Evidence of the impacts of metal mining and the effectiveness of mining mitigation measures on social–ecological systems in Arctic and boreal regions: a systematic map
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Notice bibliographique
Résumé
Background: Mining can directly and indirectly affect social and environmental systems in a range of positive and negative ways, and may result in societal benefits, but may also cause conflicts, not least in relation to land use. Mining always affects the environment, whilst remediation and mitigation efforts may effectively ameliorate some negative environmental impacts. Social and environmental systems in Arctic and boreal regions are particularly sensitive to impacts from development for numerous reasons, not least of which are the reliance of Indigenous peoples on subsistence livelihoods and long recovery times of fragile ecosystems. With growing metal demand, mining in the Arctic is expected to increase, demanding a better understand its social and environmental impacts. We report here the results of a systematic mapping of research evidence of the impacts of metal mining in Arctic and boreal regions. Methods: We searched multiple bibliographic databases and organisational websites for relevant research using tested search strategies. We also collected evidence from stakeholders and rightsholders identified in the wider 3MK project (Mapping the impacts of Mining using Multiple Knowledges, https://osf.io/cvh3u). We screened articles at three stages (title, abstract, and full text) according to a predetermined set of inclusion criteria, with consistency checks between reviewers at each level. We extracted data relating to causal linkages between actions or impacts and measured outcomes, along with descriptive information about the articles and studies. We have produced an interactive database along with interactive visualisations, and identify knowledge gaps and clusters using heat maps. Review findings: Searches identified over 32,000 potentially relevant records, which resulted in a total of 585 articles being retained in the systematic map. This corresponded to 902 lines of data on impact or mitigation pathways. The evidence was relatively evenly spread across topics, but there was a bias towards research in Canada (35% of the evidence base). Research was focused on copper (23%), gold (18%), and zinc (16%) extraction as the top three minerals, and open pit mines were most commonly studied (33%). Research most commonly focused on operation stages, followed by abandonment and post-closure, with little evidence on early stages (prospecting, exploration, construction; 2%), expansion (0.2%), or decommissioning/closure (0.3%). Mitigation measures were not frequently studied (18% articles), with groundwater mitigation most frequently investigated (54% of mitigations), followed by soil quality (12%) and flora species groups (10%). Control-impact study designs were most common (68%) with reference sites as the most frequently used comparator (43%). Only 7 articles investigated social and environmental outcomes together. the most commonly reported system was biodiversity (39%), followed by water (34%), societies (20%), and soil/geology (6%), with air the least common (1%). Conclusions: The evidence found highlights a suite of potential knowledge gaps, namely: on early stages prior to operation; effectiveness of mitigation measures; stronger causal inference study designs; migration and demography; cumulative impacts; and impacts on local and Indigenous communities. We also tentatively suggest subtopics where the number of studies could allow systematic reviews: operation, post-closure, and abandonment stages; individual faunal species, surface water quality, water sediment quality; and, groundwater mitigation measure effectiveness. Supplementary Information: The online version contains supplementary material available at 10.1186/s13750-022-00282-y.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle