IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v65y2017icp87-97.html
   My bibliography  Save this article

Downscaling heterogeneous household outcomes in dynamic CGE models for energy-economic analysis

Author

Listed:
  • Melnikov, Nikolai B.
  • O’Neill, Brian C.
  • Dalton, Michael G.
  • van Ruijven, Bas J.

Abstract

Downscaling methods for dynamic computable general equilibrium models are developed and analyzed. The methods produce outcomes for a variety of different household types by downscaling the aggregate quantities from an economic growth model with a representative household. This approach uses household survey data and long-term population projections for different household types to compare the performance of the downscaling methods vs. a general equilibrium model with multiple household groups under a variety of conditions, including demographic change, technological change, and a carbon tax. Both recursive-dynamic and forward-looking downscaling methods produce results that approximate well a multiple household model run. The recursive-dynamic downscaling method is applied to an illustrative example estimating impacts of a carbon tax on aggregate CO2 emissions and the energy demand of different household groups for a middle of the road development scenario.

Suggested Citation

  • Melnikov, Nikolai B. & O’Neill, Brian C. & Dalton, Michael G. & van Ruijven, Bas J., 2017. "Downscaling heterogeneous household outcomes in dynamic CGE models for energy-economic analysis," Energy Economics, Elsevier, vol. 65(C), pages 87-97.
    • Handle: RePEc:eee:eneeco:v:65:y:2017:i:c:p:87-97
    DOI: 10.1016/j.eneco.2017.04.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988317301305
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2017.04.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christoph Böhringer & Jared C. Carbone & Thomas F. Rutherford, 2012. "Efficiency and Equity Implications of Alternative Instruments to Reduce Carbon Leakage," Working Papers V-346-12, University of Oldenburg, Department of Economics, revised Jun 2012.
    2. Sebastian Rausch & Thomas Rutherford, 2010. "Computation of Equilibria in OLG Models with Many Heterogeneous Households," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 171-189, August.
    3. Dalton, Michael & O'Neill, Brian & Prskawetz, Alexia & Jiang, Leiwen & Pitkin, John, 2008. "Population aging and future carbon emissions in the United States," Energy Economics, Elsevier, vol. 30(2), pages 642-675, March.
    4. Hielke Buddelmeyer & Nicolas Hérault & Guyonne Kalb & Mark van Zijll de Jong, 2012. "Linking a Microsimulation Model to a Dynamic CGE Model: Climate Change Mitigation Policies and Income Distribution in Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 5(2), pages 40-58.
    5. Maurizio Bussolo & Rafael E De Hoyos & Denis Medvedev, 2010. "Economic growth and income distribution: linking macro-economic models with household survey data at the global level," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 92-103.
    6. O'Neill, Brian C. & Ren, Xiaolin & Jiang, Leiwen & Dalton, Michael, 2012. "The effect of urbanization on energy use in India and China in the iPETS model," Energy Economics, Elsevier, vol. 34(S3), pages 339-345.
    7. Nabil Annabi & Maxime Fougere & Min Li, 2013. "Foreign Competition and Income Distribution in Canada: A Dynamic Microsimulation CGE Model Analysis," International Economic Journal, Taylor & Francis Journals, vol. 27(4), pages 525-547, December.
    8. Thomas F. Rutherford & David G. Tarr, 2014. "Poverty effects of Russia's WTO accession: Modeling “real” households with endogenous productivity effects," World Scientific Book Chapters, in: APPLIED TRADE POLICY MODELING IN 16 COUNTRIES Insights and Impacts from World Bank CGE Based Projects, chapter 12, pages 287-306, World Scientific Publishing Co. Pte. Ltd..
    9. Hanan G. Jacoby & Mariano Rabassa & Emmanuel Skoufias, 2015. "Distributional Implications of Climate Change in Rural India: A General Equilibrium Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(4), pages 1135-1156.
    10. Kober, Tom & Summerton, Philip & Pollitt, Hector & Chewpreecha, Unnada & Ren, Xiaolin & Wills, William & Octaviano, Claudia & McFarland, James & Beach, Robert & Cai, Yongxia & Calderon, Silvia & Fishe, 2016. "Macroeconomic impacts of climate change mitigation in Latin America: A cross-model comparison," Energy Economics, Elsevier, vol. 56(C), pages 625-636.
    11. Rausch, Sebastian & Metcalf, Gilbert E. & Reilly, John M., 2011. "Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households," Energy Economics, Elsevier, vol. 33(S1), pages 20-33.
    12. Giulia Colombo, 2010. "Linking CGE and microsimulation models: a comparison of different approaches," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 72-91.
    13. Krey, Volker & O'Neill, Brian C. & van Ruijven, Bas & Chaturvedi, Vaibhav & Daioglou, Vassilis & Eom, Jiyong & Jiang, Leiwen & Nagai, Yu & Pachauri, Shonali & Ren, Xiaolin, 2012. "Urban and rural energy use and carbon dioxide emissions in Asia," Energy Economics, Elsevier, vol. 34(S3), pages 272-283.
    14. van Ruijven, Bas J. & O’Neill, Brian C. & Chateau, Jean, 2015. "Methods for including income distribution in global CGE models for long-term climate change research," Energy Economics, Elsevier, vol. 51(C), pages 530-543.
    15. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935, October.
    16. John Reilly & Sergey Paltsev & Ken Strzepek & Noelle Selin & Yongxia Cai & Kyung-Min Nam & Erwan Monier & Stephanie Dutkiewicz & Jeffery Scott & Mort Webster & Andrei Sokolov, 2013. "Valuing climate impacts in integrated assessment models: the MIT IGSM," Climatic Change, Springer, vol. 117(3), pages 561-573, April.
    17. Böhringer, Christoph & Carbone, Jared C. & Rutherford, Thomas F., 2012. "Unilateral climate policy design: Efficiency and equity implications of alternative instruments to reduce carbon leakage," Energy Economics, Elsevier, vol. 34(S2), pages 208-217.
    18. Jamie Sanderson & Sardar M. N. Islam, 2007. "Climate Change and Economic Development," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-59012-0, December.
    19. van Ruijven, Bas & Urban, Frauke & Benders, René M.J. & Moll, Henri C. & van der Sluijs, Jeroen P. & de Vries, Bert & van Vuuren, Detlef P., 2008. "Modeling Energy and Development: An Evaluation of Models and Concepts," World Development, Elsevier, vol. 36(12), pages 2801-2821, December.
    20. Babiker, Mustafa & Gurgel, Angelo & Paltsev, Sergey & Reilly, John, 2009. "Forward-looking versus recursive-dynamic modeling in climate policy analysis: A comparison," Economic Modelling, Elsevier, vol. 26(6), pages 1341-1354, November.
    21. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198, October.
    22. Goulder, Lawrence H., 2013. "Climate change policy's interactions with the tax system," Energy Economics, Elsevier, vol. 40(S1), pages 3-11.
    23. Cullenward, Danny & T. Wilkerson, Jordan & Wara, Michael & Weyant, John P., 2016. "Dynamically estimating the distributional impacts of U.S. climate policy with NEMS: A case study of the Climate Protection Act of 2013," Energy Economics, Elsevier, vol. 55(C), pages 303-318.
    24. Massetti, Emanuele & Tavoni, Massimo, 2012. "A developing Asia emission trading scheme (Asia ETS)," Energy Economics, Elsevier, vol. 34(S3), pages 436-443.
    25. Mendelsohn, Robert & Dinar, Ariel & Williams, Larry, 2006. "The distributional impact of climate change on rich and poor countries," Environment and Development Economics, Cambridge University Press, vol. 11(2), pages 159-178, April.
    26. Calvin, Katherine & Clarke, Leon & Krey, Volker & Blanford, Geoffrey & Jiang, Kejun & Kainuma, Mikiko & Kriegler, Elmar & Luderer, Gunnar & Shukla, P.R., 2012. "The role of Asia in mitigating climate change: Results from the Asia modeling exercise," Energy Economics, Elsevier, vol. 34(S3), pages 251-260.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mun Ho & Wolfgang Britz & Ruth Delzeit & Florian Leblanc & Roberto Roson & Franziska Schuenemann & Matthias Weitzel, 2020. "Modelling Consumption and Constructing Long-Term Baselines in Final Demand," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 5(1), pages 63-108, June.
    2. De Cian, Enrica & Dasgupta, Shouro & Hof, Andries F. & van Sluisveld, Mariësse A.E. & Köhler, Jonathan & Pfluger, Benjamin & van Vuuren, Detlef P., 2020. "Actors, decision-making, and institutions in quantitative system modelling," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    4. Richard S. J. Tol, 2023. "Navigating the energy trilemma during geopolitical and environmental crises," Papers 2301.07671, arXiv.org.
    5. Jieting Yin & Qingyou Yan & Kaijie Lei & Tomas Baležentis & Dalia Streimikiene, 2019. "Economic and Efficiency Analysis of China Electricity Market Reform Using Computable General Equilibrium Model," Sustainability, MDPI, vol. 11(2), pages 1-22, January.
    6. Wang, Yuanping & Hou, Lingchun & Hu, Lang & Cai, Weiguang & Wang, Lin & Dai, Cuilian & Chen, Juntao, 2023. "How family structure type affects household energy consumption: A heterogeneous study based on Chinese household evidence," Energy, Elsevier, vol. 284(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Ruijven, Bas J. & O’Neill, Brian C. & Chateau, Jean, 2015. "Methods for including income distribution in global CGE models for long-term climate change research," Energy Economics, Elsevier, vol. 51(C), pages 530-543.
    2. Volker Krey, 2014. "Global energy-climate scenarios and models: a review," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(4), pages 363-383, July.
    3. Lucas, Paul L. & Nielsen, Jens & Calvin, Katherine & L. McCollum, David & Marangoni, Giacomo & Strefler, Jessica & van der Zwaan, Bob C.C. & van Vuuren, Detlef P., 2015. "Future energy system challenges for Africa: Insights from Integrated Assessment Models," Energy Policy, Elsevier, vol. 86(C), pages 705-717.
    4. Vandyck, Toon & Weitzel, Matthias & Wojtowicz, Krzysztof & Rey Los Santos, Luis & Maftei, Anamaria & Riscado, Sara, 2021. "Climate policy design, competitiveness and income distribution: A macro-micro assessment for 11 EU countries," Energy Economics, Elsevier, vol. 103(C).
    5. Fankhauser, Samuel & Jotzo, Frank, 2017. "Economic growth and development with low-carbon energy," LSE Research Online Documents on Economics 86850, London School of Economics and Political Science, LSE Library.
    6. O'Neill, Brian C. & Ren, Xiaolin & Jiang, Leiwen & Dalton, Michael, 2012. "The effect of urbanization on energy use in India and China in the iPETS model," Energy Economics, Elsevier, vol. 34(S3), pages 339-345.
    7. Ciarli, Tommaso & Savona, Maria, 2019. "Modelling the Evolution of Economic Structure and Climate Change: A Review," Ecological Economics, Elsevier, vol. 158(C), pages 51-64.
    8. van Vliet, Oscar & Krey, Volker & McCollum, David & Pachauri, Shonali & Nagai, Yu & Rao, Shilpa & Riahi, Keywan, 2012. "Synergies in the Asian energy system: Climate change, energy security, energy access and air pollution," Energy Economics, Elsevier, vol. 34(S3), pages 470-480.
    9. N. B. Melnikov & A. P. Gruzdev & M. G. Dalton & M. Weitzel & B. C. O’Neill, 2021. "Parallel Extended Path Method for Solving Perfect Foresight Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 517-534, August.
    10. Calvin, Katherine & Clarke, Leon & Krey, Volker & Blanford, Geoffrey & Jiang, Kejun & Kainuma, Mikiko & Kriegler, Elmar & Luderer, Gunnar & Shukla, P.R., 2012. "The role of Asia in mitigating climate change: Results from the Asia modeling exercise," Energy Economics, Elsevier, vol. 34(S3), pages 251-260.
    11. Lucas, Paul L. & Shukla, P.R. & Chen, Wenying & van Ruijven, Bas J. & Dhar, Subash & den Elzen, Michel G.J. & van Vuuren, Detlef P., 2013. "Implications of the international reduction pledges on long-term energy system changes and costs in China and India," Energy Policy, Elsevier, vol. 63(C), pages 1032-1041.
    12. Ritchie, Justin & Dowlatabadi, Hadi, 2017. "Why do climate change scenarios return to coal?," Energy, Elsevier, vol. 140(P1), pages 1276-1291.
    13. García-Muros, Xaquín & Morris, Jennifer & Paltsev, Sergey, 2022. "Toward a just energy transition: A distributional analysis of low-carbon policies in the USA," Energy Economics, Elsevier, vol. 105(C).
    14. van der Zwaan, Bob & Kober, Tom & Calderon, Silvia & Clarke, Leon & Daenzer, Katie & Kitous, Alban & Labriet, Maryse & Lucena, André F.P. & Octaviano, Claudia & Di Sbroiavacca, Nicolas, 2016. "Energy technology roll-out for climate change mitigation: A multi-model study for Latin America," Energy Economics, Elsevier, vol. 56(C), pages 526-542.
    15. Julien Lefevre, 2018. "Modeling the Socioeconomic Impacts of the Adoption of a Carbon Pricing Instrument – Literature review," CIRED Working Papers hal-03128619, HAL.
    16. Floater, Graham & Rode, Philipp & Robert, Alexis & Kennedy, Chris & Hoornweg, Dan & Slavcheva, Roxana & Godfrey, Nick, 2014. "Cities and the New Climate Economy: the transformative role of global urban growth," LSE Research Online Documents on Economics 60775, London School of Economics and Political Science, LSE Library.
    17. Nieves, J.A. & Aristizábal, A.J. & Dyner, I. & Báez, O. & Ospina, D.H., 2019. "Energy demand and greenhouse gas emissions analysis in Colombia: A LEAP model application," Energy, Elsevier, vol. 169(C), pages 380-397.
    18. Ian Parry & Baoping Shang & Nate Vernon & Philippe Wingender & Tarun Narasimhan, 2020. "Evaluating policies to implement the Paris Agreement: a toolkit with application to China," Chapters, in: Graciela Chichilnisky & Armon Rezai (ed.), Handbook on the Economics of Climate Change, chapter 2, pages 32-67, Edward Elgar Publishing.
    19. Pereira, Alfredo & Pereira, Rui, 2017. "The Role of Electricity for the Decarbonization of the Portuguese Economy - DGEP Technical Report," MPRA Paper 84782, University Library of Munich, Germany.
    20. Ottmar Edenhofer & Susanne Kadner & Christoph von Stechow & Gregor Schwerhoff & Gunnar Luderer, 2014. "Linking climate change mitigation research to sustainable development," Chapters, in: Giles Atkinson & Simon Dietz & Eric Neumayer & Matthew Agarwala (ed.), Handbook of Sustainable Development, chapter 30, pages 476-499, Edward Elgar Publishing.

    More about this item

    Keywords

    Computable general equilibrium; Economic growth; Demographic heterogeneity; Energy demand; Carbon tax;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:65:y:2017:i:c:p:87-97. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.