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

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

Bibliographic record

VenueJurnal Ekonomi Syariah Teori dan Terapan · 2022
Typearticle
Languageid
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEnergy consumptionWelfare economicsEconomicsPolitical scienceEngineering

Abstract

fetched live from 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. 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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.222
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations6
Published2022
Admission routes1
Has abstractyes

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