An Estimation of Market-Based Carbon-Emission Prices Using Comparative Analogy: A Korean Case.

• Author: Moon, Saedaseul

1. INTRODUCTION

   Korea emits the seventh largest amount of carbon dioxide (C[O.sub.2]) in the world (Olivier et al. 2016). Greenhouse gas (GHG) emissions per capita increased steadily from 6.8 Metric tons of carbon dioxide equivalent (MtC[O.sub.2]eq) in 1990 to 13.7 MtC[O.sub.2]eq in 2014 (GHG Information Center 2016). Although Korea was classified as an involuntary reduction country that is not obligated to reduce carbon emissions by the Kyoto Protocol, it has voluntarily introduced systems to reduce carbon emissions in order to participate in international eforts to prevent global warming and take environmental responsibility.

   Korea joined the Climatic Change Convention in 1993 to take part in international eforts to prevent global warming. In 2005, Korea introduced the GHG Reduction Performance Registration System to manage corporate GHG emissions (Korea Energy Agency 2017). In 2009, Korea set a voluntary reduction target at 30% reduction from business-as-usual (BAU) by 2020, and established a basic law for low carbon and green growth for harmonious development of the economy and the environment (Korea Low Carbon Green Growth Basic Law Article 1 2009). In 2010, the GHG Energy Target Management System was established. It designates companies with high GHG emissions and energy consumption and encourages them to set their own reduction target and manage it. In 2012, Korea implemented the Forest Carbon Ofset System that enables and promotes trading of carbon stocks acquired through forest-based projects (Korea Forest Service 2017).

   However, there was criticism that the existing carbon management system recognizes only direct reduction and is too rigid. Therefore, in order to complement this rigidity, Korea introduced the Emission Trading System (ETS) in 2015, which is considered to be a more effective carbon reduction system. ETS is a scheme for GHG reduction as set out in Article 17 of the Kyoto Protocol that allows entities with excess and unused emission units to sell their emission units to entities that go over their targets (UNFCCC 2017). Among the various methods, the ETS is recognized as a very effective method to promote reductions of C[O.sub.2] (Dormady 2014, Tang et al. 2015).

   Although there are various forms of ETS that differ in size, scope, and design (Perdan and Azapagic 2011), the European Union which has introduced the ETS market in a pioneering manner explains the overall procedures as follows (European Commission 2016):

   "The EU ETS works on the 'cap and trade' principle. A cap is set on the total amount of certain greenhouse gases that can be emitted by installations covered by the system. The cap is reduced over time so that total emissions fall. Within the cap, companies receive or buy emission allowances which they can trade with one another as needed. They can also buy limited amounts of international credits from emission-saving projects around the world. The limit on the total number of allowances available ensures that they have a value. After each year a company must surrender enough allowances to cover all its emissions, otherwise heavy fines are imposed. If a company reduces its emissions, it can keep the spare allowances to cover its future needs or else sell them to another company that is short of allowances. Trading brings flexibility that ensures emissions are cut where it costs least to do so. A robust carbon price also promotes investment in clean, low-carbon technologies."

   Since the introduction of EU ETS in 2005, a burgeoning academic literature has started to examine the factors that shape the price of carbon. Ellerman and Buchner (2007), and Convery (2009) published an early literature review on the development of carbon price. Thereafter Chevallier (2013) reviewed mainly economic and econometric studies which have identified inter-relationships between the price of carbon on the one hand, and its main fundamentals that allow to explain and forecast its variation overtime on the other hand. The study categorized the drivers of carbon price into institutional decisions, energy prices, weather, and macroeconomic factors, and financial market shocks.

   Early studies the determinants of carbon price include Christiansen et al. (2005) and Mansanet-Bataller, Pardo, and Valor (2007). Christiansen et al. (2005) analyzed United Kingdom carbon trading data before the EU-ETS began and concluded that policy, regulations, market fundamentals, weather, and production levels are all key factors affecting the price of carbon credit. Mansanet-Bataller, Pardo, and Valor (2007) analyzed EU market during the implementation of the EU-ETS first phase using multiple regression analysis and showed that energy (mainly petroleum, coal, and natural gas) prices were major influential factors of carbon prices. Also, they showed that extreme weather has an effect on carbon prices. Hintermann (2010) extended Maeda (2004)'s theoretical model of the carbon trading scheme and presented a theory for carbon price equilibrium. He also conducted an empirical analysis that compared market forces on the market-based carbon price before and after the price-crash, which is the price collapse occurred in EU-ETS first phase, through linear regression analysis and showed that natural gas prices, coal prices, temperature, and precipitation were the main forces. Keppler and Mansanet-Bataller (2010) used the Granger causality tests to assess the short-term and long-term relationship between carbon emission price and some energy prices or climate variables based on the data in the first and second EU-ETS phases. They concluded that the prices of coal, natural gas, and electricity were major factors.

   Some studies to investigate the relationship between the price of carbon and macroeconomic factors (e.g., industrial production, the financial market, economic recessions) in addition to the impact of energy prices and weather have been conducted. Alberola, Chevallier, and Cheze (2008) examined how industrial production affects carbon prices by analyzing the EU ETS first phase data. As a result, it was found that the industrial production sectors of combustion, paper, and iron affected carbon prices. Chevallier (2009) investigated the empirical relationship between the returns on carbon futures and changes in macroeconomic conditions. He concluded that the returns on carbon futures could be weakly predicted based on stock and bond markets. Chevallier (2011) showed that industrial production impacts the carbon market and found a linkage between the macroeconomic variables and the price of carbon. Declercq, Delarue, and D'haeseleer (2011) showed the significant effects of the economic recession in 2008-2009 on carbon prices through analyzing the carbon footprint of the European power sector. Lanz and Rausch (2016) analyzed the effects of government regulation on polluting firms on emissions trading market.

   Recently the research on the price of carbon to identify other factors such as renewable energy can be found. Koch et al. (2014) studied the reasons for the steady decline in the price of carbon after EU ETS second phase; in particular, they studied economic recession, the Clean Development Mechanism (CDM), and renewable policies. As a result of analyzing data of 2008-2013, they concluded that variations in economic activity and the growth of wind and solar electricity production robustly affected the price of carbon. Examining the potential determinants of carbon prices during the second phase of EU ETS, they claimed that the results were very sensitive to the selection of the energy price series, and only economic activity and hydropower provisions provided a robust explanation. Van den Bergh et al. (2013) analyzed EU-ETS 2007-2010 data and concluded that deployment of renewable energies lowered the price of carbon. After the key factors on the price of carbon were identified, studies to forecast the price of carbon have followed. Zhu (2012) proposed a multi-scale ensemble forecasting model combining the empirical mode decomposition, genetic algorithms, and artificial neural networks. His proposed model was more predictive than previous models for the price of carbon futures, which have different maturities. Zhu and Wei (2013) built three hybrid models that combined autoregressive integrated moving average (ARIMA) and least squares support vector machine (LSSVM) to forecast carbon prices, and they proposed some simple approaches to a robust prediction of the non-linear behavior of carbon prices. However, there are few empirical studies on the carbon prices in countries other than Europe like Korea since carbon emission trading markets in those countries are still in an embryonic stage (Wu et al. 2016).

   The Korea Carbon Emission Trading System (KETS) aims to reduce GHG emissions by 37% compared to the GHG emission forecast (BAU) by 2030. Companies that produce more than 125,000 tons of C[O.sub.2] a year or have plants that produce more than 25,000 tons of C[O.sub.2] a year are obligated to participate; other companies can participate voluntarily. In total, there are more than 500 companies participating in the system. As shown by Table 1, the sectors covered in KETS are similar with EU ETS. The major sectors which captured about 85% of total emissions in KETS such as Power, Iron and steel plants, Petrochemicals, Cement, Oil refineries and Glass, Paper, Aviation are also included in EU ETS. Furthermore, it is found that the sector of Nonferrous metal in KETS includes the EU sectors of Aluminium, Nitric, adipic and glyoxylic acid production.

   It is possible to allocate 100% of the initial allowance without any cost in the first phase (2015 to 2017), but this free allocation ratio will be decreased to 97% in the second phase (2018 to 2020) and 90% in the third phase (2021-2025). However, it is allowed for only energy-sensitive industries to be able to allocate 100% of the initial allowance without any cost to...