Oil, Gas, and Mining: A Sourcebook for Understanding the Extractive Industries

Cameron, Peter; Stanley, Michael C.

doi:10.1596/978-0-8213-9658-2

Cited by 35 publications

(27 citation statements)

References 31 publications

(36 reference statements)

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“…Built on strategies developed in Latin America and the Caribbean in 1996, governments have been challenged with remaining transparent, providing stable institutional arrangements, investment certainty, and fiscal responsibility whilst mining companies have been charged with increasing consultation with affected people and mitigating negative social, economic and environmental impacts [7]. Drawing on guidance from these initiatives [4,5,22] and relevant literature [6,[23][24][25][26][27][28] a code to modern day mining practices can be organized around 10 main themes including: Legal frameworks, economic and fiscal policies, institutional reforms, CSR and participation of affected peoples, access to mining activities, ongoing obligations, regulatory aspects, ancillary licenses and permits, investment contracts, and environmental and social matters ( Table 1). These criteria establish the foundation for what can be considered a MMc and provide a lens through which Vietnam's current mining context was examined.…”

Section: Towards a Modern Mining Codementioning

confidence: 99%

“…To avoid repeating history, regulatory frameworks governing extractive industries in developing nations are required to promote principles of sustainable development [3]. Social scientists and international development organizations Worldbank [4], Cameron and Stanley [5] have worked to improve such frameworks to attract more investments in the mining sector [6] while promoting sustainable development principles to enhance livelihoods, reduce poverty, and ensure ecological health [3]. Debate over the most appropriate regulatory structure persists but, as Onorato et al [7] argued, modernization of regulatory frameworks [or a Modern Mining code (MMc)] is necessary to ensure fair, transparent, and sustainable outcomes from mining [7].…”

Section: Introductionmentioning

confidence: 99%

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The Regulatory Framework and Minerals Development in Vietnam: An Assessment of Challenges and Reform

2019

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This paper aims to identify the key components of a modern mining regulatory framework and examines the extent to which the Vietnamese context reflects these characteristics. Through detailed observation of two research locations in Vietnam—Phuoc Son and Bong Mieu—the paper investigates how national policy operates at a local level. Findings show that, over the past decade, the Vietnamese Government has initiated substantial reforms to the regulatory frameworks governing mining, with the central objective to attract foreign direct investment (FDI). However, it has become apparent that these reforms have numerous deficiencies and loopholes that have led to a range of unintended economic, social and environmental consequences. To cope with challenges such as rent-seeking, limited capacities and capabilities of government staff, failure of institutions and neglect of local communities, policy makers will need to think differently and strategically about the mining industry and how regulations are implemented. Only by addressing these weaknesses will the path be paved for the sustainable growth of Vietnam’s mining industry into the future.

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Section: Towards a Modern Mining Codementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Regulatory Framework and Minerals Development in Vietnam: An Assessment of Challenges and Reform

2019

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“…More specifically, governments may choose to design their revenue systems with a view on generating a steady flow of income and protecting public revenue from price oscillations in the world market, rather than maximizing public rents over the project life cycle. In those cases, revenue policies will place more emphasis on royalties and withholding taxes and less on corporate income or windfall profit taxes (Boadway & Keen, 2010, Collier, 2010, International Monetary Fund, 2012b, Luca & Mesa Puyo, 2016, Cameron & Stanley, 2017. Political control over state-owned enterprises (SOE) is another mechanism by which governments may strive to protect public revenue from excessive volatility (Barma, Kaiser, Le, & Viñuela, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Does the Political Resource Curse Affect Public Finance? The Vulnerability of Tax Revenue in Resource‐Dependent Countries

Haldenwang

Ivanyna²

2018

J of Intl Development

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This paper explores the extent to which government revenue is affected by external shocks and whether these effects are different for resource-dependent (RD) as compared with non-RD countries. We are particularly interested in the fate of poorer countries, as we assume they will find it more difficult to implement the policies needed to offset the effect of shocks. Based on data from the International Centre for Taxation and Development Government Revenue Dataset for 1980-2010, we measure the elasticity of tax revenue with respect to terms-of-trade shocks. We find that revenue in RD countries is more vulnerable to such shocks. It is above all the richer countries that appear to be adversely affected, compared with their non-RD counterparts. In contrast, the difference between RD and non-RD countries is less pronounced in the group of poorer countries. We also find that resource-dependent countries became less vulnerable in the 2000s as compared with previous decades. At the same time, political regime type does not seem to matter for the vulnerability of government revenue in these countries.

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“…Most developed countries adopt frameworks for mining regulation that attempt to mitigate mining disasters whilst respecting community needs, the environment and keeping hazard risks under control [2]. Unfortunately, many developing countries still face a myriad of challenges, such as weak governance, lack of transparency, and lack of accountability in extractive industries, preventing inclusive growth, while threatening both people and the environment.…”

Section: Introductionmentioning

confidence: 99%

Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

Balaniuk

Isupova

Reece

2020

Sensors

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This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.

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Oil, Gas, and Mining: A Sourcebook for Understanding the Extractive Industries

Cited by 35 publications

References 31 publications

The Regulatory Framework and Minerals Development in Vietnam: An Assessment of Challenges and Reform

The Regulatory Framework and Minerals Development in Vietnam: An Assessment of Challenges and Reform

Does the Political Resource Curse Affect Public Finance? The Vulnerability of Tax Revenue in Resource‐Dependent Countries

Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

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