Nexus between economy-wide metal inputs and the deterioration of sustainable development goals

Resources, Conservation & Recycling 149 (2019) 12–19

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Nexus between economy-wide metal inputs and the deterioration of sustainable development goals

T

Keisuke Nansaia,b, , Yasushi Kondoc, Damien Giurcod, David Sussmane, Kenichi Nakajimaa, Shigemi Kagawaf, Wataru Takayanagia, Yosuke Shigetomig, Susumu Tohnoh ⁎

a

Center for Material Cycles and Waste Management Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506, Japan Integrated Sustainability Analysis (ISA), School of Physics, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia c Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo, 169-8050, Japan d Institute for Sustainable Futures, University of Technology Sydney, 235 Jones St, Ultimo, 2007, Australia e Global Development and Environment Institute, Tufts University, 44 Teele Avenue, Somerville, MA, 02144, USA f Faculty of Economics, Kyushu University, 744 Motooka, Nish-ku, Fukuoka, 819-0395, Japan g Faculty of Environmental Science, Nagasaki University, 1-14, Bunkyo-machi, Nagasaki, 852-8521, Japan h Graduate School of Energy Science, Kyoto University, Sakyo-ku, Kyoto, 606-8501, Japan b

ARTICLE INFO

ABSTRACT

Keywords: Sustainable development Social impact Scarce metals International trade Mining Material flow

This study investigates, at the country level, the adverse effects of changes in metal inputs on the achievement of sustainable development goals (SDGs). It also highlights the relationships between metals use and various socioeconomic consequences that urgently require decoupling in order to achieve the SDGs. We performed panel data analysis to evaluate, on a national scale and over a ten-year period (2004–2013), the impact of changes in the material flows of 11 metals on 96 SDG indicators corresponding to the 17 SDGs defined by the UN. On average, an increase in the material flow of the targeted metals was found to be correlated with a deterioration in approximately 10% of the 96 indicators. Among the affected SDGs, the adverse impact of metals on SDG 3 (Health), SDG 8 (Economic Growth), and SDG 16 (Peace, Justice, and Strong Institutions) was particularly noteworthy. More SDGs were negatively impacted in metal-mining countries than in metal-importing countries.

1. Introduction Society’s current approach to sustainable material use (Bruckner et al., 2012; Giljum et al., 2014; O’Rourke and Lollo, 2015; Schandl et al., 2016; UNEP, 2011) appears broken, as rising populations and deepening industrialization lead to modern society’s ever-growing appetite for raw materials, reflected notably in an eightfold rise in extraction over the past century (Krausmann et al., 2009). Establishing sustainable material use requires increased focus on decoupling miningrelated production and consumption activities from negative environmental and social impacts (Bebbington et al., 2008; Franks et al., 2014). Mining industries have been tackling such decoupling via the concept of a “social license to operate” (Demuijnck and Fasterling, 2016; Dare et al., 2014; Prno and Slocombe, 2012) designed to avoid, in advance, environmental pollution and social impacts on the communities around mining sites that may impede the mining operation. Today, extending this same concept to the entire mining supply chain is becoming

essential to establish “responsible mining” that promotes coordination with downstream industries, i.e. those using the materials in manufacturing, in order to mitigate social and environmental impacts (Giurco et al., 2014). Practical attempts to minimize the adverse social and environmental effects of extraction are being propelled forward by the policies and actions of various actors on multiple scales. For example, the 2003 Extractive Industries Transparency Initiative (EITI) encourages accountability by countries, international financial institutions, companies, and civil society actors in order to reduce social and environmental harms from mining, while the 2010 National Resource Charter outlines principles for resource governance (EITI, 2017; Natural Resource Governance Institute, 2014). The 2010 Dodd-Frank Act enacted in the US is an example of legislation requiring corporations to mitigate social impacts generated during resource extraction (Ayogu and Lewis, 2011). The 2015 United Nations Sustainable Development Goals (SDGs)

⁎ Corresponding author at: Center for Material Cycles and Waste Management Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan. E-mail address: [email protected] (K. Nansai).

https://doi.org/10.1016/j.resconrec.2019.05.017 Received 12 February 2019; Received in revised form 9 May 2019; Accepted 16 May 2019 Available online 28 May 2019 0921-3449/ © 2019 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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(United Nations, 2015) represent a shared recognition by governments around the world of the imposing challenges of sustainable development by establishing 17 goals with 169 targets. To accomplish these goals and meet these targets, the supply chain of materials use needs to be tied in some way (Tseng et al., 2019), directly or indirectly, to all the SDGs. Some recent research papers have mapped these connections, although without providing quantitative measures (Columbia Center on Sustainable Investment (CCSI), 2016). However, several recent systematic literature reviews have found that most investigations connecting mining to SDGs focused on the environmental aspect, leaving a significant gap with respect to the social goals of sustainable development (Fernandes de Mesquita et al., 2017), with the social impacts of mining relating mainly to three areas: land use and territorial aspects, environmental impacts affecting health, and human rights (Mancini and Sala, 2018). Another review (Tost et al., 2018) on environmental sustainability in mining concluded that the mining industry is at risk of falling behind society’s expectations with regard to climate change. The little research that does address these social issues often considers only a single country or region (Antwi et al., 2017; Yakovleva et al., 2017). One sound approach to identifying factors impeding the achievement of SDGs within the material use supply chain is bottom-up surveys on individual mining sites and industrial groups (Bebbington and Humphreys Bebbington, 2018), which can yield highly detailed and accurate assessment (Amirshenava and Osanloo, 2018, 2019; Dialga, 2018; van den Brink et al., 2019). Given the wide spectrum of SDG targets, however, this approach would be neither comprehensive nor rapid, and would be prohibitively expensive on a global scale. In addition, such narrowly focused efforts cannot be aggregated or relied on to draw broader conclusions about the impact of material flows on SDGs. Taking an opposite approach to make up for these shortcomings, this study aims to identify materials associated with a deterioration in various SDGs. It then addresses efficient prioritization of the environmental and social impacts that should be decoupled from material use from a global and macroscopic perspective. Specifically, focusing on base and scarce metals, we adopt a top-down approach in which the relationship between metal use and various indicators corresponding to the SDGs is statistically determined using panel data (cross sectional time-series data) for the targeted 10-year period.

measuring human prosperity, or moving on from GDP altogether (Stiglitz et al., 2018). Population, for its part, is more often considered in terms of the SDGs’ impact on population, rather than vice versa (Abela et al., 2016). Studies have also shown governance (e.g., effectiveness, regulations, and the rule of law) and development to be positively correlated (Keser and Gökmen, 2018) (Ayre and Callway, 2013). In addition, some research has considered the relationship between the energy needed to support the development of human societies and the impact of its use on global sustainability (Brand-Correa and Steinberger, 2017). These concerns about energy use and sustainability have led to a focus on decoupling (Akizu-Gardoki et al., 2018). Most relevant to this investigation, there is potential for mining, and the raw materials thereby accessed, to play a constructive role in helping countries meet the defined Sustainable Development Goals (World Economic Forum, 2016). Yet there is still uncertainty regarding how exactly to measure their association. This research need occurs at a time of rapidly growing evidence of aggregate adverse human influence on the planet, including concerns about resource limits, especially given the fact that minerals and metals remain essential for sustainable technologies and lifestyles (Arrobas et al., 2017). Overall, there is a need to develop further means of comparing the influence of macroeconomic variables, particularly those relating to metals, on SDGs. The literature cited motivated us to formulate Eq. (1) to describe the relationship between these factors along with the material flow of metals and the aforementioned WB-SDG indicators. A regression analysis using Eq. (1) and the country-level panel data that we developed was performed to identify metals for which increasing material flows were significantly related to a worsening of SDG indicators. Of the various material flow indicators available, direct material input (DMI) (Eurostat, 2001) was adopted here as an indicator of the amount of metal directly input into a given country, because of its widespread use for analyzing economy-wide material flows (Fischer-Kowalski et al., 2011), and the ease of data compilation for continuous calculation and management by individual countries. Here, a separate DMI was calculated for each metal. Given that the social and environmental impacts occurring at the time of mining have received considerable attention in the past, we separated the DMI pathway into domestically mined metal and metal imported from elsewhere:

2. Material and methods

yit =

0

+

2.1. Panel data analysis

+

1log(GDPit ) + 2 log(POPit ) + 3log(GOVit ) + 4 log(ENEit ) M M T MINEkit + IMPORTkit + D + ui k=1 k k=1 M+k s = 2 s st

+ vit (i = 1, …, N ; t = 1, …, T )

This study used panel data analysis to demonstrate the association between metal use on a national scale and a wide range of issues related to SDGs. Specifically, to quantify the status of the SDGs, we assembled panel data on the quantitative indicators included in the Sustainable Development Goals Database (WB-SDGs) developed by the World Bank (The World Bank, 2017). This database provides multiple quantitative indicators corresponding to each SDG, with scores for each indicator broken down by country and year. In this study, a formulation of panel data analysis has been developed that fits within and builds on the literature concerning the association between macroeconomic variables and socio-economic indicators such as the Sustainable Development Goals. This relationship can be assessed on a variety of scales. While there is value in examining relevant indicators on a per capita basis (El-Maghrabi et al., 2018), it is also worthwhile to consider SDG measures at the national level (Kanbur et al., 2018). Other research has focused on the interconnections and interrelationships among the SDGs (Pradhan et al., 2017). A number of macroeconomic indicators have been compared with social outcomes. There is a recognized link between economic growth and human development (Ranis et al., 2000). However, GDP, which serves as one numerical yardstick, cannot capture all the intricacies of an economy, such as inequality or sustainability (Stiglitz et al., 2018). Some researchers have promoted the use of other indicators for

(1)

where i is the economic entity (country or region); t is the fiscal year; j , k , and s are parameters to be estimated; yit is an indicator of the WBSDGs; and GDPit , POPit , GOVit , and ENEit are the indicators for GDP, population, political governance score, and primary energy consumption, respectively; MINEkit and IMPORTkit are the DMI (mined and imported amounts) for metal k = 1, …, M ; Dst is a time dummy for year s such that Dst = 1 when s = t , and Dst = 0 otherwise; ui is an unobserved (time-constant) effect; and vit is an idiosyncratic error term. Letting x it refer to the vector of all the explanatory variables above, we assumed that E(vit x it , ui) = 0 (t = 1, …, T ), E(vit2 xit , ui ) = v2 (t = 1, …, T ), and E(vis vit x it , ui) = 0 (s, t = 1, …, T ; s t ). Whereas parameters j , k , and s are constant across economic entities and over time, they are allowed to vary with the WB-SDG indicators. We applied the fixed-effects method to estimate the parameters (Wooldridge, 2010), where the effects of time-invariant variables are controlled and whatever effects those variable have are assumed to be more or less the same across countries because of random assignment. For each of the indicators, we first used all the explanatory variables and estimated the parameters. Next, we eliminated from Eq. (1) the metal-related explanatory variables for which the p-values were larger than 0.3 and then re-estimated the parameters. We considered an explanatory variable to affect the indicator under study when its coefficient was significant at the 5% level. The same procedure was repeated 13

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for every indicator. Analyses were performed with Matlab2018a using the panel analysis function developed by Alvarez et al. (Álvarez et al., 2017)

unprocessed ore. For regenerated metals, we estimated amounts using USGS estimates (Fe, Cu, Pb, Zn, Al), market survey data (Pt) (O’Connell et al., 2018), recycled stainless (Ni, Cr) (World Bureau of Metal Statistics (WBMS), 2015; Daigo et al., 2010) and recycling rates (Co in the US only) from USGS(U.S. Geological Survey, 2018). No regenerated volumes were estimated for Nd or Mo, and for Co in the US only. Metals in the mined waste rocks were not included, because DMI focuses on resources entering the industrial economy for further processing (Adriaanse et al., 1997). We attempted to comprehensively capture the amount of metal used in a given country by including not only ore and unprocessed metal, but also the amount contained in raw materials and products that are imported. Because no database exists that would enable direct assessment of the amounts of metals imported, we estimated the amounts of metals contained in materials and products that are transferred between countries through trade using existing methods (Nakajima et al., 2017; Nansai et al., 2014) and created time-series data for imported metals. Descriptive statistics for the explanatory variables are provided in Table S2 of the SI.

2.2. Data compilation 2.2.1. Explained variables The WB-SDG database contains 316 indicators pertaining to 17 SDGs for 263 countries and regions for every year between 1990 and 2017. However, some of the indicators are lacking data for certain countries and regions or fiscal years. Given that the most recent year with usable data for all explanatory variables was 2013, we chose the 10-year period from 2004 to 2013 as the observation period for this study. As dependent variables, we only used indicators with multi-year data within the observation period for 20 countries or more (i.e., an unbalanced panel). Many of the 316 indicators in the WB-SDG database are similar or related to other indicators. By de-selecting such redundancies, we ended up with 96 indicators as dependent variables. Descriptive statistics and the corresponding SDGs for each of the indicators are provided in Table S1 in Supporting Information (SI). For indicators whose histograms indicated that their distribution was substantially skewed, we used the log-transformed indicator score as an explained variable, yit , in Eq. (1). In all other cases, we did not logtransform indicators and used their levels as explained variables. In Table S1, indicators for which the log-transformed score was used are identified as “log.”

2.3. Identification of positive and negative contributions to SDG indicators The coefficient, k , for a material flow-related explanatory variable represents its marginal effect on the explained variable — either a WBSDG indicator or its log-transformed value; that is, an increase (or decrease) in the indicator associated with a unit (for metals, 1 t = 106g) increase in material flow. The direction of change, or the sign of the marginal effect, is of considerable interest and varies according to the indicator. Specifically, there are indicators such as the Primary complete rate for which a decrease in score indicates a worsening of an issue, and indicators such as Poverty headcount ratio at $1.90 a day, for which a decrease in score indicates an improvement. In this study, to determine the impact of increasing the material flow of a given metal in a given country on social indicators based on the coefficient estimates, ˆ , obtained from panel data analysis, we grouped the indicators into k those where a positive coefficient indicates a worsening of the social issue and those where a negative coefficient indicates a worsening of the social issue. The direction of change and estimated parameters ( j , k ) for each indicator are shown in Table S3 in the SI. We also performed the following two calculations using ˆk to determine the breadth and strength of the impact of each metal on SDGs. First, to quantify the breadth of impact of each metal on SDG-related issues, we counted the number of negatively significant indicators. The greater the number of indicators impacted, the broader the impact of the material flow of a given metal on SDG-related issues. The number of indicators impacted was summed separately for each of the 17 SDGs. In addition, to investigate the breadth of impact of different metal categories on SDGs, we divided the metals into categories and calculated the mean impact by dividing the number of adversely impacted indicators by the number of explanatory variables in each category. The categories were: Metal (= all metals), Base (= base metals), Scarce (= scarce metals), Mine (= mined metals from both terrestrial and urban mines), and Import (= imported metals). Second, to quantify the strength of impact of each metal on each indicator, we focused on the metal with the largest absolute value of k as identified above (p < 0.05, with sign indicating adverse impact) on each indicator among all mined and imported metals. In short, only one mined metal or imported metal was selected as the metal having the greatest adverse impact on each indicator. By adding up the number of indicators on which the various metals had the greatest impact, sorted by their associated SDGs, we summarized the relationship between a unit increase in the DMI of mined or imported metals and the SDGs whose deterioration should receive the greatest attention.

2.2.2. Explanatory variables For individual countries, we used real GDP values (at constant 2005 prices) from UNstat, and populations as published by the UN in World Population Prospects. For governance, we used Political Stability and Absence of Violence/Terrorism scores from the Worldwide Governance Indicators issued by the World Bank (World Bank Development Research Group, 2016). This indicator measures “perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism.” For primary energy consumption, we used tonnes of oil equivalent (1toe = 41.868 GJ) values from the International Energy Agency (IEA). For DMI (mined and imported amounts), which we used as a measure of the material flows of metal in individual countries, we independently compiled time series data. For mined metal amounts, we included metals recovered from ‘urban mines’ (i.e., discarded items). In light of data availability for compiling time series, we selected five base metals (Fe, Cu, Pb, Zn, and Al) and six scarce metals (Nd, Co, Pt, Ni, Cr, and Mo). The base metals have long been in widespread use in multiple applications, while the scarce metals are presently being used in small quantities in specific applications in connection with new energy technologies. For example, neodymium (Nd) is a key element of parament magnets necessary for electric and hybrid vehicle motors (Alonso et al., 2012b; Nansai et al., 2015, 2014), while cobalt (Co) is needed for the lithium-ion batteries embedded in these vehicles and various mobile electric appliances(Nansai et al., 2015, 2014; Simon et al., 2015; Zeng and Li, 2015). Platinum (Pt) is used as an electrode catalyst for fuel cells (Alonso et al., 2012a; Nansai et al., 2015, 2014). Nickel (Ni) and chromium (Cr) are added to steel mainly to increase corrosion resistance (Nakajima et al., 2017, 2013). Molybdenum (Mo) improves the strength and hardness of steel at high temperatures (Nakajima et al., 2013). Wind power and high-efficiency fossil-fuel-based power generation as well as carbon capture and storage require steel with such characteristics (Kleijn et al., 2011). Hence, demand for the these metals is expected to rise substantially in the future as progress in carbon mitigation develops apace in the nations of the world (de Koning et al., 2018; Deetman et al., 2018; Kleijn et al., 2011; Månberger and Stenqvist, 2018; Tokimatsu et al., 2018; Vidal et al., 2013). To establish the mined amounts of each metal, we used the USGS values (U.S. Geological Survey, 2018) for the metal contents of 14

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Fig. 1. Number of SDG indicators that worsened in response to an increase in GDP, population, governance level, primary energy consumption, and metal input. The 17 goals correspond to 1: Poverty, 2: Hunger and food security, 3: Health, 4: Education, 5: Gender equality and women’s empowerment, 6: Water and Sanitation, 7: Energy, 8: Economic Growth, 9: Infrastructure, industrialization, 10: Inequality, 11: Cities, 12: Sustainable consumption and production, 13: Climate Change, 14: Oceans, 15: Biodiversity, forests, desertification, 16: Peace, justice and strong institutions, 17: Partnerships.

3. Results

Across the SDGs, increasing metal input was correlated with a worsening (in average terms) of 0.05–2.45 indicators, with the impacts on SDG 3 (Health), SDG 8 (Economic Growth), and SDG 16 (Peace, Justice and Strong Institutions) of greatest concern. In the case of SDG 3, increasing metal input was correlated with an average worsening of 1.5 indicators, indicating a narrower impact than population (4 indicators) but a broader impact than GDP or governance (both 1 indicator). In the case of SDG 8, while GDP was correlated with a worsening of five indicators, increasing metal use (considered as the average of all metals) was correlated with the worsening of 1.14 indicators and had a broader impact than population, governance level, or primary energy consumption. As for SDG 16, although the impact of increasing metal inputs to an economy was extremely small (0.05 indicators), given that the other socio-economic factors have no impact, the relationship of metal inputs to this SDG cannot be ignored.

3.1. Comparison of metals and key socio-economic factors Fig. 1 shows the number of SDG indicators that have worsened with increases in selected socio-economic factors (GDP, population, governance level, and primary energy consumption) and metal inputs. The greater the value (shown on the vertical axis), the broader is the role of a given socio-economic factor or metal input to the country in negatively influencing SDGs. The colors in the bar graph indicate the significantly affected SDG, while the numbers indicate the specific SDGrelated indicators. (There are no numbers for the “metals” category since it represents an average value for all the metals studied). The graph clearly illustrates which SDGs warrant the greatest concern in countries where metals and the four key socio-economic factors are increasing. At the same time, the factors and SDG indicators appearing in the graph are those for which decoupling should be given top priority. Because the number of SDG indicators associated with each SDG differs, it would not be appropriate to directly compare impacts across SDGs based on the number of indicators that worsened for each SDG. Rather, in this study, we used the number of indicators that worsened in response to increasing the DMI of the metal for each SDG as a reference for comparing the impacts of key socio-economic factors (GDP, population, governance level, and primary energy consumption). In doing so, we found that metal inputs warrant greater concern than the other socio-economic factors. On average, increasing the DMI of metals was significantly correlated with the worsening of 9.4, or roughly 10%, of the 96 SDG indicators. These results indicate the importance of implementing measures to mitigate adverse effects on SDGs in countries where metal inputs are increasing. The other socio-economic factors were found to impact a broader range of SDG indicators than did metal inputs, with GDP, population, and primary energy consumption impacting 29, 13, and 12 indicators, respectively. The impact of governance, which was correlated with a worsening of 9 SDG indicators, was found to be of similar magnitude to that of metal inputs to an economy. Increasing the material flows of metals, when considered collectively, was correlated with a worsening of indicators within each SDG studied. However, there was insufficient data in WB-SDGs to enable analysis for SDG 10 (Inequality), SDG 15 (Biodiversity, Forests, Desertification), and SDG 13 (Climate Change).

3.2. Mined metal vs. imported metal Fig. 2(a) shows the effect of increasing material inputs of mined (from both terrestrial and urban mines) and imported metals on SDG indicators (average number of indicators that worsened). To comprehensively capture the amount of metal used in an individual country, imported metals here includes not only ore and unprocessed metal, but also the amount contained in raw materials and products that are imported. The colors in the bar graph indicate the SDG that is significantly related. On average, increasing the input of mined metals was correlated with the worsening of 10.3 indicators, while increasing use of imported metals was correlated with the worsening of 8.6 indicators. This demonstrates that use of mined metals has a broader adverse impact on SDGs than use of imported metals. The difference between the two can be attributed to differing impacts on SDG 3 (Health) and SDG 8 (Economic Growth). In the case of SDG 3 (Health), increasing use of mined metals impacted 1.8 indicators, compared with 1.2 indicators for imported metals: a difference of 0.6 indicators. The greatest difference in impact between mined (1.6 indicators) and imported (0.64 indicators) metals was observed for SDG 8 (Economic Growth). Compared with the impact of GDP and the other socio-economic factors shown in Fig. 1, increased input of mined metals had a broader adverse impact on SDG 3 (Health) than GDP or governance, and a broader adverse impact on SDG 8 (Economic Growth) than population, primary energy consumption, or governance. While these results mirror those obtained for increased use of all metals 15

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Fig. 2. Number of SDG indicators that worsened in response to expansion of metal input: (a) comparison of mined and imported metals, (b) comparison of base and scarce metals, and (c) comparison among elements. The 17 goals are the same as in Fig. 1.

given to monitoring social indicators related to SDG 3 (Health).

described above, the impact of mined metals on SDG 7 (Energy) was found to be similar in magnitude (1 indicator) to that of GDP. Meanwhile, the impact of imported metals was found to be similar in magnitude to GDP and governance only in the case of SDG 3 (Health). Although “social license to operate” is becoming a requirement for existing mining sites, greater efforts to mitigate adverse impacts on SDGs are required in countries where the use of mined metals is increasing.

3.4. Individual metals Fig. 2(c) shows the number of SDG indicators and corresponding SDGs negatively correlated by increasing inputs of individual metals broken down by source (mined and imported). The characteristics of some metals are described briefly below. Increasing use of Fe was significantly correlated with a worsening of sustainable development indicators, with mined Fe (10 indicators) having a more extensive impact than imported Fe (8 indicators). SDGs impacted only by mined Fe include SDG 2 (Hunger and Food Security), SDG 6 (Water and Sanitation), SDG 7 (Energy), and SDG 8 (Economic Growth). In contrast, SDGs impacted only by imported Fe include SDG 5 (Gender Equality and Women’s Empowerment), SDG 12 (Sustainable Consumption and Production), and SDG 17 (Partnership). Thus, impacts warranting attention were found to differ substantially between mined and imported Fe. The only SDGs impacted by both mined and imported Fe were SDG 3 (Health) and SDG 4 (Education). However, among the individual indicators associated with these SDGs, none were impacted by both mined and imported Fe. Increasing use of Cu had the broadest impact among base metals, with mined and imported Cu being correlated with a worsening of 14 and 10 indicators, respectively. Similarly to Fe, only a small number of SDGs (SDG 4 (Education) and SDG 7 (Energy)) were impacted by both mined and imported Cu. These results suggest that, in economies where use of Cu is increasing, steps should be taken to mitigate adverse impacts on not just this one indicator, but on overall educational quality. Besides Cu, of all the metals investigated in this study, increasing use of Co had the greatest impact on SDGs, with both mined and imported Co being correlated with a worsening of 12 indicators. However, none of the indicators were impacted by both mined and imported Co. Of the SDGs impacted by mined Co, SDG 4 (Education) and SDG 8

3.3. Base metals vs. scarce metals Fig. 2(b) shows the relationship between increasing the DMI of base and scarce metals and the various SDG indicators (number of indicators that worsened, averaged for base and scarce metals). Increasing the use of base and scarce metals impacted 9.3 and 9.5 indicators, respectively; unlike the above-described categorization of mined and imported metals, no substantial difference was observed between the two metal categories. Although the quantity of base and scarce metal inputs in a given country may differ by an order of magnitude or more, metals in the two categories have a similar impact on SDGs per unit mass. This result suggests that future increases in demand for scarce metals, as anticipated with the spread of new energy technologies, may hinder attempts to achieve many or all of the SDGs. Although the relative impacts of metals in the two categories vary somewhat by SDG, they are generally of similar magnitude. The greatest difference in impact was observed for SDG 12 (Sustainable Consumption and Production), for which the impact of base metals (a worsening of 0.3 indicators) was 3.6 times that of scarce metals (0.08 indicators), followed by SDG 3 (Health), for which the impact of base metals (1.8 indicators) was approximately 1.4 times that of scarce metals (1.25 indicators). Considering that the impact on SDG 3 (Health) of GDP or governance was 1 (Fig. 1), this result suggests that, in countries where use of base metals is increasing, priority should be 16

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Fig. 3. The number of SDG indicators by SDGs to which mined and imported, base and scarce metals cause the largest adverse change. Each red flag represents one indicator. The 17 goals are the same as in Fig. 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

SDGs, 3 and 7, with one flag for each; however, mined Mo had the greatest number of red flags (seven in total) among the mined metals, on SDGs 4, 6, 7, and 8. Following Mo, the metals Ni and Cr showed the next largest number of red flags: six and five, respectively. Mined Ni had a red flag on SDG 17 only, while imported Ni had a red flag on SDGs 6, 9 and 17. Similarly to Ni, mined Cr was strongly associated only with SDG 17, but with imported Cr a strong relationship with SDGs 4, 9 and 12 was found.

(Economic Growth) contained numerous indicators related to unemployment. Although Co is not designated as a ‘conflict mineral’, child labor is known to exist, suggesting that recent efforts by companies to ensure ethical procurement of materials are justified (Umicore, 2017). Increasing use of mined and imported Pt was correlated with a worsening of 6 and 12 indicators, respectively, with no common indicators impacted. Mined Pt was found to adversely impact only one SDG, namely SDG 1 (Poverty). A responsible procurement scheme similar that for Co is needed to ensure that the increasing demand for Pt for fuel cells to improve sustainability does not come at the expense of increased poverty in countries where this metal is mined.

4. Discussion In this study we performed a regression analysis using panel data on material flows of metals and SDG indicators for each of the WB-SDGs. By analyzing changes in metal demand across the economies of individual countries, the overall influence on different SDGs was empirically evaluated. We examined 11 types of metal and 96 indicators. In cases where increasing the DMI of a metal was found to adversely impact an indicator, we investigated the relationship in more detail, including the specific contributions to adverse impacts arising from mining and importing metal. While the use of metals is essential for realizing sustainable development goals, this paper sought to highlight the oft-overlooked detrimental impacts from both imported metals and metal mining (from both terrestrial and urban mines). We identified the role of increased use of base and scarce metals on specific SDGs. This more comprehensive analysis is essential if global impacts from metal use are to be better understood and responsibly managed along the supply chain. It was found that material consumption has an impact on SDGs comparable with that of economic growth and population growth. Consequently, in countries or regions where metal demand is increasing, these empirical results indicate the need to better monitor adverse impacts on SDGs and, importantly, to adopt policies and regulations to decouple metal use from SDGs, in addition to increasing consumer awareness of the impacts of metal use. The study revealed that whereas base metals and scarce metals impact a similar number of SDG indicators, with respect to the DMI pathway, mined metals have a more extensive impact than imported metals. In terms of the magnitude of the adverse impact on indicators per unit mass of DMI (strength of impact), scarce metals have a greater impact on SDGs than base metals, with the majority of SDGs impacted being impacted strongly. This

3.5. Strength of impact on SDGs Fig. 3 indicates the number of indicators to which a metal causes the largest adverse change, totalized for each SDG. If we interpret the figure as focusing on a particular base or scarce metal (mined or imported), the figure shows with red flags the SDGs most negatively associated with an increase in the DMI of each particular metal. The metals with multiple red flags for a particular SDG imply an extensive effect on that goal. Because in Fig. 3 the comparison is on a per unit mass of DMI basis, rather than on the basis of RME (Raw Material Equivalent) (Eurostat, 2001), which converts the metal’s mass to a raw material basis, a greater number of red flags tends to be associated with scarce metals. Producing a gram of a scarce metal requires a greater volume of extracted mineral than in the case of base metals. The metal with the greatest number of red flags (18 in total) was Pt, which was found to have an adverse impact on 12 SDGs. Mined Pt strongly impacted SDGs 1, 2, 3, 4, 6, and 8, while imported Pt affected SDGs 4, 5, 7, 8, 11, and 17. The second most negatively associated metal was Nd (17 red flags), with mined Nd having a red flag on SDGs 4, 7 and 17, and imported Nd having a red flag on SDGs 3, 4, 5, 8, 9, and 17. In particular, SDG 3 had the highest number of red flags (five in total), and is therefore the social indicator that is most correlated with imported Nd. While mined Co had a red flag only on SDGs 4 and 8, imported Co had a red flag on SDGs 2, 3, 4, 8, 9, and 14. There is a tendency to focus on social issues in countries where Co is mined, but the results for imported Co, with eight red flags, show that it also has a potentially high impact on SDGs, as was seen with Pt and Nd. In contrast, imported Mo impacted only two 17

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result would indicate a strong need to decouple metal input from achieving SDGs in countries where metal mining is increasing. Scarce metals are key inputs to new energy technologies such as fuel cells and electric vehicles, which are essential for reducing greenhouse gas emissions (Grandell et al., 2016; Helbig et al., 2018). If the mining of scarce metals continues to increase as part of the global effort to transition to a low-carbon society, international attention should be paid to ensuring that countries where scarce metals are mined are able to achieve SDGs in their own right, rather than simply supporting countries that install new energy technologies to do so. As revealed by an analysis of environmental accounts (Peters, 2008) based on consumer standards such as the material footprint (Wiedmann et al., 2013), the important fact here is that the countries where the metals are used are different from those where they are mined, with the two connected by complex international supply chains. When metal-mining countries are unable to resolve social problems such as poverty, hunger, and health- or education-related issues on their own, the metal-using countries should take steps to ensure that the entire international supply chain supports sustainability in the mining countries by providing technological assistance and monetary support, and adopting responsible regulations regarding metal procurement. Whilst the need for international resource governance to support sustainable development has already been established (Ali et al., 2017), this paper has quantified the empirical relationship between metals and SDGs. In doing so, it helps unify several distinct alarms proposed by Ali et al. in relation to sustainable resource management — namely governance, environmental, social, geopolitical — by linking them to achievement of the sustainable development goals. Although this study supports the necessity of comprehensive resource governance for a sustainable society (Ali et al., 2017), this does not necessarily mean that we fully understand the causal relationships. Further research is needed to elucidate the mechanisms through which elements of the metal use supply chain adversely impact SDGs and to identify which cooperative solutions would be most effective for each of the stakeholders along the supply chain and in different countries. The wider adoption of consumption-based accounting (Lenzen et al., 2007; Munksgaard and Pedersen, 2001; Peters, 2008; Sudmant et al., 2018) practices can provide a common approach to monitoring progress against goals. Doing so, in conjunction with the analytical framework presented in this paper, will provide a compelling evidence base to advance efforts to decouple metal resources and SDGs, informed by a life cycle approach. Importantly, it will also provide a comprehensive data set that stakeholders from across government, industry and civil society can use to understand and engage with appropriate trade-offs as decoupling strategies are pursued — both with respect to specific SDGs and between countries. This paper presented an analysis of combined metal flows from both mined and recycled sources; in the future, however, developments in digitizing supply chains and extending chain-of-custody certification may allow the analysis to be refined to differentiate between mined and recycled sources. At the same time, the analysis will contribute to the creation of a circular economy (Foundation, 2018) and optimize the value of metals over their lifecycle by recovering value that is being lost through metal use.

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Acknowledgements This research was supported in part by Grants-in-Aid for Research (No. 18KT0056) from the Japanese Ministry of Education, Culture, Sports, Science and Technology and the Environment Research & Technology Development Fund (1-1601, 2-1801) of the Environmental Restoration and Conservation Agency, Japan. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.resconrec.2019.05.017. 18

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