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Assessing the costs of historical inaction on climate change
nature.com
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Full size table “each parameter configuration has its own effective baseline and costs are calculated for each parameter set relative to that baseline.” All the simulations considered here start in the year 1970. Delayed mitigation simulations are implemented by fixing the DICE emissions control rate (MIU) at zero until a given date, after which the model can freely choose that control rate for each year of the simulation. Optimal solutions are found by maximizing the 1970 present value of welfare over the entire simulation period (that is, the discounted sum of welfare from 1970 through 2150, the end of the simulation period). Welfare is negatively impacted by both emissions reduction costs and by climate damages.
Introduction “it is necessary to establish a framework which is capable of estimating the costs and benefits of different possible future actions.” A subset of integrated assessment models (IAMs), so-called benefit-cost IAMs3, are the primary type of research tool used to evaluate multiple costs and benefits of climate change policies4. Their representation of economic damages from climate change impacts, however, is usually defined as aggregate relationships between global average warming levels and damages whose parameters are defined by meta-analysis5. These “damage functions” are uncertain: there is physical model disagreement on how some key future economic damages such as extreme rainfall will scale with temperature6, and Earth System Models generally lack the complexity to fully resolve the dynamic ice-sheet processes necessary to capture the range of possible sea level rise7. From a socio-economic perspective, there is wide uncertainty in how changes in the climate system translate to impacts on the economy, and how that translation will depend on the changing vulnerability of society over time8,9. Damage functions can be estimated either on the basis of “bottom up” sectoral impact projections10,11,12,13,14 or by “top-down” methods based on statistical analysis of historical temperature-GDP relationships15,16. Representing damages at the process level is done in some “detailed process” IAMs for specific sectors (Weyant, 2017). Fuller incorporation would require coupled consideration of human and physical systems which influence factors such as agricultural productivity and habitability of environments17,18 and such capacity, where it exists at all, only describes a subset of human-climate interactions19. As a result, a wide range of climate damage functions can be justified in economic models20, and damages for a given level of warming can vary by an order of magnitude depending on the assumptions made21. The damage function in the DICE (Dynamic Integrated Climate-Economy) 2013R model used in this study22 can be modulated to span the range of damages observed in the literature (see Fig. 1(b)).
In order to make informed decisions on future emissions and climate adaptation policy, it is necessary to establish a framework which is capable of estimating the costs and benefits of different possible future actions. Such a framework is required to justify actions or in-actions in the present informed by potential costs of mitigation, adaptation, or impacts which might be incurred in the future. Such a process also allows the potential costs arising from greenhouse gas emissions to be incorporated into an established economic system, by defining a “social cost of carbon” quantifying future damages (costs of impacts) from present day emissions1,2. “As a result, a wide range of climate damage functions can be justified in economic models20, and damages for a given level of warming can vary by an order of magnitude depending on the assumptions made21.” Illustration of calibration for the (a) abatement backstop and (b) climate damage quadratic coefficient parameter range in the parameter ensemble. Points in (a) show each available member of the AR5 parameter ensemble which reports both emissions and costs as fraction of GWP in 2050. Curves show a parameter sweep of the initial 1970 backstop cost using the range in Table 1. Backstop costs decline exponentially by year at the default DICE rate of 2.5% per year - such that 2010 values are 36% of the 1970 values. Black lines in (b) illustrate the climate damages (expressed as a fraction of GWP) associated with different levels of global mean warming above pre-industrial, using the default DICE quadratic damage function. The quadratic coefficient is modulated according to the range in Table 1 to produce the family of curves illustrated here. The range is adjusted empirically to be representative of uncertainty in climate damages at 5C of warming relative to pre-industrial using estimates from5. Central estimates of GDP impacts at different warming levels are shown from a number of studies for context10,11,12,13,14,15.
Full size table “each parameter configuration has its own effective baseline and costs are calculated for each parameter set relative to that baseline.” All the simulations considered here start in the year 1970. Delayed mitigation simulations are implemented by fixing the DICE emissions control rate (MIU) at zero until a given date, after which the model can freely choose that control rate for each year of the simulation. Optimal solutions are found by maximizing the 1970 present value of welfare over the entire simulation period (that is, the discounted sum of welfare from 1970 through 2150, the end of the simulation period). Welfare is negatively impacted by both emissions reduction costs and by climate damages.