An Econometric Analysis of Relationship between Economic Growth and Petroleum Consumption in India

Habib, Musavir Ul

doi:10.1177/2278682119833195

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…As anticipated, using petroleum exhibits a favorable and noteworthy impact on economic advancement among the G20 nations. The present study aligns with previous research conducted by Ha and Ngoc [63] and Habib [64], which both found evidence supporting the notion that petroleum consumption positively influences economic growth. Ha and Ngoc's study specifically demonstrated a significant positive effect of petroleum consumption on Vietnam's economic growth.…”

Section: Discussionsupporting

confidence: 93%

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

Sijabat

2024

IJSDP

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This study examines the impacts of renewable and non-renewable energy consumption, urban population, and population size on economic growth. This study utilizes the Panel Unit Root Test, Pedroni's Residual-based Cointegration Test, and Fully Modified Ordinary Least Square (FMOLS) techniques to analyze data from 2010 to 2021 for 19 countries within the Group of Twenty (G20). The cointegration test indicates that renewable energy consumption, nonrenewable energy consumption, urban population, and total population are long-term associated with GDP growth. According to FMOLS estimates, renewable energy consumption has a positive impact on GDP growth in G20 countries. The impact of renewable energy consumption on GDP growth varies; natural gas consumption does not affect GDP growth, but petroleum consumption significantly affects GDP growth in G20 countries. Furthermore, the study identifies the urban population as a control variable that harms GDP growth. The empirical evidence derived from this study posits that it would be judicious for G20 policymakers to proactively institute a suite of policies that invigorate the advancement of renewable energy sources, given that such a course of action has been demonstrated to engender a notable augmentation in GDP growth.

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Section: Discussionsupporting

confidence: 93%

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

Sijabat

2024

IJSDP

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“…Therefore, studying the correlation between things belongs to the basic content of scientific research. In recent years, data analysis has been widely used in countless fields such as financial analysis [1] and medical research [2]. It can be seen that data analysis has penetrated all aspects of people's lives.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

Liu

Cui

et al. 2022

IEEE Access

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To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints. The algorithm constructs a suitable measure for representing the degree of node association in Bayesian networks based on the principle of random forest feature extraction; then uses the method to construct the initial Bayesian network structure and constrains the search space by setting a corresponding threshold for the probability increment centered on each node; finally takes the initial Bayesian network as the starting point and learns it by the forbidden hill-climbing search and BIC scoring method to obtain the optimal Bayesian network structure. Experimental results show that the correlation degree measure and mutual information proposed in this paper have an approximate correlation expression effect; compared with other Bayesian network structure learning algorithms of the same type, the method in this paper has a faster operation speed while ensuring the quality of the learned network. The experimental results show that the Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints is an effective and efficient Bayesian network structure learning algorithm.

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“…The relevance of energy is made clear in its consideration for the sustainable development agenda, 2030. Energy is a catalyst that accelerates industrial and capacity production in the real sector (Musavir, 2019). Access to high income and standard of living can be improved in low-income country when there is energy demand-supply balance (Iwayemi, 1998).…”

Section: Introductionmentioning

confidence: 99%

An econometric analysis of electricity consumption and real sector performance in Nigeria

Chitedze

Nwedeh

Adenikinju

et al. 2021

IJESM

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Purpose The purpose of this paper is to examine the extent at which electricity consumption (EC) has contributed to real sector performance, to identify energy-dependent sectors of the economy for appropriate sector-specific policy interventions and to avoid energy conservation policies that may retard the growth of the real sector and economic growth in general. Design/methodology/approach This paper used time series data, covering the period between 1981 and 2015. Various time series econometric analyses such as unit root test for stationarity and vector autoregressive and vector error correction models were used to establish the long-run and short-run co-integration relationship among the variables. Findings This study finds that EC displays a little and insignificant impact on manufacturing sector output, as well as agriculture and service outputs. The empirical result from causality test suggests a unidirectional causality running from agriculture to EC, as well as service sector to EC, whereas bidirectional causality runs between EC and manufacturing sector. This study therefore recommends adequate power supply to the manufacturing sector, while energy efficiency policy and regulatory reform should address agriculture and service sectors. Originality/value Few studies have examined the impact of EC on disaggregated gross domestic product. This research gap has strong policy implications on Nigerian economy as the output of real sector plays vital role in driving the economy. Given the pressing needs for Nigeria to boost real sector output and be among the world’s 20 largest economies by 2030, it becomes imperative for this sector-specific research to be conducted to ensure that sectoral purpose-driven energy interventions are formulated to address power supply challenges in the real sector.

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An Econometric Analysis of Relationship between Economic Growth and Petroleum Consumption in India

Cited by 4 publications

References 21 publications

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

An econometric analysis of electricity consumption and real sector performance in Nigeria

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