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

  article
  PublisherDOI

• The North American model of wildlife conservation is an inappropriate basis for formulating conservation policy

  Human Dimensions of Wildlife · 2026-04-27

  article
  PublisherDOI

• Future Water Yield Projections Under Climate Change Using Optimized and Downscaled Models via the MIDAS Approach

  Environments · 2025-08-29 · 1 citations

  articleOpen accessSenior author

  Climate 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.

  PublisherOA PDFDOI

• Evaluating household dynamics of wildlife preferences using toys

  Human Dimensions of Wildlife · 2025-02-13

  article
  PublisherDOI

• The Potential Impacts of Climate and Land Use Changes on Water Yield in the Croatan National Forest, USA

  Environments · 2025-12-05

  articleOpen access

  Coastal 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.

  PublisherOA PDFDOI

• 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

  article
  PublisherDOI

• Framing Climate Change Communication to Align with Cultural Cognition and Political Ideology

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Environmental and social impacts of shifting park-use patterns during the COVID-19 pandemic: Insights from state and local park managers

  Journal of Outdoor Recreation and Tourism · 2024-10-28 · 2 citations

  article
  PublisherDOI

• Birdwatching linked to increased psychological well-being on college campuses: A pilot-scale experimental study

  Journal of Environmental Psychology · 2024-04-20 · 25 citations

  article1st authorCorresponding
  PublisherDOI

• How mixed messages may be better than avoidance in climate change education

  Journal of Environmental Studies and Sciences · 2024-09-24 · 2 citations

  article
  PublisherDOI

Frequent coauthors

• Kathryn T. Stevenson

  North Carolina State University

  73 shared

• Christopher E. Moorman

  31 shared

• Tarla Rai Peterson

  The University of Texas at El Paso

  26 shared

• R. Brian Langerhans

  North Carolina State University

  20 shared

• Jianguo Liu

  Zunyi Medical University

  20 shared

• Markus J. Peterson

  Hagerstown Community College

  20 shared

• Erin Seekamp

  North Carolina State University

  18 shared

• Christopher Serenari

  Texas State University

  17 shared

Labs

• The Alliance for Inclusive Design Practice and ResearchPI

Education

• Fisheries and Wildlife, Fisheries and Wildlife

  Michigan State University

  2007

Awards & honors

• Visible Language (editor)