Evidence map›Paper›PMID 36085220›Full record

ArticleEnvironmental science and pollution research international2023

Assessment of the total-factor energy efficiency and environmental performance of Persian Gulf countries: a two-stage analytical approach.

Mohamad Nikbakht, Parviz Hajiani, Ahmad Ghorbanpur

Abstract read
In one paragraph

Article in Environmental science and pollution research international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. COP27: The Prospects and Challenges for the Middle East and North Africa (MENA).International journal of health policy and management · 2022
    Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Mohamad NikbakhtPersian Gulf University, Bushehr, Iran.
Parviz HajianiDepartment of Economics, Persian Gulf University, Bushehr, Iran. hajiani@pgu.ac.ir.
Ahmad GhorbanpurDepartment of Industrial Management, Persian Gulf University, Bushehr, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent decades, achieving sustainable economic growth and development through energy efficiency has been a key challenge for Persian Gulf countries. This study presents a two-stage analysis of the energy efficiency and environmental performance of Persian Gulf countries in 2000-2014 using data envelopment analysis and Tobit regression. The hypothesis of this study is that energy efficiency is low in the Persian Gulf countries and these countries have the potential to reduce greenhouse gas emissions. At first, using data envelopment analysis, total-factor energy efficiency and environmental efficiency of the Persian Gulf countries were measured. Then, using Tobit regression, the effects of GDP per capita, oil price, industrialization degree, population size, paper citation rate, foreign direct investment, and the degree of commercial openness on energy efficiency were investigated. The results of the first stage measurements show that Saudi Arabia and the United Arab Emirates had the highest and second highest total-factor energy efficiency, respectively, while Oman and Iran had the lowest, and second lowest, respectively. In terms of environmental performance, the UAE and Qatar proved to have the best and second best performance, respectively, while Iran and Iraq showed the weakest and second weakest performance, respectively. The results of Tobit regression revealed that GDP per capita, oil prices, industrialization degree, and population size had a direct relationship with energy efficiency while the paper citation rate (as an index of science, technology, and innovation) and foreign direct investment had an inverse relationship with energy efficiency. This study shows that the Persian Gulf countries could potentially reduce their energy consumption by up to 18%. Finally, a number of environmentally friendly economic policies and several environmental projects are proposed and it is emphasized that more innovative green technologies should be used to increase energy efficiency and optimize the energy structure to combat climate change.

Indexed as

Conservation of Energy ResourcesIndian OceanOmanSaudi ArabiaUnited Arab EmiratesData envelopment analysisGCCPersian Gulf countriesSustainable developmentTobit regressionTotal-factor energy efficiency

Identifiers

PMID36085220
PMCPMC9462623

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.