scholarly journals Mediating Effect of the Governance Indicators in the Relationship between Natural Resources Abundance and Economic Growth: Empirical Evidence from the

2014 ◽  
Author(s):  
Harun Bal ◽  
Mehmet Demiral ◽  
Emrah Eray Akça
Keyword(s):  
Natural Resources ◽  
Crude Oil ◽  
Oil Production ◽  
Gdp Per Capita ◽  
Panel Regression ◽  
Governance Indicators ◽  
Crude Oil Production ◽  
Per Capita ◽  
The Relationship ◽  
Control Of Corruption

This study purposes to identify the relationship between gross domestic product (GDP) and natural resources abundance, focusing on the mediator roles of governance indicators for selected 21 MENA and Caspian countries. Governance indicators used in the study are World Bank’s six global governance indicators. Annual panel data for the period of 1996-2012 are used. In this context, the study estimates the impact of crude oil production per capita (independent variable) on GDP per capita (dependent variable) at first, and then hierarchical panel regression analyses are conducted to determine the mediator variable roles of the governance indicators in this relationship. Sobel test is also applied to confirm whether the mediation effect is significant. Results from the pairwise panel regression analyses reveal that crude oil production per capita is negatively associated with all worldwide governance indicators, mostly with control of corruption, voice and accountability and regulatory quality. The progressive improvements of all dimensions of governance indicators, especially control of corruption, rule of law and government effectiveness, seem to promote GDP per capita. Results from the hierarchical regression analysis demonstrate that governance indicators play an important role as a partial mediator in the relationships crude oil production and GDP per capita. This evidence supports that weak governance indicators tend to hinder natural resources abundance to contribute economic growth. Overall findings highlight the increasing importance of policies intending to reduce corruption and violence, together with stimulating legitimacy, transparency and institutional quality for the countries investigated.

scholarly journals Does Good Governance Matter? Kazakhstan’s Economic Growth and Worldwide Governance Indicators

2020 ◽  
Vol 10 (1) ◽  
pp. 1-13
Author(s):  
Aikozha Absadykov
Keyword(s):  
Economic Growth ◽  
Economic Performance ◽  
Good Governance ◽  
Political Stability ◽  
Gdp Per Capita ◽  
Governance Indicators ◽  
Regulatory Quality ◽  
Per Capita ◽  
The Relationship ◽  
Control Of Corruption

Good governance is generally believed to improve country’s economic performance. This paper studies the relationship between the World Bank’s Worldwide Governance Indicators (Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, Control of Corruption) and economic growth in terms of GDP per capita in Kazakhstan. The findings of the research indicate that there is a significant positive relationship between good governance and economic performance of Kazakhstan. Specifically, results show that the Control of Corruption has the strongest impact on GDP per capita. 

Application of Machine Learning in Predicting Crude Oil Production Volume

2021 ◽  
Author(s):  
Okechukwu Prince Innocent
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Predictor Variable ◽  
Oil Production ◽  
Water Volume ◽  
Production Volume ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Crude Oil Production ◽  
The Relationship

Abstract The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume. Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.

scholarly journals A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019

2021 ◽  
Author(s):  
Serge Djoudji Temkeng ◽  
Achille Dargaud Fofack
Keyword(s):  
Crude Oil ◽  
Markov Switching ◽  
Oil Production ◽  
The Other ◽  
Shale Oil ◽  
Relative Importance ◽  
Crude Oil Production ◽  
The Usa ◽  
Switching Dynamic ◽  
The Relationship

AbstractThe structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA. Thus, using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other, it is found that for both oil production and oil relative importance, the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution. Furthermore, the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index. Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production. Finally, it is found that the intercept and the variance parameter also vary from one regime to the other, thus justifying the use of regime-dependent models.

Creation of the ‘Internet of things’ in crude oil production

2017 ◽  
Vol 10 ◽  
pp. 120-124
Author(s):  
R.S. Khisamov ◽  
◽  
R.A. Gabdrahmanov ◽  
A.P. Bespalov ◽  
V.V. Zubarev ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Crude Oil ◽  
Oil Production ◽  
The Internet ◽  
Crude Oil Production ◽  
The Internet Of Things

scholarly journals Classical and machine learning modeling of crude oil production in Nigeria: Identification of an eminent model for application

2021 ◽  
Vol 7 ◽  
pp. 3497-3505
Author(s):  
Chukwudi Paul Obite ◽  
Angela Chukwu ◽  
Desmond Chekwube Bartholomew ◽  
Ugochinyere Ihuoma Nwosu ◽  
Gladys Ezenwanyi Esiaba
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Oil Production ◽  
Crude Oil Production

Examination of Iran's Crude Oil Production Peak and Evaluating the Consequences: A System Dynamics Approach

2014 ◽  
Vol 32 (4) ◽  
pp. 673-690 ◽  
Author(s):  
S. H. Hosseini ◽  
H. Shakouri G. ◽  
B. Kiani ◽  
M. Mohammadi Pour ◽  
M. Ghanbari
Keyword(s):  
System Dynamics ◽  
Crude Oil ◽  
Oil Production ◽  
Crude Oil Production
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