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Essays on Climate Change Computable General Equilibrium Models

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dc.contributor.advisor홍종호-
dc.contributor.author김창훈-
dc.date.accessioned2017-07-14T05:31:28Z-
dc.date.available2017-07-14T05:31:28Z-
dc.date.issued2013-08-
dc.identifier.other000000012898-
dc.identifier.urihttps://hdl.handle.net/10371/124904-
dc.description학위논문 (박사)-- 서울대학교 환경대학원 : 환경계획학과, 2013. 8. 홍종호.-
dc.description.abstractThis research reviews the problems of conventional computable general equilibrium (CGE) models which are widely used for climate change policy analysis. To solve the problems, it proposes multivariate distribution approach as an alternative way of representing the production activities in model structures and assesses the possibility of its practical employment.

In the first part of this research, the basic characteristics of three well known global CGE models are reviewed and production function structures are pointed out as the main sources of the differences in carbon emission projections among models. Two experiments are introduced regarding the effects of changes in production function structures. In one experiment, the nested structure of constant elasticity substitution (CES) functions is substituting with alternative nesting structures. In another experiment, fixed input structures are partly applied for incorporating bottom-up approach with top-down mechanism of CGE models. The results show that these structural changes cause a considerable impact on the prediction results of greenhouse gas emissions and carbon prices. Also, the experiments are extended to the comparison of GDP losses among different model structures. Simulations for the case of Korea reveal that the estimations of GDP loss differs among model structures, raising some issues on applying them into practical policy making.

In the second part, the performance of a global CGE model is analyzed in marginal abatement cost estimation when data disaggregation is applied. Extraordinary carbon prices are reported for the case of relatively large share of capital in the economies of a few developing countries. Empirical evidence indicates that the abnormal phenomenon is accounted for by the proportional relationship between capital intensity and carbon price. The analysis is extended to CES functions with a numerical analysis, concluding that the unusual phenomena may be connected to distribution parameters of CES functional forms which are most widely used in CGE models.

In the last part, multivariate distribution approach is applied for an alternative description of energy related production activities. Applying theories on the microfoundations of aggregate production functions, it is shown that a set of bottom-up microscopic information can converge to specific aggregate production functions if assumptions are imposed on the statistical distribution of local production technologies. The actual characteristics of statistical distributions were reviewed for a real dataset of energy intensive manufacturing sector of Korea. To facilitate simulations and conveniently reproduce the relationships embedded in multivariate joint distribution maps, a statistical tool called copulas is introduced in advance. After the basic theory of copulas is briefly introduced, the performance of a copula model is investigated, revealing that a copula model is successful in describing heterogeneous microscopic information. After the introduction of copulas, a new type of CGE model is applied, in which an aggregation of local Leontief production functions takes over the role of conventional global production functions. A pilot model is composed to apply this scheme to a CGE model and it is shown that this new approach has some advantages: it eliminates the effect of the past time data and improves the precision of projection results.
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dc.description.tableofcontentsTable of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

I. Overall introduction . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview and outline . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6

II. Structural differences between global climate change CGE models. . . . . 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Reviews on global CGE models . . . . . . . . . . . . . . . 14
2.2.1 Models . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 Static structure . . . . . . . . . . . . . . . . . . . . 18
2.2.3 Dynamic process . . . . . . . . . . . . . . . . . . . 25
2.3 Model structure analysis . . . . . . . . . . . . . . . . . . . 27
2.3.1 Change in energy-capital bundle structures . . . . . 29
2.3.2 Replacement with fixed input structures . . . . . . . 36
2.4 Policy implications . . . . . . . . . . . . . . . . . . . . . . 43
2.4.1 Carbon price . . . . . . . . . . . . . . . . . . . . . 43
2.4.2 Estimation of GDP change . . . . . . . . . . . . . . 46
2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 48

III. Carbon prices and parameter calibration in CES function structures. . . 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Problems in regional disaggregation . . . . . . . . . . . . . 54
3.2.1 Derivation of MACC using the EPPA model . . . . . 54
3.2.2 Regional deviations in carbon price . . . . . . . . . 61
3.3 Mathematical analysis . . . . . . . . . . . . . . . . . . . . 67
3.3.1 Ratio of capital intensity . . . . . . . . . . . . . . . 67
3.3.2 Extensions to the CES function . . . . . . . . . . . 74
3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 78

IV. The statistical distribution approach for a description of production activities . . 81
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2 Functional forms and data distribution . . . . . . . . . . . . 87
4.2.1 Microfoundations of production functions . . . . . . 87
4.2.2 Data analysis . . . . . . . . . . . . . . . . . . . . . 96
4.2.3 Dependence representation of the CES function . . . 103
4.3 The copula model . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.1 Copula theory . . . . . . . . . . . . . . . . . . . . . 107
4.3.2 Construction of a copula model . . . . . . . . . . . 109
4.3.3 Performance of the copula model . . . . . . . . . . 113
4.3.4 The copula model with data disaggregation . . . . . 119
4.4 The statistical distribution approach . . . . . . . . . . . . . 128
4.4.1 Set of firms . . . . . . . . . . . . . . . . . . . . . . 128
4.4.2 Properties of cost functions . . . . . . . . . . . . . . 131
4.4.3 Elasticity of substitution . . . . . . . . . . . . . . . 137
4.5 Application of the distribution approach to CGE models . . 144
4.5.1 The pilot CGE model . . . . . . . . . . . . . . . . . 144
4.5.2 Projection results . . . . . . . . . . . . . . . . . . . 148
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
I. The structure of the pilot CGE model . . . . . . . . . . . . . 171
II. Source code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
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dc.formatapplication/pdf-
dc.format.extent2756379 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectcomputable general equilibrium (CGE) model-
dc.subjectstructural uncertainty-
dc.subjectconstant elasticity of substitution (CES) production function-
dc.subjectgreenhouse gas emission projection-
dc.subjectinput factor distribution-
dc.subjectcopula-
dc.subject.ddc711-
dc.titleEssays on Climate Change Computable General Equilibrium Models-
dc.typeThesis-
dc.description.degreeDoctor-
dc.citation.pagesxx, 210-
dc.contributor.affiliation환경대학원 환경계획학과-
dc.date.awarded2013-08-
Appears in Collections:
Graduate School of Environmental Studies (환경대학원)Dept. of Environmental Planning (환경계획학과)Theses (Ph.D. / Sc.D._환경계획학과)
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