About

Destenie Nock is an Assistant Professor in Civil and Environmental Engineering and Public Policy at Carnegie Mellon University. She received her Ph.D. in 2019 from the University of Massachusetts Amherst, where she performed energy systems modeling in New England and Sub-Saharan Africa, utilizing multi-criteria decision analysis and applied optimization to support policy makers in understanding energy planning options. Her broad research interests focus on using mathematical modeling tools to address societal challenges related to sustainability planning, energy policy, and engineering for social good. Nock has extensive professional experience working in industry, national laboratories, and government settings on issues related to energy systems and equity. Her work emphasizes the development of innovative solutions for energy accessibility, affordability, and sustainability, with particular attention to vulnerable groups and equitable energy planning.

Research topics

• Political Science
• Business
• Economics
• Computer Science
• Environmental economics
• Public economics
• Computer Security
• Engineering
• Economic growth
• Public relations
• Environmental resource management
• Ecology
• Sociology
• Microeconomics
• Social Science
• Transport engineering
• Statistics
• Chemistry
• Knowledge management
• Advertising
• Geography
• Natural resource economics
• Demographic economics
• Pedagogy

Selected publications

• Justice as a measure of energy transition success

  Nature Energy · 2026-01-28 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Characterizing uncertainties in residential electrification: Financial feasibility, climate impacts, and health outcomes

  Applied Energy · 2026-03-05

  articleOpen accessCorresponding

  Low-to-moderate-income (LMI) families in the United States face disproportionately high energy costs due to inefficient housing and systemic underinvestment. Residential end-use electrification can deliver benefits to households and the broader public, depending on fuel type, technology, valuation methods, and other factors. This study extends prior residential electrification feasibility analyses by evaluating how Inflation Reduction Act (IRA) rebates and the monetization of climate and health co-benefits jointly affect the cost-effectiveness and adoption potential of residential energy efficiency and electrification retrofits. We define adoption potential as the share of households achieving a positive net present value (NPV) from retrofits, categorized into tiers: Tier 1 (households can recover total capital costs), Tier 2 (households can recover incremental upgrade costs relative to replacing existing equipment), and Tier 3 (additional subsidies justified by public benefits help households reach Tier 2). We find that heat pump water heaters show nearly universal adoption potential, suggesting prioritizing heat pump water heaters as accessible electrification entry points. Air-source heat pump (ASHP) retrofits exhibit fuel-specific adoption patterns, with ASHP adoption potential reaching up to 38% under IRA provisions. Heat pump clothes dryers and electric cooking ranges follow similar patterns to water heaters, though less pronounced. We also find that ASHP upgrades can reduce economic damages from criteria pollutant exposure in 85–96% of households. Moreover, we find that accounting for monetized public benefits from avoided greenhouse gas emissions can more than double ASHP adoption potential. Overall, this study highlights the value of societal impact assessment and robust uncertainty analysis in policy evaluation. • Heat pump water heaters demonstrate nearly universal LMI adoption potential. • ASHP retrofits in LMI households range from 30% (natural gas) to 91% (fuel oil). • Monetizing climate benefits can increase adoption potential for gas users by >60%. • Electrifying heating in 85–96% of households would improve air quality benefits. • Fuel-specific policies can promote beneficial space heating electrification.

  PublisherDOI

• Behavioural choices shape US indoor temperatures more than technology

  Nature Energy · 2026-01-15

  articleOpen accessSenior author
  PublisherOA PDFDOI

• The role of thermostats and human behaviour in residential temperature settings in the USA

  Nature Energy · 2026-01-15

  articleSenior author
  PublisherDOI

• Capturing the distribution of costs and benefits in electricity system planning and operations

  Joule · 2026-03-27

  article
  PublisherDOI

• Examining the role of ridesourcing services during rain: A Chicago case study

  Transportation Research Interdisciplinary Perspectives · 2026-01-06

  articleOpen access

  • Estimated effects of rain on TNC ridership in Chicago with valid confidence intervals using non-parametric methods. • Rain effects are larger in densely populated, high income areas. • Rain effects relative to base ridership are larger in peripheral areas. • Areas with low transit access and car ownership show higher rain effects, suggesting TNC dependency during rain. Transportation network companies (TNCs) are an established transportation mode. Yet, uncertainty remains on the level to which rain affects TNC ridership and how this relates to socioeconomic factors. Leveraging TNC trip and weather data from Chicago we estimate rain effects on ridership using non-parametric methods and use OLS regression to reveal their associations with underlying demographics. We find rain causes ridership fluctuations between −46 % and + 140 %, with highest percentage changes observed in the periphery of Chicago. Ridership tends to decrease in areas near the Chicago Transit Authority rail lines, suggesting a possible alleviating effect of transit. OLS regression reveals areas with higher population tend to experience higher changes in ridership during rain (p < 0.001), and the same is true in areas with higher shares of high-income households (p < 0.05). In addition, higher transit access (p < 0.001) and lower shares of households with no vehicles (p < 0.05) are associated with lower rain effects on ridership.

  PublisherDOI

• Socially vulnerable communities face disproportionate exposure and susceptibility to U.S. wildfire and prescribed burn smoke

  Communications Earth & Environment · 2025-03-08 · 8 citations

  articleOpen access

  Abstract While air pollution from most U.S. sources has decreased, emissions from wildland fires have risen. Here, we use an integrated assessment model to estimate that wildfire and prescribed burn smoke caused $200 billion in health damages in 2017, associated with 20,000 premature deaths. Nearly half of this damage came from wildfires, predominantly in the West, with the remainder from prescribed burns, mostly in the Southeast. Our analysis reveals positive correlations between smoke exposure and various social vulnerability measures; however, when also considering smoke susceptibility, these disparities are systematically influenced by age. Senior citizens, who are disproportionately White, represented 16% of the population but incurred 75% of the damages. Nonetheless, within most age groups, Native American and Black communities experienced the greatest damages per capita. Our work highlights the extraordinary and disproportionate effects of the growing threat of fire smoke and calls for targeted, equitable policy solutions for a healthier future.

  PublisherOA PDFDOI

• An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks

  Decision Analytics Journal · 2025-12-23

  articleOpen access

  Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach for learning individual preferences. However, there is limited knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity, especially in the context of challenges often experienced during preference elicitation. This study evaluates four machine learning models (multinomial logistic regression, generalized additive model, twinned neural network, and Gaussian process) with respect to their capacity to learn and predict five choice rules that are important in the behavioral and social sciences (linear strong utility, monotonic strong utility, ideal point, lexicographic semiorder, and multiattribute linear ballistic accumulator). Monte Carlo experiments were performed to assess model performance when increasing a) the number of attributes in the choice alternatives, b) the number of training choice sets, and c) the choice rule’s determinism. The simulation results demonstrated that semi-parametric and non-parametric models generally outperform parametric models across all choice rules and experimental contexts. Model performance also generally improves by 6% to 96% and 0% to 55%, respectively, with an increase in training choice sets and choice rule determinism. A case study using real energy policy preference data was also conducted, where TNN performed best with a BIC of 13.351. This work demonstrated the viability and limitations of semi-parametric and non-parametric models in the context of policy-centric discrete choice modeling and showed how the choice task context should drive model selection. • Demonstrate that semi-parametric and non-parametric models improve the learning of discrete choice decision rules. • Show how increasing decision attributes affect machine learning model performance in choice analytics. • Illustrate the impact of limited choice data on predictive accuracy in preference-based decision-making. • Highlight the influence of choice rule randomness on model selection for discrete preference learning. • Recommend model choice based on context and specific challenges in discrete choice decision analysis.

  PublisherDOI

• Powering fairness in climate adaptation capabilities: Evaluating the influence of air conditioning rebates in a hot climate

  Energy Research & Social Science · 2025-07-05 · 6 citations

  articleOpen access

  Assisting households with maintaining adequate energy supply is one method for improving overall quality of life. Households experiencing energy insecurity may be unable to afford to use energy for necessary services at home (e.g., unable to purchase air conditioners). Energy efficiency (EE) can reduce energy costs for low-income households–requiring less energy for essential activities. While existing research has identified the groups that are less likely to participate in energy efficiency programs, there is limited research on how participation impacts energy insecurity among vulnerable households when they participate. Using over 138,000 households in Tallahassee, Florida we study participants in a neighborhood program that targeted underserved communities. We conduct quasi-experimental difference-in-difference comparisons for seasonal energy consumption, energy bills, and energy burden during the cooling season in response to air conditioning (AC) appliance purchases. We compare impacts for households in the program (REACH) and higher income non-REACH qualified households. We find that REACH homes, on average, save approximately 300kWh-eq on energy or $25 seasonally after purchasing an AC unit. While AC rebates reduce seasonal energy burden by 0.6 % in non-REACH homes, there is no statistically significant change in seasonal energy burden for REACH homes. The difference in energy reduction between REACH and non-REACH qualified homes could be due to increases in AC use among REACH homes after rebates. Further work could explore this trend of potential increases in efficient appliance use among low-income homes. • Air conditioning rebates impact low-income homes in a hot climate during cooling seasons. • Low-income homes save $25 on energy bills each cooling season due to rebates. • Higher-income homes, on average, save $6 more per cooling season after rebates. • Low-income homes may have increased AC use because of AC rebates. • This study shows how energy efficiency helps low-income homes afford energy needs.

  PublisherDOI

• Assessing the feasibility of temperature response models for residential energy consumption behavior analysis

  Energy and Buildings · 2025-08-25

  articleOpen accessSenior author

  • We compare different methods for modeling temperature response functions. • Lagged dependent variable models perform the best from assumption testing. • Estimated model parameters are similar across methods. • We recommend using stationary lagged dependent variable models. Temperature response functions (TRFs) are models used to predict a household’s daily energy consumption from outdoor temperature and other control variables. In recent energy equity work, TRFs are modeled as piecewise linear regressions and interpreted for inference instead of prediction. Inference of regression parameters assumes the models help identify when and how intensely households use climate controls. Utilities apply this knowledge to better understand which consumers show energy limiting behaviors (e.g., reducing usage to reduce cost). However, past studies have not evaluated whether TRF models satisfy regression assumptions. By testing these assumptions for different model formulations, we learn how to adapt the model, which can deepen our understanding of energy disparities. We analyze data from approximately 100,000 households in Tallahassee, Florida, US and 350,000 households from the US mid-Atlantic region. We found that including a lagged dependent variable as a predictor significantly improves conformity with all linear regression assumptions compared to all other formulations. Further, most parameter estimates are similar across methods, and most differences observed are expected due to the model formulations. For these reasons, we suggest using the stationary lagged dependent variable method for modeling TRFs. Overall, our work can help energy utilities better evaluate energy assistance programs and the quality of TRF analyses.

  PublisherDOI

Recent grants

• Equity and Sustainability: A framework for Equitable Energy Transition Analyses

  NSF · $448k · 2020–2024

• Active preference learning to aid public decisions

  NSF · $409k · 2021–2025

• EAGER: SAI: New Decision Paradigms by Integrating Utility Theory into Infrastructure Investments

  NSF · $316k · 2021–2024

• Collaborative Research: Energy Efficiency and Energy Justice: Understanding Distributional Impacts of Energy Efficiency and Conservation Programs and the Underlying Mechanisms

  NSF · $200k · 2023–2025

Frequent coauthors

• Yueming Qiu

  Xiamen University

  12 shared

• Millard McElwee

  Carnegie Mellon University

  12 shared

• Daniel Erian Armanios

  University of Oxford

  10 shared

• Andrea Francioni Rooney

  Johns Hopkins University

  9 shared

• Sarah Christian

  Stanford University

  9 shared

• Teagan Goforth

  9 shared

• Shuchen Cong

  Carnegie Mellon University

  9 shared

• Gerald Wang

  Cornell University

  9 shared

Education

• Ph.D.

  University of Massachusetts Amherst

  2019

Awards & honors

• Outstanding Alumni Award from the University of Massachusett…