Model Uncertainty and Energy Technology Policy

Energy modeling, numerical modeling based on economic principles, has become the dominant analytical tool in U.S. energy policy. Energy models are widely used by researchers across the public and private sectors. However, the widespread application of these models in policy analysis poses challenges to decision-makers. We are developing a framework and analysis that demonstrate how non-Bayesian decision rules can address fundamental model uncertainty in the domain of energy policy, technological change, and greenhouse gas abatement.

Numerical modeling based on economic principles has become the dominant analytical tool in U.S. energy policy. Energy models are now used extensively by public agencies, private entities, and academic researchers, and in recent years have also formed the core of integrated assessment models used to analyze the relationships among the energy system, the economy, and the global climate. However, fundamental uncertainties are intrinsic in what has become the typical circumstance of multiple models embodying different representations of the energy-economy, and producing different policy-relevant outputs that model users are compelled to interpret as equally plausible and/or valid. Because the policy implications of these outputs can diverge substantially, policy-makers are confronted with a significant degree of model-based uncertainty and little or no guidance as to how it should be addressed.

This problem of “model uncertainty” has recently been the focus of work in macroeconomics, where scholars have studied the problem of how a decision-maker should proceed in the face of uncertainty re- garding the correct model of an economic system that is the object of policy. We focus on analyzing a low-dimensional numerical integrated assessment model using the “minimax regret” metric. Specifically, we have demonstrated that deep uncertainty regarding energy-related technological change can be addressed using this approach. Our findings include comparison with expected cost minimization, to show how the interaction of solution methods and model structure affect the influence of this form of deep uncertainty on low-run CO2 emissions abatement policies. We also examined other methods assuming some prior distribu- tions over uncertain parameters for analyzing the difference between our robust solution and the non-robust solution from those methods.

We demonstrate that the fundamental model uncertainty can be represented and analyzed in the context of energy policy problems determining optimal CO2 abatement strategies. The robust solution from min-max regret method is significantly different with any solutions from sensitivity analysis over uncertain parameters or those methods assuming prior distributions over uncertain parameters. The following figure shows the difference of the robust min-max regret solution over all three uncertain parameters (the red line) and others with min-max regret solution over only one uncertain parameter, Technical Change level, while the other two parameters are used for sensitivity analysis. 

Abatement paths in computational model of min-max regret criterion with three uncertain parameters, and sensitivity analysis of min-max regret criterion with only one uncertain parameter

Abatement paths in computational model of min-max regret criterion with three uncertain parameters, and sensitivity analysis of min-max regret criterion with only one uncertain parameter

 

People

Yongyang Cai | Alan Sanstad

Alumni: Kenneth Judd 

Recent Publications

Illinois renewable portfolio standards

Recently enacted state Renewable Portfolio Standards (RPSs) collectively require that U.S. electricity generation by non-hydro renewables more than double by 2025. These goals are not certain to be met, however, because many RPSs apply cost caps that alter requirements if costs exceed targets. We have analyzed the 2008 Illinois RPS, which is fairly typical, and have found that at current electricity prices, complete implementation will require significant decreases in renewables costs, even given the continuation of federal renewables subsidies. While full implementation is possible, it is not assured.

We also find that the statutory design raises additional concerns about unintended potential consequences. First, the fact that wind power and solar carve-outs fall under a single cost cap leaves each technology vulnerable to the economics of the other. In failure mode, a less cost-effective technology can curtail deployment of a more cost-effective one. Second, adjacent-state provisions mean the bulk of the wind power requirement under the Illinois RPS can be met by existing facilities in Iowa, where new builds will likely also occur. We conclude that the Illinois RPS, and likely those of many other states, appear to combine objectives inherently in conflict and whose conflicts can create legislative failure: preferences for local jobs, for specific technolo- gies, for environmental benefits, and for low costs. Since RPSs are the principal policy mechanisms in the U.S. at present for combating climate change, it is important to revisiting existing legislation if necessary to ensure legislative success. The Illinois analysis can provide an example and guidelines for other states that will face similar pressure on their RPSs in the near future.

Click here to visit the RPS Calculator.

People:

Elisabeth Moyer | Alison BriziusSean Johnson | Lexi Goldberger | Joe Zhu

Recent Publications:

Social cost of carbon and climate impacts on economic growth

The social cost of carbon, defined as the present value change in consumption due to an incremental change in carbon emissions, is used by federal agencies in cost-benefit analysis of any regulation that changes emissions. In 2009, the Oce of Management and Budget, through an interagency process, estimated the social cost of carbon and required all agencies to use their estimate. Their central value was $21.40/tCO2 with range of -$2.7/tCO2 to $142.4/tCO2.

The government’s estimate of the social cost of carbon, which is consistent with estimates by private researchers, implicitly assumes that the economic growth continues even with substantial temperature increases. In one of the models, temperatures increase by 6.3 by the year 2300. With this temperature increase, the global economy is roughly 30 times larger than it is today on a per capita basis for the model. The apparent reason for this estimate is that damages from climate change do not affect growth. They are modeled as reducing usable output in a given year with exogenously specified growth continuing regardless of climate damages. 

We estimate the social cost of carbon when climate change reduces the growth rate of the economy. We use the same model as the OMB but modify it so that a fraction of damages from climate change affect the growth rate rather than simply reducing usable output. Growth might be reduced, for example, because resources are diverted from research to adaptation to climate change. Even relatively small growth effects produce substantial change in the social cost of carbon suggesting that (1) the estimates of the social cost of carbon are not robust to modest changes in the estimating model and (2) research into the impacts of climate change should focus on growth effects rather than level effects. 

Estimates of the social cost of carbon when accounting for climate damages.

Estimates of the social cost of carbon when accounting for climate damages.

Tipping Points in Dynamic Stochastic Integrated Assessment Models

There is great uncertainty about the impact of anthropogenic carbon on future economic wellbeing.

We use DSICE, a dynamic stochastic general equilibrium (DSGE) model of integrated climate and economy to account for abrupt and irreversible climate change. DSICE is an extension of the DICE2007 model of William Nordhaus, which incorporates beliefs about the uncertain economic impact of possible climate tipping events and uses empirically plausible parameterizations of Epstein-Zin preferences to represent attitudes towards risk. 

In this series of studies we model climate shocks in the form of a stochastic tipping points,  and investigate the impact of a tipping point externality on optimal mitigation policy. We find that the uncertainty associated with anthropogenic climate change imply carbon taxes much higher than implied by deterministic models. This analysis indicates that there is much greater urgency to immediately enact significant GHG policies than implied by DICE2007 and similar models that ignore uncertainty.

THE BASELINE CARBON TAX IN THE DETERMINISTIC DICE MODEL (WITH NO TIPPING POINT) IS SHOWN IN BLACK. THE EXPECTED ADDITIONAL CARBON TAX WHEN INCLUDING A STOCHASTIC TIPPING POINT IS INDICATED IN BLUE. RED LINES INDICATE THE ADDITIONAL CARBON TAX WHEN THE EXPONENT OF THE DAMAGE FUNCTION IN THE DETERMINISTIC DICE MODEL IS INCREASED TO FOURTH (SOLID LINE) AND SIXTH (DASHED LINE) ORDER. REPRODUCED FROM (LONTZEK ET AL 2015)

THE BASELINE CARBON TAX IN THE DETERMINISTIC DICE MODEL (WITH NO TIPPING POINT) IS SHOWN IN BLACK. THE EXPECTED ADDITIONAL CARBON TAX WHEN INCLUDING A STOCHASTIC TIPPING POINT IS INDICATED IN BLUE. RED LINES INDICATE THE ADDITIONAL CARBON TAX WHEN THE EXPONENT OF THE DAMAGE FUNCTION IN THE DETERMINISTIC DICE MODEL IS INCREASED TO FOURTH (SOLID LINE) AND SIXTH (DASHED LINE) ORDER. REPRODUCED FROM (LONTZEK ET AL 2015)

 

People

Yongyang Cai | Kenneth Judd | Timothy Lenton | Thomas Lontzek 

Recent Publications

RPS Calculator

The major policy instruments for mitigating climate change actually in use in the U.S. are subsidies provided to renewable energy. A popular means of subsidy is through state-level Renewable Portfolio Standards (RPSs), requirements enacted by many states that require a certain fraction of electricity must be derived from renewables. However, many state RPSs are infeasible because of “cost cap” provisions that do not permit renewables to be sufficiently competitive, and feasibility is generally not assessed before legislation is passed. The RPS calculator allows the user to explore the conditions for RPS success or failure in different states. The user can analyze and modify existing state statutes or design new statutes in states that do not have them. Users can explore the effects of parameters such as electricity prices, generation costs for wind and solar, interest rates, technology carveouts, and cost cap structure. Features under development include extension to all U.S. states (currently only IL and CA) and spatial variation in wind speed (wind capacity factor map).

People

Current: Elisabeth Moyer

Alumni: Sean Johnson | Lexi Goldberger | Joe Zhu

Recent Publications

CIM-EARTH: a climate change policy modeling framework

The Community Integrated Model for Energy and Resource Trajectories for Humankind (CIM-EARTH) is an open-source modeling framework that allows for the easy specification of computable general equilibrium models, running the resulting simulations, and analyzing the results.  This framework is meant to increase both the quality and transparency of integrated assessment modeling by providing open source modeling tools that incorporate the most modern computational methods.  

Frameworks:

  • The AMPL-Source CIM-EARTH Framework (ASCEF) is a first version of the CIM-EARTH framework.
  • To facilitate adoption and the integration of advanced data processing and analysis services, we undertook an effort to move the implementation from AMPL to C++ and to adopt standard input and output formats. The result is what we call the Open-Source CIM-EARTH Framework (OSCEF).

Models:

  • CE-Trade: The trade model is used for studying the impacts on international trade of policies relevant to carbon mitigation. In particular, this model is used to assess carbon leakage and mechanisms such as border tax adjustments to reduce leakage.
  • CE-Energy: The energy model expands core capabilities of CIM-EARTH in order to represent the energy sector in ways more appropriate for policy analysis> The model disaggregated the representation of the U.S. electric power system to include renewable and non-renewable sources, peak and base-load power, and transmission.
  • CE-Bio: The bio model simulates the economics and lifecycle of biofuel production and use, including a detailed representation of the biofuels market, agriculture, and related services.
  • CE-Life: The distributional impacts model disaggregates the US consumers in the CE-Trade model by income and age, resulting in a model with 720 distinct consumers. 

Structure of the production functions in the baseline CE model:

NOTE: Each node represents a production function. Nodes with vertical line inputs use Leontief functions; the other nodes are labeled with their elasticities of substitution. Table 2 shows the elasticities of substitution between domestic and imported commodities and the Armington international trade elasticities.

NOTE: Each node represents a production function. Nodes with vertical line inputs use Leontief functions; the other nodes are labeled with their elasticities of substitution. Table 2 shows the elasticities of substitution between domestic and imported commodities and the Armington international trade elasticities.


Publications

Energy Price Uncertainty and Global Land Use

Global land use research to date has focused on quantifying uncertainty effects of three major drivers affecting competition for land: the uncertainty in energy and climate policies affecting competition between food and biofuels, the uncertainty of climate impacts on agriculture and forestry, and the uncertainty in the underlying technological progress driving efficiency of food, bioenergy and timber production. The market uncertainty in fossil fuel prices has received relatively less attention in the global land use literature. Petroleum and natural gas prices affect both the competitiveness of biofuels and the cost of nitrogen fertilizers. High prices put significant pressure on global land supply and greenhouse gas emissions from terrestrial systems, while low prices can moderate demands for cropland. In this study, we assessed and compared the effects of these core uncertainties on the optimal profile for global land use and land-based GHG emissions over the coming century. FABLE integrates distinct strands of agronomic, biophysical and economic literature into a single, intertemporally consistent, analytical framework, at global scale. Our analysis accounted for the value of land-based services in the production of food, first- and second-generation biofuels, timber, forest carbon and biodiversity.

We have modeled uncertainty in future fossil fuel prices with discrete time, discrete state-space, time-homogenous Markov chain methods. The stochastic process is characterized by a state space and transition matrix describing the probabilities of particular transitions. The states represent whether world is on high, middle (reference path), or low price paths in a given time period. In our modeling, these are exogenous states because we will be using price data provided by EIA and extrapolated into the future.

We found that by mid-century, slowing population growth, coupled with ongoing agricultural productivity growth will likely bring an end to large scale cropland conversion, after which point the worlds land resources will likely become a net carbon sink. There is great uncertainty about the allocation of the worlds land resources in 2100. We have compared the land use impacts of anticipated uncertainty in climate impacts, climate regulation and energy prices and find that energy prices are the most significant source of uncertainty in global land use in 2100. High energy prices encourage more land conversion for biofuels and raise the cost of cropland intensification, while low energy prices have the opposite effect.

Model scenarios. Panels (a) and (b) pertain to climate change impacts. Panel (c) pertains to GHG regulatory scenario and panel (d) shows energy price scenarios.

Model scenarios. Panels (a) and (b) pertain to climate change impacts. Panel (c) pertains to GHG regulatory scenario and panel (d) shows energy price scenarios.

People

Tom Hertel  | Jevgenijs Steinbuks 

Publications