This article addresses one specific criticism that can be raised against economic climate change impact assessments conducted with Computable General Equilibrium (CGE) models: that of overly optimistic assumptions about markets’ ability to react to climate change induced shocks, i.e. market-driven adaptation. These models indeed usually assume frictionless and instantaneous adjustments to a new equilibrium. We demonstrate that these frictions could increase climate change costs from 0.64% to 0.87% of Gross World Product (GWP).
Key words: Climate Change Costs, Adaptation, Computable General Equilibrium Models
JEL classification: C68, Q54
This research project has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement n°266992 (Global IQ).
Suggested citation: Bosello, Francesco and Parrado, Ramiro, Climate Change Impacts and Limited Market-driven Adaptation (September 25, 2014). Review of Environment, Energy and Economics (Re3), http://dx.doi.org/10.7711/feemre3.2014.09.004
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For more than twenty years Integrated Assessment (IA) research has produced a vast literature on the cost estimates of climate change (see e.g.: IPCC, 1996, 2001, 2007; Stern 2007; Tol 2008, 2009, 2011, 2014). Briefly summarizing, climate change impacts on Gross World Product (GWP) seem to be moderately negative (reaching at the maximum the 2% of GDP; IPCC, 2007) or, according to some studies (Tol, 2002b; Mendelsohn et al., 2000), even slightly positive for temperature increases below the 2°C. Beyond that increase, they become unambiguously negative in response to a (roughly) 3°C warming, to then increase more than proportionally with temperature. The literature also stresses relevant regional asymmetries in impacts with developing countries incurring in GDP losses even at low levels of warming and when net benefits can still be observed for the world as a whole.
Also due to this crucial policy relevance, a heated debate surrounds these estimates and many authors suggest that they are likely to underestimate climate change costs. Namely, many features of climate change and dynamics are still uncertain and/or not well captured by IA Models (IAM). For instance, relatively small changes in climate sensitivity can greatly change their cost estimates (Ackermann and Stanton, 2012; Anthoff and Tol, 2013). Quantitative modeling frameworks are also ill suited to measure important social phenomena like conflicts, mass migrations, disruption of knowledge, learning and social capital potentially triggered by climate change (Anthoff and Tol, 2013; Stern, 2013). IAMs emphasize impacts on GDP, which even disregarding its deficiency as a welfare measure, capture flow but tend to overlook stock losses (Stern 2013). Risk and irreversibility associated to high damage low probability events are usually left out of the analysis. Finally, IAMs tend to be overly optimistic in describing timing and scale of adaptation processes, disregarding the fact that, while adapting, agents may not use perfect information and due to technological, economic, psychological and cultural characteristics may resist to some changes (Patt et al. 2010). This last critique is also relevant to Computable General Equilibrium (CGE) models which have been increasingly applied to climate change impact assessment. These models provide an assessment of “market-driven” adaptation since they are able to capture market adjustments triggered by price signals. However, those adjustments typically take place instantaneously and without frictions.
In this article we present a simple exercise to show that similar reasoning can be applied introducing limits in the ability of the world economic system to autonomously react to climate change shocks. We first run a standard climate change impact assessment exercise with a recursive-dynamic CGE model using updated estimates of climate change impacts. Then we perform the same exercise reducing the “market-driven” adaptation flexibility of the model. We do this by introducing some frictions in the productive processes, labor markets as well as international trade.
2. Assessment scenarios
The impact assessment is performed with ICES, a recursive-dynamic CGE which have been extensively used to economically assess climate change impacts (Bosello and Zhang, 2006; Bosello et al., 2006, 2007, 2008; Eboli et al., 2010; Bosello et. al. 2012a, 2012b, Bosello and Parrado, 2014). The social-economic reference for the analysis is the SSP2 – “Middle of the Road or Dynamics as Usual” scenario of the Shared Social Economic Pathways (O’Neill et al., 2014). This exercise considers an extended set of climate change impacts, referring to the consequences of changes in: sea level, fish stock productivity, land productivity, tourism flows, energy demand, health status and ecosystem services. Source information are bottom-up partial-equilibrium exercises performed within the framework of recently concluded and ongoing EU Sixth and Seventh Framework Program (FP6 and FP7) research projects: ClimateCost, SESAME and Global-IQ. Table 1 provides a summary of the impacts considered as well as their sources. All studies have a global coverage and, in their majority report data with a high spatial resolution. Impacts are computed for temperature increases consistent with the Representative Concentration Pathways (RCPs) 2.6, 6.0, and 8.5 (Van Vuuren et al., 2012). Thus, when necessary, impacts consistent with each RCP have been reconstructed mapping the temperature of A1B and B2 scenarios following the average trend of temperature changes for the three RCPs. They have not been obtained by direct re-running of the sectoral/bottom-up impact models.
Table 1 - Summary of climate change impacts
3. Climate change impacts in a context of full market adaptation
The impact assessment of market damages considers a framework where economic responses are those coming only from market mechanisms at play in ICES, and therefore, they could be regarded as full market adaptation. This kind of assessment provides the cost of inaction in dealing with climate change. The following results refer to the economic effects of all impacts above mentioned jointly imposed over the reference scenario. It is important to stress that these are market impacts (or damages) given that that the climate-change damages assessed relate to a smooth and non-catastrophic climate change. Figure 1 reports climate change impacts on GWP. As expected, RCP 2.6, which is a stabilization scenario, produces the lower costs, while RCP 6.0 is in the middle of the range. In 2050 total costs remain small even in RCP 8.5 reporting the higher CO2 concentrations. In that year, temperature increase is just slightly beyond the 2.5°C increase. In this context, impacts are still manageable and roughly amount to 0.64% of GDP.
Figure 1 - Climate change impacts on Gross World Product (GWP)
4. Introducing rigidities in market-driven adaptation
We model a more difficult market-driven adaptation working on three different “stages” of the production activity and focusing on three channels. The first deals with productive processes where we introduce a friction by reducing the ability of each economic sector to combine mainly primary production factors such as land, labour, capital as well as energy (Reduced factor substitution). The rigidity for the second channel works through the reduction of labour mobility across sectors within a region (Limited labour mobility). In the third case, we reduce the ability of the model to simulate international trade flows, so it is more difficult to use imported commodities either for production processes or for final consumption (Limited trade). In a final experiment, we combine all three rigidities in a worst-case scenario (All rigidities).
The climate change impacts are those calculated for the RCP 8.5. The aim of this exercise is to explicit the role of market-driven adaptation in impact smoothing, and to investigate what the consequences could be if it were more difficult than assumed by the model. In this context, for instance the possibility to move freely and instantaneously labour force form a shrinking to an expanding sector seems particularly unrealistic. But also the degree of substitutability between primary factors, albeit derived from calibration procedures or econometric estimations, presents a variability whose effects are important to investigate.
The inclusion of rigidities in the impact assessment reveals an increase of the costs of climate change. As shown in Figure 2, GWP losses rise from roughly 0.64% to almost 0.87%. A major driver of these peaking costs is the lower degree of substitution across primary factors, pushing alone losses to more than 0.73% of GWP in 2050. Facing a reduced ability to recombine primary factors intensifies the initial negative impact on the supply side of the economy. For instance, the reduced land productivity can only partly be compensated by using more of the remaining primary factors as in the full market adaptation case. Limiting the model’s flexibility related to international trade increases climate change costs also to 0.73% of GWP in 2050, showing a very similar impact as reducing primary factor substitution, but has a stronger effect in the initial simulation years. A reduced labour mobility increases costs just by 0.04 percentage points of global GWP in 2050.
Figure 2 - Climate change impacts on global GDP with market rigidities (RCP 8.5)
These aggregate figures however hide important regional asymmetries revealed by Figure 3. This figure compares regional breakdown of impacts of full market adaptation (red columns) against simulated limited adaptation scenarios. This highlights on the one hand the huge differentiation in regional exposure, sensitivity and adaptive capacity and on the other hand the usefulness of a disaggregated assessment. The strong message emerging is the higher vulnerability of developing countries to climate change impacts, particularly regions like South Asia and India losing more than 4% of their GDP, and Eastern Asia and Sub Saharan Africa losing roughly 2% of their GDP in 2050 in the RCP 8.5 scenario. This result is standard to and well established in the literature.
Figure 3 - Climate change impacts on regional GDP with market rigidities (RCP 8.5)
The scenarios with limited adaptation confirm the asymmetric distribution of negative impacts which are much higher in developing countries (see light blue and other columns). South Asia and India now losses around 5% of GDP. Conversely, developed countries are much less adversely affected: USA loose roughly 0.2% of GDP by limited adaptation, while European countries increase their GDP loss more than twice. In particular, introducing rigidities in primary factor substitution and labour mobility turns the initial slight gains from climate change into a slight loss for northern European countries. Nonetheless, some of the countries that in the case of full adaptation experienced net benefits from a changing climate (i.e. Russia, Canada, Japan, Russia and Brazil) might increase those gains within the limited adaptation scenarios. This last result depends upon two facts: firstly, the different rigidities produce a slightly different structural composition of the economic system. Therefore, notwithstanding the GDP is the same across the full and limited adaptation case, macrosectors weight differently and this can amplify/smooth positive/negative effects. Secondly, there are interaction effects trough international trade. Higher losses in some countries can induce higher gains in their competitors.
All in all, relatively minor deviations from the basic parameterization of the model concerning input substitutability/intersectoral mobility, are able to increase impacts by roughly 30% at the global level. This without the need to invoke, catastrophic events, risk, irreversibility, non-use value losses which naturally exist and should be accounted for as well.
Climate change is of concern in the longer term. It can be a relevant issue for developing and much less for developed countries, and albeit important, it is basically a distributional/equity issue rather than a “scale” issue. In fact, all caveats and limitations mentioned in section 1 should be considered for a correct interpretation of the results. Structural to the CGE approach is the inability to consider: i) non market losses, as these models typically record market transactions only; ii) stock losses, as they are based on GDP and more generally on flow values; iii) frictionless and instantaneous adjustment to a new equilibrium after a shock, implying an overly optimistic view of market adjustments, or differently said of market-driven adaptation.
In this article we investigated the role of market driven adaptation that CGE models explicitly capture through their endogenous price setting mechanisms in determining climate change cost estimates. Then, we introduced rigidities in market adjustments, and found that, these frictions do increase the cost of climate change impacts from 0.6% to 0.87% of GWP. This seems to point out that the “apparently” low climate change costs emphasized by CGE models only marginally depend on autonomous market adaptation mechanisms. In fact, this seems mostly due to other impacts which are usually omitted due to their proven difficulty to be included in a regular CGE assessment such as: extreme events, damages due to ecosystem services’ losses, as well as major disruptions due to the existence of tipping points.
We are highly indebted with many people and institutions who shared the results of their research and made the related data available for inclusion in the present report.
We thank Sally Brown Robert Nicholls, Athanasios Vafeidis and Jochen Hinkel who generated sea-level rise impacts as part of the EU Seventh Framework Programme project ClimateCost and the Met Office who elaborated climate change scenarios for sea-level rise within the same project.
We thank Silvana Mima who provided data on climate change impacts on energy demand elaborated during the EU Seventh Framework Programme project ClimateCost.
We thank Fraziska Piontek who provided data on climate change impacts on crop yields elaborated during the EU Seventh Framework Programme project Global IQ.
We thank Richard Tol who provided data on climate change impacts on tourism elaborated during the EU Seventh Framework Programme project ClimateCost as well as his elaborations on climate change impacts on mortality and morbidity.
Nevertheless please consider that the values reported in this document are our elaborations based on these data and accordingly ours is the only responsibility for any mistake or imprecision.
Finally the present article has been produced as part of the research conducted under the European Union’s Seventh Framework Programme (FP7/2007-2013) “Impacts Quantification of global changes (Global-IQ)” research project (Grant agreement no: 266992), whose financial support is gratefully acknowledged.
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