Enerji Ekonomisinde Genel Denge Modelleri Üzerine Bibliyometrik Çalışma

Year 2024, Volume: 42 Issue: 2, 244 - 266, 29.06.2024

Ezgi İpek , Pınar Derin Güre

https://doi.org/10.17065/huniibf.1381885

Abstract

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.

Keywords

Genel Denge Modelleri, CGE, DSGE, Enerjİ

Project Number

Tübitak 1004, 20AG002

A Bibliometric Study: General Equilibrium Models on Energy Economics

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.

Keywords

General Equilibrium Models, CGE, DSGE, Energy

Project Number

Tübitak 1004, 20AG002

References

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Jaccard, M. (2009). Combining top down and bottom up in energy economy models. International Handbook On The Economics of Energy.
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• Wing, I. S. (2009). Computable general equilibrium models for the analysis of the energy and climate policies. International Handbook On The Economics of Energy.
• 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
• 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
• 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
• Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629