Nighttime light images reveal spatial-temporal dynamics of global

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Characterization of Natural and Affected Environments

Nighttime light images reveal spatial-temporal dynamics of global anthropogenic resources accumulation above ground Bailang Yu, Shunqiang Deng, Gang Liu, Chengshu Yang, Zuoqi Chen, Catherine Jane Hill, and Jianping Wu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02838 • Publication Date (Web): 12 Sep 2018 Downloaded from http://pubs.acs.org on September 19, 2018

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Environmental Science & Technology

1

Nighttime light images reveal spatial-temporal dynamics of global

2

anthropogenic resources accumulation above ground

3

Bailang Yu 1,2, Shunqiang Deng 1,2, Gang Liu

4

*, 3

, Chengshu Yang 1,2, Zuoqi Chen 1,2,

Catherine Jane Hill 3, Jianping Wu 1,2

5 6 7

1

8

China Normal University, Shanghai 200241, China;

9

2

Key Laboratory of Geographic Information Science, Ministry of Education, East

School of Geographic Sciences, East China Normal University, Shanghai 200241,

10

China;

11

3

12

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

13

Denmark.

14

TOC Art

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

15 16

ACS Paragon Plus Environment


17

ABSTRACT

18

Urbanization and industrialization represent largely a process of transforming

19

materials from biosphere and lithosphere to anthroposphere. Understanding the

20

patterns of such anthropogenic material stock accumulation is thus a fundamental

21

prerequisite to assess and sustain how humans alter the biophysical movements of

22

resources around Earth. Previous studies on these anthropogenic stocks, however, are

23

often limited to the global and national scales, due to data gaps at higher spatial

24

resolutions. Here, based on a new set of national materials stock data and nighttime

25

light images, we developed a regression model to map the global anthropogenic

26

stocks of three fundamental construction materials (steel, concrete, and aluminium) at

27

a 1 km×1 km level from 1992 to 2008. We revealed an unevenly distributed pattern,

28

with over 40% found in three belts: from England across the Channel to Western

29

Europe; from eastern coast China to South Korea and Japan; and from Great Lakes

30

along eastern coast of United States to Florida. The spatial-temporal dynamics of

31

global anthropogenic stocks at smaller spatial scales reflect a combined effect of

32

physical geography, architectural and construction specifications, and socioeconomic

33

development. Our results provide useful data that can potentially support

34

policy-makers and industry on resource efficiency, waste management, urban mining,

35

spatial planning, and environmental sustainability at regional and urban scales.

36


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1. INTRODUCTION

38

Human civilization is built upon the use of natural resources. Industrialization and

39

urbanization in the past centuries have been transforming vast amount of raw

40

materials from biosphere and lithosphere to human built-environment1,2. Such a

41

continuing use of raw materials has raised concerns on both growing resource scarcity

42

and supply constraint3 and increasing environmental challenges (e.g., climate change4)

43

associated with materials production and consumption. The understanding of these

44

patterns of materials use is thus a fundamental prerequisite to explore pathways

45

towards a sustainable materials management and strategies for environmental impacts

46

mitigation.

47 48

Many previous efforts5,6 have been devoted to characterizing the patterns of global

49

and national material flows and developing indicators to inform relevant resource

50

policy. For example, EU member states have started to compile economic-wide

51

material flow accounts (e.g., domestic material extraction and total material

52

requirement) since 20077. The patterns of the accumulation of these materials in our

53

societies (e.g., buildings, infrastructure, and consumer goods), often referred as in-use

54

stocks, built environment stocks, or anthropogenic stocks (which is used consistently

55

hereafter in this paper), however, are less understood comparing to flows. These

56

anthropogenic stocks are important for ensuring both human development and

57

environmental sustainability because: (i) They are actively used by households,

58

governments, the public, or industries, over years and decades to satisfy service



59

demand (e.g., shelter and transportation) and to facilitate industrial production8–10; (ii)

60

They set boundary conditions for upstream raw materials demand and availability of

61

secondary materials at their end of life that can be recovered through urban mining4,11;

62

and (iii) They shape the physical setting of our economy and society and thus have

63

lock-in effects on societal energy use and emissions both directly (the operation of

64

stocks, e.g., fuel consumption of cars) and indirectly (the construction of stocks, e.g.,

65

energy use of cement production)4,10.

66 67

Existing studies on the characterization of anthropogenic stocks deploy either a

68

top-down approach or a bottom-up approach12. With a top-down approach, the

69

historical consumption of materials in different applications (e.g., buildings and

70

transportation) and their corresponding lifetime are used to simulate the

71

anthropogenic stocks. Case studies include metals such as iron and steel13, copper14,

72

aluminum11, and rare earth elements15 and nonmetal materials such as cement16 and

73

carbon17. However, this approach is practically limited to a global or national level

74

due to data gaps (e.g., trade data or apparent consumption data often not available on

75

the regional level) and lacks a spatial resolution. The estimation of initial stock can be

76

a challenge too especially for products with long life time like buildings and

77

infrastructure18. This might be complemented by a bottom-up approach, with which

78

all the relevant goods in a city or region and their corresponding material composition

79

are counted and then aggregated. However, these studies are usually very data and


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labor intensive and depend highly on statistics, and thus usually cover only very few

81

case cities19,20 or countries18,21 and years21.

82 83

Recently, the satellite and remote sensing data and techniques are increasingly applied

84

in socio-economic indicators22,23 and urban characteristics studies24, and open a new

85

window for the estimation of geographically refined materials stock. For example,

86

nighttime lights images are found to correlate well with anthropogenic material stocks

87

and thus regression models are used to disaggregate anthropogenic stocks on the

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country level to a more refined level25–28. However, these attempts often lack a good

89

basis of stock estimation on the country level and use only one or a few reported

90

values as a starting point in the regression. For example, Rauch27 used only some 6-16

91

samples of anthropogenic metal stock on the country level as a basis to simulate

92

stocks of other countries using their correlation with gross domestic product (GDP).

93

These results are also mostly for static years27,28 and selected sectors and materials

94

(predominately for steel26,29), and thus could not inform temporal trends and include

95

other important building materials such as concrete and aluminum.

96 97

Here, based on a new set of anthropogenic material stocks data for all world countries

98

in the past decades and a generated long-term dataset of nighttime lights images

99

obtained by the Defense Meteorological Satellite Programs’ Operational Line-scan

100

System (DMSP/OLS), we show to date the most comprehensive global mapping of

101

anthropogenic stocks (using the three key construction materials, cement, steel, and



102

aluminum, as a proxy) by 1km-1km grids from 1992 to 2008. We then discuss drivers

103

behind the spatial-temporal dynamics of global anthropogenic stocks at smaller spatial

104

scales and implications on resource efficiency, waste management, urban mining,

105

spatial planning, and environmental sustainability.

106 107

2. MATERIALS AND METHODS

108

2.1 National anthropogenic stocks of aluminum, cement, and steel. We used data

109

in our earlier studies and other literature for national anthropogenic stocks of cement

110

(1992-2009; or cement equivalent in concrete)16, steel (1992-2008)30, and aluminum

111

(1992-2010)11 for over 130 world countries or regions in the past decades. These data

112

were generated based on a top-down approach as briefly described as follows (more

113

methodology details can be found in our earlier studies11,16): For each material, its

114

cycle from production to use and end-of-life management was simulated and all

115

relevant anthropogenic stocks and flows within the cycle were quantified. Starting

116

points were either production or domestic shipment, which were further adjusted by

117

international trade flows of materials in semis and final products to calculate the flows

118

entering the use phase. Different end-use categories (such as buildings and

119

constructions, transportation, machinery and equipment, and packaging) were

120

considered and their corresponding lifetimes following a normal distribution were

121

assumed and differentiated by category and by country to simulate the flows exiting

122

from the use phase and eventually the anthropogenic stocks accumulated.

123


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2.2 Preprocessing of multi-temporal datasets of nighttime light data. Acquired

125

from the National Geophysical Data Center (NGDC) of NOAA

126

(http://ngdc.noaa.gov/eog/), satellite imageries of DMSP/OLS Nighttime Stable

127

Lights (NSL) Time Series (Version 4) applied in this study cover a 19-year timespan

128

from 1992 to 2010 and almost the whole globe. The images are obtained from six

129

individual satellites (F10, F12, F14, F15, and F16) of various periods and they are

130

annually composited. With digital number (DN) values of 0∼63 and spatial resolution

131

of 30 arc seconds (∼ 1km), the NSL data record light frequency on specifically

132

observed pixels. Since the DMSP/OLS sensor has no in-flight calibration, the original

133

composites of different satellites cannot be compared directly.

134 135

A saturated correction was considered because the range of the data is limited to 0∼63

136

and cannot express even higher light intensity, so we performed inter calibration and

137

saturated correction to build up a comparable time series of nighttime light data based

138

on a dependable image registration approach named invariant target method31.

139

Specifically, we first identified an invariant region (Hegang city, China, in this case)

140

where light intensity was stable during the whole study period. Then we chose the

141

DMSP/OLS image of year 2006 as a reference and built a power function model using

142

pixels of Hegang city as samples. In the end, we estimated the coefficients and

143

applied the model to the whole image to have saturated pixels corrected. This inter

144

calibrated method may lead to uncertainty and gaps between the start year and the end

145

year and their adjacent years because they do not have any year to refer to.



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As anthropogenic stocks are mainly found in buildings and infrastructure, especially

148

common seen in urban areas, we implemented a natural-city extraction method

149

proposed by Jiang32 to limit the estimated anthropogenic stocks inner urban area and

150

set light intensity outside natural city regions to zero. Using zonal statistics method

151

provided by ArcGIS, values of a nighttime light image for all natural city areas inner a

152

specific country or region are summarized as the indicator of light intensity for that

153

country or region, so that a corresponding dataset of light intensity and anthropogenic

154

stocks on the country level can be generated to conduct the regression models.

155 156

2.3 Development of regression models between anthropogenic stocks and

157

nighttime light intensity. Both anthropogenic stocks and nighttime light data are

158

indicators of anthropogenic activities. Previous studies have shown that nighttime

159

lights correlate relatively well with social-economic indicators such as GDP27,

160

regional population33, electricity consumption34, and carbon emissions35 by either

161

linear regression models or double-log models. In this study, we assumed that an

162

empirical power function is appropriate, as suggested by previous studies26, to

163

describe the relationship between anthropogenic stocks and nighttime light data:

= ℎ ∗ 164

(1)

Which can be transformed to double-log form as:

log = + ∗ log ℎ


(2)

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Where stock and light represent corresponding mass of anthropogenic stocks and

166

intensity of nighttime light locally, and , are coefficients of the regression model.

167

Since the absolute quantity of national anthropogenic stocks is of great difference due

168

to individual heterogeneity, we normalized the anthropogenic stocks and nighttime

169

light data by total natural city area of each country or region. Thus the model can be

170

revised as:

ln__ = + ∗ ln ℎ__

(3)

171

Where __ and ℎ__ are stocks mass and light intensity in

172

an unit area (1km * 1km grid). For each type of anthropogenic stocks and every year

173

in the given time ranges, we are able to build a simple double-log regression model.

174 175

Figure 1. Scatterplot of the relationship between anthropogenic steel stocks and

176

nighttime light intensity for the year 2008. For scatterplots of all materials and all

177

years, see section 1.1, Figure S1-S54, in the Supporting Information.

178



179

Figure 1 is a case of relationships between anthropogenic steel stocks and nighttime

180

light intensity for the year 2008. To avoid potential influence of outliers, a

181

density-based spatial clustering of applications with noise (DBSCAN) is implemented

182

and some outlier points are consequently removed. Using an ordinary least square

183

estimator we get a strong correlation with R2 = 0.90 under 20 test samples. Similarly,

184

we can generate regression models for each year and each construction material to

185

describe the relationship between anthropogenic stocks and nighttime light intensity,

186

and the coefficients of the models are all significant at a 95% confidence (see details

187

in Tables S1-S3 in the Supporting Information).

188 189

2.4 Model applications and gridded anthropogenic stocks. Applying the simple

190

double-log linear regressions represented above, we were able to estimate the

191

parameters by country and by material given in the models, and thus, calculate the

192

quantity of anthropogenic stocks at a 1km grid level per country per year. For the

193

convenience of visualization at a sub-country level, we adopted boundary line data of

194

cities from the Database of Global Administrative Areas (GADM; https://gadm.org/),

195

which may be slightly different from data from statistics.

196 197

3. RESULTS AND DISCUSSION

198

3.1 Spatial-temporal dynamics of anthropogenic stocks at different geographical

199

levels. We found that material stocks are widely distributed in human settlements

200

across the world (Figure 2), and rapid industrialization36 and massive urbanization37 in


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the past decades have absorbed vast amount of construction materials into buildings

202

and infrastructure. The global anthropogenic stocks have doubled in two decades from

203

about 36.3 billion tons in 1992 to over 75.4 million tons in 2008. Figure 2 also shows

204

that global anthropogenic stocks are not evenly distributed, ranging from averagely

205

about 26,000 tons/km2 in Africa to 38,000 tons/km2 in Asia to over 40,000 tons/km2

206

in Europe, America, and Oceania. Three regions or belts possess over 40% of global

207

anthropogenic stocks in 2008: (1) Most of Europe, from England across the Channel

208

to Germany, southern to Italy and Spain; (2) East Asia, from eastern coast China to

209

South Korea and Japan; (3) East and northeastern United States, along Great Lakes

210

and eastern coast to Florida. These identified regions are in general consistent with

211

results of Rauch27 for the world’s anthropogenic stock of four metals (iron, aluminium,

212

copper, and zinc) in 2000.

213 214

At the country level, it can be seen that the North China Plain, the Yangtze River

215

Delta, and the Pearl River Delta of China, where drastic urbanization is taking

216

place37,38, have become clearly most important anthropogenic stocks deposits, while

217

western Europe, the main island of Japan, and eastern United States shows a relatively

218

more even distribution due to its steady urbanization and economic growth. New

219

York metropolitan regions have the world’s highest anthropogenic stocks density (up

220

to approximately 59,000 tons/km2 on average), due to its dense population and

221

crowded skyscrapers. In addition, a few other world regions also possess large

222

quantity of anthropogenic stocks, such as regions around Johannesburg, Rio de



223

Janeiro, the Nile Valley, west bank of the Persian Gulf, and the north side of Malacca,

224

where there are important seaports, estuary, or regional central cities.

225

Figure 2. Global mapping of anthropogenic stocks (adding up cement, steel, and aluminum) in 2008 (tons/km2) at a 1 km1 km grid level. a) Part of Europe and North Africa; b) East Asia (eastern China, South Korea, and Japan); c) North America (mainly USA and southern Canada). Figures for specific materials can be found in the Supplementary Information (Section 1 Figure S55-S125).

226

We visualized the varying temporal dynamics of anthropogenic stocks at smaller

227

spatial scales in different regions in Figure 3, based on RGB (with red, green, and


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blue representing the total anthropogenic stocks in 1992, 2000, and 2008, respectively)

229

composite images.

230

•

In Figure 3a, for example, southern Europe is dominated by white and is of greater

231

brightness than North Africa, indicating a steady yet overall larger amount of

232

anthropogenic stocks in the long run (fluctuating between 48,000 tons/km2 and

233

52,000 tons/km2). On the contrary, North Africa shows more blue areas, indicating

234

a relatively lower (about 23,000 tons/km2) but increasing pattern of anthropogenic

235

stocks accumulation between 2000 and 2008. This manifests the rapid

236

socioeconomic

237

Valley and the southern bank of the Mediterranean in the first decade of the 21st

238

century.

239

•

development (e.g., due to booming oil price) along the Nile

Similar circumstances can be found in East Asia (Figure 3b), where China, South

240

Korea, and Japan represent different stages of socioeconomic development from

241

1992 to 2008. China is still undergoing a rapid development process (the bright

242

blue spots), so both the absolute anthropogenic stocks and its spatial distribution

243

have greatly increased. While in South Korea, we see large regions in bright white

244

or green, which indicates a major increase of anthropogenic stocks from 1992 to

245

2000. Japan shows a high and stable anthropogenic stocks distribution across the

246

country (all the bright white areas), as it had reached rather high level of

247

development at an earlier time before 1992. With the continuing urbanization and

248

industrialization, East Asia has reached approximately the same level of

249

anthropogenic stocks as Europe (averagely over 40,000 tons/km2).



250

•

The Greater Lakes region (Figure 3c) has shown a rather widely and evenly

251

distributed pattern of anthropogenic stock, due to its early history of urbanization

252

and industrialization. These areas are highly populated and urbanized and have

253

dense transportation networks, resulting in a stock level as high as 59,000

254

tons/km2.

255

•

In the Middle East (Figure 3d), where deserts and barren lands could not support

256

many human settlements, the anthropogenic stocks appear much less. Only the oil

257

producing regions near the Persian Gulf and some parts of Saudi Arabia show

258

relatively high deposits of anthropogenic stocks. In particular, cities like Dubai

259

and Abu Dhabi show a clear pattern of stock increase, owing to their

260

diversification industry strategies (for non-oil economy) and heavy investments on

261

real estate and tourism. On the contrary, a few places (the red spots in Figure 3d)

262

in the Middle East and northeast Africa (e.g., in Iraq, Iran, and Sudan) witnessed a

263

decrease of anthropogenic stocks, assumedly due to destruction caused by wars.


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Figure 3. RGB (red, green, blue) composite images for the

2008

spatial-temporal dynamics of regional anthropogenic stocks. 2000

The red band is for the year 1992, green band is for the year 1992

2000, and the blue band is for the year 2008. a) Southern Europe and North Africa; b) East Asia; c) Part of the USA and Canada; d) Middle East near the Persian Gulf.

264 265

3.2 Drivers behind the growth of anthropogenic stocks. The regional differences of

266

anthropogenic stocks at more geographically refined levels than earlier global and

267

national estimates shown in Figures 2 and 3 may reflect a combined effect of physical

268

geography (e.g., terrain conditions, local climate, and resource availability),

269

architectural and construction specifications (e.g., materials choice and architectural



270

style), and socioeconomic development (e.g., economy and stages and models of

271

urbanization), though a quantitative analysis of these drivers is not possible.

272 273

Figure 4 shows the distribution of anthropogenic stocks of a few selected metropolitan

274

areas at a higher resolution. Figure 4a and 4b demonstrate clear results of

275

transit-oriented development: anthropogenic stocks congregate along the rail networks

276

(including Shinkansen) in the Kanto region of Japan and along the coastal ports (e.g.,

277

Ancona, Ravenna, and Venice) and their connecting railway lines in Northeastern Italy.

278

The Tokyo (Figure 4a) and New York metropolitan areas (Figure 4c) contain multiple

279

closely-connected urban agglomerations with high anthropogenic stocks, reflecting

280

the dense buildings (e.g., steel skyscrapers) and infrastructure (e.g., over 750

281

passenger cars per 1000 people in the US), high and even socioeconomic

282

development, and terrain conditions (vast plains) in these urbanized areas. On the

283

contrary, Figure 4d shows that anthropogenic stocks distribute more in the city centers

284

in Beijing and Tianjin than sub-urbans and surrounding rural areas, which are still

285

experiencing a rapid expansion process.

286


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Roads/ routes

railway

10,000

30,000

tons 58, 58,000

Figure 4. High-resolution mapping of anthropogenic stocks in 2008 for selected areas: (a) Kanto region of Japan, around the Greater Tokyo area; (b) Northeastern Italy, along the western coast of the Adriatic Sea; (c) Eastern United States, around the New York Metropolitan area; and (d) the Beijing-Tianjin-Hebei Metropolitan region, China. 287 288

3.3 Robustness and uncertainty of our estimation. Our results provide a first crude

289

estimate of the global anthropogenic stocks on a high resolution level (1 by 1 km) and

290

a fast-estimation technique based on night-time light images with much less data and

291

time cost. These results, of course, bear unavoidably uncertainties from both

292

nighttime light data handling and its correlation with anthropogenic stocks.

293

First, the full nighttime light saturation effect27 in city centers could not be fully



294

solved without additional urban related data, and we had to adopt a natural city

295

extraction concept to limit our stock analysis in the urban areas.

296

Second, nighttime lights reflect only patterns of anthropogenic stocks above

297

ground but can hardly indicate the amount of anthropogenic stocks underground

298

(such as tunnels and pipelines).

299

Third, the robustness of the power function correlations between anthropogenic

300

stocks and nighttime lights vary by material (e.g., less robust for cement than

301

iron), by sub-category of stocks (e.g., less robust for mobile stocks such as

302

consumer goods than buildings and infrastructure), and by region at different

303

economic development levels (we normalized the nighttime light and stock

304

values by total natural city area to reduce regional difference); therefore such a

305

correction established on the country scale may be imprecise in reflecting both

306

heterogeneity of countries and global unitarity.

307

Lastly but most importantly, scale effect occurs when downscaling the regression

308

relationship established at the country/regional level to grid levels. Although our

309

results reach a best resolution of 1 by 1 km in the present circumstances,

310

comprehensive validation is not possible due to lack of primary data at higher

311

resolution (values are heterogeneous even under 1km grid level).

312 313

In short, these generated data should be interpreted with care at a high-resolution level.

314

More empirical case studies on anthropogenic stocks at sub-country, city, or sub-city

315

levels and improved data and methods on nighttime light handling could help address


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these limitations and improve gridded anthropogenic data in the future.

317 318

Nevertheless, the overall spatial-temporal trends and fine-resolution values are

319

reasonable. For example, on the country level, our estimated results of anthropogenic

320

aluminium stock of China in 2005 agree to that mentioned by Gerst and Graedel39

321

within a range of 10%, and our results of anthropogenic cement stock of the U.S. in

322

2000 is consistent with Kapur et al.40 within 5%. Moreover, our results are in good

323

accordance (differing from 9% to 32% in Table 1) with the few earlier studies at a

324

sub-country level (e.g., from district to city to province). These differences are

325

encouraging considering the level of details we provided.

326

Table 1. Comparison between our results with previous estimates Region/City

Year

Material

Connecticut, US

2000

Aluminium

Salford Quays, Manchester, UK Wakayama city centre, Japan Hannan, China Chinese provinces

2000

Cement

327

200841); bottom-up (Tanikawa and 42

Hashimoto 2009 )

This study

Difference

0.9 Mt

25%

~ 35, 000 tons/km2

13%

~0.86 Mt a (Tanikawa 2004

Cement

and Hashimoto

0.75 Mt

13%

0.155 Mt

9%

42

2009 ) 2005

Aluminium

2008

Steel

2010

and civil engineering only)

Vienna, Austria

1.2 Mt (Recalde et al. ~40, 000 tons/km2

Steel (buildings Aichi, Japan

Earlier estimate

2013

0.17 Mt (Lou and Shi 200843); bottom-up ~ 300 Mt on average 26

(Liang et al. 2014 ) ~39 Mt (Liang et al. 201644)

~270 Mt on average ~33 Mt (total stock;

5.9 Mt (Kleemann et.

4 Mt (total

only)

al 201645); bottom-up

stock; 2008)

328


15%

2008)

Steel (buildings

Note: a. Based on a concrete-to-cement conversion factor of 8.4.

10%

32%


329

3.4 Implications and discussion. More spatially refined anthropogenic stocks

330

information we generated has significant implications for facilitating more in-depth

331

research on resource management and urban sustainability and supporting policy

332

discussion on resource and environmental sustainability, especially at regional and

333

urban scales.

334

•

Resource recovery via urban mining becomes increasingly important in the

335

circular economy and climate policy agenda worldwide. However, our

336

understanding of the quantity, quality, age, and location of urban mines is still

337

much poorer comparing to that of underground mines. Such a spatially refined

338

mapping of anthropogenic stocks could provide quantitative guidance on

339

potentials (e.g., quality and location) and costs (e.g., transportation costs) for

340

construction industry, demolition companies, and waste handlers. Since

341

anthropogenic stock covers the total amount of material quantities that have been

342

identified through prospection and exploration (often using models), regardless

343

their economic feasibility to retrieve under certain conditions, further research on

344

the “reserves” of urban mines (vis-à-vis geological reserves) is urgently needed.

345

•

Urbanization induced building and infrastructure development couples human

346

well-being development (e.g., shelter and mobility) and environmental impacts

347

along their entire life cycle from construction, maintenance, use, to end-of-life

348

management. Characterization of the these anthropogenic stocks on a high

349

geographical resolution could connect the previously disconnected research fields

350

on their direct impacts in use (e.g., urban geography on transportation behavior


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and work-live spaces) and indirect impacts embodied in materials production and

352

construction (e.g., industrial ecology on life cycle environmental impacts). This

353

could potentially open up a new line of research that enables transformative,

354

dynamic, and integrative understanding of urban sustainability and provide a full

355

exploration of dematerialization and decarbonization strategies for cities.

356

•

Stock growth patterns vary in different countries and cities. Previous

357

understanding on the socioeconomic drivers behind these stock patterns exists

358

only on the global and national levels. The anthropogenic stocks data on a more

359

refined level could help explore patterns and drivers (e.g., urban form and

360

transportation mode) of stock growth from a micro view and thus provide

361

benchmark for development of less developed regions and cities.

362

•

Cities are the core of both problems and solutions to global sustainability

363

challenges. Traditional urban sustainability indicators are based exclusively on

364

flow parameters such as water, energy, emissions, and income. Growing

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information on anthropogenic stocks at the city or sub-city level could facilitate

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the establishment of urban sustainability indicators from a critical, but hitherto

367

largely overlooked, stock perspective (e.g., the weight of cities), which could

368

complement the current array of urban sustainability indicators and related policy.

369 370

AUTHOR INFORMATION

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Corresponding Author



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

373

[email protected].

374

ORCID

375

Gang Liu: 0000-0002-7613-1985

376

Notes

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The authors declare no competing financial interest.

378 379

ASSOCIATED CONTENT

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Supporting Information. Supplementary data and figures on the regression and

381

gridded anthropogenic stock results by material by year, and link to freely available

382

GeoTIFF format files for all the anthropogenic stocks estimation results in Havard

383

Dataverse46. The Supporting Information is available free of charge on the ACS

384

Publications website at DOI: XXX.

385 386

ACKNOWLEDGEMENTS

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We acknowledge financial support from National Natural Science Foundation of

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China (41728002, 41471449, 41871331, and 41801343), Independent Research Fund

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Denmark (CityWeight, 6111-00555B), and the Innovation Program of Shanghai

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Municipal Education Commission (15ZZ026).

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REFERENCES

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