Near-term Investments Research Articles

Techno-economic optimisation of steel supply chains in the clean energy transition: A case study of post-war Ukraine

The steel industry's clean energy transition can enable new market creation and economic growth stimulation. Yet, the most efficient and feasible pathway to decouple the sector from fossil fuels remains unclear, particularly within developing nations and unstable socio-political contexts. Here, a blueprint for reconfiguring plant locations and reallocating resources is developed through a Ukrainian case study under two scenarios, which capture potential post-war conditions. Framed by regrowth of Ukraine's export-oriented steel industry and prospective European Union accession, green iron and steel trade strategies are devised. A steel supply chain optimisation model underpins the techno-economic, spatially granular analysis of energy and material flows, which utilises the inputs from a separate cost-minimised renewable energy, green hydrogen, and green ammonia production model. Results show that optimal supply chain configurations rely on mixed emissions-free energy profiles, the emergence of new steelmaking sites nearby high-quality renewables, regional alliances for green iron and steel market creation, and multi-billion-dollar investment. Mature nuclear and hydro power critically reduce costs in the near-term, whilst the rapid expansion of solar and wind energy infrastructure underpins production system scale-up. To simultaneously rebuild the 22 million-tonnes-a-year Ukrainian steel industry and transition to near-zero emissions by 2050, infrastructure investment surmounts to $62 billion, given full liberation of Ukrainian territory. Near-term investment is necessary to ease the pace of change, and although mobilising capital of this magnitude will be challenging, convincing carbon prices favour decarbonisation efforts.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Near-term investments in forest management support long-term carbon sequestration capacity in forests of the United States.

The forest carbon sink of the United States offsets emissions in other sectors. Recently passed US laws include important climate legislation for wildfire reduction, forest restoration, and forest planting. In this study, we examine how wildfire reduction strategies and planting might alter the forest carbon sink. Our results suggest that wildfire reduction strategies reduce carbon sequestration potential in the near term but provide a longer term benefit. Planting initiatives increase carbon sequestration but at levels that do not offset lost sequestration from wildfire reduction strategies. We conclude that recent legislation may increase near-term carbon emissions due to fuel treatments and reduced wildfire frequency and intensity, and expand long-term US carbon sink strength.

Transition to Low-Carbon Electric Power: Portfolios, Flexibility, and Option Value.

With the increased focus on responding to climate change by accelerating a transition to a low-carbon energy system, differing views remain on the combination of energy technologies that will best achieve this goal. Identifying technological pathways is complicated by wide uncertainties in economic and technological factors. Analyses that neglect these uncertainties can produce pathways for a low-carbon energy future that are highly granular and specific, but which are based on a particular assumption about future conditions and imply a need to make specific technology commitments over a long period of time. We frame the energy transition problem as the identification of one near-term investment strategy that is flexible across a wide range of possible future costs, followed by many alternative subsequent investment plans, each of which responds to realized future costs to achieve an aggressive emissions reduction target. Using an example of planning a low-carbon power system under uncertainty, we demonstrate the option value of not ruling out some energy technologies in the near term.

Investigating alternative power supply solutions under long term uncertainty for offgrid-offshore fish farm: The case of Hinnøya island, Norway

The aim of this paper is to explore potential least-cost decarbonisation solutions for an off-grid and offshore fish farm power supply system under long term uncertainty. The Hinnøya island in Norway is used as a use case. Three distinctive off-grid and grid-based alternative power supply solutions were proposed and studied as a replacement to the existing diesel power solution in a three-stage stochastic model and under two critical long-term uncertainties: (1) access to strong grid and (2) storage battery cost. The TIMES modelling framework is applied. The stochastic model results reveal that grid integrated storage is an optimal near-term investment for a storage battery cost of 295 €/kWh and less by 2025. In scenarios with no access to strong grid, grid integrated storage continues to be a least-cost solution in the long-term as well, whereas in those scenarios with strong grid access, new investment in storage is not required after 2030. Contrary to the stochastic model runs, the equivalent deterministic model runs showed that a hybrid wind and diesel solution is an optimal near-term investment. From 2030, however, a similar technology pattern in both stochastic and deterministic model runs are observed. Nevertheless, the results are very sensitive to the assumed storage battery costs. Higher storage costs (as high as 704 €/kWh by 2025) would make the hybrid wind and diesel solution an optimal solution instead of the grid integrated storage solution in near-term investment in both stochastic and deterministic model runs.

Zero-emission public transit could be a catalyst for decarbonization of the transportation and power sectors

Abstract Reaching carbon neutrality will require investment on an unprecedented scale. Here we suggest that there is an underappreciated opportunity to leverage public funds to mobilize private capital in support of these aims. We illustrate the point using examples from public transit. Although the fuelling energy requirements of public fleets represent a small fraction of the eventual total demand across the transportation sector, the predictable and long-term nature of the refuelling profiles can reduce the financing risk. With appropriate coordination across the energy supply chain, near-term investments can be used to support scale-up of wider efforts to decarbonize the transportation sector and electric grid. We present two examples from California—one related to overnight power for battery electric bus charging and the other related to medium-scale supply chains for zero-carbon hydrogen production—to illustrate how this might be achieved.

Emergency deployment of direct air capture as a response to the climate crisis

Though highly motivated to slow the climate crisis, governments may struggle to impose costly polices on entrenched interest groups, resulting in a greater need for negative emissions. Here, we model wartime-like crash deployment of direct air capture (DAC) as a policy response to the climate crisis, calculating funding, net CO2 removal, and climate impacts. An emergency DAC program, with investment of 1.2–1.9% of global GDP annually, removes 2.2–2.3 GtCO2 yr–1 in 2050, 13–20 GtCO2 yr–1 in 2075, and 570–840 GtCO2 cumulatively over 2025–2100. Compared to a future in which policy efforts to control emissions follow current trends (SSP2-4.5), DAC substantially hastens the onset of net-zero CO2 emissions (to 2085–2095) and peak warming (to 2090–2095); yet warming still reaches 2.4–2.5 °C in 2100. Such massive CO2 removals hinge on near-term investment to boost the future capacity for upscaling. DAC is most cost-effective when using electricity sources already available today: hydropower and natural gas with renewables; fully renewable systems are more expensive because their low load factors do not allow efficient amortization of capital-intensive DAC plants.

Building conflict uncertainty into electricity planning: A South Sudan case study

This paper explores electricity planning strategies in South Sudan under future conflict uncertainty. A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is presented. Strategies that hedge against future conflict have the greatest economic value in moderate conflict-related damage scenarios by avoiding expensive near-term investments in infrastructure that may be subsequently damaged. Model results show that solar photovoltaics can play a critical role in South Sudan's future electric power system. In addition to mitigating greenhouse gas emissions and increasing access to electricity, this analysis suggests that solar can be used to hedge against economic losses incurred by conflict. While this analysis focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.

A cost-benefit analysis of smoking cessation prescription coverage from a US payer perspective.

IntroductionSmoking drives substantial direct health care spending, comprising 8.7% ($168 billion) of annual United States aggregated spending. Smoking cessation (SC) prescription use is an effective strategy to improve health outcomes, increase quit rates, and reduce economic burden. However, patient out-of-pocket costs may limit the use. Health care payers play a vital role in driving use through formulary decisions and copayment policies but must consider both the near-term financial investment as well as downstream effects of increased coverage on health care budgets. This study estimates the return on investment (ROI) of providing Affordable Care Act (ACA)-recommended prescription SC coverage.MethodsA cost–benefit analysis (CBA) estimates the ROI of providing prescription SC coverage, based on pharmacy costs and savings from smoking-attributable medical expenditures among Medicare, Medicaid, and commercial plan enrollees over 10 years. The CBA incorporated national-level population demographics, smoking prevalence estimates, proportion of smokers attempting to quit, and the utilization of SC products. A five-state Markov chain model simulated patterns of quit attempts, relapse, and cessation assuming two quit attempts per year, no patient cost-sharing, and 25.4% utilization of prescription SC aids. Results include number of quitters, annual pharmacy and smoking-attributable medical costs, and ROI.ResultsAfter initial investment in SC treatment, smoking-attributable medical benefits accrue over time, generating a positive ROI by year 4 for commercial (11.3%) and Medicaid (78.4%) plans and by year 3 for Medicare (30.6%). Over 10 years, an average return of $1.18, $2.50, and $3.22 savings per dollar spent on SC prescriptions for commercial, Medicaid, and Medicare plans, respectively, may be realized.DiscussionGiven the proven efficacy of SC pharmacotherapy, near-term investments in supporting ACA-recommended SC coverage translate into a positive ROI. As smoking is a leading cause of morbidity and mortality, increased access to prescription SC medications may improve health outcomes and reduce smoking-attributable costs to payers over time.

Future cost and performance of water electrolysis: An expert elicitation study

The need for energy storage to balance intermittent and inflexible electricity supply with demand is driving interest in conversion of renewable electricity via electrolysis into a storable gas. But, high capital cost and uncertainty regarding future cost and performance improvements are barriers to investment in water electrolysis. Expert elicitations can support decision-making when data are sparse and their future development uncertain. Therefore, this study presents expert views on future capital cost, lifetime and efficiency for three electrolysis technologies: alkaline (AEC), proton exchange membrane (PEMEC) and solid oxide electrolysis cell (SOEC). Experts estimate that increased R&D funding can reduce capital costs by 0–24%, while production scale-up alone has an impact of 17–30%. System lifetimes may converge at around 60,000–90,000 h and efficiency improvements will be negligible. In addition to innovations on the cell-level, experts highlight improved production methods to automate manufacturing and produce higher quality components. Research into SOECs with lower electrode polarisation resistance or zero-gap AECs could undermine the projected dominance of PEMEC systems. This study thereby reduces barriers to investment in water electrolysis and shows how expert elicitations can help guide near-term investment, policy and research efforts to support the development of electrolysis for low-carbon energy systems.

Technology Decisions Under Architectural Uncertainty: Informing Investment Decisions Through Tradespace Exploration

Although NASA has yet to choose an architecture for human spaceflight beyond Earth orbit, they must pursue near-term investment in the enabling technologies that will be required for these future systems. Given this architectural uncertainty, it is difficult to define the value proposition of technology investments. This paper proposes a method for evaluating technology across a tradespace defined by architectural decisions. Main effects analysis is taken from design of experiments to quantify the influence that a technology has on the system being considered. This analysis also identifies couplings between technologies that are mutually exclusive or mutually beneficial. This method is applied to the architecture tradespace of transportation for future human exploration at Mars with a set of possible propellant, propulsion, and aerobraking technologies. The paper demonstrates that the evaluation of technologies against an individual reference architecture is flawed when the range of architectures being pursued remains diverse. Furthermore, it is shown that comparisons between fuzzy Pareto optimal architectures and heavily dominated architectures will distort the evaluated benefit of a technology. The resulting tradespace can be structured as the sequence in which technology decisions should be made, in order of their impact on the tradespace and their coupling to other decisions.

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.

What are robust strategies in the face of uncertain climate threshold responses?

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy’s expected performance for a significant increase in a strategy’s performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM.

Critical mid-term uncertainties in long-term decarbonisation pathways

Over the next decade, large energy investments are required in the UK to meet growing energy service demands and legally binding emission targets under a pioneering policy agenda. These are necessary despite deep mid-term (2025–2030) uncertainties over which national policy makers have little control. We investigate the effect of two critical mid-term uncertainties on optimal near-term investment decisions using a two-stage stochastic energy system model.The results show that where future fossil fuel prices are uncertain: (i) the near term hedging strategy to 2030 differs from any one deterministic fuel price scenario and is structurally dissimilar to a simple ‘average’ of the deterministic scenarios, and (ii) multiple recourse strategies from 2030 are perturbed by path dependencies caused by hedging investments. Evaluating the uncertainty under a decarbonisation agenda shows that fossil fuel price uncertainty is very expensive at around £20 billion. The addition of novel mitigation options reduces the value of fossil fuel price uncertainty to £11 billion. Uncertain biomass import availability shows a much lower value of uncertainty at £300 million.This paper reveals the complex relationship between the flexibility of the energy system and mitigating the costs of uncertainty due to the path-dependencies caused by the long-life times of both infrastructures and generation technologies.

Incentivizing wood-based Fischer–Tropsch diesel through financial policy instruments: An economic assessment for Norway

The objective of this study is to evaluate a select set of financial incentive instruments that can be employed by the Norwegian government for encouraging early investment and production experience in wood-based Fischer–Tropsch diesel (FTD) technologies as a means to accelerate reductions in greenhouse gas emissions (GHG) stemming from road-based transport. We start by performing an economic analysis of FTD produced from Norwegian forest biomass at a pioneer commercial plant in Norway, followed with a cost growth analysis to estimate production costs after uncertainty in early plant performance and capital cost estimates are considered. Results after the cost growth analysis imply that the initial production cost estimates for a pioneer producer may be underestimated by up to 30%. Using the revised estimate we then assess, through scenarios, how various financial support mechanisms designed to encourage near-term investment would affect production costs over a range of uncertain future oil prices. For all policy scenarios considered, we evaluate trade-offs between the levels of public expenditure, or subsidy, and private investor profitability. When considering the net present value of the subsidy required to incentivize commercial investment during a future of low oil prices, we find that GHG mitigation via wood-FTD is likely to be considered cost-ineffective. However, should the government expect that mean oil prices in the coming two decades will hover between $97 and 127/bbl, all the incentive policies considered would likely spur investment at net present values ≤$-100/tonne-fossil-CO 2-equivalent avoided.

Stepping stones to the future: Achieving a sustainable lunar outpost

The current emphasis in the US and internationally on lunar robotic missions is generally viewed as a precursor to possible future human missions to the Moon. As initially framed, the implementation of high level policies such as the US Vision for Space Exploration (VSE) might have been limited to either human lunar sortie missions, or to the testing at the Moon of concepts-of-operations and systems for eventual human missions to Mars [White House, Vision for Space Exploration, Washington, DC, 14 January, 2004. [1]]. However, recently announced (December 2006) US goals go much further: these plans now place at the center of future US—and perhaps international—human spaceflight activities a long-term commitment to an outpost on the Moon. Based on available documents, a human lunar outpost could be emplaced as early as the 2020–2025 timeframe, and would involve numerous novel systems, new technologies and unique operations requirements. As such, substantial investments in research and development (R&D) will be necessary prior to, during, and following the deployment of such an outpost. It seems possible that such an outpost will be an international endeavor, not just the undertaking of a single country—and the US has actively courted partners in the VSE. However, critical questions remain concerning an international lunar outpost. What might such an outpost accomplish? To what extent will “sustainability” be built into the outpost? And, most importantly, what will be the outpost's life cycle cost (LCC)? This paper will explore these issues with a view toward informing key policy and program decisions that must be made during the next several years. The paper will (1) describe a high-level analytical model of a modest lunar outpost, (2) examine (using this model) the parametric characteristics of the outpost in terms of the three critical questions indicated above, and (3) present rough estimates of the relationships of outpost goals and “sustainability” to LCC. The paper will also consider possible outpost requirements for near-term investments in enabling research in light of experiences in past advanced technology programs.

A control model of policy uncertainty and energy R&D investments

Using an optimal control model, we explore the reaction of a firm's optimal investment into an RD first, that the influence of risk on investment decisions depends on model formulation. Second, near-term investments decrease in risk in the magnitude of a carbon tax, but increase in uncertainty in the timing of a carbon tax.

Future evolution of the liberalised European gas market: Simulation results with a dynamic model

Abstract Strategic behaviour by gas producers is likely to affect future gas prices and investments in the European Union (EU). To analyse this issue, a computational game theoretic model is presented that is based on a recursive-dynamic formulation. This model addresses interactions among demand, supply, pipeline and liquefied natural gas (LNG) transport, storage and investments in the natural gas market over the period 2005–2030. Three market scenarios are formulated to study the impact of producer market power. In addition, tradeoffs among investments in pipelines, LNG liquefaction and regasification facilities, and storage are explored. The model runs indicate that LNG can effectively compete with pipelines in the near future. Further, significant decreases in Cournot prices between 2005 and 2010 indicate that near-term investments in EU gas transport capacity are likely to diminish market power by making markets more accessible.

How Sensitive is Investment to Cash Flow when Financing is Frictionless?

I analyze the sensitivity of a ¢rm’s investment to its own cash £ow in the benchmarkcase where ¢nancing is frictionless. This sensitivity has been proposed as a measure of ¢nancing constraints in earlier studies. I ¢nd that the investment^cash £ow sensitivities that obtain in the frictionless benchmarkare very similar, both in magnitude and in patterns they exhibit, to those observed in the data. In particular, the sensitivity is higher for ¢rms with high growth rates and low dividend payout ratios. Tobin’s q is shown to be a more noisy measure of near-term investment plans for these ¢rms. DOES A HIGH SENSITIVITYof a ¢rm’s investment to its own cash £ow indicate that the ¢rm is ¢nancially constrained? A large body of research, starting with Fazzari, Hubbard, and Petersen (1988) (hereafter FHP), suggests that ¢rms facing ¢nancing constraints should exhibit high investment^cash £ow sensitivities, re£ecting the wedge between the costs of external and internal funds. Empirical ¢ndings seem to give support to this hypothesis, since ¢rms classi¢ed as constrained on a priori criteria, such as size, dividend payout, or leverage, do have higher cash £ow sensitivities, even after controlling for their investment opportunities by conditioning on Tobin’s q. However, the reliability of these results critically depends on whether q is a proper control for the investment opportunity set. If q performs worse for certain classes of ¢rms, higher sensitivities may obtain for these ¢rms simply because cash £ow re£ects information about investment opportunities. This critique is not new. In his discussion of FHP, Poterba (1988) was the ¢rst to point out that measurement error in q may cloud the empirical results; similar concerns have been raised in subsequent studies as well.While the qualitative point is well known, its quantitative impact on the investment^cash £ow sensitivity has not been analyzed. Can the linkbetween cash £ow and the investment opportunity set account for the observed magnitudes of the sensitivity? Or are these magnitudes too large to be explained without ¢nancing frictions?

Modeling uncertainty of induced technological change

Modeling uncertainty of induced technological change