에너지경제연구 제13권 제2호

KOREAN ENERGY ECONOMIC REVIEW Korean Energy Economic Review

차 례 에너지경제연구제 13 권제 2 호 전봉걸 윤원철 김현석 김수현김지효허은녕 안일환강승진 신동현김동하조하현 오승환이철용 조철흥전의찬

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 1~25 기업의온실가스관련비용과행태 : 온실가스의효율적감축방안모색을위하여 * 1

Pr 8

arg min 9

ln 11

< 표 1> 분석에이용된변수 ln 12

ln < 표 2> 설비투자의분위수회귀추정결과 14

< 표 3> 생산성의분위수회귀추정결과 15

< 표 4> 생산액당비용의분위수회귀추정결과 17

접수일 (2014 년 3 월 7 일 ), 게재확정일 (2014 년 7 월 7 일 ) 19

, 2011. LMDI,, 10(1), 4976., 2012. CO2,, 11(1), 87119., 2002. :,, 13(2), 6193., 2011. :,, 17(2), 124151., 2012.,, 18(2), 137193., 2009. :,, 8(2), 128., 2012.,, 30(3), 6392., 2012.,, 34(2), 173204., 2011.,, 10(1), 124., 2006. :,, 44(4), 149176., 2009.,,., 2012.,, 30(2), 141166. 20

, 2008., 08-04,., 2012.,., 2009.. Alcantara, V., and E. Padilla. 2006. An Input-output Analysis of the Key Sectors in CO2 Emissions from a Production Perspective: An Application to the Spanish Economy, Working paper 0702. Barnes, M. L., and A. W. Hughes. 2002. A Quantile Regression Analysis of the Cross Section of Stock Market Returns, memio. Bitler, M. P., J. B. Gelbach, and H. Hilary. 2003. What Mean Impact Miss: Distributional Effects of Welfare Reform Experiments, NBER working paper 10121. Buchinsky, M. 1998. The Dynamics of Changes in the Female Wage Distribution in the USA: A Qauntile Regression Approach, Journal of Applied Econometrics, 13. 130. Cameron, A., and P. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge. Card, D. 1996. The Effect of Unions on the Structure of Wages: A Longitudinal Analysis, Econometrica, 64 (4), 957979. Friedlander, D., and P. K. Robins. 1997. The Distributional Impacts of Social Programs, Economic Review, 21 (5), 531553. Gowdy. J. M., and J. L. Miller. 1987. Technological and Demand Change in Energy Use: An Input-output Analysis, Environmental and Planning, 19, 13871398. Koenker, R., and G. Bassett. 1978. Regression Quantile, Econometrica, 46 (1), 3350. Koenker, R., and K. F. Hallock. 2000. Quantile Regression: An Introduction, memio. Meyer, B. D., and J. X. Sullivan. 2001. The Effects of Welfare and Tax Reform: The Material Well-Being of Single Mothers in the 1980s and 1990s, NBER working paper 8928. 21

Nahm, J. W. 2002. Nonparametric Quantile Regression Analysis of R&D-Sales Relationship for Korean Firms, Empirical Economics, 26, 259270. Rangvid, B. S. 2001. Educational Peer Effect: Quantile Regression Evidence from Denmark with PISA 2000 data, memio. 22

< 부표 1> 변수의기술통계량 ln ln 23

< 부표 2> 변수별상관관계 ln ln 24

ABSTRACT The Korean government is introducing the emission trading system as a means of achieving the Korea's mid-term Greenhouse Gases emission reduction target. However, it is well known that manufacturing sector in Korea is characterized by the energy intensive structure. Therefore, economic analysis on how carbon dioxide emission reduction might impact on the economy, especially firm's competitiveness, is necessary in the context of preparing for the global environment change. This study amis to analyze the relationship between the costs associated with the Greenhouse Gases and the firm's characteristics, such as productivity and investment. We empirically investigate how firm's investment affects its costs associated with Greenhouse Gases. This study, which is different from previous studies in terms of the estimation approach, applies the quantile regression methods. The estimation results show that in general, the effects of firm's investment and the productivity on the costs associated Greenhouse Gases are positive. Key Words : Green-house gases, Firm's performance, Investment, JEL Codes : L20, Q50 Quantile regression 25

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 27~43 석유제품전자상거래의도입효과분석 * 27

[ 그림 1] 이중차분모형에서정책시행효과 32

DIRECT i TIME i P OLICY i DUBAI i FX i i 33

DIRECT i TIME i P OLICY i DIRECT i TIME i DUBAI i FX i 34

[ 그림 2] 직접거래주유소와전국평균비교 35

[ 그림 3] 대리점경유주유소와전국평균비교 36

< 표 1> 인센티브시행에따른가격인하효과 37

< 표 2> 이중차분모형을활용한가격인하효과추정결과 DUBAI i MOP S i FX i DUMMY i i DUBAI i MOP S i FX i DUMMY i 38

< 표 3> 단순회귀분석을활용한경쟁파급효과추정결과 39

접수일 (2014 년 7 월 28 일 ), 게재확정일 (2014 년 9 월 17 일 ) 41

,, (), 2012.,, 20, 2012, 1-21., :, 59, 2011, 77-99. Hastings, J., Vertical Relationships and Competition in Retail Gasoline Markets: Empirical Evidence from Contract Changes in Southern California, American Economic Review 94, 2004, 317-328. Johnson, N. and Romeo, J. The Impact of Self-Service Bans in the Retail Gasoline Market, The Review of Economics and Statistics 82, 2000, 625-633. 42

ABSTRACT This study quantitatively analyzes the effects of spot e-commerce trading for petroleum products. Especially, it tests the effectiveness of various incentives introduced by the government since July 2012. For this purpose, the introduction of spot e-commerce trading or incentives is quantitatively analyzed in terms of price reduction and competition spillover effects. According to the empirical results, the diesel sales prices of gas stations participating to the e-commerce trading are estimated to be lower than national averages by 16 to 19 wons per liter. In addition, the sales prices of gas stations directly participating to e-commerce trading turn out to be lower than those of gas stations indirectly participating to e-commerce trading by 10 wons per liter. In the aspect of the competition spillover effect, the national average prices of diesel are lowered by 24 wons per liter. Key Words : E-Commerce Trading, Diesel Prices, Differences-in-Differences Model JEL Codes : C51, L11, G40 43

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 45~69 배출권시장에서이윤율헤징전략의수익성 * 45

[ 그림 1] 값변화에따른기대효용의변화 55

< 표 1> 구매전략별기대효용 < 표 2> 구매전략별기대효용의이점비교검정결과 (t- 통계량 ) 63

< 표 3> EU ETS 선물가격의분산비검정결과 65

접수일 (2014 년 7 월 23 일 ), 게재확정일 (2014 년 9 월 2 일 ) 67

Fishburn, P.C. 1977. Mean-Risk Analysis Associated with Below-Target Returns. American Economic Review 67:116-126 Holthausen, D.M. 1981. A Risk-Return Model with Risk and Return Measured as Deviations from a Target Return. American Economic Review 71:182-188. Kim, H.S., B.W. Brorsen, and K.B. Anderson. 2010. Profit Margin Heding. American Journal of Agricultural Economics 92:638-653. Lo, A.W., and A.C. MacKinlay. 1988. Stock Market Prices Do Not Follow Random Walks: Evidence from Simple Specification Test. Review of Financial Studies 1: 41-66. Parcell, J., and V. Pierce. 2009. An Introduction to Hedging Agricultural Commodities with Futures Risk Management Series, University of Missouri Extension. Available at http://agebb.missouri.edu/mgt/risk/introfut.htm. Purcell, W.D., and S.R. Koontz. 1999. Agricultural Futures and Options: Principles and Strategies. Upper Saddle River, NJ: Prentice Hall. Yang, S.-R., and B.W. Brorsen. 1993. Nonlinear Dynamics of Daily Futures Prices: Conditional Heteroskedasticity or Chaos? Journal of Futures Markets 13:175-191. 68

ABSTRACT This article theoretically analyzes profit marin hedging can be optimal strategy to buy emission allowances. Target utility function is used to show the expected utility of profit margin strategy is greater than that of other strategies such as always hedging and buying at expiration with spot price. Additionally, this article provides a mathematical proof that Profit margin hedging is more profitable than other strategies if futures prices of emission allowances are mean reverting. Simulations are conducted to compare the expected utility of a profit margin hedging strategy with the expected utility of other strategy. In addition, variance ratio test is conducted to test mean reversion in futures prices of emission allowances. Key Words : emission allowances, expected utility, mean reversion, profit margin hedging, target utility function JEL Codes : C12, C15, Q54 69

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 71~101 국제에너지시장의편의수익연구 * 71

< 표 1> 에너지시장의편의수익을분석한선행연구 75

ln 79

[ 그림 1] 북미석유시장의선 현물가격및재고 85

[ 그림 2] 유럽석유시장의선 현물가격및재고 [ 그림 3] 북미가스시장의선 현물가격및재고 86

< 표 2> IAB와콜옵션가치의기초통계 87

[ 그림 4] 북미석유시장의표준화된재고, IAB 및콜옵션가치 88

[ 그림 5] 유럽석유시장의표준화된재고, IAB 및콜옵션가치 [ 그림 6] 북미가스시장의표준화된재고, IAB 및콜옵션가치 89

< 표 3> 가설 1 에대한추정결과 : IAB 와재고간관계 90

< 표 4> 가설 1 에대한추정결과 : 콜옵션가치와재고간관계 91

< 표 5> 가설 2에대한추정결과 : IAB와재고간관계 < 표 6> 가설 2 에대한추정결과 : 콜옵션가치와재고간관계 93

접수일 (2014 년 6 월 13 일 ), 게재확정일 (2014 년 8 월 26 일 ) 97

. 2014,.., 2007.. 16(2): 213-239., 2003..., 2007..., 2000. : OPEC.., 2004.. 13(4): 735-761., 2011.., 2012.. 49(3): 338-349. Bentzen, J. 2007. Does OPEC influence crude oil prices? Testing for co-movements and causality between regional crude oil prices. Applied Economics 39(11): 1375-1386. Black, F. and Sholes, M. 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81(3): 637-654. Bodie, Z., Kane, A. and Marcus, A. 2010. Investments. McGraw-Hill. Brennan, M. J. 1958. The supply of storage. The American Economic Review 48(1): 50-72. Chen, T-F., Lin, M-I. and Wang, K. 2006. The Information Content of the Implied Convenience Yield: Using Copula Based American GARCH Call Option Pricing Model. Working Paper. Cho, D. W. and McDougall, G. S. 1990. The supply of storage in energy futures markets. The Journal of Futures Markets 10(6): 611-621. Considine, T. J. and Heo, E. 2000. Price and inventory dynamics in petroleum product 98

markets. Energy Economics 22(5): 527-548. Energy Charter Secretariat. 2007. Putting a Price on Energy. Fama, E. F. 1970. Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25(2): 383-417. Fama, E. F. and French, K. R. 1987. Commodity futures prices: some evidence on forecast power, premiums, and the theory of storage. The Journal of Business, 60(1): 55-73. Fama, E. F. and French, K. R. 1988. Business cycles and the behavior of metals prices. The Journal of Finance, 43(5): 1075-1093. Fattouh, B. 2007. WTI Benchmark Temporarily Breaks Down: Is It Really a Big Deal?. Oxford Institute for Energy Studies. Gao, A. H. and Wang, G. H. K. 2005. Asymmetric volatility of basis and the theory of storage. The Journal of Future Markets 25(4): 399-418. Gary, R. W. and Peck, A. E. 1981. The chicago wheat futures market: Recent Problems in Historical Perspective. Food Research Institute Studies 18(1): 89-115. Geman, H. and Nguyen, V. 2005. Soybean inventory and forward curve dynamics. Management Science 51(7): 1076-7091. Geman, H. and Ohana, S. 2009. Forward curves, scarcity and price volatility in oil and natural gas markets. Energy Economics 31(4): 576-585. Heinkel, R., Howe, M. E. and Hughes J. S. 1990. Commodity convenience yields as an option profit. The Journal of Futures Markets 10(5): 519-533. Hochradl, M. and Rammerstorfer, M. 2012. The convenience yield implied in European natural gas hub trading. The Journal of Futures Markets 32(5): 459-479. Hotelling, H. 1931. The economics of exhaustible resources. The Journal of Political Economy 39(2): 137-175. Kaldor, N. 1939. Speculation and economic stability. The Review of Economic Studies 7(1): 1-27. Kim, J., Kim J. and Heo, E. 2013. Evolution of the international crude oil market mechanism. Geosystem Engineering 16(4): 265-274. 99

Kocagil, A. E. 2004. Optionality and daily dynamics of convenience yield behavior: An Empirical Analysis. Journal of Financial Research 27(1): 143-158. Kucher, O. and Kurov A. 2012. Energy Commodity Basis, Returns and the Business Cycle. 31 st USAEE/IAEE North American Conference(Nov. 4-7, Austin, Texas). Lautier, D. 2009. Convenience yield and commodity markets. Working Paper. Lin, W. T. and Duan, C. 2007. Oil convenience yields estimated under demand/supply shock. Review of Quantitative Finance and Accounting 28(2): 203-225. Milonas, N. T. and Henker, T. 2001. Price spread and convenience yield behavior in the international oil market. Applied Financial Economics 11(1): 23-36. Milonas N. T. and Thomadakis, S. B. 1997. Convenience yields as call options: An empirical analysis. The Journal of Futures Markets 17(1): 1-15. Roy, A. 2006. Convenience yields modelling as call options: Indian wheat market. Working Paper. Symeonidis, L., Prokopczuk, M. Brooks, C. and Lazar, E. 2012. Futures basis, inventory and commodity price volatility: An empirical analysis. Economic Modelling 29(6): 2651-2663. Telser, L. G. 1958. Futures trading and the storage of cotton and wheat. The Journal of Political Economy 66(3): 233-255. West, J. 2012. Convenience yields in bulk commodities: The case of thermal coal. The International Journal of Business and Finance Research 6(4): 33-44. Working, H. 1948. Theory of the inverse carrying charge in futures markets. Journal of Farm Economics 30(1): 1-28. Working, H. 1949. The theory of price of storage. The American Economic Review 39(6): 1254-1262. Zulauf, C. R., Zhou, H., and Roberts, M. C. 2006. Updating the estimation of the supply of storage. The Journal of Futures Markets 26(7): 657-676., www.petronet.com Energy Information Administration, www.eia.gov 100

ABSTRACT This study investigates convenience yield which is one of the primary factors explaining changes in energy markets. This study analyzes the convenience yields in Northern American oil market, European oil market, and Northern American natural gas market from January 2000 to July 2013 and tests the following two hypotheses. The first hypothesis is that convenience yield is inversely proportional to inventory. The empirical results support the first hypothesis when the convenience yield is approximated by the interest adjusted basis(iab). On the other hand, they do not support the first hypothesis when the convenience yield is approximated by the call-option value. The second hypothesis is that convenience yield is approximated by call-option value better than IAB. The empirical results do not support this hypothesis. Key Words : Convenience Yield, Energy Market, Interest Adjusted Basis, Call-option Value JEL Codes : Q3, G1, C5 101

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 103~129 한국전력도매시장 (CBP) 계통한계가격 (SMP) 변동성실증분석 103

104

105

정수 실수 106

107

108

ln ln ln ln ln 109

110

< 표 1> ADF 단위근검정 ln ln ln < 표 2> 분수차분차수추정치 ln ln ln 111

[ 그림 1] ln 의피리오도그램과스펙트럼 112

[ 그림 2] SMP 시리즈 < 표 3> 각시리즈의기초통계량 ln 113

[ 그림 3] ln 와 시리즈의 ACF 와 PACF ln 114

[ 그림 4] SMP 의연율표시역사적변동성 (30, 60, 90, 120 일이동평균 ) log 115

< 표 4> 정보기준에의한 모형선택 116

117

< 표 5> 단일시계열변동성모형추정결과 118

< 표 6> 표준잔차가중융 - 박스검정 < 표 7> 표준잔차제곱가중융 - 박스검정 119

< 표 8> 가중 ARCH LM 검정결과 < 표 9> 모형의추정치정확도 (Accuracy) 120

[ 그림 5] 분포모형시리즈와실현변동성 [ 그림 6] 분포모형의연율표시 실현변동성 121

[ 그림 7] 분포모형시리즈와 조건부표준편차예측 시점의연율표시변동성 연간거래일수 122

< 표 10> 예측치의정확도 123

접수일 (2014 년 2 월 6 일 ), 수정일 (2014 년 8 월 14 일 ), 게재확정일 (2014 년 9 월 5 일 ) 124

, 2005, ARIMA, 2005,., 2002,, 2,., 2010, Forecasting Hourly Electricity Loads of South Korea: Innovations State Space Modeling Approach,, 17 2, pp.301-317., 2013, 2012 (2012 Annual Report Electricity Market Trends & Analysis) Amjady, N., F. Keynia, 2011, A new prediction strategy for price spike forecasting of day-ahead electricity markets, Applied Soft Computing, 11 pp.4246-4256. Baillie, R. T., T. Bollerslev, H. O. Mikkelsen, 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 74, 3-30. Barnett, A., P. Baker, 2014, Package season Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. Box, G. E. P., G. M. Jenkins, 1976, Time Series Analysis : Forecasting and Control revised ed., Holden-Day, San Francisco. Catalão, J. P. S., S.J.P.S. Mariano, 2007, Short-term electricity prices forecasting in a competitive market: A neural network approach, Electric Power Systems Research, 77 1297-1304. Chan, K. F., P. Gray, B. van Campen, 2008, A new approach to characterizing and forecasting electricity price volatility, International Journal of Forecasting 24 pp.728-743. Engle, R. F., 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, No.4, 987-1007. 125

Engle, R. F., and T. Bollerslev, 1986, Modelling the persistence of conditional variances, Econometric Reviews, 5, pp.1-50. Engle, R. F., and G. Lee, 1999, A permanent and transitory component model of stock return volatility, in Robert F. Engle and Halbert L. White, ed.: Cointegration, Causality and Forecasting: A Festschrift in Hornor of Clive W. J. Granger, Oxford University Press, New York. Faria, J. C., P. Grosjean, E. G. Jelihovschi, R. Pietrobon, Tnn-R Editor-GUI for R Language and Environment, User Guide. Fraley, C., F. Leisch, M. Maechler, Reisen, V., A. Lemonte, Package fracdiff Geweke, J., and Porter-Hudak, S., 1983, The estimation and application of long memory time series models, Journal of Time Series Analysis, 4, 221-238. Ghalanos. A., 2014, Package rugarch Glosten, L. R., Jagannathan, R., Runkle, D., 1993, On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779-1801. Haugom, E., C. J. Ullrich, 2012, Forecasting spot price volatility using the short-term forward curve, Energy Economics, 34 pp.1826-1833. Higgs, H., 2009, Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets, Energy Economics, 31 pp.748-756. Hosking, J. R. M., 1981, Fractional differencing, Biometrika, 68, pp.165-176. Huisman, R., 2008, The influence of temperature on spike probability in day-ahead power prices, Energy Economics, 30 pp.2697-2704. Hyndman, R., S. Razbash, D. Schmidt, Z. Zhou, Y. Khan, C. Bergmeir, E. Wang, 2014, Package forecast Karakatsani, N. V., D. W. Bunn, 2008, Forecasting electricity prices: The impact of fundamentals and time-varying coefficients, International Journal of Forecasting, 24 pp.764-785. Knittel, C. R., M. R. Roberts, 2005, An empirical examination of restructured electricity 126

prices, Energy Economics, 27 pp.791-817. Li G., J. Shi, X. Qu, 2011, Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market-a state-of-the-art review, Energy, 36 pp.4686-4700. Nelson, D. B., 1991, Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, pp.347-370. Pirrong, C., M. Jermakyan, 2008, The price power: The valuation of power and weather derivatives, Journal of Banking & Finance, 32 pp.2520-2529. Reisen, V. A, 1994, Estimation of the fractional difference parameter in the ARIMA(p, d, q) model using the smoothed periodogram,. Journal of Time Series Analysis, 15, 335-350. Reisen, V. A., Alessandro, J. Q. S., Neyval, C. R. Jr., Céline, L., Jane, M. S., 2014, Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility, Environmental Modelling & Software, 51 pp.286-295. Shafie-khah, M., M. P. Moghaddam, M. K. Sheikh-El-Eslami, 2011, Price forecasting of day-ahead electricity markets using a hybrid forecast method, Energy Conversion and Management, 52 pp.2165-2169. Theodorou, P., D. Karyampas, 2008, Modeling the return and volatility of the Greek electricity marginal system price, Energy Policy, 36 pp.2601-2609. Tishler, A., I. Milstein, C. K. Woo, 2008, Capacity commitment and price volatility in a competitive electricity market, Energy Economics, 30 pp.1625-1647. Ullrich, C. J., 2012, Realized volatility and price spikes in electricity markets: The importance of observation frequency, Energy Economics, 34 pp.1809-1818. Veenstra, J. Q., A. I. McLeod, 2014, Package arfima Weron, R., A. Misiorek, 2008, Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models, International Journal of Forecasting, 24 pp.744-763. 127

Zachmann, G., 2013, A stochastic fuel switching model for electricity prices, Energy Economics, 35 pp.5-13. Zareipour, H., K. Bhattacharya, C. A. Cañizares, 2007, Electricity market price volatility: The case of Ontario, Energy Policy, 35 pp.47394748. Zeile, A., G. Grothendieck, J. A. Ryan, F. Andrews, 2014, Package zoo Zhang, G. P., 2003, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50 pp.159-175. http://www.r-project.org/ 128

ABSTRACT In this paper, we were introduced to Mark-to-market valuation model and that evaluated level of volatility for system marginal price in the Korean Electricity wholesale market. The SMP time series has a volatility clustering and a fat tail of its distribution. The estimates of fractional difference ( ) confirmed that it is stationary in the mean level and has long-memory in the long-run. Therefore, the student t-distribution model was specified for SMP volatility. We found out that the SMP volatility was not sensitive ( ) to the market variation but it was very persistence ( ). As a result, the annualized average volatility (Realized volatility) estimate was about 312%. And the annualized average historical volatility estimate was about 297%. We propose a time-varying volatility models as an alternative to analyze the volatility of electricity spot prices. Key Words : ARFIMA-GARCH, SMP(System Marginal Price), Volatility, JEL Codes : C22, C53 Wholesales Electricity Market 129

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 131~169 구조변화를고려한우리나라전력소비의 변동성증가에관한연구 131

132

133

134

135

max ln exp 136

< 표 1> 구조변화에따른비조건부분산 137

max max 138

139

140

141

142

143

144

max max 145

.15 [ 그림 1] 우리나라전력소비증가율 ( 로그차분 ).10.05.00 -.05 -.10 -.15 1970 1975 1980 1985 1990 1995 2000 2005 2010 146

< 표 2> 자료들의연도별변동성 147

148

< 표 3> 전력소비의조건부평균과조건부분산의구조변화검정결과 < 표 4> 전력소비의비조건부분산변화 149

150

[ 그림 2] 부문별전력소비증가율및 Growth Contribution 151

< 표 5> 부문별전력소비증가율의변동성변화 152

153

< 표 6> 전력소비증가율에대한다수의구조변화검정결과 < 표 7> 부문별전력소비의변동성변화 154

< 표 8> 생산 가격충격을고려한전력소비증가율의변동성변화 155

156

157

< 표 9> 기온충격을고려한전력소비증가율의변동성변화 158

159

160

접수일 (2014 년 3 월 31 일 ), 게재확정일 (2014 년 9 월 4 일 ) 161

, 2007..., 2006.. EU., 2002..., 2009..., 2007... Ahed, S., Levin, A. and Wilson, B. A. 2004. Recent U.S. Macroeconomic Stability: Good Policies, Good Practices, or Good Luck? The Review of Economics and Statistics 86(3) : pp824-832 Andrews, D. W. K. 1993. Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica 61(4) : pp821-856. Andrews, D. W. K., Ploberger, W. 1994. Optimal Tests when a Nuisance Parameter is Present Only under the Alternative. Econometrica 62(6) : pp1383-1414. Bai, J. 1994. Least squares estimation of a shift in linear processes. Journal of Time Series Analysis 15(5) : pp453-472. Bai, J. 1997. Estimating Multiple Breaks One at a Time. Econometric Theory 13(3) : pp315-352. Bai, J., Perron, P. 1998. Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica 66(1) : pp47-78. Bai, J., Perron, P. 2003. Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics 18(1) : pp1-22. Bai, J., Perron, P. 2006. Multiple Structural Change Models: A Simulation Analysis. Econometric Theory and Practice: Frontiers of Analysis and Applied Research : pp212-237. Cambridge University Press. 162

Bentai, L. 2008. The great moderation in the United Kingdom. Journal of Money, Credit and Banking 40(1) : pp121-147. Blanchard, O. and Simon, J. 2001. The Long Decline in U.S. Output Volatility. Brookings Papers on Economic Activity 32(1) : pp135-173. Canova, F. 2010. What Explains the Great Moderation in the U.S.? A Structural Analysis. Journal of the European Economic Association 7(4) : pp697-721. Chauvet, M. and Potter, S. 2001. Recent Changes in the U.S. Business Cycle. The Manchester School 69(5) : pp481-508. Chow, G. C. 1960. Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica 28(3) : pp591-605. Daslsgaard, T., Elmeskov, J. and Park, C.-Y. 2002. Ongoing Changes in the Business Cycle: Evidence and Causes. OECD Economics Department Working Papers 315. Davidian, M. and Carroll, R. J. 1987. Variance Function Estimation. Journal of the American Statistical Association 82(400) : pp1079-1091. Davies, R. B. 1977. Hypothesis Testing When a Nuisance Parameter is Present Only Under the Alternative. Biometrika 64(2) : pp247-254. Del Negro, M. and Otrok, C. 2003. Time-Varying European Business Cycles. Mimeo, University of Virginia. Doyle, B. M. and Faust, J. 2005. Breaks in the Variability and Comovement of G-7 Economic Growth. The Review of Economics and Statistics 87(4) : pp721-740. Fritshe, U. and Kuzin, V. 2005. Declining Output Volatility in Germany: Impulses, Propagation, and the Role of Monetary Policy. Applied Economics 37 : pp2445-2457. Giannone, D., Reichlin, L. and Lenza, M. 2008. Explaining the Great Moderation: It Is Not the Shocks. Journal of the European Economic Association 6(2-3) : pp621-633. Hansen, B. E. 1997. Approximate Asymptotic p Values for Structural-Change Tests. Journal of Business and Economic Statistics 15(1) : pp60-67. 163

Hamilton, J. D. 1994. Time Series Analysis. Vol. 2. Princeton: Princeton University Press. Herrera, A. M. and Pesavento, E. 2005. The Decline in U.S. Output Volatility: Structural Changes and Inventory Investment. Journal of Business & Economic Statistics 23(4) : pp462-472. Kahn, J. A., McConnell, M. M. and Perez-Quiros, G. 2002. On the Causes of the Increased Stability of the U.S. Economy. Economic Policy Review 8 : pp183-206. Kim, C.-J. and Nelson, C. R. 1999. Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle. The Review of Economics and Statistics 81(4) : pp608-616. Kim C.-J., Nelson, C. R. and Piger, J. 2004. The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations. Journal of Business & Economic Statistics 22(1) : pp80-93. Koop, G. and Korobilis, D. 2009. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends in Econometrics 3(4) : pp267-358. McConnell, M. M., Mosser, P. and Perez-Quiros, G. 1999. "A Decomposition of the Increase Stability of GDP Growth." Current Issues in Economics and Finance 5(13) : pp1-6. McConnell, M. M. and Perez-Quiros, G. 2000. Output Fluctuations in the United States: What Has Changed Since the Early 1980 s? American Economic Review 90(5) : pp1464-1476. Mills, T. and Wang, P. 2003. Have Output Growth Rates Stabilized? Evidence from the G-7 Economies. Scottish Journal of Political Economy 50(3) : pp232-246. Sensier, M. and van Dijk, D. 2004. Testing for Volatility Changes in U.S. Macroeconomic Time Series. The Review of Economics and Statistics 86(3) : pp833-839. Simon, J. 2001. The Decline in Australian Output Volatility. Reserve Bank of Australia. 164

Stock, J. H. and Watson, M. W. 2003. Has the Business Cycle Changed and Why? In NBER Macroannual 2002 17 : pp159-230. MIT Press. Stock, J. H. and Watson, M. H. 2005. Understanding Changes in International Business Cycle Dynamics. Journal of the European Economic Association 3(5) : pp968-1006. Quandt, R. 1960. Tests of the Hypothesis That a Linear Regression Obeys Two Separate Regimes. Journal of the American Statistical Association 55(290) : pp324-330. Warnock, M. C. and Warnock, F. E. 2000. The Declining Volatility of U.S. Employment: Was Arthur Burns Right? Board of Governors of the Federal Reserve System. Zivot, E. and Andrews, D. K. 1992. Further Evidence On The Great Crash, The Oil Price Shock, and The Unit Root Hypothesis. Journal of Business and Economic Statistics 10(10) : pp25170 165

< 부표 1> 부문별 연도별전력소비와관련변수들간의상관관계 166

< 부표 2> 부문별전력소비와 GC 에대한구조변화검정결과 < 부표 3> 생산 가격및각충격에대한구조변화검정결과 167

< 부표 4> 부문별전력소비증가율및 GC 에대한추정결과 < 부표 5> 개별충격을고려한전력소비모형추정결과 168

ABSTRACT This study examines volatility changes of electricity consumption in South Korea. The results of empirical analysis using quarterly data from 1970:1 to 2013:2 shows the volatility increase. Structural changes of conditional mean and variance result in this increase. Electricity use in industrial, commercial service and residential sector are examined for identifying the main sources of the increase. With respect to the reasons of these changes, economic shocks, such as GDP and price of electricity, and temperature shock, such as HDD CDD, are checked. The specific results show that the main source which have charge of the volatility increase is commercial service sector whose electricity consumption has a relatively large proportion in its total energy use. In addition, increase in the effect of GDP, Price and temperature shock to the electricity use leads to the volatility expansion. These empirical results suggest that effective management in the volatility of electricity consumption should consider the effect of economic climatic shock as well as the volatility of electricity use itself. Key Words : Electricity consumption, Volatility change, Multiple structural breaks analysis, economic climatic shock JEL Codes : C22, C34, Q43 169

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 171~197 국내신재생에너지 R&D 사업의 경제적성과분석 * 171

172

173

174

175

< 표 1> PSM 방법론을사용한정부사업의성과평가사례 176

177

178

Pr 179

min 지원을받은기업의성향지수 지원을받지않은기업의성향지수 정부의지원을받지않은기업전체 180

181

< 표 2> 분석에따른분류 182

< 표 3> R&D 지원을받은기업과지원을받지않은기업들의 기초통계량비교 183

184

< 표 4> R&D 지원사업의효과 ( 지원받은신재생에너지기업 vs. 일반 제조업기업 ) 185

< 표 5> R&D 지원사업의효과 ( 지원받은신재생에너지기업 vs. 지원받지않은신재생에너지기업 ) 186

< 표 6> R&D 지원사업의효과 ( 지원받은태양광기업 vs. 일반제조업기업 ) 187

< 표 7> R&D 지원사업의효과 ( 지원받은태양광기업 vs. 지원받지않은신재생에너지기업 ) 188

< 표 8> R&D 지원사업의효과 ( 지원받은비태양광기업 vs. 일반제조업기업 ) 189

< 표 9> R&D 지원사업의효과 ( 지원받은비태양광기업 vs. 지원받지않은신재생에너지기업 ) 190

< 표 10> R&D 지원사업의효과정리 191

192

접수일 (2014 년 8 월 12 일 ), 게재확정일 (2014 년 9 월 15 일 ) 193

, 2012., 15, 649-674, 2013. PSM DID, Information systems review 15, 141-150., 2008., R&D,, 16(4), 1-33., 2011. R&D :, 19, 29-53., 2007. R&D,, 10, 1-21., 2000,,, 1-134, 2013.,., 1998, R&D - -, 6(2), 159-177, 2009., 10, 200-208. Almus, M. and Czarnitzki, D. 2003. The effects of public R&D subsidies on firms innovation activities: the case of Eastern Germany, Journal of Business & Economic Statistics, 21(2), 226-236. Becker, S. O., & Ichino, A. 2002. Estimation of average treatment effects based on propensity scores. The Stata Journal, 2(4), 358-377. Bloomberg New Energy Finance. 2013. Global Renewable Energy Market Outlook Blundell, R. and Costa Dias, M. 2000. Evaluation methods for nonexperimental data, 194

Fiscal Studies, 21(4), 427-468. Busom, I. 2000. An Empirical Evaluation of The Effects of R&D Subsidies, Economics of Innovation and New Technology, 9(2), 111-148. Caliendo, M. 2006. Microeconometric evaluation of labour market policies. Berlin: Springer. Carboni, O. A. 2011. R&D subsidies and private R&D expenditures: evidence from Italian manufacturing data, International Review of Applied Economics, 25(4), 419-439. Czarnitzki, D., Ebersberger, B., and Fier, A. 2007. The relationship between R&D collaboration, subsidies and R&D performance : Empirical evidence from Finland and Germany, Journal of Applied Econometrics, 22, 1347-1366 David, P. A., Hall, B. H., & Toole, A. A. 2000. Is public R&D a complement or substitute for private R&D? A review of the econometric evidence. Research Policy, 29(4), 497-529. Gorg, H. and Strobl, E. 2007. The Effect of R&D Subsidies on Private R&D, Economica, 74(294), 215-234. Klein, A.., Held. A., Ragwitz. M., Resch. G., and Faber.T. 2006. Evaluation of different feed-in tariff design options- Best practice paper for the International Feed-in Cooperation Koshi, H. 2008. Public R&D subsidies and empolyment growth-microeconomic evidence from Finnish firms, Keskusteluaiheita-Discussion Paper, 1143. Lerner, J. 1999. The Government as Venture Capitalist: The Long-Run Impact of the SBIR Program, The Journal of Business, 72(3), 285-318 Neyman, J. and Iwaszkiewicz, K. 1935. Statistical problems in agricultural experimentation, Supplement to the journal of the Royal Statistical Society, 2(2), 107-180. Piekkola, H. 2007, Public funding of R&D and Growth : Firm-level evidence from Finland, Economics of Innovation and New Technology, 16(3), 195-210 195

Quandt, R. E. 1972. A new approach to estimating switching regressions, Journal of the American Statistical Association, 67(338), 306-310. Rosenbaum, P. R., & Rubin, D. B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. Rubin, D. B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies, Journal of educational Psychology, 66(5), 688-701. Wallsten, S. J. 2000. The effects of government-industry R&D programs on private R&D: the case of the Small Business Innovation Research program, The RAND Journal of Economics, 31(1), 82-100. 196

ABSTRACT The importance of renewable energy is all the more growing sharply as the necessity for climate change mitigation and energy security enhancement has been increasing. Korean Government also has been increasing the R&D support for renewable energy consistently and there has been considerable achievements in research paper publications and patent applications. Despite these kinds of efforts, however, as there has been questions whether the achievements of R&D in renewable energy is led to the creation of economic value. Therefore, the paper aims at analyzing the connection between the R&D support and the economic performance. Propensity Score Matching method which solves the issue of selectivity bias has been used for analysis. The result shows that the R&D support in Solar PV sector has a statistically significant effect on the growth and innovation of corporations while but not significant in non-pv sector. Key Words : Renewable Energy, R&D, Technology Commercialization, PSM, Outcome Evaluation JEL Codes : C19, H59, Q42 197

에너지경제연구 Korean Energy Economic Review Volume 13, Number 2, September 2014 : pp. 199~230 에너지 기후변화정책의양립가능성평가 *: 한국의전력부문을중심으로 199

200

201

202

203

[ 그림 1] 발전설비의에너지원별발전용량및연식구조 (2010 년 ) 204

[ 그림 2] 6 차계획의기술별신규발전용량 205

[ 그림 3] 6 차계획의누적 (2010~2029) 신규발전용량및투자비 206

< 표 1> 6 차계획의전력수요전망 207

[ 그림 4] 전력소비량의연평균증가율 208

[ 그림 5] 연간총전력소비량실적및전망 209

[ 그림 6] 발전원별연료비용변화 210

[ 그림 7] 발전기술별전력균등화비용 (2010 년 ) 및이용률 211

< 표 2> 주요발전기술의기준이용률과최대이용률전제 212

< 표 3> 시나리오의구성과전제 213

214

[ 그림 8] 에너지원별발전용량및비중 215

[ 그림 9] 주요시나리오의에너지원별발전량 216

217

[ 그림 10] 수요감소와재생에너지확대가천연가스와석탄발전에미치는영향 218

[ 그림 11] 시나리오별온실가스배출경로 219

220

[ 그림 12] 시나리오별총연료비용 221

[ 그림 13] 시나리오별유연탄발전과가스복합발전의이용률변화 222

223

[ 그림 14] 확정설비의자연퇴화에따른온실가스배출경로 224

225

접수일 (2014 년 1 월 22 일 ), 수정일 (2014 년 8 월 16 일 ), 게재확정일 (2014 년 6 월 2 일 ) 226

, 2011, 3, 2014,,,,,,,, 2011, 2020 : (. 2011.7.12.), 2013,, 2012, 2011, 2013,, https://epsis.kpx.or.kr, 2012, 5 (2010~2024), 2013, 6 (2013~2027), 2012, Bertram, Christoph, Nils Johnson, Gunnar Luderer, Keywan Riahi, Morna Isaac, and Jiyong Eom., 2013, Carbon lock-in through capital stock inertia associated with weak near-term climate policies, Technological Forecasting and Social Change Cho, C.H., 2013, Technological Advancement and Implication for Optimal Carbon Mitigation Portfolio in Korean Power Sector, Ph.D. Thesis, Sejong University. Davis, Steven J., Ken Caldeira, and H. Damon Matthews, 2010, Future CO 2 emissions and climate change from existing energy infrastructure, Science 329.5997: 1330-1333. Fischer, C., & Newell, R. G., 2008, Environmental and technology policies for climate mitigation, Journal of Environmental Economics and Management, 55(2), 142 162. doi:10.1016/j.jeem.2007.11.001 GEA, 2012, Global Energy Assessment - Toward a Sustainable Future, Cambridge University Press, Cambridge, UK and New York, NY, USA and the International 227

Institute for Applied Systems Analysis, Laxenburg, Austria. IEA and NEA, 2010, Projected Costs of Generating Electricity, 2010 Edition, International Energy Agency, Nuclear Energy Agency. IEA, 2012, World Energy Outlook 2012, International Energy Agency. Messner, S., & Strubegger, M., 1995, User s Guide for MESSAGE III, WP-95-69, International Institute for Applied Systems Analysis(IIASA), Laxenburg, Austria. Metz, B., 2001, Climate change 2001: Mitigation: contribution of Working Group III to the third assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press. Metz, B., 2007, Climate Change 2007-Mitigation of Climate Change: Working Group III Contribution to the Fourth Assessment Report of the IPCC, Cambridge University Press. Nakićenović, N., Grübler, A., McDonald, A., 1998, Global Energy Perspectives, Cambridge University Press. Nakicenovic, N., Swart, R., 2000, IPCC Special Report on Emissions Scenarios(SRES), Intergovernmental Panel on Climate Change, Geneva. O Neill, B. C., Riahi, K., & Keppo, I., 2010, Mitigation implications of midcentury targets that preserve long-term climate policy options, Proceedings of the National Academy of Sciences, 107(3), 1011-1016. Riahi, K., Grübler, A., & Nakicenovic, N., 2007, Scenarios of long-term socio-economic and environmental development under climate stabilization, Technological Forecasting and Social Change, 74(7), 887-935. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., Rafaj, P., 2011, RCP 8.5A scenario of comparatively high greenhouse gas emissions, Climatic Change 109, 33-57. Roelfsema, M., Elzen, M. D., Höhne, N., Hof, A. F., Braun, N., Fekete, H.,... & Larkin, J., 2013, Are major economies on track to achieve their pledges for 2020? An assessment of domestic climate and energy policies, Energy Policy 67, 781-796. 228

Paul, A., Palmer, K., Woerman, M., 2013, Modeling a clean energy standard for electricity: Policy design implications for emissions, supply, prices, and regions, Energy Economics 36, 108-124. Schrattenholzer, L., Miketa, A., Riahi, K., & Roehrl, R. A. (Eds.), 2004, Achieving a Sustainable Global Energy System: Identifying Possibilities Using Long-term Energy Scenarios, Edward Elgar Publishing. UNEP, 2013, The Emissions Gap Report 2013 - A UNEP Synthesis Report, Nairobi, Kenya: UNEP UNFCCC, 2010, Decision 1/CP.16, The Cancun Agreements, UNFCCC document FCCC/CP/2010/7/Add.1, <http://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf#page=2> 229

ABSTRACT This study assesses the compatibility of current energy policy with climate change policy by simulating the feasible domain of GHG emissions in Korean electricity sector and evaluating whether the mitigation pledge can be achieved, based on the 6th Basic Plan on Electricity Supply and Demand. With electricity generation capacity fixed until 2030 according to the 6th Plan, we simulate 6 alternative scenarios, each of which is the different combination of electricity demand, utilization of renewable technologies, and fuel substitution. We found that even the lowest emission pathway, which requires unprecedented combination of demand reduction, enhanced renewable generation, and fuel substitution, can not reach the pledge level in 2020. The lowest emission scenario also inevitably incurs additional fuel cost and stranded cost on already-installed coal capacity. These findings provide a supporting evidence that current energy and climate change policy are incompatible and either of them is not binding at least in an economic sense. The characteristics of both energy system and associated climate change problem imply that energy policy making should take a long-term perspective and secure the consistency with climate policy goal. Such harmonized forwarding-looking energy policy is necessary conditions for cost-effective carbon mitigation. Key Words : Energy Policy, Climate Change Policy, Electricity Sector, Energy Model, Reduction Pledge, Emission Gap JEL Codes : Q21, Q41, Q47, Q48 230

에너지경제연구 투고안내, 2.. - 다음 - 200 80 (A4 15 ), 3, 9 (031-420-2100, journal@keei.re.kr), (htt://www.keei.re.kr) (: ).