Journals / Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 42 Sayı: 2

A Bibliometric Study: General Equilibrium Models on Energy Economics

Enerji Ekonomisinde Genel Denge Modelleri Üzerine Bibliyometrik Çalışma

Ezgi İpek(ORTA DOĞU TEKNİK ÜNİVERSİTESİ)0000-0002-5807-8703
Pınar Derin Güre(ORTA DOĞU TEKNİK ÜNİVERSİTESİ)0000-0001-6128-5116
Pages
244–266
Publication Date
2024-06-29
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

They offer a benchmark for analyzing changes in the overall economy due to new policies, shocks, or technological improvements, using the data from the social accounting matrices and input-output tables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:General equilibrium (GE) models are commonly used for economic impact analysis.
📊
Key Findings & Data:This study uses bibliometric analysis to examine the networks between the existing literature between 1990-2020.
💡
Conclusion & Impact:They offer a benchmark for analyzing changes in the overall economy due to new policies, shocks, or technological improvements, using the data from the social accounting matrices and input-output tables.
🔬Design:Review & Systematic Survey
📍Scope:2020 Verileri
⚙️Variables:Genel Denge Modelleri · CGE · DSGE

Abstract

General equilibrium (GE) models are commonly used for economic impact analysis. They offer a benchmark for analyzing changes in the overall economy due to new policies, shocks, or technological improvements, using the data from the social accounting matrices and input-output tables. GE models are widely used for analyzing the areas of energy economics, as the focus on energy-related issues has become critical throughout the years. Therefore, a broad literature focuses on the GE models and energy economics. This study uses bibliometric analysis to examine the networks between the existing literature between 1990-2020. No other paper uses this method to focus on the selected literature. The data for the bibliometric analysis is subtracted from the Web of Science. The keywords are "computable general equilibrium, dynamic stochastic general equilibrium, and energy." Dynamic Stochastic Models are added to expand the scope of the dataset. In addition, the paper reviews the ten most cited articles based on the data retrieved from the Web of Science. The main results of the bibliometric analysis show that the GE models were highly used after 2005, with the introduction of international energy agreements. The focus of these models is usually renewable energy and mitigation policies.

Özet

Genel denge (GD) modelleri, ekonomik etki analizi için yaygın olarak kullanılmaktadır. Bu modeller sosyal muhasebe matrislerinden ve girdi-çıktı tablolarından elde edilen verileri kullanarak, yeni politikalar, şoklar veya teknolojik gelişmeler nedeniyle oluşan değişiklikleri analiz etmek için bir kıyaslama sunarlar. GD modelleri, enerji ile ilgili konuların yıllar içinde daha kritik hale gelmesiyle, bu alanların analizi için yaygın olarak kullanılmaktadır. Bu nedenle, geniş bir literatür GD modellerine ve enerji ekonomisine odaklanmaktadır. Bu çalışma, 1990-2020 yılları arasındaki mevcut literatür arasındaki ağları incelemek için bibliyometrik analizi kullanmaktadır. Daha önceki hiçbir çalışmada, seçilen literatüre odaklanmak için bu yöntem kullanılmamıştır. Bibliyometrik analiz için veriler Web of Science'tan alınmıştır. Anahtar kelimeler “hesaplanabilir genel denge, dinamik stokastik genel denge ve enerji”dir. Dinamik Stokastik Modeller veri setinin dahil ettiği alanı genişletmek amacıyla eklenmiştir. Çalışma ayrıca Web of Science'tan alınan verileri kullanarak en çok atıf yapılan on makaleyi de gözden geçirmektedir. Bibliyometrik analizin ana sonuçları, 2005 yılından itibaren, uluslararası enerji anlaşmalarının yürürlüğe girmesinin de etkisiyle GD modellerinin yüksek oranda kullanıldığını göstermektedir. Analiz GD modellerinin odak noktasının genellikle yenilenebilir enerji ve karbon azaltma politikaları olduğunu göstermektedir.

🔖

Cite This Article

İpek, E., Güre, P. D. (2024). A Bibliometric Study: General Equilibrium Models on Energy Economics. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 42 Sayı: 2, 244–266. https://doi.org/10.17065/huniibf.1381885

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (39)

  1. Babatunde, K. A., Begum, R. A., & Said, F. F. (2017). Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review. Renewable and Sustainable Energy Reviews, 78, 61-71. https://doi.org/10.1016/j.rser.2017.04.064
  2. Babiker, M. H. (2005). Climate change policy, market structure, and carbon leakage. Journal of International Economics, 65(2), 421-445. https://doi.org/10.1016/j.jinteco.2004.01.003
  3. Bardazzi, E., & Bosello, F. (2021). Critical reflections on water-energy-food nexus in computable general equilibrium models: A systematic literature review. Environmental Modelling & Software, 145, 105201.https://doi.org/10.1016/j.envsoft.2021.105201
  4. Bauer, N., Baumstark, L. & Leimbach, M. The REMIND-R model: the role of renewables in the low-carbon transformation—first-best vs. second-best worlds. Climatic Change 114, 145–168 (2012).https://doi.org/10.1007/s10584-011-0129-2
  5. Bhattacharyya, S. C. (1996). Applied general equilibrium models for energy studies: A survey. Energy Economics, 18(3), 145-164. https://doi.org/10.1016/0140-9883(96)00013-8
  6. Böhringer, C. (1998). The synthesis of bottom-up and top-down in energy policy modeling. Energy Economics, 20(3), 233-248. https://doi.org/10.1016/S0140-9883(97)00015-7
  7. Böhringer, C., & Rutherford, T. F. (2008). Combining bottom-up and top-down. Energy Economics, 30(2), 574-596. https://doi.org/10.1016/j.eneco.2007.03.004
  8. Böhringer, C., Löschel, A., Moslener, U., & Rutherford, T. F. (2009). EU climate policy up to 2020: An economic impact assessment. Energy economics, 31, S295-S305. https://doi.org/10.1016/j.eneco.2009.09.009
  9. Bosetti, V., Carraro, C., Galeotti, M., Massetti, E., & Tavoni, M. (2006). A World Induced Technical Change Hybrid Model. The Energy Journal, 27, 13 - 37. http://www.jstor.org/stable/23297044
  10. Burns, A., Djiofack Zebaze, C., & Prihardini, D. (2018). Energy Subsidy Reform Assessment Framework: Modeling Macroeconomic Impacts and Global Externalities. World Bank, Washington, DC.https://documents1.worldbank.org/curated/en/815971530883640016/pdf/ESRAF-note-7-Modeling-Macroeconomic-Impacts-and-Global-externalities.pdf
  11. Carbone, J. C., Rivers, N., Yamazaki, A., & Yonezawa, H. (2020). Comparing applied general equilibrium and econometric estimates of the effect of an environmental policy shock. Journal of the Association of Environmental and Resource Economists, 7(4), 687-719.http://dx.doi.org/10.1086/708734
  12. Chen, H. Q., Wang, X., He, L., Chen, P., Wan, Y., Yang, L., & Jiang, S. (2016). Chinese energy and fuels research priorities and trend: A bibliometric analysis. Renewable and Sustainable Energy Reviews, 58, 966-975. DOI:10.1016/j.rser.2015.12.239
  13. Chevalier, J. M. (2007). Introduction: Energy economics and energy econometrics. e Econometrics of Energy Systems, JH Keppler, R. Bourbonnais and J. Girod (Eds.), Palgrave Macmillan, New York.
  14. Cui, L. B., Fan, Y., Zhu, L., & Bi, Q. H. (2014). How will the emissions trading scheme save cost for achieving China's 2020 carbon intensity reduction target?. Applied Energy, 136, 1043-1052. https://doi.org/10.1016/j.apenergy.2014.05.021
  15. Dai, H., Xie, X., Xie, Y., Liu, J., & Masui, T. (2016). Green growth: The economic impacts of large-scale renewable energy development in China. Applied energy, 162, 435-449. https://doi.org/10.1016/j.apenergy.2015.10.049DOI: 10.1016/j.apenergy.2015.10.049
  16. De Cian, Enrica & Bosetti, Valentina & Tavoni, Massimo. (2012). Technology innovation and diffusion in "less than ideal" climate policies: An assessment with the WITCH model. Climatic Change. 114. 121-143. 10.1007/s10584-011-0320-5.https://doi.org/10.1007/s10584-011-0320-5
  17. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  18. Du, H., Wei, L., Brown, M. A., Wang, Y., & Shi, Z. (2013). A bibliometric analysis of recent energy efficiency literatures: An expanding and shifting focus. Energy Efficiency, 6(1), 177-190.https://DOI:10.1007/s12053-012-9171-9
  19. Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: Quality measurements of scientific publication. Radiology, 255(2), 342-351.https://doi.org/10.1148/radiol.09090626
  20. Fan, J. L., Kong, L. S., & Zhang, X. (2018). Synergetic effects of water and climate policy on energy-water nexus in China: A computable general equilibrium analysis. Energy Policy, 123, 308-317.DOI: 10.1016/j.enpol.2018.09.002
  21. Golosov, M., Hassler, J., Krusell, P., & Tsyvinski, A. (2014). Optimal taxes on fossil fuel in general equilibrium. Econometrica, 82(1), 41-88. https://doi.org/10.3982/ECTA10217
  22. Hache, E., & Palle, A. (2019). Renewable energy source integration into power networks, research trends and policy implications: A bibliometric and research actors survey analysis. Energy Policy, 124, 23-35. https://doi.org/10.1016/j.enpol.2018.09.036
  23. Hassler, J., & Krusell, P. (2018). Environmental macroeconomics: the case of climate change. In Handbook of Environmental Economics (Vol. 4, pp. 333-394). Elsevier.DOI: 10.1016/bs.hesmac.2016.04.007
  24. He, P., Ng, T. S., & Su, B. (2019). Energy-economic resilience with multi-region input–output linear programming models. Energy Economics, 84, 104569.DOI: 10.1016/j.eneco.2019.104569
  25. Jaccard, M. (2009). Combining top down and bottom up in energy economy models. International Handbook On The Economics of Energy.
  26. Leimbach, M., Bauer, N., Baumstark, L., & Edenhofer, O. (2010). Mitigation costs in a globalized world: climate policy analysis with REMIND-R. Environmental modeling & assessment, 15, 155-173. doi:10.1007/s10666-009-9204-8
  27. Liang, Q. M., Fan, Y., & Wei, Y. M. (2007). Carbon taxation policy in China: How to protect energy-and trade-intensive sectors?. Journal of Policy Modeling, 29(2), 311-333. https://doi.org/10.1016/j.jpolmod.2006.11.001 Luderer, G., Bosetti, V., Jakob, M., Leimbach, M., Steckel, J. C., Waisman, H., & Edenhofer, O. (2012). The economics of decarbonizing the energy system—results and insights from the RECIPE model intercomparison. Climatic Change, 114(1), 9-37. https://doi.org/10.1007/s10584-011-0105-x
  28. Mao, G., Huang, N., Chen, L., & Wang, H. (2018). Research on biomass energy and the environment from the past to the future: A bibliometric analysis. Science of The Total Environment, 635, 1081-1090.DOI:%2010.1016/j.scitotenv.2018.04.173
  29. Mao, G., Liu, X., Du, H., Zuo, J., & Wang, L. (2015). Way forward for alternative energy research: A bibliometric analysis during 1994–2013. Renewable and Sustainable Energy Reviews, 48, 276-286. https://doi.org/ 10.1016/j.scitotenv.2018.04.173
  30. Pollitt, H., Lewney, R., & Mercure, J. F. (2019). Conceptual differences between macro-econometric and CGE models. In 27th International Input-Output Association Conference [Internet]. Glasgow, Scotland.
  31. Gitz, Vincent & Sassi, Olivier & Crassous, Renaud & Hourcade, Jean-Charles & Waisman, Henri & Guivarch, Céline. (2010). IMACLIM-R: A modelling framework to simulate sustainable development pathways. International Journal of Global Environmental Issues. 10. 5-24. 10.1504/IJGENVI.2010.030566
  32. Shobande, O. A., & Shodipe, O. T. (2019). Carbon policy for the United States, China and Nigeria: An estimated dynamic stochastic general equilibrium model. Science of The Total Environment, 697, 134130.DOI:10.1016/j.scitotenv.2019.134130
  33. Tsay, M. Y. (2008). A bibliometric analysis of hydrogen energy literature, 1965-2005. Scientometrics, 75(3), 421-438. https://doi.org/10.1007/s11192-007-1785-x
  34. Henri Waisman & Céline Guivarch & Fabio Grazi & Jean Hourcade, 2012. "The I maclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight," Climatic Change, Springer, vol. 114(1), pages 101-120, September. DOI: 10.1007/s10584-011-0387-z
  35. Wing, I. S. (2009). Computable general equilibrium models for the analysis of the energy and climate policies. International Handbook On The Economics of Energy.
  36. Wu, J., Ge, Z., Han, S., Xing, L., Zhu, M., Zhang, J., & Liu, J. (2020). Impacts of agricultural industrial agglomeration on China's agricultural energy efficiency: A spatial econometrics analysis. Journal of Cleaner Production, 260, 121011.DOI:10.1016/j.jclepro.2020.121011
  37. Xie, Y., Dai, H., Dong, H., Hanaoka, T., & Masui, T. (2016). Economic impacts from PM2. 5 pollution-related health effects in China: a provincial-level analysis. Environmental Science & Technology, 50(9), 4836-4843. https://doi.org/10.1021/acs.est.5b05576
  38. Xing, Z., Wang, J., & Zhang, J. (2018). Expansion of environmental impact assessment for eco-efficiency evaluation of China's economic sectors: An economic input-output based frontier approach. Science of the Total Environment, 635, 284-293.https://doi.org/10.1016/j.scitotenv.2018.04.076
  39. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629