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CGE 모형을 이용한 국내 산업부분별 Marginal Abatement Cost (MAC) Curves 도출
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김수덕 | - |
| dc.contributor.author | 민은주 | - |
| dc.date.accessioned | 2018-11-08T08:12:00Z | - |
| dc.date.available | 2018-11-08T08:12:00Z | - |
| dc.date.issued | 2016-08 | - |
| dc.identifier.other | 23266 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/11578 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :에너지시스템학과,2016. 8 | - |
| dc.description.tableofcontents | I. Introduction 1 A. Overview 1 B. Research Objectives 2 II. Review of Previous Studies 4 A. Energy Price and Emission Changes 4 B. Technology-Specific (Bottom-up) MAC Curves 5 C. Economy-Wide (Top-Down) MAC Curves 7 D. Other Researches on MAC Curves 8 III. A CGE Model 9 A. Overview 9 B. Social Accounting Matrices (SAM) 10 1. Simplified Schematics for SAM 10 2. Balancing a Social Accounting Matrix 16 C. The structure of the Core Static Model 19 1. Activities, Production and factor markets 19 2. Commodity Markets 21 3. Market clearing conditions 22 4. Institutions 23 5. Closure Rules 24 IV. Modeling the Korean Energy Market 25 A. SAM for Korean Economy 25 B. Energy Quantity in 2012 Korea Input-Output table 27 C. Overview of Korean Energy Market and Emission Trends 31 D. CO2 Emission Accounting 34 V. Scenarios and Simulation Results 37 A. Scenario Development 37 1. Carbon Tax Scenarios 37 2. Energy Price Scenarios 39 B. Simulation Results 49 1. Overview 49 2. Results for Carbon Tax Scenarios 49 3. Results for Energy Price Scenarios 55 4. Comparison of Two scenarios 61 VI. Conclusions 66 VII. References 67 VIII. Appendix 73 A. Comparison of scenarios for all Sectors 73 B. Mathematical Representation of the Model 85 1. Activity 86 2. Consumers behavior: 88 3. Trade Block: 88 4. Price Block 89 5. Institution 90 6. System Constraint Block 92 C. Key Elasticities of the model 94 D. Reference Tables 98 E. Final SAM Table after Balancing 100 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | CGE 모형을 이용한 국내 산업부분별 Marginal Abatement Cost (MAC) Curves 도출 | - |
| dc.title.alternative | A Derivation of Sectoral Marginal Abatement Cost (MAC) Curves for Korean Economy Using a CGE Model | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Eunju Min | - |
| dc.contributor.department | 일반대학원 에너지시스템학과 | - |
| dc.date.awarded | 2016. 8 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 758519 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000023266 | - |
| dc.subject.keyword | CGE | - |
| dc.subject.keyword | MAC Curve | - |
| dc.subject.keyword | Energy Price | - |
| dc.description.alternativeAbstract | One of the key energy policies for Korean government recently is to reduce greenhouse gas of 37% based on business as usual scenario result by 2030. This paper considers the impact of oil price shocks and the introduction of a carbon tax on the Korea economy, which is highly dependent on imported energy. Imported energy here means crude oil, coal and liquefied natural gas (LNG). In 2012, crude oil accounted for 58% of the value of energy imports. The impact of oil price changes with the co-movement of LNG and coal prices and of the introduction of carbon tax on Korea economy is investigated, by deriving sector-wise marginal abatement cost (MAC) curves using a Computable General Equilibrium (CGE) model. CGE model is calibrated for the base year of 2012 applying information from various sources including both of ‘Use’ and ‘Supply’ Input-Output tables of Korea. Careful attention is given to this monetary figures of social accounting matrix (SAM) for the proper representation of base price and the quantification of physical amount of energy and carbon dioxide. It is tested using ARDL (Autoregressive Distributed Lag) model that the coal and LNG import prices are in concert with the oil price. For the coal and LNG price scenario setting, estimation results from ARDL model have been used to get the long-term forecast prices of LNG and coal given crude oil prices. For the high price scenario, around +60% higher oil price is used, whereas for the low price scenario, around -60% lower oil price is adopted compared to the 2012 annual average crude oil price. Energy price scenario development provide an interesting result: since domestic import price of LNG is the weighted average price of all the long-term and short-term contracts made by KOGAS (Korea Gas Corporation) and is having so called S-curve shape with respect to crude oil price change for the risk hedging of both parties, buyer and seller, import LNG price is found not as responsive as coal price to the changes in crude oil price. For the carbon tax scenario, tradable emission permit (TEP) price is set from $5 to $200 with interval changes of $5. Carbon tax is calculated by multiplying the calorific value of energy in its own physical unit, carbon contents per calorific value of energy, and TEP price per carbon. It is noted here that CO2 emission accounts from this model using 2012 Input-Output table for the construction of a SAM, coupled with CO2 emission accounting method to be presented in detail yields 593.5 million CO2 tons, while CO2 emission from fuel combustion reported by Greenhouse Gas Inventory & Research Center of Korea (GIR) is 587.2 million CO2 tons, showing only 1.06% difference. Each scenario of energy price and carbon tax is applied to the disaggregated sectors of Korean economy and MAC curves for each sector is derived. It is not normally the case that relationship between energy price changes and carbon abatement potential are called MAC curve. But it is also acceptable and not incorrect to regard this relationship as another type of MAC curve, since it also presents the relationship between the abatement potential and the energy prices changes. The result of this, of course, will be useful for the policy designer. Carbon abatement potentials are calculated for both scenarios and detailed tables and figures are prepared for better presentation of results: CO2 emission abatement potentials in terms of the percentage changes are provided compared with ‘BASE’ figures for both scenarios. One of the results obtained from the model implies that power sector which utilizes many different types of fossil fuels with high carbon contents is found to be more sensitive to carbon tax than petroleum sector, while petroleum sector which utilizes the crude oil for refining is more responsive to energy prices. The loss of Gross Domestic Product (GDP) is also being calculated for both scenarios. The analysis of combined results from both scenarios provides insight to energy policy formulation in terms of reforming current energy price structure and the introduction of carbon tax to domestic market in the future. | - |
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