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Record W7067652541

Maximising the Contribution of Mining to Sustainable Development in Indonesia

2022· dissertation· en· W7067652541 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityContext (archaeology)Sustainable developmentMining industryRelevance (law)Corporate governanceResource (disambiguation)Socioeconomic development
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.280
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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