Chapter V. Conclusion
2. Policy Implications
2.3. Changing the Perception of Policymakers and Businesses
The findings of this study carry the following policy and strategic implications for policymakers and corporate decision-makers. First, consistency in execution of the ETS is critical. It is also crucial to tailor policy execution in a way so as to encourage innovation by target businesses. Constant worries about the potentially negative impact of the ETS on the economy and industries could stand in the way of consistently enforcing the scheme. Ex-ante assessments based on static models can overestimate the harm of the ETS, prompting unwarranted policy change and intervention. Our analysis demonstrates that the ETS in Korea exerted no negative effect on businesses and industries during its first phase.
Although it is still too early to determine what direct effects the ETS has had, its first phase has coincided with steady improvements in the efficiency of the targeted businesses. Insofar as it can serve as an impetus of innovation in those businesses, it will also be possible to refine the ETS so that it can lead to both reducing emissions and bringing innovation to businesses. There is room for acceptance of the Porter hypothesis (Porter and van der Linder, 1995) in carbon policy.
Second, businesses and industries need to change their perspective on the ETS. Rather than merely reacting to it as another form of regulation, businesses and industries should actively embrace it with strategies for innovation and reform. They should take signs from the ETS to develop more efficient and cost-saving processes, low-carbon products capable of boosting sales, and actively respond to climate change toward strengthening their market image and managing risk. Businesses and industries should seize upon the market-based advantages of the ETS and capitalize upon them to enhance their own competitiveness.
References
1. Domestic literature
Ahn, Y. (2018), Possibility of Utilizing Authorized Auctions to Boost Emission Permit Trading. KEEI Basic Research Report, 18-12.
Cho, S. (1985), Theory of Monetary Finance, Bibong Publishing.
Government of the Republic of Korea (2016), Roadmap to Realizing the National Target for Reducing Greenhouse Gas Emissions.
Government of the Republic of Korea (2017), Change to the Emission Allocation Plan for the Third Year of the First Phase of the ETS (draft).
Government of the Republic of Korea (2018), Revised Roadmap to Realizing the National Target for Reducing Greenhouse Gas Emissions (draft).
Greenhouse Gas Inventory and Research Center (GIR, 2019), Final Report on the Operation of the First Phase (2015-2017) of the ETS.
Han, C. (2017), Lectures on Panel Data, Bagyeongsa Publishing.
Han, G., Lim, D., and Kwak, D. (2010), Effects of the ETS on the Competitiveness of Major Industries, Korea Institute for Industrial Economics and Trade Research Report 2010-577, NRC Green Growth Research Collection 10-02-23.
Lee, S. (2011), “Weakening of the Korean Manufacturing Sector’s Competitiveness Due to Carbon Leakage and Solutions,” Policy Tasks for Low-Carbon Green Growth (Part I), Korea Economic Research Institute (KERI).
Lee, S., Cho, Y., and Lee, S. (2017). “Analysis of Performance on Reducing Greenhouse Gas Emissions: Using the GHG & Energy Management System Data and Emission Breakdowns of ETS- Subject Businesses,” Journal of the Korean Climate Change Association, 8(3), pp. 221-230.
Ministry of Environment (2014), National Emission Permit Allocation Plan for the First Phase (2015- 2017) of the ETS.
Ministry of Environment (2019), “Change to the Standard on the Carryforward of Surplus Emission Permits” (press release), June 7, 2019.
Ministry of Strategy and Finance (2014), ETS Master Plan.
Ministry of Trade, Industry and Energy (2017), Eighth Master Plan on Electricity Supply and Demand (2017 to 2031).
Ministry of Trade, Industry and Energy (2019), Third Energy Master Plan.
Oh, H., Shin, S., Ju, Y., Ju, H., and Lee, J. (2018), Analysis of the Economic Impact of the Korean ETS in Its First Phase (2015-2017). Kyung Hee University Industry-Academic Cooperation
Foundation-GIR.
Yu, J., Yu, J., Kim, J., and Lee, J. (2017), “Korea’s Greenhouse Gas Emission Reducing Policy: Before and After the ETS,” Environmental Policy, 25(2), Korea Environmental Policy and Administration Society.
2. Overseas literature
Abrell, J., Ndoye, A. and Zachmann, G. (2011), Assessing the impact of the EU ETS using firm level data. Bruegel Working Paper 2011/08, Brussels, Belgium.
Aggarwal, R., and Sundararaghavan, P. S. 1987, Efficiency of the silver futures market: an empirical study using daily data. Journal of Banking & Finance 11, pp. 49–64
Alexeeva-Talebi, V. (2011), Cost pass-through of the EU emissions allowances: Examining the European petroleum markets. Energy Economics, 33(S1), pp. S75-S83.
Anderson, B. and Di Maria, C. (2011), Abatement and Allocation in the Pilot Phase of the EU ETS.
Environmental and Resource Economics, 48(1), pp.83-103.
Anger, N. and Oberndorfer, U. (2008), Firm performance and employment in the EU Emissions Trading Scheme: An empirical assessment for Germany. Energy Policy, 36(1), pp. 12-22.
Arlt, J. and Arltová, M. (2000), VARIANCE RATIOS. Socio-Economical Applications of Statistical Methods, pp. 17-29, Wroclaw University of Economics Publishing House.
Bel, G. and Joseph, S. (2015), Emission abatement: Untangling the impacts of the EU ETS and the economic crisis. Energy Economics, 49, pp.531-539.
Borghesi, S., Cainelli, G. and Mazzanti, M. (2012), Brown sunsets and green dawns in the industrial sector: Environmental innovations, firm behavior and the European emission trading. FEEM Working Paper No. 3.2012, Milan, Italy.
Bushnell, J. B., Chong, H. and Mansur, E. T. (2013), Profiting from regulation: Evidence from the European Carbon Market. American Economic Journal: Economic Policy 5(4), pp. 78-106.
Calel, R. and Dechezlepretre, A. (2015), Environmental policy and directed technological change:
Evidence from the European carbon market. Review of Economics and Statistics, 98(1), pp. 173-191.
Capmbell, J. Y., Lo, A. W. and Mackinlay, A. C. (1997), The Econometrics of Financial Markets, Princeton University Press, Princeton, New Jersey.
Chan, H.S., Li, S. and Zhang, F. (2013), Firm competitiveness and the European Union emissions trading scheme. Energy Policy, 63, pp. 1056-1064.
Commins, N., Lyons, S., Schiffbauer, M. and Tol, N.C. (2011), Climate policy and corporate behavior.
Energy Journal, 32(4), pp. 51-68.
Cong, R. and Lo, A.Y. (2017), Emission trading and carbon market performance in Shenzhen, China.
Applied energy, 193, pp.414-425.
Constantini, V. and Mazzanti, M. (2012), On the green and innovative side of trade competitiveness?
The impact of environmental policies and innovation on EU exports. Research Policy, 41(1), pp. 132-
153.
Daskalakis, G. (2013). On the efficiency of the European carbon market: New evidence from Phase II, Energy Policy, 54, 369-375.
Daskalakis, G and Markellos, R.N. (2008). Are the European Carbon Markets Efficient?. Review of Futures Markets, 17(2), 103-128.
de Bruyn, S., Markowska, A., de Jong, F. and Bles, M. (2010), Does the energy intensive industry obtain windfall profits through the EU ETS? An econometric analysis for products from the refineries, iron and steel and chemical sectors. CE Delft Publication No. 10.7005.36, Delft, the Netherlands.
Delarue, E., Voorspools, K. and D’haeseleer, W. (2008), Fuel switching in the electricity sector under the EU ETS: review and prospective. Journal of Energy Engineering, 134(2), pp.40-46.
de Manuel Aramendía, M. (2011), Market Efficiency in the EU Emissions Trading Scheme: An outlook for the third trading period. Bruges European Economic Research (BEER) Papers 20/March 2011.
Ellerman, A.D. and Buchner, B.K. (2008), Over-allocation or abatement? A preliminary analysis of the EU ETS based on the 2005–06 emissions data. Environmental and Resource Economics, 41(2), pp.267-287.
Ellerman, A.D., Convery, F.J. and De Perthuis, C. (2010), Pricing carbon: the European Union emissions trading scheme. Cambridge University Press.
Environomist (2016), Environomist China Carbon Market Research report 2016. Beijing, Environomist Ltd.
Fabra, N. and Reguant, M. (2014), Pass-through of emissions costs in electricity markets. American Economic Review, 104(9), pp. 2872-2899.
Fama, E.F., (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383–417
Filc, W. (2007), Inefficient markets: causes and consequences.
Greene, W. H. (2012), Econometric analysis. Pearson Education India.
Hoque, H.A.A.B., Kim, J.H. and Pyun, C.S., 2007. A comparison of variance ratio tests of random walk: a case of Asian emerging stock markets. International Review of Economics and Finance 16, 488–502.
Ibikunle, G., Gregoriou, A., Hoepner, A.G. and Rhodes, M. (2016), Liquidity and market efficiency in the world's largest carbon market. The British Accounting Review, 48(4), pp.431-447.
Jaraite-Kažukauske, J. and Di Maria, C. (2016). Did the EU ETS make a difference? An empirical assessment using Lithuanian firm-level data. The Energy Journal, 37(1).
Jegadeesh, N. (1990), Evidence of predictable behavior of security returns. Journal of Finance, 45, 881 – 898.
Kenber, M., Haugen, O. and Cobb, M. (2009), The effects of EU climate legislation on business competitiveness: A survey and analysis, Washington, DC: German Marshall Fund of the United States.
Kim, J. H. and Shamsuddin, A. (2008). Are Asian stock markets efficient? Evidence from new multiple variance ratio tests. Journal of Empirical Finance, 15, 518-532.
Klemetsen, M.E., Rosendahl, K.E. and Jakobsen, A.L. (2016). The impacts of the EU ETS on Norwegian plants' environmental and economic performance (No. 833). Discussion Papers.
Kossoy, A., Guigon, P. (2012), State and Trends of the Carbon Market 2012. World Bank Research Report.
Lacombe, R.H. (2008), Economic impact of the European Union Emission Trading Scheme: Evidence from the refining sector. Master’s thesis, Massachusetts Institute of Technology.
Lee, C.I., Gleason, K.C., and Mathur, I. (2000), Efficiency tests in the French derivatives market.
Journal of Banking & Finance, 24, pp. 787–807.
Liski, M. (2001), Thin versus thick CO2 market. Journal of Environmental Economics and Management, 41, pp. 295-311.
Liu, C.Y., and He, J. (1991), A variance ratio test of random walks in foreign exchange rates. Journal of Finance, 96, pp. 773–785.
Lo, A.W., and MacKinlay, A.C. (1988), Stock market prices do not follow random walks: evidence from a simple specification test. Review of Financial Studies, 1, pp. 41–66.
Lofgren, A., Wrake, M., Hagberg, T. and Roth, S. (2013), The Effect of EU ETS on Swedish industry’s investment in carbon mitigating technologies. Working Papers in Economics 565, Department of Economics, University of Gothenburg.
Martin, R., Muuls, M. and Wagner, U.J. (2016), The impact of the European Union Emission Trading Scheme on regulated firms: what is the evidence after ten years? Review of Environmental Economics and Policy, 10(1), pp. 129-148.
Martin, R., Muuls, M., De Preux, L.B. and Wagner, U.J. (2013) Carbon markets, carbon prices and innovation: Evidence from interviews with managers. Paper presented at the Annual Meetings of the American Economic Association, San Diego.
Martin, R., Muuls, M., De Preux, L.B. and Wagner, U.J. (2014), Industry compensation under relocation risk: A firm-level analysis of the EU Emissions Trading Scheme. American Economic Review, 104(8), pp. 2482-2508.
Miller, M. H., Muthuswamy, J. and Whaley, R. E. (1994), Mean reversion of standard and poor's 500 index basis changes: arbitrage-induced or statistical illusion? Journal of Finance 49, 479–513.
Mills, T.C., and Markellos, R.N. (2008), The Econometric Modelling of Financial Times Series, 3rd edition (Cambridge University Press, Cambridge, UK).
Mittal, S.K. and Thakral, M. (2018), Testing weak form of efficient market hypothesis: Empirical evidence for bullions and base metal segment of Indian commodity market. Economic Affairs, 63(2), pp.575-581.
Montagnoli, A. and De Vries, F.P. (2010), Carbon trading thickness and market efficiency. Energy Economics, 32(6), pp.1331-1336.
Montgomery, W. (1972), Markets in licenses and efficient pollution control programs. Journal of
Economic Theory 5, 395–418.
Mundlak, Y. (1978), On the pooling of time series and cross section data. Econometrica: journal of the Econometric Society, pp.69-85.
Murray, B. C. and Maniloff, P. T. (2015), Why have greenhouse emissions in RGGI states declined?
An econometric attribution to economic, energy market, and policy factors. Energy Economics, 51 : 581-589.
Oberndorfer, U., Alexeeva-Talebi, V. and Loschel, A. (2010), Understanding the competitiveness implications of future phases of EU ETS on the industrial sectors. Discussion Paper No. 10-044, ZEW Centre for European Economic Research, Mannheim, Germany.
Paolella, M.S. and Taschini, L. (2008), An econometric analysis of emission allowance prices. Journal of Banking and Finance 32, 2022–2032.
Petrick, S. and Wagner, U.J. (2014), The impact of carbon trading on industry: Evidence from German manufacturing firms. Kiel Working Paper No. 1912, Kiel, Germany.
Porter, M.E. and Van der Linde, C. (1995), Toward a new conception of the environment- competitiveness relationship. Journal of Economic Perspectives, 9(4), pp. 97-118.
Reinaud, J. (2008), Climate policy and carbon leakage: Impacts of the European emissions trading scheme on aluminium. IEA Information Paper. OECD/IEA, Paris, France.
Seifert, J., Uhrig-Homburg, M., and Wagner, M. (2008), Dynamic behavior of CO2 spot prices- theory and empirical evidence. Journal of Environmental Economics & Management, 56, 180-194
Sijm, J., Neuhoff, K. and Chen, Y. (2006), CO2 cost pass through and windfall profits in the power sector. Climate Policy, 6(1), pp. 49-72.
UN (1992), United Nations Framework Convention on Climate Change, New York, 9 May 1992.
UN (1998), Kyoto Protocol to the United Nations Framework Convention on Climate Change. Kyoto, 11 December 1997.
Veith, S., Werner, J.R. and Zimmermann, J. (2009), Capital market response to emission rights returns:
Evidence from the European power sector. Energy Economics, 31(4), pp. 605-613.
Wagner, U.J., Muuls, M., Martin, R. and Colmer, J. (2013), An evaluation of the impact of the EU emissions trading scheme on the industrial sector: Plant-level evidence from France. Paper presented at the AERE Conference, Banff, Canada.
Wang, Q. and Wang, S. (2014), Study on market efficiency of china's carbon Trading market. Social Science Journal, 4:30–6.
Wirl, F. (2009), Oligopoly meets oligopsony: the case of permits. Journal of Environmental Economics and Management 58, 329–337.
Wooldridge, J.M. (2010), Econometric analysis of cross section and panel data. MIT press.
Wright, J.H. (2000), Alternative Variance-Ratio Tests Using Ranks and Signs. Journal of Business &
Economic Statistics, 18(1), 1-9.
Zhao, Xi., Wu, L. and Li, A. (2017), Research on the efficiency of carbon trading market in China.
Renewable and Sustainable Energy Reviews, 79, 1-8.
Zachmann, G. and von Hirschhausen, C. (2008), First evidence of asymmetric cost pass-through of EU emissions allowances: Examining wholesale electricity prices in Germany. Economics Letters, 99(3), pp. 465-469.
Zhao, X., Jiang, G. and Nie, D. (2016), How to improve the market efficiency of carbon trading: a perspective of China. Renewable and Sustainable Energy Review, 59:1229–45.
Zhou, W. and Chen, Y. (2011), The status quo, problems and countermeasures of china's carbon trading market. Journal of JiangXi University Finance and Economics, 3:12–7.
3. Websites
EU (2003), Directive 2003/87/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 13 October 2003 establishing a scheme for greenhouse gas emission allowance trading within the Community and amending Council Directive 96/61/EC”, Official Journal of the European Union, https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1578615473691&uri= CELEX:32003L0087, last accessed January 10, 2020.
National Greenhouse Gases Management System (NGMS, 2019), Emissions Breakdown and Statistics,
https://ngms.gir.go.kr/link.do?menuNo=30130103&link=/websquare/websquare.html%3Fw2xPath%3 D/cm/bbs/OGCMBBS023V.xml%26menu%3D30130103, last accessed October 30, 2019.
NICE (2019), KisValue, https://www.kisvalue.com/web/index.jsp, last accessed January 10, 2020.
National Law Information Center (NLIC, 2019a), Framework Act on Low-Carbon Green Growth
(enacted January 13, 2010), http://www.law.go.kr/lsInfoP.do?lsiSeq=98467&ancYd=20100113&ancNo=09931&efYd=20100414
&nwJoYnInfo=N&efGubun=Y&chrClsCd=010202#0000, last accessed October 30, 2019.
NLIC (2019b), Guidelines on the Management of the GHG & Energy Management System (all amended
October 10, 2014), http://www.law.go.kr/LSW//admRulLsInfoP.do?chrClsCd=&admRulSeq=2100000005757, last
accessed December 6, 2019.
Korea Energy Statistics and Information System (KESIS, 2019), General Energy Balance, http://www.kesis.net/, last accessed January 10, 2020.
Financial Supervisory Service (2019), FSS Electronic Disclosure System (DART), http://dart.fss.or.kr/, last accessed April 1, 2019.
Republic of Korea, (2016), INDC Submission by the Republic of Korea on June 30, https://www4.unfccc.int/sites/submissions/INDC/Submission%20Pages/submissions.aspx, last accessed January 10, 2020.
ETRS (2019), INDCs as communicated by parties, https://www4.unfccc.int/sites/submissions/INDC/Submission%20Pages/submissions.aspx, last accessed January 10, 2020.
Korea Exchange (2019), Market Information on General Commodities: Emissions Trading Market
http://marketdata.krx.co.kr/mdi#document=070301, last accessed March 5, 2019.
Appendix
1. Estimates by Industry
In this section can be found the results of applying, for the purpose of testing the fixed- and random- effect models, Equation (3) to the F&B, paper, glass and ceramics, cement, and non-iron metal industries, as well as the results of applying to the same industries Equation (2), the standard model of analysis described in Chapter II, and removing important variables from that equation.
Appendix Table 1. Model Test Results (Fixed- or Random-Effect): F&B Dependent variable: ghg
energyi,t 0.0181
energyi 0.0185
(0.0126) (0.0225)
energyi,t∗Af2014 -0.00316
energy∗Af2014i 0.176
(0.0017) (0.1471)
energyi,t∗ETSi,t -0.000312
energy∗ETSi
-0.203 (0.1323) (0.0027)
revenuei,t 0.00560
revenuei -0.00894
(0.0065) (0.0129)
revenuei,t∗ETSi,t 0.00557
revenue∗ETSi
-0.00546 (0.0163) (0.0036)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0279
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i
-0.0286
(0.0246) (0.0233)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t -0.00443
adjusted tangible asset∗ETSi
0.0738
(0.0116) (0.0556)
Af2014 3.555
Af2014 -135.6
(3.7421) (124.3110)
ETSi,t -1.746
ETSi
89.70 (57.5673) (3.4153)
DYear2012 -0.0212
- (3.4708)
DYear2013 -4.644
(2.7595)
DYear2015 -2.988
(2.6613)
DYear2016 -1.288
(2.5029)
DYear2017 -2.514
(3.6771)
Constant term 41.49
(49.6545)
N 23 Total obs. 144
Note: Figures in parentheses indicate standard errors. *p < 0.05, **p < 0.01, ***p < 0.001.
Appendix Table 2. Equation (2) Estimates: F&B
Model (1) (2) (3) (4)
Dependent variable ghg ghg ghg ghg
energyi,t
0.0181 0.0428** 0.0196
(0.0122) (0.0124) (0.0129)
energyi,t∗Af2014 -0.00313 -0.00779** -0.00310
(0.0016) (0.0023) (0.0016)
energyi,t∗ETSi,t -0.000276 0.00255 0.00150
(0.0027) (0.0031) (0.0030)
revenuei,t 0.00551 -0.00133 0.0123
(0.0064) (0.0061) (0.0113)
revenuei,t∗ETSi,t 0.00544 0.00151 0.00665
(0.0035) (0.0023) (0.0045)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0279 0.0265 0.0520*
(0.0238) (0.0291) (0.0205)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t -0.00440 0.00523 -0.0138
(0.0112) (0.0154) (0.0094)
ETSi,t -2.402 -2.297 -2.375 -2.922
(3.3215) (3.0865) (3.7072) (4.4939)
Af2014
3.459 9.416 3.698 -1.234
(3.5980) (5.4131) (3.7076) (2.4724)
DYear2012
-0.00616 -0.233 0.685 1.680
(3.3523) (3.1488) (3.7341) (4.6019)
DYear2013 -4.661 -7.249* -4.361 -1.144
(2.6636) (3.4300) (2.6352) (2.7478)
DYear2015
-2.219 -3.045 -1.088 -1.743
(2.2557) (3.0700) (1.6406) (2.1496)
DYear2016
-0.507 -2.205 -0.376 1.837
(2.2302) (2.7002) (1.6951) (2.5427)
DYear2017 -1.601 -3.318 -0.604 0.775
(3.4362) (3.2529) (2.8414) (3.8865)
Constant term 55.49** 24.69 59.18** 77.21***
(17.9977) (23.4825) (20.5199) (11.6412)
σν 44.71 24.48 45.56 64.58
σɛ 8.952 13.29 9.149 10.38
N 23 24 23 23
Total obs. 144 153 144 144
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 3. Model Test Results (Fixed- or Random-Effect): Paper
Dependent variable: ghg energyi,t
0.0135*
energyi 0.133*
(0.0054) (0.0533)
energyi,t∗Af2014
0.00269
energy∗Af2014i -0.0932
(0.0014) (0.2369)
energyi,t∗ETSi,t
-0.00921
energy∗ETSi
-0.132
(0.2621) (0.0072)
revenuei,t
0.144
revenuei
0.859
(0.0799) (0.6973)
revenuei,t∗ETSi,t 0.00838
revenue∗ETSi
-1.988
(1.6479) (0.0165)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.137
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i
-2.899*
(0.1177) (1.1792)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t
0.209
adjusted tangible asset∗ETSi
5.554
(0.2018) (3.4983)
Af2014
0.535
Af2014
-230.4*
(5.1238) (94.0738)
ETSi,t
-0.786
ETSi
0
(.) (6.2777)
DYear2012
1.619
- (2.9422)
DYear2013 0.384
(3.7357) DYear2015
2.400 (4.9256) DYear2016
8.637 (5.8493)
DYear2017 0
(.) Constant term
171.0**
(61.8163)
Number of businesses 40 Total obs. 273
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 4. Equation (2) Estimates: Paper
Model Model (1) Model (2) Model (3) Model (4)
Dependent variable ghg ghg ghg ghg
energyi,t 0.0136* 0.0144** 0.0144*
(0.0053) (0.0044) (0.0054)
energyi,t∗Af2014 0.00270 0.00234 0.00288**
(0.0014) (0.0015) (0.0010)
energyi,t∗ETSi,t -0.00912 -0.00507 -0.0107
(0.0071) (0.0048) (0.0062)
revenuei,t 0.144 0.198** 0.234**
(0.0787) (0.0634) (0.0746)
revenuei,t∗ETSi,t 0.00838 0.0335 0.0143
(0.0162) (0.0190) (0.0251)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.139 0.231 0.258
(0.1162) (0.1553) (0.2867)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t 0.207 0.267 0.0228
(0.1993) (0.1586) (0.0766)
ETSi,t -0.846 -5.378 2.137 -10.91*
(6.1929) (5.1024) (4.9169) (5.0678)
Af2014
0.539 2.850 -2.717 4.950
(5.0477) (4.6758) (4.8337) (4.3321)
DYear2012
1.630 1.922 0.387 4.246
(2.8975) (2.9582) (3.0643) (3.8349)
DYear2013 0.389 0.828 -1.006 3.633
(3.6798) (3.7040) (3.9898) (3.4130)
DYear2015
2.434 6.363 -0.261 3.348
(4.8439) (6.1191) (4.9456) (5.3175)
DYear2016
8.657 9.234 7.143 9.954
(5.7609) (6.1555) (6.0112) (6.1540)
DYear2017 0 0 0 0
(.) (.) (.) (.)
Constant term 80.63** 75.68*** 108.7*** 86.39*
(25.4527) (15.3837) (17.2409) (33.2858)
σν 109.9 110.8 122.6 124.8
σɛ 19.27 19.83 19.73 22.21
Number of businesses 40 40 40 40
Total obs. 273 274 273 273
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 5. Model Test Results (Fixed- or Random-Effect): Glass/Ceramics
Dependent variable: ghg energyi,t
0.0776***
energyi -0.202*
(0.0034) (0.0788)
energyi,t∗Af2014
0.0000183
energy∗Af2014i 2.945*
(0.0040) (1.2295)
energyi,t∗ETSi,t
-0.00375
energy∗ETSi
-3.230*
(1.4596) (0.0033)
revenuei,t
-0.0141
revenuei
2.763***
(0.0239) (0.6215)
revenuei,t∗ETSi,t 0.0613
revenue∗ETSi
-6.281***
(1.6153) (0.0318)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0844
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i
-6.290*
(0.0928) (2.7311)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t
-0.0444*
adjusted tangible asset∗ETSi
9.553
(0.0183) (7.5227)
Af2014
-13.36**
Af2014
-700.3
(5.0656) (380.8715)
ETSi,t
0.302
ETSi
1713.2**
(587.4465) (2.3020)
DYear2012
-12.91**
- (4.5675)
DYear2013 -18.25*
(8.0030) DYear2015
-0.784 (3.1394) DYear2016
3.688 (3.5283)
DYear2017 0.109
(3.6055) Constant term
-414.6**
(137.5415)
Number of businesses 19 Total obs. 130
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 6. Equation (2) Estimates: Glass/Ceramics
Model Model (1) Model (2) Model (3) Model (4)
Dependent variable ghg ghg ghg ghg
energyi,t 0.0776*** 0.0765*** 0.0801***
(0.0033) (0.0030) (0.0034)
energyi,t∗Af2014 0.0000183 0.000481 0.000379
(0.0039) (0.0036) (0.0035)
energyi,t∗ETSi,t -0.00375 -0.000927 0.00219
(0.0032) (0.0025) (0.0016)
revenuei,t -0.0141 0.0123 0.0335
(0.0229) (0.0072) (0.0229)
revenuei,t∗ETSi,t 0.0613 0.0271 0.151***
(0.0305) (0.0190) (0.0138)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0844 0.0416 -0.0139
(0.0891) (0.0460) (0.0807)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t -0.0444* -0.00587 -0.119***
(0.0176) (0.0232) (0.0298)
ETSi,t 0.308 5.145 0.519 -21.99***
(2.2091) (5.1384) (1.5946) (5.0243)
Af2014
-13.35* -10.13* -12.49** -26.28
(4.8633) (4.0974) (4.2314) (16.8094)
DYear2012
-12.91** -10.16* -11.51* -22.52
(4.3850) (3.7248) (4.0627) (18.4279)
DYear2013 -18.25* -14.86* -17.00* -28.86
(7.6835) (6.0833) (6.5780) (17.0927)
DYear2015
-0.792 -6.121 -0.783 -4.812
(3.0146) (4.5617) (3.0084) (7.4582)
DYear2016
3.682 -2.116 2.363 7.025
(3.3879) (4.7822) (2.6761) (5.9911)
DYear2017 0.103 -5.271 -1.141 5.091
(3.4619) (5.1075) (3.8383) (4.3890)
Constant term 26.46 17.08 16.47 291.1***
(16.3580) (12.4694) (16.3370) (18.7649)
σν 321.0 267.7 313.1 522.8
σɛ 17.86 16.61 18.22 36.02
Number of businesses 19 26 19 19
Total obs. 130 163 130 130
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 7. Model Test Results (Fixed- or Random-Effect): Cement
Dependent variable: ghg energyi,t
0.00553
energyi -0.00376
(0.0061) (0.0068)
energyi,t∗Af2014
-0.000409
energy∗Af2014i 1.222*
(0.0050) (0.5882)
energyi,t∗ETSi,t
-0.00416
energy∗ETSi
-1.183
(0.7895) (0.0055)
revenuei,t
-0.202
revenuei
-2.363***
(0.6899) (0.7167)
revenuei,t∗ETSi,t 0.325
revenue∗ETSi
1.523
(1.4785) (0.4646)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -1.534
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i
8.401***
(3.1257) (2.2390)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t
-0.0469
adjusted tangible asset∗ETSi
-10.73
(0.7678) (6.1285)
Af2014
54.86
Af2014
536.3
(52.1977) (602.4809)
ETSi,t
-7.141
ETSi
1899.5
(1390.0779) (49.1686)
DYear2012
-13.13
- (61.9021)
DYear2013 27.81
(64.2993) DYear2015
-40.76 (32.3301) DYear2016
19.65 (44.3554)
DYear2017 -27.59
(51.6972) Constant term
-1092.1 (936.4331)
Number of businesses 21 Total obs. 133
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 8. Equation (2) Estimates: Cement
Model Model (1) Model (2) Model (3) Model (4)
Dependent variable ghg ghg ghg ghg
energyi,t 0.00572 0.00616 0.00538
(0.0059) (0.0064) (0.0058)
energyi,t∗Af2014 -0.000563 0.00134 -0.000834
(0.0047) (0.0044) (0.0044)
energyi,t∗ETSi,t -0.00371 -0.00172 -0.000773
(0.0054) (0.0076) (0.0071)
revenuei,t -0.162 -0.162 -0.0225
(0.6839) (0.5939) (0.6883)
revenuei,t∗ETSi,t 0.274 0.223 0.0871
(0.4668) (0.3110) (0.5102)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -1.510 -1.591 -1.520
(2.9706) (2.7798) (3.0035)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t -0.0191 0.161 -0.00209
(0.7413) (0.5455) (0.7615)
ETSi,t 4.906 4.970 28.65 18.72
(48.5171) (43.1767) (46.0808) (58.6531)
Af2014
62.65 83.41 53.41 25.26
(48.2289) (80.6756) (54.7367) (44.8195)
DYear2012
-4.975 15.01 -11.08 -19.19
(59.0669) (74.8374) (61.7682) (63.3505)
DYear2013 36.08 73.39 26.72 7.248
(61.2342) (111.4609) (72.9828) (58.0300)
DYear2015
-48.99 -46.94 -50.72 -48.83
(30.6675) (28.8324) (31.0653) (28.2710)
DYear2016
28.44 36.85 23.90 23.26
(42.7018) (30.8182) (48.5058) (39.5072)
DYear2017 -20.48 -26.70 -30.83 -28.86
(50.7563) (56.6705) (68.6949) (35.4725)
Constant term 2392.5*** 2129.2*** 2372.4*** 2451.5***
(383.4949) (246.0685) (406.6630) (400.2849)
σν 3543.1 3150.8 3514.7 3581.7
σɛ 230.3 231.0 228.6 230.4
Number of businesses 21 21 21 21
Total obs. 133 133 133 133
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 9. Model Test Results (Fixed- or Random-Effect): Non-Iron Metals
Dependent variable: ghg energyi,t
0.0899***
energyi -0.0778*
(0.0124) (0.0341)
energyi,t∗Af2014
-0.00104
energy∗Af2014i -0.0696
(0.0015) (0.1083)
energyi,t∗ETSi,t
-0.000889
energy∗ETSi
0.213**
(0.0702) (0.0021)
revenuei,t
0.000819
revenuei
-0.000139
(0.0031) (0.0146)
revenuei,t∗ETSi,t -0.000815
revenue∗ETSi
-0.00397
(0.0333) (0.0012)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.0247
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i
-0.794**
(0.1606) (0.2715)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t
-0.0528
adjusted tangible asset∗ETSi
1.835***
(0.0558) (0.4136)
Af2014
-7.371
Af2014
82.62
(8.7562) (63.0549)
ETSi,t
6.215
ETSi
-152.5***
(42.0123) (6.9043)
DYear2012
-20.66*
- (8.8270)
DYear2013 -14.35
(7.8701) DYear2015
4.151 (3.3080) DYear2016
-2.055 (4.1381)
DYear2017 7.854
(6.1451) Constant term
19.08 (37.6167)
Number of businesses 25 Total obs. 163
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
Appendix Table 10. Equation (2) Estimates: Non-Iron Metals
Model Model (1) Model (2) Model (3) Model (4)
Dependent variable ghg ghg ghg ghg
energyi,t 0.0899*** 0.0897*** 0.0898***
(0.0121) (0.0155) (0.0120)
energyi,t∗Af2014 -0.00109 -0.00125* -0.00111
(0.0015) (0.0006) (0.0015)
energyi,t∗ETSi,t -0.000818 -0.00235 -0.000416
(0.0020) (0.0021) (0.0016)
revenuei,t 0.000779 0.000882 0.0154
(0.0030) (0.0034) (0.0169)
revenuei,t∗ETSi,t -0.000829 -0.00182 -0.00704
(0.0012) (0.0011) (0.0050)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.0217 -0.0198 0.441**
(0.1553) (0.1559) (0.1327)
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t∗ETSi,t -0.0550 -0.0704 0.275**
(0.0541) (0.0578) (0.0910)
ETSi,t 6.527 4.774 6.494 -15.86
(6.7543) (5.6596) (6.7447) (9.1869)
Af2014
-7.188 -7.800 -7.484 2.024
(8.4682) (10.8379) (8.3375) (10.1359)
DYear2012
-20.97* -21.53* -21.17* 2.715
(8.6011) (7.7896) (8.7411) (8.7015)
DYear2013 -15.16 -16.26 -15.57 6.621
(7.6643) (10.2299) (7.8731) (13.8450)
DYear2015
4.009 4.073 3.676 6.802
(3.2266) (2.8199) (3.0458) (14.0537)
DYear2016
-2.195 -0.563 -2.817 39.71
(4.0299) (5.6002) (4.5258) (25.9550)
DYear2017 7.723 9.978 7.047 40.05
(5.9831) (7.8365) (5.7996) (29.7640)
Constant term -95.18 -97.73 -93.67 176.6**
(83.5996) (76.9565) (83.2968) (54.4412)
σν 160.9 163.7 160.6 505.0
σɛ 34.74 34.64 34.50 78.64
Number of businesses 25 25 25 25
Total obs. 163 163 163 163
Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.
4. Non-Revenue-Adjusting Statistical Thresholds
Below can be seen the statistical thresholds used in the evaluation, in Chapter III, of efficiency of the Korean emissions market during the first phase of the ETS.
Appendix Table 11. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=880)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.698 -2.724 -2.495
2.5% -2.025 -2.006 -2.023
5.0% -1.638 -1.665 -1.689
95.0% 1.601 1.524 1.618
97.5% 1.929 1.883 2.023
99.5% 2.556 2.481 2.428
5
0.5% -2.505 -2.573 -2.302
2.5% -1.961 -2.052 -1.957
5.0% -1.676 -1.713 -1.712
95.0% 1.604 1.593 1.539
97.5% 2.044 2.084 1.884
99.5% 2.665 2.661 2.597
10
0.5% -2.616 -2.584 -2.548
2.5% -1.911 -1.935 -1.977
5.0% -1.656 -1.704 -1.650
95.0% 1.522 1.480 1.570
97.5% 1.834 1.947 1.941
99.5% 2.759 2.523 2.732
20
0.5% -2.442 -2.398 -2.442
2.5% -1.879 -1.917 -1.931
5.0% -1.724 -1.716 -1.616
95.0% 1.483 1.569 1.559
97.5% 1.800 1.916 2.054
99.5% 2.583 2.448 2.835
40
0.5% -2.306 -2.285 -2.320
2.5% -1.886 -1.888 -1.805
5.0% -1.681 -1.669 -1.615
95.0% 1.284 1.271 1.504
97.5% 1.686 1.716 1.965
99.5% 2.497 2.495 3.058
Appendix Table 12. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=879)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.551 -2.640 -2.733
2.5% -2.056 -2.047 -1.857
5.0% -1.748 -1.782 -1.585
95.0% 1.483 1.494 1.518
97.5% 1.765 1.749 1.923
99.5% 2.652 2.686 2.597
5
0.5% -2.517 -2.524 -2.562
2.5% -2.046 -1.978 -2.069
5.0% -1.748 -1.735 -1.601
95.0% 1.484 1.447 1.527
97.5% 1.858 1.845 1.897
99.5% 2.382 2.493 2.661
10
0.5% -2.656 -2.736 -2.368
2.5% -1.994 -2.012 -1.969
5.0% -1.716 -1.729 -1.673
95.0% 1.486 1.449 1.622
97.5% 1.774 1.770 1.916
99.5% 2.243 2.353 2.584
20
0.5% -2.441 -2.421 -2.263
2.5% -1.916 -1.978 -1.886
5.0% -1.756 -1.750 -1.632
95.0% 1.398 1.433 1.559
97.5% 1.709 1.821 1.846
99.5% 2.390 2.568 2.423
40
0.5% -2.250 -2.266 -2.218
2.5% -1.927 -1.912 -1.802
5.0% -1.772 -1.750 -1.598
95.0% 1.327 1.419 1.462
97.5% 1.760 1.803 1.814
99.5% 2.335 2.420 2.567
Appendix Table 13. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=385)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.709 -2.857 -2.804
2.5% -2.198 -2.179 -1.990
5.0% -1.880 -1.865 -1.682
95.0% 1.566 1.503 1.580
97.5% 1.839 1.840 1.886
99.5% 2.294 2.381 2.702
5
0.5% -2.467 -2.435 -2.643
2.5% -2.130 -2.070 -1.974
5.0% -1.886 -1.889 -1.675
95.0% 1.476 1.483 1.638
97.5% 1.823 1.819 1.935
99.5% 2.446 2.499 2.680
10
0.5% -2.404 -2.288 -2.388
2.5% -2.044 -2.017 -1.821
5.0% -1.748 -1.829 -1.603
95.0% 1.278 1.325 1.573
97.5% 1.710 1.635 2.008
99.5% 2.492 2.291 2.594
20
0.5% -2.351 -2.265 -2.316
2.5% -2.012 -1.998 -1.756
5.0% -1.771 -1.800 -1.581
95.0% 1.165 1.169 1.572
97.5% 1.567 1.590 1.865
99.5% 2.190 2.342 2.656
40
0.5% -1.992 -2.003 -1.930
2.5% -1.793 -1.776 -1.686
5.0% -1.689 -1.687 -1.512
95.0% 1.107 1.076 1.452
97.5% 1.503 1.539 1.878
99.5% 2.485 2.707 2.544
Appendix Table 14. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=384)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.678 -2.784 -2.552
2.5% -2.071 -2.134 -1.939
5.0% -1.805 -1.820 -1.633
95.0% 1.501 1.544 1.633
97.5% 1.797 1.730 1.840
99.5% 2.441 2.396 2.552
5
0.5% -2.624 -2.646 -2.292
2.5% -1.998 -2.124 -1.883
5.0% -1.757 -1.770 -1.623
95.0% 1.452 1.495 1.584
97.5% 1.872 1.874 1.883
99.5% 2.560 2.585 2.292
10
0.5% -2.352 -2.386 -2.292
2.5% -1.905 -1.913 -1.863
5.0% -1.718 -1.745 -1.578
95.0% 1.470 1.441 1.614
97.5% 1.772 1.841 1.929
99.5% 2.402 2.640 2.594
20
0.5% -2.204 -2.175 -2.154
2.5% -1.824 -1.895 -1.773
5.0% -1.721 -1.742 -1.645
95.0% 1.341 1.261 1.646
97.5% 1.647 1.779 2.033
99.5% 2.820 2.609 2.888
40
0.5% -1.932 -1.952 -1.911
2.5% -1.787 -1.791 -1.681
5.0% -1.665 -1.678 -1.525
95.0% 1.132 1.058 1.650
97.5% 1.444 1.411 2.064
99.5% 2.384 2.621 2.948
Appendix Table 15. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=171)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.409 -2.584 -2.371
2.5% -1.985 -2.029 -1.916
5.0% -1.782 -1.779 -1.759
95.0% 1.456 1.486 1.606
97.5% 1.822 1.794 1.759
99.5% 2.319 2.300 2.371
5
0.5% -2.400 -2.330 -2.346
2.5% -1.966 -2.002 -2.010
5.0% -1.720 -1.748 -1.675
95.0% 1.498 1.474 1.620
97.5% 1.726 1.755 2.010
99.5% 2.457 2.517 2.625
10
0.5% -2.165 -2.155 -2.134
2.5% -1.927 -1.908 -1.889
5.0% -1.681 -1.708 -1.653
95.0% 1.383 1.283 1.582
97.5% 1.775 1.623 2.025
99.5% 2.396 2.345 2.967
20
0.5% -1.919 -1.901 -1.902
2.5% -1.752 -1.706 -1.711
5.0% -1.631 -1.632 -1.573
95.0% 1.168 1.053 1.336
97.5% 1.524 1.429 1.988
99.5% 2.222 2.337 3.148
40
0.5% -1.611 -1.597 -1.565
2.5% -1.540 -1.524 -1.468
5.0% -1.470 -1.460 -1.404
95.0% 0.605 0.545 1.269
97.5% 1.112 0.994 1.663
99.5% 1.896 1.850 3.225
Appendix Table 16. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=170)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.671 -2.701 -2.762
2.5% -2.121 -2.074 -1.994
5.0% -1.766 -1.786 -1.687
95.0% 1.526 1.514 1.534
97.5% 1.883 1.800 1.841
99.5% 2.628 2.621 2.301
5
0.5% -2.569 -2.519 -2.325
2.5% -2.078 -2.062 -1.822
5.0% -1.756 -1.765 -1.596
95.0% 1.570 1.489 1.540
97.5% 2.009 1.930 1.764
99.5% 2.566 2.567 2.493
10
0.5% -2.208 -2.237 -2.290
2.5% -1.966 -1.915 -1.717
5.0% -1.802 -1.762 -1.545
95.0% 1.292 1.369 1.655
97.5% 1.859 1.774 1.891
99.5% 2.426 2.462 2.645
20
0.5% -1.977 -1.978 -1.954
2.5% -1.759 -1.711 -1.645
5.0% -1.655 -1.635 -1.525
95.0% 1.013 1.045 1.494
97.5% 1.532 1.444 1.902
99.5% 2.281 2.334 2.521
40
0.5% -1.605 -1.609 -1.582
2.5% -1.515 -1.510 -1.476
5.0% -1.448 -1.443 -1.410
95.0% 0.493 0.622 1.317
97.5% 0.987 0.973 1.589
99.5% 1.855 1.863 2.663
Appendix Table 17. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=249)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.734 -2.757 -2.598
2.5% -2.052 -2.164 -2.091
5.0% -1.812 -1.779 -1.711
95.0% 1.554 1.531 1.458
97.5% 1.925 1.850 1.711
99.5% 2.452 2.654 2.091
5
0.5% -2.460 -2.392 -2.314
2.5% -2.045 -2.005 -1.990
5.0% -1.753 -1.734 -1.668
95.0% 1.450 1.418 1.527
97.5% 1.733 1.734 1.851
99.5% 2.521 2.618 2.315
10
0.5% -2.287 -2.263 -2.286
2.5% -1.894 -1.907 -1.821
5.0% -1.682 -1.680 -1.641
95.0% 1.347 1.321 1.506
97.5% 1.652 1.690 1.889
99.5% 2.306 2.207 2.692
20
0.5% -2.095 -2.093 -2.046
2.5% -1.888 -1.878 -1.780
5.0% -1.705 -1.755 -1.622
95.0% 1.057 1.000 1.464
97.5% 1.500 1.395 1.928
99.5% 2.103 2.236 2.719
40
0.5% -1.844 -1.855 -1.726
2.5% -1.696 -1.708 -1.617
5.0% -1.594 -1.617 -1.491
95.0% 0.704 0.681 1.278
97.5% 1.147 1.119 1.813
99.5% 1.931 1.926 2.445
Appendix Table 18. Statistical Thresholds for Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=248)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.675 -2.647 -2.414
2.5% -2.062 -2.123 -1.905
5.0% -1.699 -1.727 -1.778
95.0% 1.564 1.567 1.651
97.5% 1.947 1.836 1.905
99.5% 2.617 2.452 2.922
5
0.5% -2.502 -2.548 -2.435
2.5% -2.014 -2.064 -1.878
5.0% -1.769 -1.727 -1.646
95.0% 1.592 1.571 1.646
97.5% 1.873 2.012 2.017
99.5% 2.659 2.865 2.945
10
0.5% -2.311 -2.278 -2.257
2.5% -1.989 -1.985 -1.881
5.0% -1.725 -1.765 -1.566
95.0% 1.417 1.447 1.648
97.5% 1.881 1.794 1.874
99.5% 2.463 2.508 2.768
20
0.5% -2.080 -2.050 -2.164
2.5% -1.836 -1.840 -1.743
5.0% -1.677 -1.654 -1.577
95.0% 1.185 1.203 1.523
97.5% 1.600 1.637 1.884
99.5% 2.308 2.415 2.794
40
0.5% -1.781 -1.754 -1.736
2.5% -1.635 -1.643 -1.573
5.0% -1.548 -1.564 -1.473
95.0% 0.789 0.774 1.437
97.5% 1.156 1.373 1.946
99.5% 1.716 2.096 2.513
Appendix Table 19. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU18, Number of Open Days (n=153)
Duration Cumulative
probability R1 R2 S1
2
0.5% -2.488 -2.551 -2.668
2.5% -2.037 -2.125 -2.021
5.0% -1.653 -1.711 -1.698
95.0% 1.443 1.375 1.698
97.5% 1.745 1.866 2.021
99.5% 2.639 2.619 2.506
5
0.5% -2.230 -2.377 -2.480
2.5% -1.956 -1.965 -1.889
5.0% -1.755 -1.752 -1.653
95.0% 1.456 1.450 1.597
97.5% 1.794 1.799 2.127
99.5% 2.334 2.504 2.658
10
0.5% -2.201 -2.223 -2.150
2.5% -1.863 -1.861 -1.806
5.0% -1.706 -1.699 -1.624
95.0% 1.238 1.211 1.597
97.5% 1.584 1.572 2.104
99.5% 2.255 2.208 3.616
20
0.5% -1.872 -1.887 -1.881
2.5% -1.713 -1.737 -1.656
5.0% -1.610 -1.617 -1.529
95.0% 1.080 0.939 1.390
97.5% 1.422 1.420 1.985
99.5% 2.215 2.043 3.076
40
0.5% -1.535 -1.525 -1.504
2.5% -1.483 -1.488 -1.434
5.0% -1.437 -1.442 -1.366
95.0% 0.479 0.427 1.065
97.5% 0.826 0.746 1.505
99.5% 1.790 1.709 2.885