Energy Transition

The specification of parameters is a crucial task in the development of economic models. The objective of this paper is to improve the standard parameter specification of computable general equilibrium (CGE) models. On that account, we illustrate how Optimal Fingerprint Detection Methods (OFDM) can be used to identify appropriate values for various parameters. These methods originate from climate science and combine a simple model validation exercise with a structured sensitivity analysis. The new approach has three main benefits: 1) It uses a structured optimisation procedure and does not revert to ad-hoc model improvements. 2) It accounts for uncertainty in parameter estimates by using information on the distribution of parameter estimates from the literature. 3) It can be applied for the specification of a range of parameters required in CGE models; for example, for the definition of elasticities or productivity growth rates.

Effectively balancing existing technology adoption and new technology development is critical for successfully managing carbon dioxide (CO2) emissions from the fossil-dominated electric power generation sector. The long infrastructure lifetimes of power plant investments mean that deployment decisions made today will influence carbon dioxide emissions long into the future. New technology development and R&D decisions can help reduce the overall costs of reducing emissions, but there are multiple technology investments to choose from, and returns to R&D are inherently uncertain. These features of the technology “deployment versus development” question create unique challenges for decision makers charged with managing cumulative carbon dioxide emissions from the electricity sector.

Unfortunately, current quantitative decision-support tools ultimately lack one or more of three overarching features jointly necessary to provide useful insights about an optimal balance between R&D program and power plant investments. They lack (1) resolution of the critical structure of the electricity sector, (2) an explicit endogenous representation of the effects of learning-by-searching technological change, and/or (3) an efficient decision-analytic framework to explore multiple technology investment options under uncertainty in the returns to R&D.

This dissertation presents a new quantitative decision-support framework that allows for the study of socially optimal R&D and capital investment decisions for the power generation sector. Through a novel integration of classical electricity generation investment planning methods, economic modeling of endogenous R&D-driven technological change, and emerging numerical stochastic optimization techniques, the new framework (1) explicitly accounts for the complementary roles that generating technologies play within the electric power system, (2) considers the characteristics of the uncertainty in the technology innovation process, and (3) identifies flexible, adaptive R&D investment strategies for multiple technologies for decision makers to consider.

A series of numerical experiments with the new model reveal that (1) the optimal near-term R&D investment strategy under technological change uncertainty and adapting between decisions can be different than the optimal strategy assuming perfect foresight, and may be higher or lower; (2) the timing that a technology should be deployed to meet a specific carbon target dictates the direction and magnitude of the difference in these decisions; (3) increasing the level of uncertainty tends to increase near-term R&D investments; and (4) increasing right-skewness of the uncertainty (i.e., decreasing the likelihood of higher than average returns), reduces R&D spending throughout the planning horizon.

[Link to "Full Document" provides the abstract only; link to full thesis is forthcoming.]

In times of increasing importance of wind power in the world’s energy mix, this study focuses on a better understanding of the influences of large-scale climate variability on wind power resource over Europe. The impact of the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the El Niño Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO) are investigated in terms of their correlation with wind power density (WPD) at 80 m hub height. These WPDs are calculated based on the MERRA Reanalysis data set covering 31 years of measurements. Not surprisingly, AO and NAO are highly correlated with the time series of WPD. This correlation can also be found in the first principal component of a Principal Component Analysis (PCA) of WPD over Europe explaining 14% of the overall variation. Further, cross-correlation analyses indicates the strongest associated variations are achieved with AO/NAO leading WPD by at most one day. Furthermore, the impact of high and low phases of the respective oscillations has been assessed to provide a more comprehensive illustration. The fraction of WPD for high and low AO/NAO increases considerably for northern Europe, whereas the opposite pattern can be observed for southern Europe. Similar results are obtained by calculating the energy output of three hypothetical wind turbines for every grid point over Europe. Thus, we identified a high interconnection potential between wind farms in order to reduce intermittency, one of the primary challenges in wind power generation. In addition, we observe significant correlations between WPD and AMO.

We investigate the impact of climate policies on Canada's oil sands industry, the largest of its kind in the world. Deriving petroleum products such as gasoline and diesel from oils sands involves significant amounts of energy, and that contributes to a high level of CO2 emissions. We apply the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the world economy, augmented to include detail on the oil sands production processes, including the possibility of carbon capture and storage (CCS). We find: (1) without climate policy, annual Canadian bitumen production increases almost 4-fold from 2010 to 2050; (2) with climate policies implemented in developed countries, Canadian bitumen production drops by 32% to 68% from the reference 4-fold increase, depending on the viability of large-scale CCS implementation, and bitumen upgrading capacity moves to the developing countries; (3) with climate policies implemented worldwide, the Canadian bitumen production is significantly reduced even with CCS technology, which lowers CO2 emissions at an added cost. This is mainly because upgrading bitumen abroad is no longer economic with the global climate policies.

What will large-scale global bioenergy production look like? We investigate this question by developing a detailed representation of bioenergy in a global economy-wide model. We develop a scenario with a global carbon dioxide price, applied to all anthropogenic emissions except those from land-use change, that rises from $15 per metric ton in 2015 to $59 in 2050. This creates market conditions favorable to biomass energy, resulting in global non-traditional bioenergy production of ~150 exajoules (EJ) in 2050. By comparison, in 2010 global energy production was primarily from coal (139 EJ), oil (175 EJ) and gas (108 EJ). With this policy, 2050 emissions are 16% less in our Base Policy case than our Reference case, although extending the scope of the carbon price to include emissions from land-use change would reduce 2050 emissions by 57% relative to the same baseline. Our results from various policy scenarios show that lignocellulosic (LC) ethanol may become the major form of bioenergy, if its production costs fall by amounts predicted in a recent survey and ethanol blending constraints disappear by 2030; however, if its costs remain higher than expected or the ethanol blend wall continues to bind, bioelectricity and bioheat may prevail. Higher LC ethanol costs may also result in expanded production of first-generation biofuels (ethanol from sugarcane and corn) so that they remain in the fuel mix through 2050. Deforestation occurs if emissions from landuse change are not priced, although the availability of biomass residues and improvements in crop yields and conversion efficiencies mitigate pressure on land markets. As regions are linked via international agricultural markets, irrespective of the location of bioenergy production, natural forest decreases are largest in regions with the lowest political constraints to deforestation. The combination of carbon price and bioenergy production increases food prices by 2.6%–4.7%, with bioenergy accounting for 1.3%–2.6%.

 

Be Cautious about Bioenergy, but Don't Rule It Out

Research commentary by Niven Winchester in response to the World Resources Institute report on biofuels and bioenergy.

A report published by the World Resources Institute (WRI) in January has reignited the old debate over biofuels and other forms of bioenergy, casting doubt on their climate benefits and effects on food availability. With recent low oil prices, and the EPA set to announce updated biofuel standards in the spring, the stakes for considering what role bioenergy will play in a sustainable future are higher than ever.

While the WRI report’s authors outline valid reasons to be cautious about bioenergy, the report contains misleading statements about the role of bioenergy in abating emissions.

The report claims that bioenergy made from fuel crops does not reduce direct carbon emissions because, say, diverting maize from food to ethanol does not result in additional absorption of carbon. While this is true, focusing on direct emissions is short-sighted because it doesn’t take into account the bigger picture of where energy comes from. Regardless of whether maize is used for food or fuel, emissions absorbed will end back into the atmosphere, mainly through respiration in the former and combustion in the latter. However, if maize is used for ethanol, it displaces oil and reduces carbon emissions.

If direct emissions were used to evaluate solar power, this technology would also be judged not to reduce carbon emissions, as no emissions are either stored or released when sunlight is converted to electricity. This is not to say that the carbon benefits from bioenergy are equal to the emissions from the fossil energy that it displaces. Growing bioenergy crops requires energy to plant, harvest and process, and may result in deforestation. Emissions from these activities reduce the carbon benefits of bioenergy, but there is still the potential for it to reduce emissions.

The report also claims that bioenergy analyses double-count land by assuming that biomass for other purposes, like food and lumber, continue to be met when land is used for bioenergy. In fact, studies that consider the economy-wide effects of bioenergy cultivation explicitly represent resource constraints, so land used for bioenergy cannot be used for other purposes. In these studies, as in the real world, increased demand for land to grow bioenergy crops will increase land prices.

The effects of the higher land prices propagate throughout the economy in several ways. Land may be used more intensively by, for example, developing new cultivars. On the other side of the equation, demand for food can be reduced through greater use of refrigeration and packaging to reduce food waste.

That is, incentives for bioenergy can drive efficiency improvements in crop production and use that allow food and fuel demands to be met using limited resources. These efficiency improvements come at a cost, but so do other carbon abatement options.

Bioenergy is not a “silver bullet” against climate change, has been over-hyped in some circles, and governments should guard against negative impacts on water and biodiversity. At the same time, bioenergy should not be erroneously excluded from our toolkit for fighting climate change, because we will need all the tools available to us.

Niven Winchester is an environmental energy economist at the Joint Program on the Science and Policy of Global Change. He is a coauthor of the recent report "The Contributions of Biomass to Emissions Mitigation under a Global Climate Policy."

Increases in the U.S. Corporate Average Fuel Economy (CAFE) Standards for 2017 to 2025 model year light-duty vehicles are currently under consideration. This analysis uses an economy-wide model with detail in the passenger vehicle fleet to evaluate the economic, energy use, and greenhouse gas (GHG) emissions impacts associated with year-on-year increases in new vehicle fuel economy targets of 3%, 4%, 5%, or 6%, which correspond to the initially proposed rates of increase for the 2017 to 2025 CAFE rulemaking. We find that across the range of targets proposed, the average welfare cost of a policy constraint increases non-linearly with target stringency, because the policy targets proposed require increasingly costly changes to vehicles in the near term. Further, we show that the economic and GHG emissions impacts of combining a fuel tax with fuel economy standards could be positive or negative, depending on underlying technology costs. We find that over the period 2015 to 2030, a 5% CAFE policy would reduce gasoline use by about 25 billion gallons per year, reduce CO2 emissions by approximately 190 million metric tons per year, and cost $25 billion per year (net present value in 2004 USD), relative to a No Policy baseline.

China’s recently-adopted targets for developing renewable electricity—wind, solar, and biomass—would require expansion on an unprecedented scale in China and relative to existing global installations. An important question is how far this deployment will go toward achieving China’s low carbon development goals, which include a carbon intensity reduction target of 40–45% relative to 2005 and a non-fossil primary energy target of 15% by 2020. During the period from 2010 to 2020, we find that current renewable electricity targets result in significant additional renewable energy installation and a reduction in cumulative CO2 emissions of 1.2% relative to a no policy baseline. After 2020, the role of renewables is sensitive to both economic growth and technology cost assumptions. Importantly, we find that CO2 emissions reductions due to increased renewables are offset in each year by emissions increases in non-covered sectors through 2050. By increasing reliance on renewable energy sources in the electricity sector, fossil fuel demand in the power sector falls, resulting in lower fossil fuel prices, which in turn leads to greater demand for these fuels in unconstrained sectors. We consider sensitivity to renewable electricity cost after 2020 and find that if cost falls due to policy or other reasons, renewable electricity share increases and results in slightly higher economic growth through 2050. However, regardless of the cost assumption, projected CO2 emissions reductions are very modest under a policy that only targets the supply side in the electricity sector. A policy approach that covers all sectors and allows flexibility to reduce CO2 at lowest cost—such as an emissions trading system—will prevent this emissions leakage and ensure targeted reductions in CO2 emissions are achieved over the long term.

A major uncertainty in future energy and greenhouse gas (GHG) emissions projections for China is the evolution of demand for personal transportation modes. This paper explores the implications of divergent personal transportation scenarios, either favoring private vehicles, or emphasizing a sector including all purchased transport (including local public transit, rail and aviation) as substitute for vehicle travel. Motivated by a wide range of forecasts for transport indicators in the literature, we construct plausible scenarios with low-, medium- and high-transport demand growth, and implement them in a technology-rich model which represents opportunities for fuel economy improvement and switching to plug-in hybrid-electric vehicles (PHEVs). The analysis compares primary energy use and GHG emissions in China in the absence and presence of climate policies. We find that a policy that extends the current Chinese emissions-intensity goals through 2050 mostly affects other sectors with lower abatement costs, and so only lightly engages household transport, permitting nearly the same large increases in refined oil demand (by more than five times) and private vehicle stocks (to 430–500 million) as in the reference case. A stringent climate stabilization policy affects household transport, limiting vehicle ownership and petroleum demand, but drives up the share of household spending on transport, and carries high economy-wide costs. The large projected scale of vehicle fleets, refined oil use and transport purchases all suggest that the rate and type of travel demand growth deserves attention by policymakers, as China seeks to address its energy, environmental, and economic goals.

Solar electricity generation is one of very few low-carbon energy technologies with the potential to grow to very large scale. As a consequence, massive expansion of global solar generating capacity to multi-terawatt scale is very likely an essential component of a workable strategy to mitigate climate change risk. Recent years have seen rapid growth in installed solar generating capacity, great improvements in technology, price, and performance, and the development of creative business models that have spurred investment in residential solar systems. Nonetheless, further advances are needed to enable a dramatic increase in the solar contribution at socially acceptable costs. Achieving this role for solar energy will ultimately require that solar technologies become cost-competitive with fossil generation, appropriately penalized for carbon dioxide (CO2) emissions, with — most likely — substantially reduced subsidies.

This study examines the current state of U.S. solar electricity generation, the several technological approaches that have been and could be followed to convert sunlight to electricity, and the market and policy environments the solar industry has faced. Our objective is to assess solar energy’s current and potential competitive position and to identify changes in U.S. government policies that could more efficiently and effectively support the industry’s robust, long-term growth.

We focus in particular on three preeminent challenges for solar generation: reducing the cost of installed solar capacity, ensuring the availability of technologies that can support expansion to very large scale at low cost, and easing the integration of solar generation into existing electric systems. Progress on these fronts will contribute to greenhouse-gas reduction efforts, not only in the United States but also in other nations with developed electric systems. It will also help bring light and power to the more than one billion people worldwide who now live without access to electricity.

We analyze the economic and emissions impacts on U.S. commercial aviation of the Federal Aviation Administration’s renewable jet fuel goal when met using advanced fermentation (AF) fuel from perennial grasses. These fuels have recently been certified for use in aircraft and could potentially provide greater environmental benefits than aviation biofuels approved previously. Due to uncertainties in the commercialization of AF technologies, we consider a range of assumptions concerning capital costs, energy conversion efficiencies and product slates. In 2030, estimates of the implicit subsidy required to induce consumption of AF jet fuel range from $0.45 to $20.85 per gallon. These correspond to a reference jet fuel price of $3.23 per gallon and AF jet fuel costs ranging from 4.01 to $24.41 per gallon. In all cases, as renewable jet fuel represents around 1.4% of total fuel consumed by commercial aviation, the goal has a small impact on aviation operations and emissions relative to a case without the renewable jet fuel target, and emissions continue to grow relative to those in 2005. Costs per metric ton of carbon dioxide equivalent abated by using biofuels range from $42 to $652.

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