DOI QR코드

DOI QR Code

A Review on Probabilistic Climate-economy Models and an Application of FUND

기후경제 모형의 불확실성 분석 방법 비교분석 및 FUND 모형 응용

  • Received : 2017.07.04
  • Accepted : 2017.08.09
  • Published : 2017.09.30

Abstract

Uncertainty is central to energy and climate policy. A growing number of literature show that almost all components of energy and climate models are, to some extent, uncertain and that the effect of uncertainty on the model outputs, in turn policy recommendations, is significantly large. Most existing energy and climate-economy models developed and used in Korea, however, do not take uncertainty into account explicitly. Rather, many models conduct a deterministic analysis or do a simple (limited) sensitivity analysis. In order to help social planners to make more robust decisions (across various plausible situations) on energy and climate change issues, an uncertainty analysis should be conducted. As a first step, this paper reviews the theory of decision making under uncertainty and the method for addressing uncertainty of existing probabilistic energy and climate-economy models. In addition, the paper proposes a strategy to apply an uncertainty analysis to energy and climate-economy models used in Korea. Applying the uncertainty analysis techniques, this paper revises the FUND model and investigates the impacts of climate change in Korea.

에너지 및 기후변화 정책을 수립하는 데 있어 경제모형은 중요한 역할을 한다. 자원의 효율적인 사용에 관한 경제이론을 바탕으로 공공의 정책을 평가하고 나아가야 할 방향을 제시할 수 있기 때문이다. 개인의 의사결정뿐 아니라 공공의 의사결정에서 불확실성은 중요한 영향을 미친다. 최근 학계를 중심으로 에너지 및 기후변화 문제와 관련하여 불확실성이 모형의 결과에 미치는 영향이 강조되고 있으며, 몇몇 모형들에서는 불확실성을 명시적으로 반영하고 있다. 그러나 국내에서 사용하고 있는 에너지 및 기후경제 모형의 경우 대체로 결정론적인 분석틀을 사용하고 있어 에너지 및 기후변화 문제가 갖고 있는 불확실성을 반영하지 못한다는 문제점이 있다. 발생 가능한 다양한 경우에 대해서도 견고한 의사결정의 중요성이 강조되고 있다는 측면에서도 불확실성 분석의 필요성은 더욱 커지고 있다. 이에 이 논문은 불확실성 분석에 관한 이론을 검토하고, 이론에 근거해 불확실성과 관련한 에너지 및 기후경제 모형의 최근 연구 결과를 분석하며, 국내 모형들이 불확실성 분석을 수행할 수 있는 방법을 제안한다. 또한 수치 모형 응용으로서 FUND 모형을 불확실성을 반영해 수정한 후 국내 기후변화 피해비용을 분석하였다. 이 논문은 에너지 및 기후경제 모형과 관련하여 불확실성 분석 관련 내용을 종합적으로 검토하고 향후 국내 모형에서 적용할 수 있는 방안을 제시한다는 점에서 기여하는 바가 있다.

Keywords

References

  1. 강성원. 박창석. 이윤하. 구윤모, 온실가스 감축정책 평가를 위한 환경경제모형 개발.운용(3), 한국환경정책.평가연구원, 2016.
  2. 김용건, 한국형 상하향식 온실가스 통합 감축 시스템 개발, 환경기술개발사업 연차(단계) 보고서(2년차(단계)), 환경부, 2016.
  3. 산업통상자원부, 제2차 에너지기본계획, 2014.
  4. 산업통상자원부. 한국에너지공단.에너지경제연구원, 에너지 연소부문 온실가스 인벤토리작성 및 품질개선, 2016.
  5. 전봉걸. 심성희. 황인창. 진상현, "발전부문 온실가스 배출량의 불확도 분석: 전문가 판단 조사방법을 이용한 몬테카를로 시뮬레이션," 환경정책, 제22권, 제1호, 2014, pp. 1-29.
  6. 황인창, 불확실성과 학습효과를 반영한 기후경제 모형 방법론 연구, 한국환경정책.평가연구원, 2017.
  7. 황인창, 기후경제통합-지역평가모형 비교분석 및 국내 모형개발을 위한 기초연구, 한국환경정책. 평가연구원, 2015.
  8. 황인창, "MAED 모형을 이용한 서울시 에너지 수요 전망," 환경정책, 제23권 제3호, 2015, pp. 47-76.
  9. 황인창, "사회후생함수를 중심으로 한 기후경제통합-지역평가모형 비교분석," 자원.환경경제연구, 제25권 제1호, 2016, pp. 27-60.
  10. 황인창. 진상현, "에너지분야 온실가스 인벤토리의 불확도에 관한 연구: Tier 1 에러전파방법을 이용한 추정," 자원. 환경경제연구, 제23권 제2호, 2014, pp. 249-280.
  11. Anthoff, D., F. Estrada, and R. S. J. Tol, "Shutting Down the Thermohaline Circulation," American Economic Review, Vol. 106, No. 5, 2016, pp. 602-606. https://doi.org/10.1257/aer.p20161102
  12. Bhattacharyya, S. C., Energy Economics: Concepts, Issues, Markets and Governance, Springer, 2011.
  13. Bosello, F., "Building Uncertainty into the Adaptation Cost Estimation in Integrated Assessment Models," Proceedings of the European Association of Environmental and Resource Economists 2016, 2016.
  14. Cai, Y., T. M. Lenton, and T. S. Lontzek, "Risk of Multiple Interacting Tipping Points should Encourage Rapid $CO_2$ Emission Reduction," Nature Climate Change, Vol. 6, 2016, pp. 520-525. https://doi.org/10.1038/nclimate2964
  15. Chen, Y. -H., S. Paltsev, J. M. Reilly, J. F. Morris, and M. H. Babiker, "The MIT EPPA6 Model: Economic Growth, Energy Use, and Food Consumption," MIT Joint Program on the Science and Policy of Global Change Report, No. 278, 2015.
  16. De Groot, M. H., Optimal statistical decisions, McGraw-Hill, 1970.
  17. Frey, C. and L. Hanle, "Uncertainties," In IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Intergovernmental Panel on Climate Change, 2006.
  18. Gillingham, K., W. D. Nordhaus, D. Anthoff, G. Blanford, V. Bosetti, P. Christen, H. McJeon, J. Reilly, and P. Sztorc, "Modeling uncertainty in climate change: A multi-model comparison," Cowles Foundation Discussion Paper, No. 2022, 2015.
  19. Gollier, C., The Economics of Risk and Time, The MIT Press, 2001.
  20. Ehrlich, P. R. and J. P. Holdren, "Impact of Population Growth." Science, Vol. 171, No. 3977, 1971, pp. 1212-1217. https://doi.org/10.1126/science.171.3977.1212
  21. Hope, C., "The PAGE09 Integrated Assessment Model: A Technical Description," Cambridge Judge Business School Working Paper Series 4/2011, 2011.
  22. Hwang, I.C., "A Recursive Method for Solving a Climate-economy Model: Value Function Iterations with Logarithmic Approximations," Computational Economics, Vol. 50, No. 1, 2017, pp. 95-110. https://doi.org/10.1007/s10614-016-9583-2
  23. Hwang I. C., F. Reynes, and R. S. J. Tol, "Climate Policy Under Fat-tailed Risk: an Application of DICE," Environmental and Resource Economics, Vol. 56, No. 4, 2013, pp. 415-436. https://doi.org/10.1007/s10640-013-9654-y
  24. Hwang I. C., R. S. J. Tol, and M. Hofkes, "Fat-tailed Risk about Climate Change and Climate Policy," Energy Policy, Vol. 89, 2016a, pp. 25-35. https://doi.org/10.1016/j.enpol.2015.11.012
  25. Hwang I. C., R. S. J. Tol, and M. Hofkes, "Active Learning and Optimal Climate Policy," Proceedings of the European Association of Environmental and Resource Economists, 2016, 2016b.
  26. Hwang I. C., F. Reynes, and R. S. J. Tol, "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Vol. 48, 2017, pp. 1-18. https://doi.org/10.1016/j.reseneeco.2017.01.001
  27. Iman, R. L. and J. C. Helton, "An investigation of uncertainty and sensitivity analysis techniques for computer models," Risk Analysis, Vol. 8, 1988, pp. 71-90. https://doi.org/10.1111/j.1539-6924.1988.tb01155.x
  28. IPCC, Climate Change 2014 Synthesis Report, Intergovernmental Panel on Climate Change, 2014.
  29. IWGSCC, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis-Under Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United States Government, 2013.
  30. Judd, K. L., L. Maliar, and S. Maliar, "Numerically Stable and Accurate Stochastic Simulation Approaches for Solving Dynamic Economic Models," Quantitative Economics, Vol. 2, 2011a, pp. 173-210. https://doi.org/10.3982/QE14
  31. Judd, K. L., L. Maliar, and S. Maliar, "How to Solve Dynamic Stochastic Computing Expectations Just Once," National Bureau of Economic Research Working Paper 17418, 2011b.
  32. Kann, A. and J. P. Weyant, "Approaches for performing uncertainty analysis in large-scale energy/economic policy models," Environmental Modeling and Assessment, Vol. 5, 2000, pp. 29-46. https://doi.org/10.1023/A:1019041023520
  33. Kelly, D. L. and C. D. Kolstad, "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Vol. 23, 1999, pp. 491-518. https://doi.org/10.1016/S0165-1889(98)00034-7
  34. Kelly, D. L. and Z. Tan, "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Vol. 72, pp. 98-122.
  35. Kennedy, J. J., N. A. Rayner, R. O. Smith, D. E. Parker, and M. Saunby, "Reassessing Biases and Other Uncertainties in Sea Surface Temperature Observations Measured in Situ Since 1850: 2. Biases and Homogenization," Journal of Geophysical Research, Vol. 116, 2011, D14104. https://doi.org/10.1029/2010JD015220
  36. Klibanoff, P., M. Marinacci, and S. Mukerji, "A smooth model of decision making under ambiguity," Econometrica, Vol. 73, No. 6, 2005, pp. 1849-1892. https://doi.org/10.1111/j.1468-0262.2005.00640.x
  37. Leach, A. J., "The climate change learning curve," Journal of Economic Dynamics and Control, Vol. 31, 2007, pp. 1728-1752. https://doi.org/10.1016/j.jedc.2006.06.001
  38. Lemoine, D. M. and C. Traeger, "Watch your step: Optimal policy in a tipping climate," American Economics Journal: Economic Policy, Vol. 6, No. 1, pp. 137-166.
  39. Mathiesen, L., "An algorithm based on a sequence of linear complementarity problems applied to a Walrasian equilibrium model: An example," Mathematical Programming, Vol. 37, 1987, pp. 1-18. https://doi.org/10.1007/BF02591680
  40. Morgan, M. G. and M. Henrion, Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis, Cambridge University Press, 1990.
  41. Lemoine, D. and C. Traeger, "Watch your step: Optimal policy in a tipping climate," American Economic Journal: Economic Policy, Vol. 6, No. 1, 2014, pp. 137-166. https://doi.org/10.1257/pol.6.1.137
  42. Loulou, R., U. Remme, A. Kanudia, A. Lehtila, and G. Goldstein, "Documentation for the TIMES model," Energy Technology Systems Analysis Programme, 2005.
  43. Loulou, R., M. Labriet and A. Kanudia, "Deterministic and Stochastic Analysis of Alternative Climate Targets Under Differentiated Cooperation Regimes," Energy Economics, Vol. 31, 2009, pp. S131-S143. https://doi.org/10.1016/j.eneco.2009.06.012
  44. Nakicenovic, 2000, A special report of working group 3 of the International Panel on Climate Change. IPCC.
  45. Narayanan, B., T. Hertel, and T. Walmsley, "GTAP 8 data base documentation," Purdue University, 2012.
  46. Nordhaus, W., A Question of Balance, Yale University Press, 2008.
  47. Paltsev S., "Moving from static to dynamic general equilibrium economic models," MIT Joint Program on the Science and Policy of Global Change. Technical Note No. 4, 2004.
  48. Paltsev S., "Energy scenarios: The value and limits of scenario analysis," MIT Center for Energy and Environmental Policy Research CEEPR WP 2016-007, 2016.
  49. Parson, E., V. R. Burkett, K. Fisher-Vanden, D. W. Keith, L. O. Mearns, H. M. Pitcher, C. E. Rosenzweig, and M. D. Webster, Global Change Scenarios: Their Development and Use, Sub-report 2.1B of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, Department of Energy, 2007.
  50. Pye, S., N. Sabio, and N. Strachan, "An integrated systematic analysis of uncertainties in UK energy transition pathways," Energy Policy, Vol. 87, 2015, pp. 673-684. https://doi.org/10.1016/j.enpol.2014.12.031
  51. Refsgaard, J. C., J. P. van der Sluijs, A. L. Hojberg, and P. A. Vanrolleghem, "Uncertainty in the environmental modelling process: A framework and guidance," Environmental Modelling and Software, Vol. 22, 2007, pp. 1543-1556. https://doi.org/10.1016/j.envsoft.2007.02.004
  52. Roe, G. H. and M. B. Baker, "Why is climate sensitivity so unpredictable?" Science, Vol. 318, 2007, pp. 629-632. https://doi.org/10.1126/science.1144735
  53. Savage, L. J., "The theory of statistical decision," Journal of the American Statistical Association, Vol. 46, No. 253, 1951, pp. 55-67. https://doi.org/10.1080/01621459.1951.10500768
  54. Sokolov, A. P., P. H. Stone, C. E. Forest, R. Prinn, M. C. Sarofim, M. Webster, "Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters," Journal of Climate, Vol. 22, 2009, pp. 5175-5204. https://doi.org/10.1175/2009JCLI2863.1
  55. Stern, N., Stern review: The economics of climate change, Cambridge University Press, 2007.
  56. Stern, N., "The structure of economic modeling of the potential impacts of climate change:Grafting gross underestimation of risk onto already narrow science models," Journal of Economic Literature, Vol. 51, No. 3, 2013, pp. 838-859. https://doi.org/10.1257/jel.51.3.838
  57. Trutnevyte, E., C. Guivarch, R. Lempert, and N. Strachan, "Reinvigorating the scenario technique to expand uncertainty consideration," Climatic Change, Vol. 135, 2016, pp. 373-379. https://doi.org/10.1007/s10584-015-1585-x
  58. UN, World Population Prospects: Methodology of the United Nations Population Estimates and Projections, United Nations, 2015.
  59. von Neumann, J. and Morgenstern, O., Theory of games and economic behavior, Princeton University Press, 1944.
  60. Walker, W. E., P. Harremoes, J. Rotmans, J. P. van der Sluijs, M. van Asselt, P. Janssen, and M. P. Krayer von Krauss, "Defining uncertainty: A Conceptual Basis for Uncertainty Management in Model-based Decision Support," Integrated Assessment, Vol. 4, No. 1, 2003, pp. 5-17. https://doi.org/10.1076/iaij.4.1.5.16466
  61. Warmink, J. J., J. A. Janssen, M. J. Booij, and M. S. Krol, "Identification and Classification of Uncertainties in the Application of Environmental Models," Environmental Modelling and Software, Vol. 25, 2010, pp. 1518-1527. https://doi.org/10.1016/j.envsoft.2010.04.011
  62. Watson, J., R. Gross, I. Ketsopoulou, and M. Winskel, "The Impact of Uncertainties on the UK's Medium-term Climate Change Targets," Energy Policy, Vol. 87, 2015, pp. 685-695. https://doi.org/10.1016/j.enpol.2015.02.030
  63. Webster, M. D., M. Babiker, M. Mayer, J. M. Reilly, J. Harnisch, R. Hyman, and C. Wang, "Uncertainty in Emissions Projections for Climate Models," Atmospheric Environment, Vol. 36, 2002, pp. 3659-3670. https://doi.org/10.1016/S1352-2310(02)00245-5
  64. Webster, M. D., S. Paltsev, J. E. Parsons, J. M. Reilly, and H. D. Jacoby, Uncertainty in Greenhouse Gas Emissions and Costs of Atmospheric Stabilization, MIT Joint Program on the Science and Policy of Global Change Report No. 165, 2008.
  65. Webster, M. D., L. Jakobovits, and J. Norton, "Learning about climate change and implications for near-term policy," Climatic Change, Vol. 89, 2008, pp. 67-85. https://doi.org/10.1007/s10584-008-9406-0
  66. Weitzman, M., "A review of the Stern Review on the economics of climate change," Journal of Economic Literature, Vol. 45, No. 3, 2007, pp. 703-724. https://doi.org/10.1257/jel.45.3.703
  67. Weyant, J. and E. Kriegler, "Preface and introduction to EMF27," Climatic Change, Vol. 123, 2014, pp. 345-352. https://doi.org/10.1007/s10584-014-1102-7