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Enregistrement W4210295873 · doi:10.20473/vol9iss20221pp118-130

KAUSALITAS PERTUMBUHAN EKONOMI, ENERGI TERBARUKAN DAN DEGRADASI LINGKUNGAN PADA NEGARA ORGANISASI KERJASAMA ISLAM

2022· article· id· W4210295873 sur OpenAlexaboutno aff
Adelia De Tsamara Khansa, Tika Widiastuti

Notice bibliographique

RevueJurnal Ekonomi Syariah Teori dan Terapan · 2022
Typearticle
Langueid
DomaineEnergy
ThématiqueEnergy, Environment, and Transportation Policies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRenewable energyEnergy consumptionWelfare economicsEconomicsPolitical scienceEngineering

Résumé

récupéré en direct d'OpenAlex

ABSTRAKPenelitian ini bertujuan mengetahui hubungan kausalitas antara konsumsi energi konvensional, pertumbuhan ekonom, emisi karbon dioksida, dan konsumsi energi terbarukan di 39 negara Organisasi Kerjasama Islam (OKI) periode 1992-2018. Metode yang diterapkan ialah uji kausalitas Dumitrescu-Hurlin (2012) yang memperbolehkan adanya heterogenitas dan cross-sectional dependence. Temuan dari penelitian ini ialah terdapat interdependensi antara konsumsi energi konvensional dengan pertumbuhan ekonomi, sedangkan konsumsi energi terbarukan dipengaruhi oleh pertumbuhan ekonomi sebagaimana teori RKC U-shaped. Pertumbuhan ekonomi menyebabkan emisi karbon dioksida sebagaimana teori EKC-Kuznets. Tidak ditemukannnya hubungan kausalitas antara konsumsi energi konvensional dan terbarukan dengan emisi karbon dioksida. Penerapan kebijakan konservasi dapat diterapkan dengan memperhatikan pertumbuhan ekonomi. Penelitian terdahulu, menguji hubungan kausalitas tanpa memperhatikan cross-sectional dependence dan tidak memisahkan antara konsumsi energi konvensional dengan energi terbarukan. Kata Kunci: Energi terbarukan, Degradasi Lingkungan, Kausalitas. ABSTRACTThis research aims to find causality between conventional energy consumption, economic growth, carbon dioxide emissions, and renewable energy consumption in 39 countries of Organization of Islamic Cooperation (OIC) on 1992-2018. The method used Dumitrescu-Hurlin Causality Test (2012) that allows heterogeneity and cross-sectional dependence. The outcome affirms that there is interdependency between conventional energy consumption and economic growth, but renewable energy consumption affected economic growth that confirms RKC U-Shaped theory. The impact of economic growth affects environmental degradation, carbon dioxide emissions which accept EKC-Kuznets theory. The neutral hypothesis confirmed between conventional and renewable energy consumption and carbon dioxide emissions. Conservation policy could be implementing by considering economic growth. Previous study, testing causality relationship without considering cross-sectional dependence and differentiate between conventional and renewable energy consumption.Keywords: Renewable energy, Environmental Degradation, Causality. DAFTAR PUSTAKAAdams, S., & Nsiah, C. (2019). Reducing carbon dioxide emissions; Does renewable energy matter? Science of the Total Environment, 693(25), 1-9. https://doi.org/10.1016/j.scitotenv.2019.07.094Alfarabi, M. A., Hidayat, M. S., & Rahmadi, S. (2014). Perubahan struktur ekonomi dan dampaknya terhadap kemiskinan di provinsi Jambi. Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, 1(3), 171-178. https://doi.org/10.22437/ppd.v1i3.1551Antonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Energy consumption, CO2 emissions and economic growth: An ethical dilemma. Renewable dan Sustainable Energy Reviews, 68(P1), 808-824.Banday, U. J., & Aneja, R. (2018). Energy consumption, economic growth and CO2 emissions: evidence from G7 countries. World Journal of Science, Technology and Sustainable Development, 16(1), 22-39. https://doi.org/10.1108/WJSTSD-01-2018-0007Banday, U. J., & Aneja, R. (2020). Renewable and non-renewable energy consumption, economic growth and carbon emission in BRICS: Evidence from bootstrap panel causality. International Journal of Energy Sector Management, 14(1), 248-260.Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460.EIA. (2021). Carbon dioxide emisssions coefficients. Retrieved from EIA: https://www.eia.gov/environment/emissions/co2_vol_mass.phpField, B. C., & Olewiler, N. D. (2015). Environmental economics. Toronto: MacGraw-Hill Ryerson.Grafström, J. (2018). Divergence of renewable energy intention efforts in Europe: An econometric analysis based on patent counts. Environmental Economics and Policy Studies, 20(4), 829-859.Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement. The quarterly journal of impacts, 110(2), 353-377.Huang, B.-N., Huang, M. J., & Yang, C. W. (2008). Causal relationship between energy consumptionand GDP growth revisited: A dynamicpanel data approach. Ecological Economics, 67(1), 41-54.Irijanto, T. T., Zaidi, M. A., Ismail, A. G., & Arshad, N. C. (2015). Al Ghazali's thoughts of economic growth theory, a contribution with system thinking. Scientific Jounal of PPI-UKM, 2(5), 233-240.Jaelani, A., Firdaus, S., & Jumena, J. (2017). Renewable energy policy in Indonesia: The Quranic Scientific signals in Islamic economics perspective. International Journal of Energy Economics and Policy, 193-204.Kahouli, B. (2018). The causality link between energy electricity consumption, CO2 emissions, R&D stocks and economic growth in Mediterranean countries (MCs). Energy, 145, 388-399.Khan, S. H., & Akram, M. H. (2018). Renewable energy profile of OIC Countries. Pakistan: COMSTECH.Lopez, L., & Weber, S. (2017). Testing for granger causality in panel data. The Stata Journal, 17(4), 972-984.Lu, W.-C. (2017). Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis for 16 Asian countries. International Journal of environmental research and public health, 14(11), 14-36.Muhammad, A. A., Arshed, N., & Kousar, N. (2017). Renewable energy consumption and economic growth in member of OIC countries. European Online Journal of Natural and Social Science, 6(1), 111-129.Naf'an. (2014). Ekonomi makro tinjauan ekonomi syariah. Yogyakarta: Graha Ilmu.Pesaran, M. (2004). General diagnostic test for cross sectional independence in panel. Journal of Econometrics, 68(1), 79-110.Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics, 22(2), 265-312.Ranjan, A., Banday, U. J., Hasnat, T., & Koçoglu, M. (2017). Renewable and non renewable energy consumption and economic growth: Empirical evidence from panel error correction model. Jindal Journal of Business Research, 6(1), 1-10.Ritchie, H. (2021, May 5). What are the safest and cleanest sources of energy? Retrieved from https://ourworldindata.org/safest-sources-of-energySaad, N. M., Kassim, S., & Hamiid, Z. (2016). Best practices of waqf: Experiences of Malaysia and Saudi Arabia. Journal of Islamic Economics Lariba, 2(2), 57-74.SESRIC. (2019). OIC environment report 2019. Ankara: SESRIC.______. (2020). OIC economic outlook 2020. Ankara: SESRIC.Shafie, S., & Salim, R. A. (2014). Non renewable and renwable energy consumption and CO2 emissions in OECD countries: A comparative analysis. Energy Policy, 66, 547-556.Sharif, A., Raza, S. A., Ozturk, I., & Afshan, S. (2019). The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations. Renewable Energy, 133, 685-691.Tietenberg, T., & lewis, L. (2018). Environmental & natural resource economics. New Jersey: Pearson Education.Toumi, S., & Toumi, H. (2019). Asymmetric causality among renewable energy consumption, CO2 emissions, and economic growth in KSA: Evidence from a non-linear ARDL model. Environmental Science and Pollution Research, 26(5), 16145-16156.Tugcu, C. T., & Topcu, M. (2018). Total, renewable and non renewable energy consumption and economic growth: Revisiting the issue with an asymmetric point of view. Energy, 152(C), 64-74.Tuna, G., & Tuna, V. E. (2019). The asymmetric causal relationship between renewable and non-renewable energy consumption and economic growth in the ASEAN-5 countries. Resources Policy, 62, 114-124.WaCIDS. (2021, August 23). Green waqf: Wakaf sebagai solusi perbaikan alam dan kemandirian energi. Retrieved from https://wacids.or.id/2021/08/23/green-waqf-sebagai-solusi-perbaikan-alam-dan-kemandirian-energi/WHO. (2018). COP24 special report health & climate change. Geneva: WHO.World Bank. (2019). Economy. Retrieved from https://datatopics.worldbank.org/world-development-indicator/themes/economy.htmlWorld Bank. (2021). State and trends carbon pricing 2021. Washington DC: World Bank.Yamane, T. (1967). Statistics: An introductory analysis. New York: Harper anda Row.Yao, S., Zhang, S., & Zhang, X. (2019). Renewable energy, carbon emission and economic growth: A revised environmental Kuznets Curve perspective. Journal of Cleaner Production, 1338-1352.Zaidi, S. A., Danish, Hou, F., & Mirza, F. M. (2018). The role of renewable and non-renewable energy consumption in CO2 emissions: a disaggregate analysis of Pakistan. Environmental Science and Pollution Research, 25(31, 31616-31629.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,724
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,012
Tête enseignante GPT0,222
Écart entre enseignants0,211 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
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

Citations6
Publié2022
Routes d'admission1
Résumé présentoui

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