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Record W3018108401

Сравнение методик дисконтированного денежного потока и энергетической рентабельности инвестиций (EROI): обзор экономических оценок нефтегазовых ресурсов // Comparative Study of Discounted Cash Flow and Energy Return on Investment: Review of Oil and Gas Resource Economic Evaluation

2020· article· ru· W3018108401 on OpenAlexaboutno aff
Jie Yan, Lianyong Feng, S. Fu, Ц. Янь, Fen Liu, Ш. Фу

Bibliographic record

VenueФинансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice · 2020
Typearticle
Languageru
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInternal rate of returnNet present valueDiscounted cash flowReturn on investmentRate of returnFossil fuelInvestment (military)Cash flowPresent valueResource (disambiguation)EconomicsEconomic evaluationProduction (economics)Environmental economicsBusinessFinanceEngineeringMacroeconomicsComputer scienceMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The aim of the paper is to develop a methodology for evaluating oil and gas fields return on investments based on not only finance, but also environmental and social interrelations. The subject of the study is a comparison of methods for calculating return on investments on the example of China, Canada and Russia’s oil and gas companies. The authors used a comparative method of calculations, as well as a case study — a comparison of return on investments methods on the example of oil and gas enterprises. In the paper, the authors analyze the next traditional methods of economic assessment: net present value, differential rent, reserve and multiple costs. The authors suggest using a new assessment method that determines the energy return on investment (EROI). This method does not rely on traditional analysis of net present value (NPV), internal rate of return (IRR), and financial sensitivity. It comprehensively takes into account the costs of energy production, environmental protection and energy efficiency. Based on the results of the study, the authors conclude that the advantages of various methods of economic assessment should be integrated in order to avoid disadvantages and create a new dynamic integrated system of economic assessment. Oil and gas companies may use the results of the study to implement the energy return on investment methodology concerning oil and gas fields’ evaluation. A promising direction for further research may be to compare the energy return on investment at oil and gas enterprises in different countries as well as developing corporate reporting concerning energy return on investment improving efficiency. Цель статьи — развитие методологии оценки рентабельности инвестиций в месторождения нефти и газа на основе не только финансовых, но и экологических, и социальных взаимосвязей. Предметом исследования является сравнение способов расчета рентабельности инвестиций на примере нефтегазовых предприятий Китая, Канады и России. Авторы использовали сравнительный метод расчетов, а также case-study — сравнение оценок инвестиций на примере нефтегазовых предприятий. Проанализированы традиционные методы экономической оценки: чистой приведенной стоимости, дифференциальной ренты, резервных и множественных затрат. Авторы предлагают использовать новый метод экономической оценки, определяющий энергетическую рентабельность инвестиций (EROI). Этот метод не опирается на традиционный анализ чистой приведенной стоимости (NPV), внутренней нормы доходности (IRR) и финансовой чувствительности. Он всесторонне учитывает затраты на выпуск энергии, экологическую безопасность и энергоэффективность. По результатам исследования авторы делают вывод, что преимущества различных методов экономической оценки должны быть интегрированы, чтобы избежать недостатков и создать динамичную комплексную систему экономической оценки. Результаты исследования могут быть использованы нефтегазовыми компаниями для внедрения методологии энергетической рентабельности инвестиций на месторождениях. Перспективным направлением дальнейшего исследования может быть сравнение энергетической рентабельности инвестиций на нефтегазовых предприятиях разных стран и разработка корпоративной отчетности в направлении повышения эффективности энергетической рентабельности инвестиций.

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)0.024
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.349
Teacher spread0.303 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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