This study uses the global climate–economy–biosphere (CoCEB) model developed in Part 1 to investigate economic aspects of deforestation control and carbon sequestration in forests, as well as the efficiency of carbon capture and storage (CCS) technologies as policy measures for climate change mitigation. We assume – as in Part 1 – that replacement of one technology with another occurs in terms of a logistic law, so that the same law also governs the dynamics of reduction in carbon dioxide emission using CCS technologies. In order to take into account the effect of deforestation control, a slightly more complex description of the carbon cycle than in Part 1 is needed. Consequently, we add a biomass equation into the CoCEB model and analyze the ensuing feedbacks and their effects on per capita gross domestic product (GDP) growth. Integrating biomass into the CoCEB and applying deforestation control as well as CCS technologies has the following results: (i) low investment in CCS contributes to reducing industrial carbon emissions and to increasing GDP, but further investment leads to a smaller reduction in emissions, as well as in the incremental GDP growth; and (ii) enhanced deforestation control contributes to a reduction in both deforestation emissions and in atmospheric carbon dioxide concentration, thus reducing the impacts of climate change and contributing to a slight appreciation of GDP growth. This effect is however very small compared to that of low-carbon technologies or CCS. We also find that the result in (i) is very sensitive to the formulation of CCS costs, while to the contrary, the results for deforestation control are less sensitive.
This paper is the second part of a two-part study that formulates,
tests, and applies a simplified Coupled Climate–Economy–Biosphere
(CoCEB) model. Part 1 of the study (Ogutu et al., 2015; hereafter
Paper 1) presented the model structure and the coupling of economic
equations and physical equations. The economic activities are
represented through a Cobb–Douglas output function with constant
returns to scale of the two factors of production: per capita physical
capital
In this part, we introduce in the model a simulation of the reduction
of
Most of the scenario studies that aim to identify and evaluate climate change mitigation strategies (e.g. Hourcade and Shukla, 2001; Morita et al., 2001) focus on the energy sector (van Vuuren et al., 2006, p. 166). Examples of studies that focus on the energy sector are the RICE (Regional Dynamic Integrated model of Climate and the Economy) and DICE (Dynamic Integrated model of Climate and the Economy) (Nordhaus and Boyer, 2000) models, which consider emissions from deforestation as exogenous (see also, Tol, 2010, p. 97). Nevertheless, greenhouse gas (GHG) emissions from deforestation and current terrestrial uptake are significant, so including GHG mitigation in the biota sinks has to be considered within integrated assessment models (IAMs), cf. Wise et al. (2009).
Several studies provide evidence that forest carbon sequestration can
reduce atmospheric
Our goal is to build a reduced-complexity model that incorporates the climate–economy–biosphere (CoCEB) interactions and feedbacks, while using the smallest number of variables and equations needed to capture the main mechanisms involved in the evolution of the coupled system. We merely wish to trade greater detail for more flexibility in the analysis of the dynamical interactions between the different variables. Our CoCEB model is not a quantitative tool for climate change impacts: it is an exercise in simplicity and transparency. The modeling framework here 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.
In Paper 1, we analyzed the abatement share and considered abatement activities to be geared toward investment in increase of overall energy efficiency of the economy and decrease of overall carbon intensity of the energy system. In this paper, we study relevant economic aspects of deforestation control and carbon sequestration in forests, as well as the widespread application of CCS technologies as alternative policy measures for climate change mitigation.
We seek to show that: (i) low investment in CCS contributes to
reducing industrial carbon emissions and to increasing GDP growth, but
further investment leads to a smaller reduction in emissions, as well
as in the incremental GDP growth. (ii) Enhanced deforestation control
contributes to a reduction in both deforestation emissions and
atmospheric
A large range of hypotheses on CCS costs appears in the literature, and our modeling framework permits to span this range and check the sensitivity of results.
In the next section, we briefly revisit the CoCEB model as developed in Paper 1 for completeness. In Sect. 2, we introduce the biomass equation and the effect on the carbon emissions of CCS and of deforestation control. Section 3 presents the numerical simulations and their results. In Sect. 4, we test the sensitivity of the results to the parameters setting the price of CCS and of deforestation control. Section 5 summarizes, discusses the results, and formulates our conclusions with caveats and avenues for future research.
The climate–economy part of the CoCEB model is represented by five
variables: per capita physical capital
The model is reproduced below:
In Paper 1, the formulation of industrial
There is uncertainty regarding the costs of carbon capture,
transportation and storage (Morita et al., 2000, 2001; IPCC, 2005,
p. 354; Al-Juaied and Whitmore, 2009; Kalkuhl et al., 2015). The total
cost of abating carbon through CCS is subject to research: very
diverse estimates have been reported in the recent literature. These
estimates span the wide range given by USD 71–615
The estimated
Keeping in mind this range of emissions reduction and of prices, we
calibrated the parameter
Uzawa (1991, 2003) extended the analysis of the
In order to include fertilization effects in the Uzawa model, van
Wassenhove (2000) proposed a model of the interaction between biomass
and
Equation (13) is not different from the
This section follows the work of Eriksson (2013) who investigated the role of the forest in an IAM of the climate and the economy. In that work, deforestation does not change the growth rate but leads to a smaller stock of biomass – which is subject to that growth – as well as to a smaller carrying capacity, i.e. a smaller area where forest can potentially re-grow.
Deforestation is formulated in terms of forest biomass volume and not
in terms of land area. The maximum forest biomass carrying capacity is
modeled to decrease with deforestation as follows:
Deforestation is considered exogenous; we model it in our CoCEB model
in agreement with Nordhaus and Boyer (2000), who prescribed carbon
emissions from deforestation to decrease in time according to:
The total carbon emissions are hence assumed here to be the sum of
industrial fossil fuel use emissions
The rental cost – that is, the rental payment to the landowner to
hinder conversion of forested land – of avoiding direct release of
carbon in one time period is given by the marginal cost function
(Kindermann et al., 2008; Eriksson, 2013):
The total cost of avoiding deforestation can be written as
The capital stock is hence assumed to grow with investment in land,
i.e. conversion of land to agricultural land and urbanization or
infrastructure. Deforestation is mainly caused by these two types of
conversions, and hence the capital stock increases with
deforestation. The accumulated investment in land is here assumed to
be implicit in the total capital stock and does not affect the
development of the total capital stock when following the baseline
deforestation pattern. Reducing the baseline deforestation is here
equivalent to a disinvestment of land capital resulting in a smaller
net investment in the total capital stock. The per capita cost of
avoiding deforestation is thus
Through a meta-analysis of published works, Phan et al. (2014)
estimated the cost of carbon emissions reduction due to deforestation
control to range from 0.11 to USD 246
Following the latter result, we take
Finally, including the biosphere module and deforestation control, the
evolution of total per capita capital accumulation
The model is now described by Eqs. (1b), (1c), (9), (12), (13), and
(20). The equations are grouped for the reader's convenience below:
As in Paper 1, we confine our investigations to the transition path
for the next 110
The scenarios studied herein are summarized in Table 2. We perform 33 integrations: the first is a control integration, with biomass evolution included but no CCS and no deforestation control. This run is equivalent to a Business as Usual (BAU) simulation in the IPCC terminology, but not the same as the BAU run described in Paper 1. The difference lies in the presence of interactive biomass that exchanges carbon with the atmosphere.
Next we perform 12 integrations using CCS investments but no
deforestation control,
The values of
In Table 3, a summary of the behavior of the BAU integration with
inclusion of the biomass is shown. The results of the BAU integration
of Paper 1 (reported in the 1st line of the table for comparison) and
in the present paper's BAU are qualitatively similar, yet the new BAU
has
There is no contradiction in the fact that these higher
The model's behavior in response to inclusion of biomass agrees with
Mackey et al.'s (2013) claims that the capacity of terrestrial
ecosystems to store carbon is finite and that the current
sequestration potential primarily reflects depletion due to past
land-use. Therefore, avoiding emissions from land carbon stocks and
refilling depleted stocks reduces atmospheric
The effects of including CCS into the model, via a fraction
On the other hand, when
The inclusion of CCS investment tends to reduce industrial
From the table, we notice that 100 % investment in CCS, i.e.
In the
In Fig. 1, the time-dependent evolution of the reduction in
In Table 5, the CCS investment share is taken to be 0 and we analyze
the effect of increasing deforestation control with different values
of
For instance, we note that increasing
The reduction in atmospheric
Even though it is beyond this study's ability to predict a realistic international emissions mitigation regime, CoCEB simulations suggest that best results are obtained by combining the various mitigation measures discussed. This was found in Table 4 and Fig. 1, where we noted that 100 % investment in CCS or low-carbon technologies is slightly less efficient than the combined investment in both technologies.
For illustration purposes, we chose now a 30 % investment in CCS
technologies and a deforestation control of
For the scenarios corresponding to
Figure 2 plots the per capita GDP growth curves with time for the
Later though, as the damages from climate change accumulate on the BAU path, GDP growth in the BAU scenario slows down and falls below the level on the other paths, i.e. the paths cross and mitigation strategies pay off in the longer run. We also observe that the growth in Fig. 2b – with 30 % investment in CCS technologies and 70 % investment in low-carbon technologies, together with a deforestation control of 10 % – is slightly higher than that in Fig. 2a.
The estimates for the cost of CCS and of deforestation control are
still very uncertain in the mitigation literature. For this reason, we
conducted an analysis to ascertain the robustness of the CoCEB model's
results and to clarify the degree to which they depend on two key
parameters: the CCS abatement efficiency parameter
We modify the value of the parameter
Each entry in the table – for total emissions reduced, CCS abatement
cost, and the per capita GDP growth – appears as three numbers: the
standard integrated values for
Comparing the efficiency of CCS and low-carbon technologies, which depend on their cost estimation, we note that given the uncertainties, low-carbon can be either slightly more efficient or equally efficient. The qualitative result that a mix of the two is better than 100 % of the one or 100 % of the other is quite robust.
Taking
We now vary simultaneously the
In this paper we described the completion of the CoCEB model by the
addition of the biomass equation and the related exchanges of
This extended version of the CoCEB model has been used here to investigate the relationship between the long-term effects of using CCS and deforestation control, and the long-term growth rate of the economy under threat from climate change–related damages. The abatement share and investment in low-carbon technologies was considered in Paper 1. The framework developed allows one to investigate policy sensitivity to the choice of key parameters. We analyzed in particular the effect of the parameters setting the costs of the different means of climate change mitigation: in the present work, the parameter values tested spanned the range of cost values found in the mitigation literature.
We have shown that: (i) low investment in CCS contributed to
a reduction in industrial carbon emissions and to an increase in GDP
growth, but a further investment leads to a decrease in the reduction
of emissions, as well as in the incremental GDP growth, (ii) enhanced
deforestation control contributes to a reduction in both deforestation
emissions and atmospheric
We found that per capita GDP growth on the paths with nonzero abatement share lies below growth on the Business as Usual (BAU) path for the earlier time period, approximately for 1990 to 2060, while GDP growth in the BAU scenario slows down and falls below the level on the other paths, i.e. the paths cross and mitigation strategies pay off in the longer run.
In the climate modeling literature, the role of a full hierarchy of models, from the simplest to the most detailed ones, is well understood (e.g. Schneider and Dickinson, 1974; Ghil, 2001, and references therein). There is an even greater need for such a hierarchy to deal with the higher-complexity problems at the interface of the physico-chemical climate sciences and of socio-economic policy.
The CoCEB model lies toward the highly idealized end of such a hierarchy: it cannot, nor does it claim to, represent the details of the real world, but its simplicity is also a strength. Simple models do not allow one to provide a quantitative description of the fully coupled dynamics of the real climate–economy–biosphere system; on the other hand, though, the study of such models makes it possible to understand the qualitative mechanisms of the coupled-system processes and to evaluate their possible consequences.
More than just a simple model, CoCEB is a formal framework in which it is possible to represent in a simple way several components of the coupled system and their interactions. In this paper, we showed as an example how to insert the effects of CCS and deforestation control. Several choices are possible in modeling these effects.
In this paper, formulations taken from the literature have been integrated into the CoCEB framework. Doing so allowed us to treat low-carbon technologies, CCS and deforestation control consistently, and to translate the range of uncertainties on their relative cost into long-term effects on the climatic and economic system. The CoCEB framework also allowed us to evaluate the sensitivity of the results on the cost parameters.
Given the recent scientific evidence on global warming and its consequences, as documented in the numerous IPCC reports, the importance of climate change mitigation policies represents by now a consensus that is widely accepted by the climate community. Delaying action may mean that high temperatures and low growth are approached on a path that becomes irreversible. To prevent human society's engaging on such a path, the IPCC reports (IPCC, 1995, 2007a, 2014) propose a significant number of policy measures to prevent further emission of GHGs and a further rise of global temperature.
As measures leading toward a low-carbon economy, the IPCC Fourth
Assessment Report emphasizes the role of technology policies to
achieve lower
Forestry policies, particularly reduced deforestation, also emerge as
additional low-cost measures for the reduction of carbon
emissions. Reduced deforestation would cut carbon emissions and
increased afforestation would sequester
In the present study and in Paper 1, we considered technological abatement activities, as well as deforestation control to reduce the sources and enhance the sinks of GHGs, thereby lessening the radiative forcing that leads to temperature rise and economic impacts. Our results indicate that a pure CCS policy or a pure low-carbon technologies policy carry their own specific risks of being less efficient in combating climate change, a sentiment echoed by Riahi et al. (2004a, b), Uyterlinde et al. (2006), Akashi et al. (2014), Kalkuhl et al. (2015), among others.
Through our CoCEB framework, we have demonstrated that best results are obtained by combining the various mitigation measures discussed in this study, i.e. high investment in low-carbon technologies and low investment in CCS technologies, as well as inclusion of deforestation control. While we have also shown that certain results are robust to very substantial variations in parameter values, uncertainties do remain. Further research is, therefore, necessary, to reduce these uncertainties in the cost of the CCS technologies and of deforestation control.
Recent academic work has argued for a greater urgency to implement effective climate policies to combat climate change. Yet, to the best of our knowledge, no study has sufficiently explored the possibility of bringing together all the three mitigation measures under one coherent framework – including their impact on economic growth – as suggested here.
Another essential issue that has not been sufficiently addressed so far is how to reconcile and couple the IPCC's Representative Concentration Pathways (RCPs) and the Shared Socio-economic Pathways (SSPs) being developed in the framework of more detailed integrated assessment models (IAMs) by the impacts, adaptation, and vulnerability communities; see Ebi et al. (2014); Kriegler et al. (2014); O'Neill et al. (2014); Rozenberg et al. (2014); Vuuren et al. (2014). We hope this study will serve as an illustrative pointer in this direction.
The CoCEB model can be extended in several directions. The next most interesting item on the research agenda is to let the biomass colonization rate and human population growth depend on the availability and quality of water, and to investigate how this will affect model feedbacks. Doing so will require a simple treatment of the water cycle.
Furthermore, the CoCEB model can be regionalized, while maintaining its essential simplicity. For example, one might want to establish separate energy balance modules for the tropical and extratropical areas, and extend a similar separation to the economic module.
Finally, even though there are several truly coupled IAMs (e.g. Nordhaus and Boyer, 1998; Ambrosi et al., 2003; Stern, 2007), these IAMs disregard variability and represent both climate and the economy as a succession of equilibrium states without endogenous dynamics. This can be overcome by introducing business cycles into the economic module (e.g. Akaev, 2007; Hallegatte et al., 2008) and by taking them into account in considering the impact of both natural, climate-related and purely economic shocks (Hallegatte and Ghil, 2008; Groth et al., 2014).
This work was supported by Dedan Kimathi University of Technology (DeKUT) and the Embassy of France in Kenya, whose views it does not claim to represent.
List of new variables with respect to Paper 1, parameters and their values.
The scenarios studied herein.
Variable values for year 2100 for the
model with no biomass (
Variable values for year 2100 with deforestation emissions in parentheses, for the runs with investment in CCS scenario.
Variable values for year 2100, with deforestation emissions in parenthesis, for runs with inclusion of deforestation control scenario.
Target values of key variables for our
policy scenarios at year 2100, with
Effect of varying
Evolution in time of reduction in
GDP growth over time, with biomass module (