månberger, A.; Stenqvist, B. Global Metal Flows in the Renewable Energy Transition: Exploring the Effects of Substitutes, Technological Mix and Development. Energy Policy 2018, 119, 226– 241. https://doi.org/10.1016/j.enpol.2018.04.056. (35) Zimmermann, T.; Rehberger, M.; Gößling-Reisemann, S. Material Flows Resulting from Large Scale Deployment of Wind Energy in Germany. Resources 2013, 2 (3), 303–334. https://doi.org/10.3390/resources2030303. (36) Fishman, T.; Graedel, T. E. Impact of the Establishment of US Offshore Wind Power on Neodymium Flows. Nat. Sustain. 2019. https://doi.org/10.1038/s41893-019-0252-z. (37) Müller, E.; Hilty, L. M.; Widmer, R.; Schluep, M.; Faulstich, M. Modeling Metal Stocks and Flows: A Review of Dynamic Material Flow Analysis Methods. Environ. Sci. Technol. 2014, 48 (4), 2102–2113. https://doi.org/10.1021/es403506a. (38) Busch, J.; Steinberger, J. K.; Dawson, D. A.; Purnell, P.; Roelich, K. Managing Critical Materials with a Technology-Specific Stocks and Flows Model. Environ. Sci. Technol. 2014, 48 (2), 1298– 1305. https://doi.org/10.1021/es404877u. (39) Restrepo, E.; Løvik, A. N.; Wäger, P.; Widmer, R.; Lonka, R.; Müller, D. B. Stocks, Flows, and Distribution of Critical Metals in Embedded Electronics in Passenger Vehicles. Environ. Sci. Technol. 2017, 51 (3), 1129–1139. https://doi.org/10.1021/acs.est.6b05743. (40) Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Koehler, A.; Hellweg, S. Wind Power Electricity: The Bigger the Turbine, The Greener the Electricity? Environ. Sci. Technol. 2012, 46 (9), 4725– 4733. https://doi.org/10.1021/es204108n. (41) Müller, D. B. Stock Dynamics for Forecasting Material Flows—Case Study for Housing in The Netherlands. Ecol. Econ. 2006, 59 (1), 142–156. https://doi.org/10.1016/j.ecolecon.2005.09.025. (42) Liu, G.; Bangs, C. E.; Müller, D. B. Stock Dynamics and Emission Pathways of the Global Aluminium Cycle. Nat. Clim. Change 2012, 3 (4), 338–342. https://doi.org/10.1038/nclimate1698.

29 ACS Paragon Plus Environment

Environmental Science & Technology

587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620

Page 30 of 30

(43) Cao, Z.; Shen, L.; Zhong, S.; Liu, L.; Kong, H.; Sun, Y. A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock. J. Ind. Ecol. 2018, 22 (2), 377–391. https://doi.org/10.1111/jiec.12579. (44) Lacal-Arántegui, R. Materials Use in Electricity Generators in Wind Turbines – State-of-the-Art and Future Specifications. J. Clean. Prod. 2015, 87, 275–283. https://doi.org/10.1016/j.jclepro.2014.09.047. (45) Danish Energy Agency. Technology Data for Energy Plants for Electricity and District Heating Generation; 2018. (46) Danish Energy Agency. Master Data Register of Wind Turbines; 2018. (47) The Wind Power. Wind Energy Database; 2017. (48) Cristina Vázquez Hernández; Thomas Telsnig; Anahí Villalba Pradas. JRC Wind Energy Status Report–2016 Edition; 2017. (49) Habib, K.; Parajuly, K.; Wenzel, H. Tracking the Flow of Resources in Electronic Waste - The Case of End-of-Life Computer Hard Disk Drives. Environ. Sci. Technol. 2015, 49 (20), 12441– 12449. https://doi.org/10.1021/acs.est.5b02264. (50) Qu, S.; Guo, Y.; Ma, Z.; Chen, W.-Q.; Liu, J.; Liu, G.; Wang, Y.; Xu, M. Implications of China’s Foreign Waste Ban on the Global Circular Economy. Resour., Conserv. Recycl. 2019, 144, 252– 255. https://doi.org/10.1016/j.resconrec.2019.01.004. (51) Jensen, J. P.; Skelton, K. Wind Turbine Blade Recycling: Experiences, Challenges and Possibilities in a Circular Economy. Renew. Sust. Energ. Rev. 2018, 97, 165–176. https://doi.org/10.1016/j.rser.2018.08.041. (52) Ciacci, L.; Vassura, I.; Cao, Z.; Liu, G.; Passarini, F. Recovering the “New Twin”: Analysis of Secondary Neodymium Sources and Recycling Potentials in Europe. Resour., Conserv. Recycl. 2019, 142, 143–152. https://doi.org/10.1016/j.resconrec.2018.11.024. (53) Liu, P.; Meng, F.; Barlow, C. Y. Wind Turbine Blade End-of-Life Options: An Eco-Audit Comparison. J. Clean. Prod. 2019, 212, 1268–1281. https://doi.org/10.1016/j.jclepro.2018.12.043. (54) Yang, Y.; Walton, A.; Sheridan, R.; Güth, K.; Gauß, R.; Gutfleisch, O.; Buchert, M.; Steenari, B.M.; Gerven, T. V.; Jones, P. T.; et al. REE Recovery from End-of-Life NdFeB Permanent Magnet Scrap: A Critical Review. J. Sustain. Metall. 2017, 3 (1), 122–149. https://doi.org/10.1007/s40831-016-0090-4. (55) Ziegler, L.; Gonzalez, E.; Rubert, T.; Smolka, U.; Melero, J. J. Lifetime Extension of Onshore Wind Turbines: A Review Covering Germany, Spain, Denmark, and the UK. Renew. Sust. Energ. Rev. 2018, 82, 1261–1271. https://doi.org/10.1016/j.rser.2017.09.100.

30 ACS Paragon Plus Environment