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Enregistrement W7067652541

Maximising the Contribution of Mining to Sustainable Development in Indonesia

2022· dissertation· en· W7067652541 sur OpenAlexaboutno aff

Notice bibliographique

RevueFigshare · 2022
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGene expression and cancer classification
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainabilityContext (archaeology)Sustainable developmentMining industryRelevance (law)Corporate governanceResource (disambiguation)Socioeconomic development
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

As a mineral-rich country, Indonesia may benefit from the strong growth of world mineral demand. However, it needs to be coupled with resource governance actions that enable the country to use the opportunity well. Mining can generate many benefits, but poor management may result in mining working against sustainable development. Sustainable development in mining, or sustainable mining, emphases on efforts to maximise the benefits of mining and minerals projects for sustainable development while at the same time improving environmental and social sustainability. This thesis constructs legitimate arguments, emphasise this study's relevance in the context of space and time globally and confirms the importance of this study for the country. The elaborations are devided into three main chapters (4,5, and 6). The thesis first specifically focuses to analyse socioeconomic impacts and sustainability of mining, by exploring the lessons learnt from the historical tin mining on Singkep Island in Indonesia. Tin mining was the only major industry on the island from 1812-1992. A 27 question survey with 170 respondents, semi-structured interviews, and statistical data analysis were used to analyse the impacts during active mining and after closure. This research finds that tin mining contributed around 65% -90% of the local economy, provided 2 452 out of 8 716 direct jobs, operated 2 out of 39 primary schools, built infrastructure and controlled the hospital, airport, power plant and piped water. Despite the significant contributions of during the active mining, substantial mining benefits turned very quickly into long-term losses after closure. Job opportunities became unemployment, economic contributions became economic collapse, and infrastructure assets became liabilities. Environmental degradation was a negative impact during and after mining. Education was relatively unaffected because most children attended state schools. This case highlights two important, perhaps the most central, challenges in mining governance: sudden mine closure and mining dependency. In addition to the case of Singkep Island, two worldwide case studies in which mining regions faced sudden mine closure were then reviewed: Blyvooruitzicht, South Africa (gold mining 1942 - 2013), and Sussex, Canada (potash mining 1983-2016). Singkep Island and Blyvooruitzicht represent unsustainable development where mining benefits were lost soon after closure. The Sussex case study concerns a mining region that maintained sustainable growth despite the sudden mine closure. The results of this comparison shows that unsustainable development is still the main threat in mining regions worldwide, not least because of the risk of unplanned closure. There are few published in-depth case studies of unplanned closure situations, despite the significant number of mines that subject to sudden and premature closure. In this section, this research finds that the sudden mine closure would have devastating impacts if mining regions had at least one of these preconditions: lack of economic diversification, job (opportunities and skill) dependent on mining, massive environmental degradation, and low human capital. A combination of these may arise when a weak government exist and the mining industry's attractiveness often blocks the awareness that these preconditions are already rooted in the region. This study suggests the following avoiding these preconditions: ensuring early shared-use of mining infrastructure, using mining facilities to improve local workforce skills, ongoing community engagement, and implementation of progressive closure and preparation of a contingency plan. These mitigation strategies to generate resilient mining communities are not the sole responsibility of mining companies. They require strengthening the government role, with regional governments and communities taking more significant initiatives. Finally, this thesis analyses regional mining dependency in five mining regions in Indonesia and considered how countries could improve the governance of their mining sectors to ensure that they contribute to sustainable development. Gross Regional Domestic Product data from 2000 to 2017 show that four mining regions, namely Mimika, Luwu Timur, Kutai Kartanegara, and Muara Enim, have a positive story of increasing mining revenues but still have high resource dependency. Kolaka is moving to be more dependent on mining. The regions are vulnerable to socioeconomic setbacks that may happen anytime and be related to global issue beyond their control, such as commodity prices crashes or global actions to combat climate change. Limited fiscal capacity, ineffective spending of mineral revenue, and irresponsible operation of mines are the challenges facing Indonesia. This thesis further proposes recommendations for the country to help mining regions grow out of resource dependence. The country should maximise mineral revenues through downstream integration and formalisation of ASM activities. It should also introduce a heavily regulated distribution and allocation policy for adequate mineral revenue spending. The country needs also promote strong institutions involved in resource governance and transparency to ensure practical policy implementations, eliminate mineral revenue losses, eradicate corruptions and minimise environmental rehabilitation costs because of irresponsible mining operations. Although the recommendations in this thesis mainly made in reference to Indonesia, other countries or mining regions can adapt the points suggested here to fit their context.

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.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,209
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,280
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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