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Uncertainty in Greenhouse Emissions and Costs of Atmospheric Stabilization

We explore the uncertainty in projections of emissions, and costs of atmospheric stabilization applying the MIT Emissions Prediction and Policy Analysis model, a computable general equilibrium model of the global economy. Monte Carlo simulation with Latin Hypercube Sampling is applied to draw 400 samples from probability distributions for 100 parameters in the EPPA model, including labor productivity growth rates, energy efficiency trends, elasticities of substitution, costs of advanced technologies, fossil fuel resource availability, and trends in emissions factors for urban pollutants. The resulting uncertainty in emissions and global costs is explored under a scenario assuming no climate policy and four different targets for stabilization of atmospheric greenhouse gas concentrations. We find that most of the IPCC emissions scenarios are outside the 90% probability range of emissions in the absence of climate policy, and are consistent with atmospheric stabilization scenarios. We find considerable uncertainty in the emissions prices under stabilization. For example, the CO2 price in 2060 under an emissions constraint targeted to achieve stabilization at 650 ppm has a 90% range of $14 to $88 per ton CO2, and a 450 ppm target in 2060 has a range of $241 to $758. We also explore the relative contribution of uncertainty in different parameters to the resulting uncertainty in emissions and costs and find that, despite the significant uncertainty in future energy supply technologies, the largest drivers of uncertainty in costs of atmospheric stabilization are energy demand parameters, including elasticities of substitution and energy efficiency trends.

Uncertainty in the oceanic heat and carbon uptake and its impact on climate projections

The impact of uncertainty in the rate of heat and carbon uptake by the deep ocean on climate response to increases in greenhouse gas concentrations is studied by means of numerical simulations with the two-dimensional climate-chemistry model developed in the framework of the MIT Global Change Joint Program. This model incorporates parameterizations of most physical processes, includes fully interactive atmospheric chemistry and calculates carbon uptake by the ocean and, therefore, simulates the main nonlinear interactions taking place in the climate system. At the same time, it is much more computationally efficient than coupled atmosphere-ocean general circulation models. Results of the simulations with calculated CO2 concentrations are compared with those of simulations with a prescribed CO2 increase. This comparison shows that the uncertainty in the increase in global mean surface temperature due to uncertainty in the rate of oceanic heat uptake is enhanced by taking into account the related uncertainty in oceanic carbon uptake, while the uncertainty in sea level rise is decreased.

© 1998 American Geophysical Union

Uncertainty in the Oceanic Heat and Carbon Uptake and Their Impact on Climate Projections

The impact of uncertainty in the rate of heat and carbon uptake by the deep ocean on climate response to increases in greenhouse gas concentrations is studied by means of numerical simulations with the two-dimensional climate-chemistry model developed in the framework of the MIT Global Change Joint Program. This model incorporates parameterizations of most physical processes, includes fully interactive atmospheric chemistry and calculates carbon uptake by the ocean and, therefore, simulates the main nonlinear interactions taking place in the climate system. At the same time, it is much more computationally efficient than coupled atmosphere-ocean general circulation models. Results of the simulations with calculated CO2 concentrations are compared with those of simulations with a prescribed CO2 increase. This comparison shows that the uncertainty in the increase in global mean surface temperature due to uncertainty in the rate of oceanic heat uptake is enhanced by taking into account the related uncertainty in oceanic carbon uptake, while the uncertainty in sea level rise is decreased.

Unemployment Effects of Climate Policy

This paper models the unemployment effects of restrictions on greenhouse gas emissions, embodying two of the most significant types of short term economic imperfections that generate unemployment: sectoral rigidities in labor mobility and sectoral rigidities in wage adjustments. A labor policy is also analyzed that would reduce the direct negative economic effects of the emissions restrictions.
    The politics of limiting greenhouse gas emissions are often dominated by relatively short term considerations. Yet the current economic modeling of emissions limitations does not embody economic features that are likely to be particularly important in the short term, in particular, the politically sensitive unemployment rate. Moreover, only a few of these studies also consider policies that would offset the negative direct economic effects of emissions restrictions. For plausible estimates of the parameters, the model shows that, with the labor market imperfections, if there were no offsetting policies, the reductions in GNP in the U.S. in the first ten years after emissions restrictions were imposed would be as much as 4 per cent. However, if there were two policies, instead of just one: a counteracting labor market policy, as well as the emissions restrictions, the negative direct economic effects could be completely eliminated.

© 2007 Elsevier Ltd.

Unemployment Effects of Climate Policy

This paper models the unemployment effects of restrictions on greenhouse gas emissions, embodying two of the most significant types of short term economic imperfections that generate unemployment: sectoral rigidities in labor mobility and sectoral rigidities in wage adjustments. A labor policy is also analyzed that would reduce the direct negative economic effects of the emissions restrictions.
    The politics of limiting greenhouse gas emissions are often dominated by relatively short term considerations. Yet the current economic modeling of emissions limitations does not embody economic features that are likely to be particularly important in the short term, in particular, the politically sensitive unemployment rate. Moreover, only a few of these studies also consider policies that would offset the negative direct economic effects of emissions restrictions. For plausible estimates of the parameters, the model shows that, with the labor market imperfections, if there were no offsetting policies, the reductions in GNP in the U.S. in the first ten years after emissions restrictions were imposed would be as much as 4 per cent. However, if there were two policies, instead of just one: a counteracting labor market policy, as well as the emissions restrictions, the negative direct economic effects could be completely eliminated.

Unintended Environmental Consequences of a Global Biofuels Program

Biofuels are being promoted as an important part of the global energy mix to meet the climate change challenge. The environmental costs of biofuels produced with current technologies at small scales have been studied, but little research has been done on the consequences of an aggressive global biofuels program with advanced technologies using cellulosic feedstocks. Here, with simulation modeling, we explore two scenarios for cellulosic biofuels production and find that both could contribute substantially to future global-scale energy needs, but with significant unintended environmental consequences. As the land supply is squeezed to make way for vast areas of biofuels crops, the global landscape is defined by either the clearing of large swathes of natural forest, or the intensification of agricultural operations worldwide. The greenhouse gas implications of land-use conversion differ substantially between the two scenarios, but in both, numerous biodiversity hotspots suffer from serious habitat loss. Cellulosic biofuels may yet serve as a crucial wedge in the solution to the climate change problem, but must be deployed with caution so as not to jeopardize biodiversity, compromise ecosystems services, or undermine climate policy.

Urban-Scale Impacts on the Global-Scale Composition and Climate Effects of Anthropogenic Aerosols

A reduced form metamodel has been produced to simulate the effects of physical, chemical, and meteorological processing of highly reactive trace species in hypothetical urban areas, which is capable of efficiently simulating the urban concentration, surface deposition, and net mass flux of these species. A polynomial chaos expansion and the probabilistic collocation method have been used for the metamodel, and its coefficients were fit so as to be applicable under a broad range of present-day and future conditions. The inputs upon which this metamodel have been formed are based on a combination of physical properties (average temperature, diurnal temperature range, date, and latitude), anthropogenic properties (patterns and amounts of emissions), and the surrounding environment (background concentrations of certain species).

Probability Distribution Functions (PDFs) of the inputs were used to run a detailed parent chemical and physical model, the Comprehensive Air Quality Model with Extensions (CAMx), thousands of times. Outputs from these runs were used in turn to both determine the coefficients of and test the precision of the metamodel, as compared with the detailed parent model. The deviations between the metamodel and the parent mode for many important species (O3, CO, NOx, and BC) were found to have a weighted RMS error less than 10% in all cases, with many of the specific cases having a weighted RMS error less than 1%. Some of the other important species (VOCs, PAN, OC, and sulfate aerosol) usually have their weighted RMS error less than 10% as well, except for a small number of cases. These cases, in which the highly non-linear nature of the processing is too large for the third order metamodel to give an accurate fit, are explained in terms of the complexity and non-linearity of the physical, chemical, and meteorological processing. In addition, for those species in which good fits have not been obtained, the program has been designed in such a way that values which are not physically realistic are flagged.

Sensitivity tests have been performed, to observe the response of the 16 metamodels (4 different meteorologies and 4 different urban types) to a broad set of potential inputs. These results were compared with observations of O3, CO, formaldehyde, BC, and PM10 from a few well observed urban areas, and in most of the cases, the output distributions were found to be within ranges of the observations.

Overall, a set of efficient and robust metamodels have been generated which are capable of simulating the effects of various physical, chemical, and meteorological processing, and capable of determining the urban concentrations, mole fractions, and fluxes of species, important to human health and the climate.

The point of developing these computationally efficient metamodels of urban processing is so that they can be used in the context of global modeling efforts. In specific, urban-scale processing has long been excluded in global 3D chemical transport models due to its large computational demands. In this thesis, the metamodel is used to simulate this processing, and compare a set of results against the more traditional approach of dilution of emissions into large grid boxes. This metamodel provides a tool to simulate, in a global 3D model, the effects of cities around the world on aerosol chemistry, physics, and radiative effects at the global scale.

It is then demonstrated that a significant Bias Error = (Dilution Approach - Urban Processing) / Urban Processing is incurred due to the ignoring of urban processing. Specifically, the globally averaged monthly minimum, monthly maximum, and monthly average bias error caused by ignoring urban processing on the total aerosol surface concentration (+0.23, +0.28, and +0.26), the total aerosol column abundance (+0.43, +0.61, and +0.51), the AOD (+0.35, +0.50, and +0.42), and the AAOD (+0.01, +0.18, and +0.09), respectively. This leads to a significant Error = (Dilution Approach - Urban Processing) for the globally averaged monthly minimum, monthly maximum, and monthly average error for the top of the atmosphere radiative forcing (−0.414, −0.168, and −0.272 W/m2), the surface radiative forcing (−1.02, −0.352, and −0.448 W/m2), and the atmospheric radiative forcing (−0.004, +0.849, and +0.176 W/m2), respectively. These results show that failure to consider urban scale processing leads to significantly more negative aerosol radiative forcing in the dilution case, as compared to when detailed urban scale processing is considered.

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