Control integration: run with biomass, no CCS and no deforestation control (new BAU) 52

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Background to the problem

It is widely accepted that climate change will have major impacts on humankind. Depending on the magnitude of twenty-first century climate change, human societies and ecosystems are expected to be greatly affected by climate change (IPCC 2007b) and in particular by the frequency and intensity of extreme events (e.g., Changnon et al. 1996; Ciais et al. 2005; IPCC 2012a). Negative impacts are expected on water, food, human health and conflict (IPCC 2001b, p. 238; IPCC 2007b) and ultimately economic growth (Dell et al. 2014 and the citations therein; Nordhaus 2008; Stern 2007). Global carbon dioxide (CO2) emissions, which are the largest contributor to anthropogenic climate change (Farmer and Cook 2013, p. 4; Mokhov et al. 2012; Stern 2008; Stott et al. 2000), have, to date, been highly correlated with economic output (Barker et al. 1995). As a result there is a negative feedback between climate change and economic growth that is mediated by CO2 emissions: an increase in human wealth causes an increase in emissions and global warming, but the warming damages human wealth, slowing its rise or even making it fall. Although some integrated assessment models (IAMs) do include the climate-economy-biosphere feedback albeit only weakly (Nordhaus 2008), this feedback is typically neglected in a standard climate change assessment (Soden and Held 2006), which is largely a serial process going from socioeconomic scenarios to emissions to climate change to impacts (Cox and Stephenson 2007) (see Figure 1). A feasible sensitivity of the economy to the climate results in important emergent processes and feedbacks which need to be better understood inorder to address the climate change challenge.
This study focuses on the feedbacks between the climate, economy, and biosphere systems. Because full realistic coupled climate models are so complex, analyses of the various potential feedbacks have been rather limited. Thus, potentially important mechanisms are better initially described in low or intermediate complexity models. The use of a reduced scale model in this study is meant to bring out the interplay between the climate, economy, and biosphere. General Circulation Models (GCMs) by far the most sophisticated tools for performing global climate simulations are ill-suited for the task of policy-oriented global and/or regional climate change assessment, in that the computational costs required in performing long-term simulations are largely prohibitive. Although substantial resources have been devoted to calibrating and building GCMs, there remains substantial uncertainty about many of their integral parts. Concerns about the role of clouds, the generation of precipitation, the role of ice, the interaction with oceans, soils, and the biosphere, and the role of other gases in the atmosphere remain problematic. Further, the models still struggle to reproduce the current regional climates of earth (Mendelsohn and Rosenberg 1994). Global climate models are, in addition, unable to provide the degree of flexibility, ease-of-use, and transparency that policy-oriented modeling requires. Moreover, it is impossible for the moment to incorporate large-scale climate models into decision-analytic frameworks.
A reduced-scale model was selected for its simplicity and transparency. Simple models do not allow us to make a quantitative description of the coupled climate–economy–biosphere system dynamics; conversely, the study of such models makes it possible to understand the qualitative mechanisms of the coupled system processes and to evaluate their possible consequences.
The effort undertaken in this study operated under a critical chain of assumptions (Figure 1):
! human activities will result in greenhouse gas emissions
! atmospheric CO2 concentrations will increase
! increased atmospheric CO2 concentrations will cause atmospheric warming
! atmospheric warming will threaten living conditions
! threatened living conditions will require measures to mitigate the threat
! climate change mitigation strategies will affect climate change or its impacts through a variety of additional processes
Fig. 1 Schematic of climate-economy-biosphere interactions (see also, Kellie-Smith and Cox 2013)

Statement of the problem and justification

Climate change represents one of the greatest environmental, social, and economic threats facing planet Earth today. The global climate has been changing due to human activities and is projected to keep changing even more rapidly. The consequences of climate change could be devastating,
with increased atmospheric greenhouse gas concentrations resulting in large-scale, high-impact, non-linear, and potentially abrupt and/or irreversible changes in physical and biological systems (Mitchell 2009).
In developing countries, climate change will have a significant impact on the livelihoods and living conditions of the poor. Increasing temperatures and shifting rain patterns across the Earth’s continents reduce access to food and create effects that impact regions, farming systems, households, and individuals in varying ways. Additional global changes, including changed trade patterns and energy policies, have the potential to exacerbate the negative effects of climate change on some of these systems and groups.
Thus, analyses of the biogeophysical, biogeochemical and socioeconomic factors that determine exposure, mitigation and/or adaptation, and the capacity to mitigate and/or adapt to climate change are urgently needed so that policymakers can make more informed decisions.

Objectives of the study

Global climate models offer the best approach to understanding the physical climate system. At various resolutions, they capture the basic behaviour of the physical processes that drive the climate. However, these models focus only on natural systems, and do not represent socio-economic systems that affect and are affected by natural systems. The most common approach to combining socio-economic and biophysical systems involves applying projected trends (scenarios) to “drive” the climate model. But such an approach disregards the existing dynamic feedbacks.
To bridge such gaps, the general objective of this research is to study the interactions and feedbacks between the climate, economy, and biosphere systems including the climate change related damages.
The specific objectives of the study are:
i) To develop a reduced- complexity Coupled Climate-Economy-Biosphere (CoCEB) model.
ii) Application of the reduced-scale model to examine the interactions and feedbacks between the climate, economy, and biosphere systems and the sensitivity to the implementation of the various climate change mitigation policy measures with their associated costs.

Significance of the study

The CoCEB is a formal framework in which it is possible to represent in a simple and clear way different elements of the coupled system and their interactions as well as feedbacks, while using the minimum number of variables and equations needed to capture the fundamental mechanisms involved and can thus help clarify the role of the different variables and parameters. The model developed, being an exercise in simplicity and transparency and not a predictive tool for climate change impacts, brings together and summarizes information from diverse fields in the literature on climate change mitigation measures and their associated costs, and allows comparing them in a coherent way.

Research methodology and outline of the study

The model describes the temporal dynamics of six variables: per capita physical capital K , per capita human capital H , the average global surface air temperature T , the CO2 concentration in the atmosphere C , biomass/vegetation B , and industrial CO2 emissions EY .
The study came up with a set of modules, which will be linked and will represent a crucial step in efforts to assess the influence that policy choice is likely to have on future climate. The study considered the nature of the relation between K , H , T , C , B , EY . Consequently by the use of a set of nonlinear, coupled Ordinary Differential Equations (ODEs), the temporal dynamics of these six variables are described by deriving a reduced-scale climate-economy-biosphere model composed of various modules the climate module, economy module, biosphere module that is used to explore the consequences of various climate change mitigation measures on economic growth.
The simplicity of the model makes it easier to clearly identify the relationships in the complex system. After the relationships are found, the mechanisms for these relationships are discussed and comparisons with observations or other studies, made, to evaluate their reasonability or correctness. The model structure and numerical analysis derives some of its parameters from previous climatic and economic studies (see, e.g., Eriksson 2013; Greiner 2004; Greiner and Semmler 2008; IPCC 2001a; Nordhaus 1994, 2007, 2013; Nordhaus and Boyer 2000; McGuffie and Henderson-Sellers 2005; Schwartz 2007, 2008; Uzawa 2003; van Wassenhove 2000; among others).
This chapter reviews some of the literature related to climate change modelling and integrated assessment modelling. Nowadays there are numerous climate change models; they function to predict future changes in climatic conditions and to help formulate mitigation policies. Integrated assessment models are especially useful in these regards, since they can provide insight into the interaction between different sectors of a larger system. The component models of individual sciences (natural or social) cannot do this.

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.


Integrated assessment modelling (IAM)

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).
Other policy evaluation models include Asian-Pacific Integrated Model (AIM), Model for Energy Supply Strategy Alternatives and their General Environmental impacts (MESSAGE) (Gusti et al., 2008), etc. These models are not subject to the constraints of optimization models, and therefore can incorporate greater complexity in their representations of natural and social processes at the regional scale without losing detail. Thus, they are generally applied to comparisons of the consequences (e.g., regional economic and environmental impacts) of alternative emissions scenarios. But even with these detailed descriptive capabilities, they are not appropriate to optimize the economic activities of the energy-economy sector.

Application of integrated assessment models

Integrated Assessment Modelling is usually comprehensive, but it produces less detailed models than conventional climate- or socio-economic-centred approaches. It is based on an understanding that feedbacks and interconnections in the climate-society-biosphere system drive its evolution over time (Davies and Simonovic 2008). Rotmans et al. (1997, p. 36) state that integrated assessments “are meant to frame issues and provide a context for debate. They analyze problems from a broad, synoptic perspective.”
Integrated assessment modelling is not a new concept; it rather has a long history of being applied to many problems. Over the past decade or so, integrated assessment models (IAMs) have been widely utilized to analyze the interactions between human activities and the global climate (Weyant et al. 1996). The first IPCC report referenced two IAMs, the Atmospheric Stabilization Framework from US Environmental Protection Agency (EPA) and the Integrated Model for the Assessment of the Global Environment (IMAGE) model from the Netherlands (van Vuuren et al. 2006a). These were employed to assess the factors controlling the emissions and concentrations of GHGs over the next century. Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) was then developed to account ocean heat transport and a carbon cycle component to respond the land-use change; it is a multi-box energy balance model (Meinshausen et al. 2008). Later, MAGICC modelling framework became a foundation for the IPCC process, as it can easily show the climate implications of different emissions scenarios and can be benchmarked to have climate responses that mimics those of any of the GCMs.
Rotmans et al. (1997), mention that the integrated assessment approach allows for an exploration of the interactions and feedbacks between subsystems and provides flexible and fast simulation tools. It also identifies and ranks major uncertainties, and supplies tools for communication between scientists, the public, and policy makers. Davies (2007) provides some examples of integrated assessment models including the Integrated Model to Assess the Greenhouse Effect, IMAGE 2.0 (Alcamo et al. 1994), the Asian Pacific Integrated Model, AIM (Matsuoka et al. 1995), the Model for Evaluating Regional and Global Effects of GHG reduction policies, MERGE (Manne et al. 1995), the Tool to Assess Regional and Global Environmental and health Targets for Sustainability, TARGETS (Rotmans and de Vries 1997), the Integrated Global System Model, IGSM (Prinn et al. 1999), Integrated Climate Assessment Model, ICAM (Dowlatabadi 2000), the Dynamics Integrated Climate-Economy model, DICE (Nordhaus and Boyer 2000), the Feedback-Rich Energy-Economy model, FREE (Fiddaman 1997; Fiddaman 2002), and World3 (Meadows et al. 2004). The list of IAMs and Computable General Equilibrium (CGE) models used in climate policy analyses is long. The reader can refer to Ortiz and Markandya (2009) and Stanton et al. (2008) for a literature review of some of these models.
Most IAMs consist of (i) an economy module in which the interactions among economic sectors and agents are represented; (ii) a climate module representing the relationships between GHG emissions and concentrations and temperature changes; and (iii) predetermined relationships between both modules; i.e. damage functions representing the impact of temperature changes in the economy, and abatement cost functions summarizing the available climate change mitigation options. The level of details employed in each of these components characterizes and differentiates the existing models (Ortiz et al. 2011).
It has been predicted that global climate change will have significant impacts on society and the economy, and that the adaptation measures to tackle global climate change will be accompanied with very large economic burden. It is estimated that GHG emissions will increase to over one-half of total global emissions by the end of the next century (Akhtar 2011, p. 42). The Integrated Assessment Model (IAM) provides a convenient framework for combining knowledge from a wide range of disciplines; it is one of the most effective tools to increase the interaction among these groups.

Table of contents :

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
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
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
6.1 Summary
6.2 Discussion


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