Integrations without and with investment in low-carbon technologies and with no CCS, biomass or deforestation control

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Climate change and climate variability

Climate change and climate variability are two important characteristics of climate. According to United Nations Framework Convention on Climate Change (UNFCCC 1992), climate change is a change of climate which is attributed directly or indirectly to any human activity that alters the composition of the global atmosphere and which is in addition to natural variability observed over comparable time periods. On the other hand, climate variability is the departure from normal or the difference in magnitude between climatic episodes.
The history of scientific study of climate change is long. More than a century ago, for example, Fourier (1824, 1888) was the first to notice that the Earth is a greenhouse, kept warm by an atmosphere that reduces the loss of infrared radiation. The overriding importance of water vapor as a greenhouse gas was recognized even then. In the late 1890s, Arrhenius (1896) was the first to quantitatively relate the concentration of CO2 in the atmosphere to global surface temperature. Given this long-standing history, one might lament the fact that – perhaps owing, in part, to the politically-charged nature of the topic – many people mistakenly assume that the science that underlies our current understanding of climatic change is, in some way, suspect or unreliable. Of course, the nature of the greenhouse debate is far too complex and multifaceted to lend itself well to simplistic “is it happening or isn’t it?” characterizations. The vast evidence that the climate of the Earth is changing due to the anthropogenic increase in greenhouse gases (GHGs) is compiled in the successive reports of the Intergovernmental Panel on Climate Change (IPCC 1996a, 2001a, 2007a, 2013), CO2 being the largest contributor (Farmer and Cook 2013, p. 4; Stern 2008; Stott et al. 2000). Typically, the effect of global warming on the economic system is modeled using integrated assessment models (IAMs). IAMs are motivated by the need to balance the dynamics of carbon accumulation in the atmosphere and the dynamics of de-carbonization of the economy (Nordhaus 1994a). A specific goal of these studies is to evaluate different abatement scenarios as to economic welfare and their effects on GHG emissions.

The emergence of IAMs as a science-policy interface

With the immense enhancement in computer technology, integrated modelling surfaced in the mid- 1980s as a new paradigm for interfacing science and policy concerning complex environmental issues such as climate change. In the second half of the eighties, it was believed that integrated modelling would be the optimal way to interface science with policy. According to Parson (1994): “To make rational, informed social decisions on such complex, long-term, uncertain issues as global climate change, the capacity to integrate, reconcile, organize, and communicate knowledge across domains to do integrated assessment is essential.” Therefore, integrated assessment models are believed to produce insights that cannot be easily derived from the individual natural or social science component models that have been developed in the past (Weyant 1994); see also, Meyers (2012, pp. 5399 5428) and Rasch (2012, Ch. 8) for a further discussion.
According to Beltran et al. (2005, p. 70), Integrated Assessment (IA) can be defined as an interdisciplinary process of combining, interpreting and communicating knowledge from diverse scientific disciplines in such a way that the whole cause-effect chain of a problem can be evaluated from a synoptic perspective with two characteristics: (i) it should have added value compared to single disciplinary assessment; and (ii) it should provide useful information to decision makers.

Classification of IAMs

Nowadays IAMs are capable of reflecting a range of modelling approaches that aim to provide policy-­‐relevant information, and most can be summarized by: (i) policy optimization that seeks optimal policies and (ii) policy evaluation models that assess specific policy measures. The complexity of optimization models is limited, however, because of the requirement of a large number of numerical algorithms in optimization. Therefore these models tend to be based on compact representations of both the socioeconomic and natural science systems. They thus contain a relatively small number of equations, with a limited number of geographic regions. Apart from policy optimization, policy evaluation models tend to be descriptive and can contain much greater modelling detail on bio-geo-physical, economic or social aspects. These models are often referred to as simulation models, and are designed to calculate the consequences of specific climate policy strategies in terms of a suite of environmental, economic, and social performance measures. An early example of this type of model is the Integrated Model to Assess the Global Environment (IMAGE) (Rotmans 1990; Alcamo et al. 1998).

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Challenges for IAM studies

The foremost challenge for IAM Studies is the integration of the natural and socioeconomic systems in order to better model the relationship between human activities and the global environment. To the present, many integrated assessment models share the same basic framework.
Whether current IAMs have reached a level of development where they can serve as the adequate basis for judgments in formulating actual global environmental measures is debatable. Modellers appear to agree, however, that for the most part the framework itself is acceptable. The integrated assessment of global environmental issues from the perspectives of the natural and social sciences is not a field of learning involving the pursuit of truth. Rather, it is a practical science that aims at providing useful guidance to policy makers seeking to establish rules and policies that help smooth the relationships between natural rule, the global environment and humanity. Conventionally, it is possible to encapsulate the relationships between such practical scientific studies and the real world in a relatively simple framework.

Table of contents :

DECLARATION
Declaration by the candidate
Declaration by supervisors
DEDICATION
ACKNOWLEDGEMENTS
ABSTRACT
RÉSUMÉ
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
OPERATIONAL DEFINATIONS OF TERMS AND CONCEPTS
ABBREVIATIONS AND ACRONYMS
INTRODUCTION
1.1 Background to the problem
1.2 Statement of the problem and justification
1.3 Objectives of the study
1.4 Significance of the study
1.5 Research methodology and outline of the study
LITERATURE REVIEW
2.1 Climate change and climate variability
2.2 Integrated assessment modelling (IAM)
2.2.1 The emergence of IAMs as a science-policy interface
2.2.2 Classification of IAMs
2.2.3 Application of integrated assessment models
2.2.4 Challenges for IAM studies
2.2.5 Improvements of IAMs
2.2.6 This study
MODEL DESCRIPTION
3.1 Climate module
3.2 Economy module
3.3 Industrial CO2 emissions
3.3.1 Inclusion of CCS in the industrial CO2 emissions equation
3.3.2 Cost of CCS
3.3.3 Damage function
3.4 Inclusion of a Biosphere module: CO2-biomass interactions
3.4.1 Carbon flux from deforestation and deforestation control
3.4.2 Cost of the deforestation activity
3.5 Climate change abatement measures
3.5.1 Abatement policies
3.5.2 Abatement share
3.5.3 Deforestation control and afforestation
3.6 Summary: CoCEB, the Coupled Climate-Economy-Biosphere model
NUMERICAL SIMULATIONS AND ABATEMENT RESULTS
4.1 Experimental design
4.2 Integrations without and with investment in low-carbon technologies and with no CCS, biomass or deforestation control
4.3 Control integration: run with biomass, no CCS and no deforestation control (new BAU) 52
4.4 Using CCS methods but no deforestation control
4.5 Integrations with inclusion of deforestation control
4.6 A mix of mitigation measures
SENSITIVITY ANALYSIS
5.1 Damage function parameters m1 and χ
5.2 Robustness to changes in the low-carbon abatement efficiency parameter ατ
5.3 Robustness to changes in the CCS abatement efficiency parameter αω
5.4 Robustness to changes in the deforestation control cost parameters
CONCLUSIONS AND WAY FORWARD
6.1 Summary
6.2 Discussion
REFERENCES
APPENDICES
Appendix 1: Table of conversions
Appendix 2: Abstracts of selected publications

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