
Matthew Peterson
· Associate Professor of Graphic & Experience DesignVerifiedNorth Carolina State University · Graphic and Experience Design
Active 1964–2026
About
Matthew Peterson is a member of the Alliance for Inclusive Design at NC State University, which focuses on advancing inclusive design through research and industry collaboration. The Alliance aims to influence research, industry practice, and application of inclusive design, working with colleagues across NC State University, partner universities, governmental agencies, non-profit organizations, and professional design firms. While specific details about Matthew Peterson's individual research focus, background, or key contributions are not provided in the page text, his association with the Alliance indicates a commitment to promoting inclusive design practices and research within the built environment.
Research topics
- Sociology
- Political Science
- Psychology
- Geography
- Social psychology
- Social Science
- Medicine
- Pedagogy
- Environmental health
- Computer Science
- Developmental psychology
- Engineering
- Epistemology
- Gerontology
- Demography
- Clinical psychology
- Psychiatry
- Ecology
- Engineering ethics
- Medical education
- Mathematics education
- Public relations
Selected publications
Assessing public attitudes towards sharks using the Implicit Association Test
Marine Policy · 2026-02-20
articleHuman Dimensions of Wildlife · 2026-04-27
articleEnvironments · 2025-08-29 · 1 citations
articleOpen accessSenior authorClimate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation.
Evaluating household dynamics of wildlife preferences using toys
Human Dimensions of Wildlife · 2025-02-13
articleEnvironments · 2025-12-05
articleOpen accessCoastal forests are highly sensitive to both climate change and land use change, which can strongly affect hydrological processes and long-term water yield. This study quantifies the individual and combined impacts of climate change and land use/land cover (LULC) change on water yield in the Croatan National Forest (CNF), a coastal ecosystem in North Carolina, USA, from 2003 to 2070. To produce high-resolution climate projections, we extended the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach by applying a full statistical downscaling of temperature and precipitation from CMIP6–SSP5-8.5 scenarios using the Random Forest algorithm. Future LULC scenarios were generated using machine learning and Markov Chain-based modeling to predict spatial changes up to 2070. The downscaled climate and LULC data were integrated into the WaSSI hydrological model to simulate their potential effects on water yield under the following four scenarios: baseline, LULC change only, climate change only, and combined change. The results showed that climate change alone could reduce annual water yield by about 11%, while LULC change alone could increase it by roughly 3% due to lower evapotranspiration from forest-to-urban conversion. Under the combined scenario, water yield decreased by about 6%, indicating that climate change dominated, but LULC change could locally alter or influence its effects. Overall, the findings highlight that climate change could be the primary driver of reduced water yield in coastal forests, while LULC change mainly affects its spatial variability. This integrated framework improves the accuracy of regional hydrological projections and provides useful insights for climate adaptation and sustainable water resource management in coastal forest ecosystems.
Political identity as a driver of hunter responses to chronic wasting disease in a post-COVID world
Human Dimensions of Wildlife · 2024-11-06 · 2 citations
articleFraming Climate Change Communication to Align with Cultural Cognition and Political Ideology
SSRN Electronic Journal · 2024-01-01
preprintOpen accessJournal of Outdoor Recreation and Tourism · 2024-10-28 · 2 citations
articleJournal of Environmental Psychology · 2024-04-20 · 25 citations
article1st authorCorrespondingHow mixed messages may be better than avoidance in climate change education
Journal of Environmental Studies and Sciences · 2024-09-24 · 2 citations
article
Frequent coauthors
- 73 shared
Kathryn T. Stevenson
North Carolina State University
- 31 shared
Christopher E. Moorman
- 26 shared
Tarla Rai Peterson
The University of Texas at El Paso
- 20 shared
R. Brian Langerhans
North Carolina State University
- 20 shared
Jianguo Liu
Zunyi Medical University
- 20 shared
Markus J. Peterson
Hagerstown Community College
- 18 shared
Erin Seekamp
North Carolina State University
- 17 shared
Christopher Serenari
Texas State University
Labs
Education
- 2007
Fisheries and Wildlife, Fisheries and Wildlife
Michigan State University
Awards & honors
- Visible Language (editor)
- Resume-aware match score
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- AI-drafted outreach
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