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Enregistrement W7115706572 · doi:10.22034/epj.2024.20468.2471

Investigating the impact of energy subsidies on the market power of Iran's industry

2024· article· fa· W7115706572 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languefa
DomaineEnergy
ThématiqueEnergy, Environment, and Transportation Policies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSubsidyEnergy subsidiesConsumption (sociology)Energy consumptionGovernment (linguistics)Production (economics)Energy policyEnergy market

Résumé

récupéré en direct d'OpenAlex

Purpose: Iran has the second largest gas reserves in the world, after Russia, and the fourth oil reserves in the world, after Venezuela, Saudi Arabia, and Canada, and it is possible for the country to access energy for households and the economic activities (Energy Balance Sheet, 2019). Price stabilization policies have always been applied, which has caused the government to spend a huge annual subsidy on energy consumption in different sectors of Iran; in 2021, according to the report of the International Energy Agency of Iran, it would amount to 58 billion dollars (that of the world is 531 billion dollars). It has paid subsidies to all types of energy carriers, which ranks Iran second after Russia in terms of the share of subsidies in the world's total energy subsidies (11%). The rate of Iran's energy consumption was on the rise during the years 2011-2012, and this indicator was higher in the industry sector than in the whole economy. From 2011 to 2012, the energy coefficient for the whole economy was 2.3 and for the industry sector was 1.7. This was less than one for advanced countries. Therefore, in total, this volume of subsidies and energy consumption in the economy and especially the industry sector has led to development. It has not been economical and the efficiency of energy use decreases in Iran every year. Subsidies affect macroeconomic and social variables such as inflation, budget deficit, market power, business environment, and class gap. Due to its high volume, examining the economic and social effects of subsidies is of great importance. Since it is not possible to refer to all its dimensions in one study, this article only aims at the effect of energy subsidies on market power in the industrial sector. The impact of subsidies on market power may lead to a positive or negative correlation between subsidies and market power, and there are different views on this issue. Some believe that subsidies increase market power because subsidies provide companies with more finance or lead to cost reduction, so there is a positive relationship between subsidy and market power. To estimate the subsidy impact, panel data models were used. The study period is 2002-2019 at the level of four-digit ISIC codes of industries with 10 workers and more. The current research seeks to answer the following key questions: Do energy subsidies have a positive or negative effect on the market power of the country's industries? What is the impact of energy subsidies on the country's industries?Methodology: External resources (i.e. government subsidies) increase access to internal resources. In this case, a firm can protect itself from uncertainty and adverse environmental risks or reduce financial barriers, thus creating an advantage. It improves the performance of the company. In this regard, the first hypothesis is ‘government subsidies have a positive relationship with company performance’.However, inefficient allocation of resources reduces or even reverses the positive outcomes of resource accumulation. One of the major potential costs of government subsidies is the elimination of their positive effects. Research has found that government subsidies lead to overinvestment by firms, thereby harming firm performance. It is also shown that government subsidies provide firms with excess resources, leading to inefficient and complacent management, waste of resources, and stagnation in financial performance. The second hypothesis is ‘government subsidies have a negative relationship with company performance’.Results and discussion: Iran is one of the countries with the highest energy subsidy payments in the world, and subsidies have impacts on all the economic and social aspects of the society. The studies conducted by Dai Li, Chu and Wang, and Chen and Yu showed that the effect of subsidy on market power is negative. Also, in Akhani's study, the relationship between subsidy and market power was negative. This study uses the panel data method and productivity variables, the Herfindahl index, wages, and capital stock as independent variables and the Lerner index as market power (2002-2019). The estimated model is Yu and Wang's model. The results show that the relationship between subsidy and market power is negative, which is consistent with previous studies. One of the reasons for the negative relationship between subsidy and market power is the lack of innovation and creativity by companies due to cheap access to funds. They are not willing to try to gain the energy market, and there is a kind of laziness that prevails in the companies. The statistics on the intensity of energy consumption confirm this issue. In general, the energy subsidy in Iran in the industry sector did not lead to the creation of market power but increased inefficiency. Moreover, industries are not inclined to modernize and use old and energy-consuming technology.Conclusions and policy implications: Considering the positive impact of productivity and market power, the government should provide appropriate policies to support the increase of productivity in companies. Due to the influence of the market share and market power of the government through anti-trust policies, monopolies should be prevented. This study showed that subsidies have a negative effect on market power. This is because the fair distribution of energy subsidies in the country would deter the easy access of companies to subsidies. So, if the government likes subsidies not to lead to monopoly or to the increase of the power of companies, it should provide conditions for the accessibility of subsidies for all companies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,182
Tête enseignante GPT0,487
Écart entre enseignants0,306 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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