Climate Policy

Uncertainty quantification of socio-economic outcomes can be combined with scenario discovery techniques to explore a full range of outcomes and provide insight into associated likelihoods while also identifying individual scenarios of interest. This unique approach quantifies multi-sector risks, which can aid decision-making about energy and technology choices and sectoral strategies.

The Science

DOE Highlight: Quantifying Socio-Economic Uncertainty

When the 2015 Paris Agreement set a long-term goal of keeping global warming “well below two degrees Celsius, compared to pre-industrial levels” to avoid the worst impacts of climate change, it did not specify how its nearly 200 signatory nations could collectively achieve that goal. Each nation was left to its own devices to reduce greenhouse gas emissions in alignment with the 2°C target.

What choices does the world need to make to keep global warming below two degrees Celsius?

To avert the worst impacts of climate change, from extreme flooding to devastating droughts, the world will need to cap global warming at 1.5 degrees Celsius, according to the latest United Nations IPCC Report on the Earth’s climate system. Achieving that goal means that by around 2050, the planet’s total greenhouse gas emissions will need to decline to net-zero. To that end, more and more governments and businesses are setting net-zero emissions targets.

Global net-zero emissions goals: Challenges and opportunities

Currently, there is no magic bullet for fossil fuels—no one energy technology that can provide a cheap and reliable alternative capable of supporting the world’s growing energy needs. Instead, decision-makers looking to lower greenhouse gas emissions must choose from an expansive menu of technology and policy options, says MIT Joint Program on the Science and Policy of Global Change Deputy Director Sergey Paltsev.

Scaling up low-carbon energy: Challenges and opportunities

Currently, there is no magic bullet for fossil fuels—no one energy technology that can provide a cheap and reliable alternative capable of supporting the world’s growing energy needs. Instead, decision-makers looking to lower greenhouse gas emissions must choose from an expansive menu of technology and policy options, says MIT Joint Program on the Science and Policy of Global Change Deputy Director Sergey Paltsev.

MIT Joint Program Deputy Director Sergey Paltsev delivers keynote presentation on scaling up low-carbon energy at international energy symposium
Meeting U.S. greenhouse gas emissions goals with the International Air Pollution Provision of the Clean Air Act

Abstract: We explore economic, distributional and health consequences of U.S. greenhouse gas emissions objectives that could be achieved using Section 115 of the Clean Air Act (international air pollution) which has only recently received detailed legal analysis as a potential U.S. climate policy tool. Under it a national emissions target could be allocated among the states. This illustrative analysis considers 45% and 50% reductions of energy and industry-related CO2 emissions by 2030, below 2005 levels, via a model rule. Different approaches (based on legal precedent) for the interstate allocation are considered, along with alternative rates of technology improvement.

The detail needed to analyze this approach is provided by MIT’s U.S. Regional Energy Policy (USREP) model (30 individual states and regions), with its electricity sector replaced by the U.S. National Renewable Energy Laboratory’s Renewable Energy Development System (ReEDS). Air quality benefits are estimated using modeling tools developed by academic researchers and the U.S. Environmental Protection Administration.

Three-quarters of emissions reductions in 2030 come in the electric sector, while reductions elsewhere illustrate the efficiency advantage of a multi-sector policy. With all states participating in allowance trading, the resulting national emissions price is lower than in older assessments. The difference is due to lower growth expectations, recent state policies, falling costs of low carbon technologies, and an improved representation of electric system flexibility by the ReEDS model. Even ignoring climate and air quality benefits, economic welfare grows at near the baseline rate for all regions regardless of the interstate allocation approach. When states distribute allowance revenue to residents on an equal per-capita basis the policy is welfare improving to the lowest income quintile in all regions. Aggregation of control costs, the mortality effects of reduced particulates, and the value of avoided climate damages yields positive national net benefits in all cases.

An economy-wide framework for assessing the stranded assets of the energy production sector under climate policies

Abstract: Climate change mitigation efforts, which require the transition away from carbon-intensive activities, can pose financial risks for owners of fossil fuel assets and investors that the finance companies are engaged in greenhouse gas-emitting activities. For instance, fossil fuel extraction may be significantly scaled-back, and coal-power plants may be idled or even phased out prematurely, thus becoming stranded assets for the shareholders.

Using a global general equilibrium model with detailed energy sector and capital stock structures, we estimate the corresponding stranded assets under various emissions mitigation scenarios. Our findings reveal that, depending on the policy scenario, the global net present value of unrealized fossil fuel output through 2050 relative to a “no policy” scenario is between 21.5 and 30.6 trillion USD, and that of stranded assets in coal power generation is between 1.3 and 2.3 trillion USD.

The analytical framework presented in our study complements existing research, in which macroeconomic variables required for estimating the stranded assets are often derived from models with more simplified assumptions. Therefore, individual firms and financial institutions can combine our economy-wide analysis with details on their own investment portfolios to determine their climate-related transition risk exposure.

 

Representing socio-economic uncertainty in human system models

Summary: Simulation models are often used to explore future development pathways and their impacts on energy, emissions, economies and the environment. This requires making assumptions about various socio-economic conditions, such as how fast populations and economies will grow, the cost of technology options, or the amount of fossil fuels available. Different assumptions have significant impacts on model results, yet analyses typically only test a few alternatives.

Here, we develop and use probability distributions to capture this uncertainty. We draw samples from these distributions, run an energy-economic model hundreds of times, and quantify the resulting uncertainty in model outcomes, providing insight into their likelihood. We focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. We also apply approaches to find scenarios of interest from within the database of scenarios.

We find that many patterns of energy and technology development are possible under a given long-term environmental pathway (such as a 2C scenario) or a given economic outcome (such as high or low GDP). This approach can help identify biases in perceptions of what “needs” to happen to achieve certain outcomes, and shows that there are many pathways to a successful energy transition. 

Pages

Subscribe to Climate Policy