Modeling direct air carbon capture and storage in a 1.5 °C climate future using historical analogs.

Limiting the rise in global temperature to 1.5 °C will rely, in part, on technologies to remove CO2 from the atmosphere. However, many carbon dioxide removal (CDR) technologies are in the early stages of development, and there is limited data to inform predictions of their future adoption. Here, we present an approach to model adoption of early-stage technologies such as CDR and apply it to direct air carbon capture and storage (DACCS). Our approach combines empirical data on historical technology analogs and early adoption indicators to model a range of feasible growth pathways. We use these pathways as inputs to an integrated assessment model (the Global Change Analysis Model, GCAM) and evaluate their effects under an emissions policy to limit end-of-century temperature change to 1.5 °C. Adoption varies widely across analogs, which share different strategic similarities with DACCS. If DACCS growth mirrors high-growth analogs (e.g., solar photovoltaics), it can reach up to 4.9 GtCO2 removal by midcentury, compared to as low as 0.2 GtCO2 for low-growth analogs (e.g., natural gas pipelines). For these slower growing analogs, unabated fossil fuel generation in 2050 is reduced by 44% compared to high-growth analogs, with implications for energy investments and stranded assets. Residual emissions at the end of the century are also substantially lower (by up to 43% and 34% in transportation and industry) under lower DACCS scenarios. The large variation in growth rates observed for different analogs can also point to policy takeaways for enabling DACCS.

Machine learning reduces soft costs for residential solar photovoltaics.

Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs-now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.

Improving Human Health in China Through Alternative Energy.

In this study, we estimate the health benefits of more stringent alternative energy goals and the costs of reducing coal-fired power plant pollution in China projected in 2030. One of our two overarching alternative energy goals was to estimate the health benefits of complete elimination of coal energy, supplemented by natural gas and renewables. The second was a policy scenario similar to the U.S. 2013 Climate Action Plan (CAP), which played a pivotal role leading up to the 2015 Paris Climate Agreement. We used the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model created by the International Institute for Applied Systems Analysis for our model simulations. We found that 17,137-24,220 premature deaths can be avoided if coal energy is completely replaced by alternative energy, and 8,693-9,281 premature deaths can be avoided if coal energy is replaced by alternatives in a CAP-like scenario. A CAP-like scenario using emission-controls in coal plants costs $11-18 per person. Reducing coal energy in China under a CAP-like scenario would free up $9.4 billion in the annual energy budget to spend on alternatives, whereas eliminating the cost of coal energy frees up $32 billion. This study's estimates show that more stringent alternative energy targets in China are worth the investment in terms of health.

Quantifying the Effects of Expert Selection and Elicitation Design on Experts' Confidence in Their Judgments About Future Energy Technologies.

Expert elicitations are now frequently used to characterize uncertain future technology outcomes. However, their usefulness is limited, in part because: estimates across studies are not easily comparable; choices in survey design and expert selection may bias results; and overconfidence is a persistent problem. We provide quantitative evidence of how these choices affect experts' estimates. We standardize data from 16 elicitations, involving 169 experts, on the 2030 costs of five energy technologies: nuclear, biofuels, bioelectricity, solar, and carbon capture. We estimate determinants of experts' confidence using survey design, expert characteristics, and public R&D investment levels on which the elicited values are conditional. Our central finding is that when experts respond to elicitations in person (vs. online or mail) they ascribe lower confidence (larger uncertainty) to their estimates, but more optimistic assessments of best-case (10th percentile) outcomes. The effects of expert affiliation and country of residence vary by technology, but in general: academics and public-sector experts express lower confidence than private-sector experts; and E.U. experts are more confident than U.S. experts. Finally, extending previous technology-specific work, higher R&D spending increases experts' uncertainty rather than resolves it. We discuss ways in which these findings should be seriously considered in interpreting the results of existing elicitations and in designing new ones.

Net radiative forcing from widespread deployment of photovoltaics.

If photovoltaics (PV) are to contribute significantly to stabilizing the climate, they will need to be deployed on the scale of multiple terawatts. Installation of that much PV would cover substantial portions of the Earth's surface with dark-colored, sunlight-absorbing panels, reducing the Earth's albedo. How much radiative forcing would result from this change in land use? How does this amount compare to the radiative forcing avoided by substituting PV for fossil fuels? This analysis uses a series of simple equations to compare the two effects and finds that substitution dominates; the avoided radiative forcing due to substitution of PV for fossil fuels is approximately 30 times largerthan the forcing due to albedo modification. Sensitivity analysis, including discounting of future costs and benefits, identifies unfavorable yet plausible configurations in which the albedo effect substantially reduces the climatic benefits of PV. The value of PV as a climate mitigation option depends on how it is deployed, not just how much it is deployed--efficiency of PV systems and the carbon intensity of the substituted energy are particularly important