About

Yash Amonkar is a Research Assistant Professor at the Department of Environmental Sciences & Engineering at UNC Gillings School of Global Public Health and a faculty member at the Institute for Risk Management and Insurance Innovation (IRMII). He specializes in financial risk modeling for infrastructure exposed to weather variability, bridging environmental engineering, insurance innovation, and data science. His research supports the development of financial tools that help utilities, municipalities, cities, and private entities better manage extreme weather risks. Amonkar collaborates closely with government and industry partners to translate climate data into actionable risk mitigation strategies. His current projects include modeling financial risk in the power sector due to drought in the Western U.S., conducting life-cycle and techno-economic analyses for renewable biofuels, and quantifying hail damage risk to residential and utility-scale solar installations. He has received several honors and awards, including the Morton B. Friedman Memorial Prize for Excellence in 2023 from Columbia University, the LaVon Duddleson Fellowship for 2022-2023, and the Cheung-Kong Innovation Doctoral Fellowship from 2020 to 2022. His teaching interests encompass natural hazards and financial risk, as well as Python for environmental research, at both undergraduate and graduate levels.

Research topics

• Environmental science
• Engineering
• Natural resource economics
• Physics
• Computer Science
• Meteorology
• Atmospheric sciences
• Economics
• Ecology
• Environmental resource management
• Environmental economics
• Statistical physics
• Aerospace engineering
• Business
• Geography
• Simulation
• Thermodynamics
• Risk analysis (engineering)
• Electrical engineering

Selected publications

• Rules-Based Systems Modeling for Hydropower Forecasting in Multi-Objective Reservoir Systems: Application to California's Central Valley Project

  2026-05-02

  article1st authorCorresponding

  Hydropower from large multi-objective reservoirs and water management projects constitutes the bulk of global reservoir-based generation. Yet accurate forecasting remains challenging because generation is governed not only by hydrology but by complex institutional rules, environmental regulations, and infrastructure constraints. This study demonstrates that a rules-based systems modeling approach, one that explicitly integrates water allocation institutions, operational constraints, and hydrological variability, produces significantly more accurate and financially valuable hydropower forecasts than purely statistical methods. This framework is applied to California's Central Valley Project (CVP) using CALFEWS, an open-source daily timestep rules-based model of the Central Valley, extended here to include the Trinity River Diversion System and its evolving environmental flow regulations. CALFEWS is validated against 20 years of operational hydropower data (2003–2023), achieving r² = 0.84 for monthly CVP hydropower generation. Herein, we develop a 12 month two-stage October–April hydropower forecasting approach, initialized with October storage conditions and historical climatology, that incorporates water year type classification in April. The resulting forecasts achieve high fidelity, nearly matching the skill of simulations with perfect inflow foresight. Compared to statistical baselines, CALFEWS reduces hydropower forecast errors by 40% (RMSE) and hydropower financial losses relative to historic baselines by 20% ($75–115 million over a decade). While demonstrated for the CVP, the framework is directly transferable to any multi-objective reservoir system where institutional rules and competing water demands govern hydropower generation, a condition common across the U.S. and globally.

  PublisherDOI

• High-Alkalinity Algal Cultivation with Direct Air Capture: An Economic Feasibility Analysis

  2026-05-02

  article

  Weather variability and CO₂ supply costs remain key barriers to the commercial viability of algal biofuel production. Recent experimental work has demonstrated that the algae Chlorella sp. strain SLA-04 achieves high productivity in extreme alkaline growth media (pH greater than 10), where the solution chemistry enables direct capture of atmospheric CO₂, eliminating the need for costly CO₂ sparging. The extreme alkaline medium also provides resistance to microbial contamination and culture crashes. Despite these promising experimental results, the commercial-scale economic and environmental implications of this cultivation approach have not yet been assessed. Here we present the first integrated Techno Economic Analysis (TEA)/Life Cycle Analysis (LCA) of high pH-high alkalinity production, driven by 500 stochastic simulations of 20-year weather and market conditions. We compare four SLA-04 cultivation scenarios against a baseline strain cultivation scenario with Nannochloropsis oceanica and sparged CO₂. These scenarios also include the first incorporation of Trona, a naturally occurring carbonate mineral and the primary domestic source of bicarbonate in the United States, into our TEA/LCA framework as a low-cost alternative to commercial NaHCO₃ for establishing the high-alkalinity growth medium. Our results indicate that the SLA-04-Trona scenario reduces operating expenses by 60% per gallon relative to the baseline, and SLA-04 exhibits lower production variability across all seasons. The high pH-high alkalinity cultivation method also reduces the carbon intensity of algal biofuel by approximately 40%, achieving values below corn-based ethanol. These findings provide the first quantitative evidence that high pH-high alkalinity cultivation can substantially improve both the economics and environmental footprint of commercial-scale algal biofuel production.

  PublisherDOI

• A composite index-based insurance instrument for managing the financial risk of variable hydrometeorology for electric utilities

  2025-02-27

  preprintOpen access1st authorCorresponding

  Variable hydrometeorological conditions can impact electric utilities' financial stability. Extreme temperatures often increase electricity demand, raising utility costs, while drought reduces hydropower generation and often reduces revenues, with financial impacts potentially exacerbated by spikes in fuel prices, particularly natural gas. In this study, a model of the U.S. West Coast power system is combined with a financial risk model of a large California electric utility as it responds to variable hydrometeorology and market conditions, and is used to test the performance of a novel financial tool for managing risk. An insurance contract based on a composite index of measures related to streamflow, temperature, and natural gas prices is developed and its cost-effectiveness is compared against a portfolio of three currently available index contracts each based on a single index. The new composite index contract achieves an equivalent reduction in the utility’s net revenue variance as a portfolio of the three existing contract types for roughly half the cost with the cost reduction largely attributable to lower basis risk associated with the composite index contract. The utility's financial risk and the effectiveness of the new contract are also explored under an alternative regulatory scenario involving a pollution tax intended to reduce air pollution damages and emissions. Overall, this case study represents a new approach to managing financial risk arising from hydrometeorological and market variability for vertically integrated utilities, the most common utility structure.

  PublisherOA PDFDOI

• Improving the Representation of Climate Risks in Long-Term Electricity Systems Planning: a Critical Review

  UNC Libraries · 2025-03-22

  articleOpen access
  PublisherDOI

• A composite index-based insurance instrument for managing the financial risk of variable hydrometeorology for electric utilities

  Environmental Research Energy · 2025-07-16

  articleOpen access1st authorCorresponding

  Abstract Variable hydrometeorological conditions can impact electric utilities’ financial stability. Extreme temperatures often increase electricity demand, raising utility costs, while drought reduces hydropower generation and often reduces revenues, with financial impacts potentially exacerbated by spikes in fuel prices, particularly natural gas. In this study, a model of the US. West Coast power system is combined with a financial risk model of a large California electric utility as it responds to variable hydrometeorology and market conditions, and is used to test the performance of a novel financial tool for managing risk. An insurance contract based on a composite index of measures related to streamflow, temperature, and natural gas prices is developed and its cost-effectiveness is compared against a portfolio of three currently available index contracts each based on a single index. The new composite index contract achieves an equivalent reduction in the utility’s net revenue variance as a portfolio of the three existing contract types for roughly half the cost with the cost reduction largely attributable to lower basis risk associated with the composite index contract. The utility’s financial risk and the effectiveness of the new contract are also explored under an alternative regulatory scenario involving a pollution tax intended to reduce air pollution damages and emissions. Overall, this case study represents a new approach to managing financial risk arising from hydrometeorological and market variability for vertically integrated utilities, the most common utility structure.

  PublisherDOI

• A space-time simulator for hourly wind and solar energy fields

  2024-06-26

  preprintOpen access1st authorCorresponding

  Spatially distributed renewable energy generation poses unique risks to power systems since the aggregate amount of energy produced in any hour depends on the spatial correlation structure of the sources. Moreover, the spatial correlation structure can vary with the time of day and season and depend on the state of the large-scale climate. These features pose a challenge for resource adequacy risk assessment using traditional statistical or machine learning methods. A new algorithm based on spatially clustered k-nearest neighbors to capture the spatio-temporal dynamics of wind and solar fields is presented and applied to data from ERCOT, Texas. The algorithm skill is analyzed both at the aggregated field level and also at the individual site level. The algorithm's utility in assessing temporally varying risks of lower-than-expected target wind and solar energy production across ERCOT is demonstrated.

  PublisherOA PDFDOI

• Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state

  UNC Libraries · 2024-11-14

  articleOpen access

  Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

  PublisherDOI

• Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state

  Science Advances · 2024-11-01 · 10 citations

  articleOpen access

  Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

  PublisherDOI

• Improving the Representation of Climate Risks in Long-Term Electricity Systems Planning: a Critical Review

  Current Sustainable/Renewable Energy Reports · 2023 · 11 citations

  • Environmental resource management
  • Natural resource economics
  • Environmental science
  PublisherDOI

• Integrating Climate Risk into Long-Term Energy Planning: A Critical Review

  2023-07-15

  reviewOpen access

  Electricity systems face substantial and growing climate risks which are escalating due to electrification, renewable energy intermittency, population changes, and the intensifying impacts of climate change such as extreme temperatures and weather-induced infrastructure damage. This critical review investigates climate risks to the electricity sector and scrutinizes the methodologies used to represent climate risk in long-term electricity system planning studies. We find that many studies rely on weather data and socio-economic scenarios that are inadequate to fully characterize climate risks to present and future electricity systems. We advocate for more holistic assessments that incorporate comprehensive weather data, acknowledge dynamic multi-sector interactions, and employ adaptive and robust methodologies.

  PublisherOA PDFDOI

Frequent coauthors

• Upmanu Lall

  Columbia University

  13 shared

• David Farnham

  7 shared

• James Doss‐Gollin

  7 shared

• Vijay Modi

  Lamar University

  3 shared

• Mengjie Zhang

  Columbia University

  2 shared

• Adam Nayak

  Columbia University

  2 shared

• Katlyn Schmeltzer

  Rice University

  2 shared

• Lei Yan

  2 shared

Education

• PhD, Department of Earth and Environmental Engineering

  Columbia University Fu Foundation School of Engineering and Applied Science

  2023

• Master of Science, Department of Earth and Environmental Engineering

  Columbia University Fu Foundation School of Engineering and Applied Science

  2017

• Bachelor of Chemical Engineering, Department of Chemical Engineering

  Institute of Chemical Technology

  2016

Awards & honors

• Morton B. Friedman Memorial Prize for Excellence 2023
• LaVon Duddleson Fellowship 2022-2023
• Cheung-Kong Innovation Doctoral Fellowship 2020-2022