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Record W2787959522

Annual trust fund implementation progress report to development partners Australia, Canada, European Commission, Finland, Germany, Norway, Switzerland, United Kingdom

2017· article· en· W2787959522 on OpenAlexaboutno aff
Anastasiya Rozhkova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefied natural gasCommodityEconomicsAgricultural economicsBusinessEconomyInternational tradeNatural resource economicsNatural gasFinanceEngineering
DOInot available

Abstract

fetched live from OpenAlex

For countries endowed with mineral wealth, extractives sector management is a challenging task. However, one aspect of the sector that is predictable is the cyclical nature of commodity prices. Currently, the outlook for commodity prices shows an upward trend. The World Bank’s most recent Commodities Markets Outlook of October 2017 reported that in the oil market, inventories continue to fall amid strong demand, OPEC production restraint, and stabilizing U.S. shale oil production. Global gas demand has increased over the past decade and, with the increased interest in cleaner energy, is expected to grow by 2 percent per year between now and 2030. Looking ahead, demand for gas is expected to strengthen from new chemical and fertilizer capacity and from rising exports by pipeline to Mexico and via liquefied natural gas (LNG) worldwide. In 2016, global liquefied natural gas (LNG) demand reached 265 million tons, indicating an increase in net LNG imports of 17 million tons from the previous year. The World Bank’s Metals and Minerals Price Index surged by 10 percent in the third quarter of 2017 due to strong demand—particularly in China’s property, infrastructure, and manufacturing sectors—and various supply bottlenecks. Metals prices have risen in five of the past six quarters, and prices for the first nine months of the year averaged 26 percent higher than the corresponding period of 2016.These continued cycles of uncertainty faced by the sector and global economies in general continue to encourage World Bank client countries to push for reforms to improve the investment climate and sector governance transparency while improving economic, environmental, and social sustainability of the industry and increasing benefits from resource extraction to their citizens. The World Bank has abundant and diverse experience and expertise in supporting its resource-rich client countries to reform their extractives sector. It offers technical assistance on geodata collection and analysis; sector governance, including transparent licensing and tendering of assets; environmental and social management; local content development; fiscal policies; and overall legal, regulatory, and institutional frameworks. The World Bank Energy and Extractives Global Practice (Extractives Team—GEEX) annually produces approximately 50 knowledge products, which range from new research on extractives topics to communities of practice and just-in-time client support on a wide variety of sectoral issues. If the current upward trend in commodity prices persists, there will be greater need for reforms to prepare resource-rich developing and transition economies for potential new investments and sector development. Transparency and accountability, including adherence to the Extractive Industries Transparency Initiative (EITI) Standard, are key elements for an extractives sector to contribute to a sustainable, and equitable economic growth. Globally, EITI has gained enormous momentum with new countries joining the Initiative. At the same time, the EITI Standard has gone far beyond the initial scope of revenue disclosures. Over its two years of activities, the EGPS has managed to accommodate about 50 activities, with about 30 projects to be added during fiscal 2018. Through fiscal 2017, the EGPS followed a demand-based approach for financing within its four topical pillars. The EGPS offers a comprehensive approach to the reform needs of client countries and leverages, where possible, the World Bank’s broader portfolio in the extractives sector and other donor support. This approach ensures that the World Bank and developmentpartners will be better equipped to address the World Bank’s twin goals of ending extreme poverty and boosting shared prosperity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.106
GPT teacher head0.333
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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