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