Rising global demand for minerals to support continued economic development and the Green Transition is expanding areas of extraction across the planet. While mineral extraction often necessitates substantial transformation of the immediate landscape, whether these activities contribute to positive outcomes for the local communities and minimize natural resource competition of targeted areas has not been assessed globally, with previous investigations largely limited to case studies or missing an assessment of multiple dimensions of sustainability. Here we perform a comprehensive analysis of the associations between mining activities, rural household wealth, and water scarcity across for 19 major minerals (selected either due to their importance for the green energy transition or their overall volumes of extraction) and 27 countries in the Global South. Combining georeferenced information on 1373 large-scale mining locations and 63 household surveys (encompassing 1.32 million households) on wealth within a mixed effects modelling approach while controlling for a suite of economic, demographic, and environmental factors known to be associated with household wealth, we find that only 22% of countries (6 out of 27) show significant increases in household wealth associated with greater proximity to mines, suggesting that anticipated economic benefits to local communities may not be the norm. Further, depending on the focus mineral and considering much larger global dataset of mines about 8103, we find that between 45% and 50% of mine operations are located in water-scarce regions (i.e., water withdrawals more then 40% then total available blue water) and may be exacerbating water competition with local communities and other water users and needs. Finally, in directly comparing wealth and water scarcity, we identify win-win (e.g., Liberia, Gabon) and lose-lose (e.g., Armenia, Dominican Republic, India, Kyrgyzstan, Philippines, and South Africa) countries where mines tend to be associated with (un)realized household wealth benefits and (non)minimal water competition. Ensuring win-wins that both benefit the livelihoods of local communities and minimize environmental impact and natural resource competition is the crux of sustainable mining development, but our findings highlights that such socio-environmental co-benefits are often not achieved. As such, our study underscores the need for more inclusive policies that prioritize local benefits and environmental sustainability.
Physical Sciences - Article
Socio-ecological tradeoffs between household wealth and water stress in global mining areas
https://doi.org/10.21203/rs.3.rs-4438652/v1
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Since 1970s, the global economy has increased four folds, driven by approximately 3.5-fold increase in material extraction, with metals and minerals comprising about 60% of this growth1. Moreover, there is a growing literature that continues to debate the need for mineral extraction to meet the demands for the green transition as well2. The International Energy Agency’s recent assessment on minerals needed for the green energy transition recognizes that recycling alone cannot meet the projected demand across eleven technological and behavioral cases. For instance, cobalt demand by the year 2040 is expected to be between 6 to 30 times higher than current levels, depending on the pace of evolution for battery chemistry and climate policies3,4. Therefore, anticipated mineral demands to support continued economic growth together with the emerging green energy transition will likely exert significant pressure for additional and expanded extraction. This expected proliferation of mineral extraction has been the subject of considerable debate due to, on one hand, concerns about widespread potential social and environmental impacts in new mining locations and, on the other hand, purported economic and development benefits for mineral-rich nations.
Ensuring that local communities share in the benefits of mineral extraction is essential for ensuring a just and equitable green transition and broader mineral-dependent development, but there is limited work to date to understand where and how households have economically benefited from such enterprises. Indeed, past work has shown that the effects of mining on nearby communities has yielded mixed outcomes5. While some studies report that people living near mines perceive economic benefits of mining operations 6, other work has shown that local communities do not fare as well economically as the broader nation 7. Similarly some studies suggest potential health benefits8 (e.g., improved child health outcomes, such as reductions in stunting and underweight, due to better income), while in many other cases, local communities report a suite of negative social impacts, including conflict and various health issues (e.g., migration, inadequate infrastructure, and fear of malaria transmission from large ponds,)6,9,10. Recent work has also highlighted where the lands of indigenous and land-connected communities are being appropriated for the extraction of energy transition minerals11,12
Parallel to influencing socio-economic outcomes for local communities, the land transformation and altered natural resource demands of mining operations can exert substantial environmental pressure, including on forests13,14 and biodiversity15. In addition, in water scarce regions, the introduction of mining operations can substantially increase competition for limited water resources 16, disrupting ecological balance and reducing availability for downstream water users17 and potentially contributing to conflicts18–20. For example, introduction of a mining company creates perverse incentives for a new, powerful player, which can harm local communities through water pollution and supply constriants17,18.Moreover, the governmental legal frameworks that govern the distribution of water to existing users may not be adequate in water-scarce areas requiring region-specific focus19. Taken together, the socio-economic and environmental effects of mining operations have, to date, largely been evaluated through case studies, and there is limited knowledge of the co-benefits or tradeoffs that have emerged between socioeconomic and environmental outcomes across contexts. It is therefore essential to better understand whether local communities tend to benefit economically from mining activities to what extent active mines are located in water-stressed regions, and to what extent there exist socio-environmental trade-offs for rural communities between increasing competition for water resources and benefits for household wealth. Such insights are crucial for developing more nuanced and targeted development policies that can better ensure the equitable and sustainable governance of rising mineral extraction.
To this end, we perform a comprehensive assessment of the associations between mining operation location and target mineral, rural household wealth, and water stress status for 27 Global South countries (covering all countries for which data are available). First, combining georeferenced information on 1373 large-scale mining locations for 19 minerals, 63 household surveys on wealth (as well as a suite of economic, demographic, and environmental factors known to influence household wealth), we use linear mixed effects models to evaluate associations between household wealth and proximity to mines. We then overlay mining locations with geospatial information on blue water stress (i.e., water stress resulting from the excessive withdrawal of water for municipal, industrial, and agricultural expressed as a percentage of total annual available blue water – surface and groundwater) to identify potential competition for water resources and to examine how this exposure to water stress may differ by country and by mineral. Finally, we evaluate potential trade-offs between mine-associated household wealth and water competition by performing a country-level classification into win-win (i.e., wealth benefits and low water stress), lose-lose (i.e., no observable wealth benefits and high-water stress) and win-lose/lose-win categories. In doing so, this study provides important new insights into whether existing mining operations are delivering purported economic benefits for local communities – while avoiding natural resource competition – and offer a multi-country perspective on where international efforts may wish to prioritize strengthening policies and regulation to better foster sustainable practices in mining regions.
Our study across 27 countries worldwide (Fig. 1) reveals substantial between-country variations in the association between household wealth and proximity to mines – after controlling for a host of other determinants of wealth. Globally, we found that only 22% of countries (6 of 27) showed a significant negative association between rural household wealth and proximity to mines – meaning that rural household wealth tended to increase the closer a household was to a mine. Notably, in 3 (out of 18) African countries in the study (Lesotho, Sierra Leone, South Africa), we found a significant positive correlation, indicating that households closer to mines tended to have lower levels of wealth. Further, we found that rural household wealth in an additional 10 African countries showed no significant association with the distance to mines. As such, in only 28% of the studied African countries (5 out of 18) did we find a significant negative association (Gabon, Liberia, Mauritania, Namibia, Rwanda), suggestive of increasing income benefits sharing with rural communities closer to mines. We also observed that neighboring countries sometimes exhibited associations with opposite signs (e.g., Namibia vs. South Africa; Liberia vs. Sierra Leone), suggesting a strong role of policies and regulations in determining household wealth outcomes from mining, despite potentially similar mineral geographies. Within Asia, we analyzed five countries, only one of which (Tajikistan) had a significant negative association with distance to mines, while India and the Philippines had a significant positive association and Jordan and Kyrgyzstan were not significant. In fact, India showed the largest positive effect size across all study countries – indicating a strong gradient of increasing household wealth when moving farther from mines in the country. In Central America, rural wealth in the Dominican Republic had a significant positive association with distance to mines, while Guatemala showed no significant association. In Europe, rural wealth and distance to mines showed a significant positive association in Albania and no significance in Armenia (Fig. 2). Taken on their own, these findings suggest a widespread lack of effective benefits sharing with mine-hosting communities. We then utilized the Baseline Water Stress (BWS) Index (a sub-basin level measure of total annual water withdrawals for municipal, industrial, and agricultural uses as a percentage of total annual available blue water (i.e., surface and groundwater)) to assess the extent to which mining operations – disaggregated by mineral and by country – are located in water-stressed regions, suggesting that the water demands of mining in these locations may contribute to enhanced water competition. We find that mining locations for major minerals critical for the energy transition -- such as copper, chromium, cobalt, graphite, lithium, manganese, molybdenum, nickel, rare earths (lanthanides), and zinc3 – occur across a spectrum of water-stress conditions. In particular, large fractions of molybdenum (73%), lithium (51%), platinum (83%), manganese (52%), and chromium (66%) mines are located in regions facing at least medium to high water stress (i.e., BWS Index of 30% or higher) (Figure 3). This suggests that increased extraction of these minerals in these locations may exacerbate water competition with other users and with nature, as large fractions of available water are already being withdrawn. On the other hand, mines for cobalt and graphite largely occur in places where water is relative abundant and may avoid creating conditions of water competition. In addition to energy transition minerals, we also evaluated the status of water stress for locations of the most extensively extracted minerals by volume worldwide. Among these, substantial fractions of mines for uranium (64%), phosphate (65%), silver (67%), gold (46%), iron (46%), and bauxite (43%) were located in areas of high (or greater) water stress. Given the widespread distribution of operations for these minerals, enhanced extraction to meet future demand may have the most extensive influence on altering water competition.
Given the mixed outcomes observed separately for rural household wealth and for the status of water stress, we then sought to directly combine these findings to evaluate in which countries mining operations tend to be associated with wealth benefits and low water competition (i.e., win-win), where there is no evidence of wealth benefits but high water competition (i.e., lose-lose), and where there occurs a mixture of outcomes (i.e., win-lose or lose-win). Distributing countries across these win/lose quadrants, our analysis revealed that 6 of the 27 study countries – Armenia, Dominican Republic, India, Kyrgyzstan, Philippines, and South Africa – have mining activities categorized as lose-lose, in which there is no associated benefit to household wealth and high levels of water stress (and therefore an elevated chance of water competition). In other cases (i.e., Jordan, Mauritania, Namibia, Tajikistan), we observed tradeoffs that are perhaps more difficult to resolve in which there are associated household wealth benefits but high levels of water stress. In contrast, the mining activities in several other countries occur in locations that are water abundant but are not associated with significant benefits for wealth (e.g., Albania, Burkina Faso, Democratic Republic of Congo. Ethiopia, Ghana, Mali, Zambia). Only in two countries (Gabon and Liberia) do we find evidence of win-win outcomes in which mining activities are correlated with significant economic benefits sharing and there is minimal pressure on water resources. Lastly, in order to further investigate the direct dynamics between rural wealth and water scarcity in the context of mineral extraction, we carried out a final mixed-effect regression only considering those mines operating within water-stressed locations (i.e., a BWS Index of 20% or higher). Our findings indicate that for household in water-stressed areas the proximity to mining operations is associated with lower wealth (Fig. 4 and Fig. 5), suggesting a lose-lose situation in which there is a disconnect between local development and large-scale mining operations in conjunction with increased mine-related water withdrawals.
There is broad and ongoing discussion about the economic impacts of large-scale mining on national and local development. Although strong evidence supports the positive economic effects of non-fuel mining at national scales19,20, the effects of mining development on the incomes of local communities are not well understood19. Moreover, the impacts of large-scale mining operations vary by country and by mineral, with some showing beneficial outcomes for local communities5,22,23 and others showing a suite of negative impacts9,11. At the same time, when mining activities are established within a landscape, modifications to natural resource demand are a certainty, raising questions about whether and how local communities and the environment may be affected, particularly in places where natural resources may already be relatively scarce. Given the spectrum of potential economic and environmental benefits, impacts, and tradeoffs resulting from mineral extraction, it is important to understand whether certain types of mining arrangements lead to beneficial social and environmental outcomes across diverse contexts and geographies.
By evaluating such outcomes across 27 Global South countries, our study sheds important new light on the effects of mining activities in diverse contexts. For one, our analysis showed that only in 6 of 27 study countries is proximity to mines associated with significant wealth benefits for rural households – suggesting that, globally, many rural households living close to mining operations are not materially sharing in the extraction-related economic benefits. Our analyses also showed that – depending on the mineral and country – large fractions of mines are located in water-stressed areas – underscoring both the critical vulnerabilities inherent in the extraction of major and energy transition minerals as well as the risk of increased extraction (from these and future mining operations) to increase local competition for limited water resources. Together, across diverse contexts and geographies, these findings show an overall lack of consistent benefits sharing despite often elevated stress of water resources. Such socio-environmental shortfalls thus demand renewed and enhanced scrutiny of emerging policies, regulations, and strategies being developed at multiple levels of governance to shepherd the rising mineral demands of continued global development and the green energy transition.
Among the most interesting cases within our study countries are those in which there is evidence of local rural communities benefiting significantly from mining activities that are largely located in the water scarce areas (e.g., Mauritania and Namibia). In both of these countries, mining constitutes a large part of their economies, and past policies and the political economy have favored certain groups that have benefited from mining developments 24,25. The ability to derive livelihood benefits from mining rents, similar to harnessing a "demographic dividend," depends crucially on governance stability, which may not directly correlate with democratic measures, as demonstrated by both Namibia and Mauritania despite their differing democratic contexts26. Future research should focus on detailed governance attributes at the local level to better understand the specific mechanisms that link governance quality to the distribution of mining income benefits. Hence, local contexts and development histories are extremely important when the benefit of extraction activities are distributed. Another strategy that can be helpful for local communities involves implementing top-down policies in which benefit sharing and environmental sustainability are prioritized, compelling mining companies to follow such practices 27.
Additionally, at the industry level, mining companies profess their commitment to the United Nations Sustainable Development Goals (SDGs)28. Consequently, it is anticipated that they can serve as change agents to spearhead inclusive and sustainable practices within mining regions. Because water availability is a principal factor considered before initiating mining operations, this necessity drives mining companies to consider the risk that water stress may pose to their operations29. Given this prior knowledge that these companies likely possess when initiating mining activities in a location, there is also a growing demand for the mining industry to serve as local water stewards and to incorporate a broader definition of risk that includes impacts on nearby communities in their assessments30,31. Although there is some evidence suggesting that large-scale mining can enhance local water and sanitation conditions for communities close to mining sites10, it is crucial that mining companies establish such public infrastructure in ways that enable communities to sustain such improvements. Along these lines, our analysis identifies countries where it should be a priority for mining companies to effectively implement water stewardship practices.
Our current analysis encompasses all available spatially detailed data on global mining operations, household-level wealth, and water stress. As such, it represents – to the best of our knowledge – one of the first comprehensive assessments of benefits sharing and resource competition associated with mineral extraction across the Global South. However, we readily acknowledge that the datasets on mining operations and household characteristics are not complete32. Especially given the deep heterogeneity between contexts, we thus advise against extending our findings to countries that are not included in our study. As additional data may become available for other countries, our approach can be readily applied to understanding outcomes in those settings. Future research could also benefit from more concerted tracking of mining operations by local government, as well as the direct collection and reporting on resource usage by mines, specifically through water usage data collected at the site level. Despite these limitations, our research provides important new insights on the global debate concerning the mineral extraction nexus and sustainable development32.
Global mining data
The mining dataset was sourced from the S&P Capital IQ Pro database 33. It provides latitude and longitude for each mineral and metal commodity. We drew 10 km buffers around S&P dataset, following the recommendations of Maus et al. 2020, and overlaid the buffers with the mining data area from the same dataset34. We included only those mining buffers that contained Maus et al. 2020 mining areas, as these correspond to currently operational mining sites. That resulted in a total of 8,675 data points. Moreover, since only 27 countries were included due to the availability of socio-economic datasets, we had 1373 large scale mining operations within these countries. For the water scarcity analysis, we subsequently overlaid the 8,675 mine sites with the global water-scarcity dataset. Due to the overlap of mining location data and the water-scarcity dataset, we ultimately selected 8,103 mine sites.
Household wealth index
Data on the household wealth index came from the Demographic and Health Survey (DHS) database35. The wealth index is a composite measure which assesses the living standard of a household. These values are derived by considering various assets owned by the household. They range from 1 to 5, with 1 being the poorest and 5 being the wealthiest. We examined all countries (27 in total) for which georeferenced household survey data were available from 2000 to 2019 and where our mining dataset indicated the presence of mining activities. We only considered rural households, as livelihood outcomes in these communities are more directly impacted by land use decisions in adjacent areas. In total, we considered 1.32 million households distributed across 60,064 clusters. As the DHS provides geographic data at the cluster-level, we were able to analyze 60,064 population cluster across 27 countries. To protect the privacy of the surveyed population, rural clusters are offset by 5 km, with 1% randomly offset by 10km. The number of clusters per country varied, ranging from a minimum of 203 in Kyrgyzstan to 42,239 in India. The distribution of clusters reflects the geographic area and population size of each country, as well as the frequency of standard DHS surveys conducted there. Complete details regarding the number of observations for each country, alongside the number of DHS standard surveys conducted from 2000 to 2019, are provided in supplementary material Table S2. Typically, each cluster comprises 20-30 households, although this number can vary depending on survey design and country-specific requirements. For our analysis, we selected the household wealth variable for rural areas from various DHS standard surveys as the dependent variable. Median values were assigned to clusters to indicate rural cluster wealth. We calculated the distance from each cluster to the nearest mining area in each country.
Covariates of household wealth
We also assessed a number of demographic, geographic, and agro-ecological variables that are known to influence household wealth 36,37 (Table 1). For demographic factors, we examined the education level and gender of the household head, using data from the DHS standard survey for a particular year 35. Geographic factors considered included the distance to the nearest main road and urban center, and the population density around the DHS cluster. To calculate distances, we utilized OpenStreetMap data for proximity to the main road, while population density was determined using the 2015 population density dataset from the Center of International Earth Information Network 38,39. For agroecological variables, we referred to the Food and Agriculture Organization/International Institute for Applied Systems Analysis Global Agro-Ecological Zones to classify agricultural soil suitability for rainfed, high-input cereals 40. Additionally, the last covariate we used was percentage of tree cover in year 2000 within a 5 km buffer around the population cluster, drawing on forest cover data from Hansen et al. 201341.
Mixed Effects Regression Modelling
We utilized a linear mixed-effect model to estimate the wealth coefficients using the ‘lme4’ package in R (eq. 1). This model predicts the household wealth index based on the previous mentioned geographical, demographical, and environmental predicators. Moreover, we introduced random effects to account for potential variations within administrative regions and across different survey years.
where β is the intercept, β0, β1, ..., β8 coefficients represent the fixed effects of each independent variable, b1j and b2k represents the random effects for different years and different regions within the country and ∈ is the error term.
Water Scarcity Assessment
For our water scarcity analysis, we utilized BWS data developed by the World Resources Institute (WRI) Aqueduct Global Maps 2.1 data42. The estimates of water stress are derived from long-term time series data, reflecting chronic water stress rather than acute drought conditions. BWS measures total annual water withdrawals (municipal, industrial, and agricultural) expressed as a percentage of the total annual available blue water. Regions with water withdrawal of up to 10% are considered low water-scarcity areas, up to 20% are considered low to medium, 20% to 40% medium to high, and 40% to 80% high, while above 80% is extremely high, and areas with above 97% withdrawal are classified as arid. BWS maps were overlaid with maps of mining areas to determine the water stress levels occurring at each mining location. This enabled us to determine for which minerals ambient levels of water stress may increase competition for finite water resources between mining and other societal and environmental needs16
Acknowledgements
We would like to acknowledge the assistance of ChatGPT, an AI language model developed by OpenAI, for providing coding help and checking the final draft for any mistakes. ChatGPT was instrumental in facilitating the technical aspects and ensuring the accuracy of the content. All final content and interpretations are our own responsibility.
We also extend our gratitude to the Higher Education Commission of Pakistan for providing the funding that made this research possible.
Additionally, we thank the University of Queensland for allowing us to use the S&P Capital IQ Pro database.
Author Contributions
Humayoun Akram, Kyle Davis and Saleem Ali conceived of the presented idea. Humayoun Akram, Bhoktear Khan, Amirreza Meydani, Kyle Davis performed the analysis. Humayoun Akram wrote the manuscript with the help of Kyle Davis, Saleem Ali and Amirreza Meydani. Kyle Davis and Saleem Ali supervised the project.
Conflict of Interest
The authors declare that they have no conflicts of interest.
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Table 1. Variables used in multi regression analysis.
|
Variable Name |
Description |
Source |
|
Wealth Index |
The composite measure reflects the household overall living standard, calculated based on the ownership of certain assets. It’s scored from 1 to 5, with a score of 5 indicating the highest level of wealth. |
(34) |
|
Education level of household head |
The highest education attainment of the household head. It was either no education, primary, secondary schooling, or higher education. |
(34) |
|
Gender of household head |
The sex of the household head. This was given as either male or female. |
(34) |
|
Distance from urban center |
The distance of the urban areas from the survey cluster (km). |
(37) |
|
Population density |
Mean population density in a 5km radius around the cluster. |
(37) |
|
Distance to main road |
The distance of survey cluster to the main road (km). |
(38) |
|
Agriculture soil suitability |
The agricultural suitability of the soil around the population cluster. The soil is classified for 1 as highly suitable and 7 as least suitable. |
(39) |
|
Percentage tree cover in 2000 |
The percentage tree cover around 5 km buffer of |
(40) |
There is NO Competing Interest.
- SupplementaryMaterials.docx
Supplementary Tables and Figures