About

Jianbang Gan, Ph.D., is a professor in the Department of Ecology and Conservation Biology at Texas A&M University and a faculty affiliate of the Texas A&M Energy Institute. His research and teaching interests focus on the intersection of natural and socioeconomic systems, with an emphasis on forest resources. His current research centers on disturbances such as wildfire, invasions of nonnative species, pest infestations, and extreme weather events, as well as response strategies. Additionally, he studies trade and natural resource conservation, along with the economic and environmental aspects of bioeconomic development. His work aims to connect science, policy, and practice. Gan has conducted collaborative research across multiple continents, including Africa, the Americas, Asia, Europe, and Oceania. With over 25 years of undergraduate and graduate teaching experience, he has contributed significantly to education in his field. His professional background includes a B.S. in Forest Engineering from Fujian Agriculture and Forestry University, an M.S. in Forestry (Economics and Marketing), and a Ph.D. in Forestry (Economics), both from Iowa State University. His expertise encompasses forest economics, forest management and conservation, bioeconomics, and responses to environmental disturbances.

Research topics

• Economics
• Environmental science
• Waste management
• Natural resource economics
• Ecology
• Demography
• Economic geography
• Business
• Econometrics
• Macroeconomics
• Environmental resource management
• Engineering
• Geography
• Mathematics
• International trade
• Agroforestry

Selected publications

• Mean-risk stochastic integer programming approach for integrated fuel treatment and wildfire response planning with endogenous uncertainty

  Journal of Global Optimization · 2026-03-14

  articleOpen access

  Abstract Two-stage mean-risk stochastic integer programming (MR-SIP) with endogenous uncertainty provides a powerful modeling tool for real-life decision-making problems, as it allows to capture here-and-now decisions that influence future outcomes. However, such problems are difficult to solve due to their nonconvexity and large-scale nature. We derive a decomposition method for this class of MR-SIP and apply it to a critical problem in wildfire management: optimal fuel treatment planning (FTP) under uncertainty. The uncertainty stems from fuels (vegetation), fire occurrence, and weather conditions. Fuel treatment methods such as prescribed burning, mechanical thinning, mowing, grazing, and chemical treatments are aimed at reducing hazardous fuels and thus, influence the uncertainty. In this work, we formulate a novel MR-SIP FTP model that integrates fuel treatment and firefighting resource deployment planning before fires happen, which are typically addressed in isolation rather than in an integrated manner. The new model uses the expected excess risk measure, which given a target level of wildfire damage cost, minimizes the mean excess above the target level. We parameterize the FTP model through standard wildfire behavior simulation software for generating fire scenarios and apply it to a real study area in West Texas, U.S.A. The results provide several practical insights for FTP decision-making. For example, the results reveal that when considering fuel treatment alone, treatment coverage is spread across the study area to high-risk subareas. However, integrating fuel treatment with resource deployment reduces coverage near operation bases, prioritizing high-risk subareas and reducing fire damage cost by 80% on average.

  PublisherOA PDFDOI

• Private Landowners' Perspectives on Managing Feral Swine in Arkansas, Louisiana, and East Texas

  Journal of Wildlife Management · 2026-01-29

  articleOpen accessSenior author

  Abstract Feral swine ( Sus scrofa ) have inflicted extensive damage on private lands throughout the southern United States, especially in the West Gulf Region. Managing feral swine on private lands is increasingly necessary to reduce ecological and economic damage. Management is also becoming increasingly challenging with growing swine populations and the diversity of private ownership and management objectives. To improve feral swine management, we conducted a mail survey of landowners to better understand private landowners’ feral swine management decision‐making in Arkansas, Louisiana, and East Texas, a region that contains over half of the feral swine population in the United States. We documented reported management actions and motivations, described perceived effectiveness of different management strategies, and examined various factors related to landowners’ decisions to manage swine. We used analysis of variance to test differences in swine management and control activities among the 3 states and binary logistic regression to identify factors associated with landowners’ swine management decisions. Shooting and trapping activities were conducted more often than other activities (e.g., fences, repellent usage). Regression results indicated that landowner decisions to manage feral swine were related to sociodemographic characteristics (age, education) and property size, familiarity with and beliefs about feral swine impacts, and perceptions of neighbors and community. These findings offer insights into feral swine management decision‐making and can facilitate the development of incentives, education, and outreach programs that encourage landowners to manage feral swine in the West Gulf Region.

  PublisherOA PDFDOI

• Understanding private landowner strategies for wild pig management using cluster analysis and structural equation modeling

  Scientific Reports · 2026-03-04

  articleOpen accessSenior author

  Wild pigs (Sus scrofa) pose a significant threat, causing substantial ecological and economic damage to natural ecosystems, agriculture, and forestry through destructive behaviors of wallowing and rooting. Addressing this widespread issue urgently requires effective and sustained management strategies, especially involving private landowners, who are a critical stakeholder group in the West Gulf Coastal Plain (WGCP). This study aims to identify landowner typologies in wild pig management and to examine factors influencing their intentions to engage in such efforts in Arkansas, Louisiana, and East Texas. We employed a mixed method of cluster analysis and structural equation modeling (SEM) based on the Theory of Planned Behavior (TPB). Cluster analysis revealed three distinct landowner groups based on their familiarity with and experiences of wild pig damage and management efforts: Unaware Bystanders, Frontline Responders, and Cautious Observers. SEM was employed to assess the belief structures influencing behavioral intentions across the entire sample and within each identified cluster. Results indicated that beliefs and attitudes were the most influential predictors of intended behavior, which varied across the landowner clusters. The findings highlight the heterogeneity in landowner responses and offer practical implications for developing targeted outreach strategies, policy interventions, and collaborative management approaches aligned with the needs and motivations of different landowner groups.

  PublisherOA PDFDOI

• Allometric relationships between total height and diameter for volume estimation of bottomland hardwood oaks in Arkansas

  Frontiers in Forests and Global Change · 2026-04-29

  articleOpen accessSenior author

  Bottomland hardwood (BLH) forests are a unique ecosystem that plays an important role in the provision of ecosystem services, both ecologically and economically. This study modeled allometric relationships between total tree height and diameter at breast height (DBH) for seven oak species ( Quercus spp.), including overcup ( Quercus lyrata ), cherry bark ( Quercus pagoda ), Nuttall ( Quercus texana ), Shumard ( Quercus shumardii ), water ( Quercus nigra ), willow ( Quercus phellos ), and post oaks ( Quercus stellata ) that prevail in BLH forests in the Lower Mississippi Alluvial Valley region of eastern Arkansas. Twelve candidate models (Schumacher, Bufford, Curtis, Meyer, Micment, Power, Logistic, Weibull, Chapman, Gompertz, Ratkowsky, and Prodan) were fitted using data from 2,762 individual trees across 34 Forest Inventory and Analysis (FIA) plots collected between 2015 and 2023. The results showed that the three-parameter Weibull model was the best-fitting model based on the relative rank sum scores and was therefore selected as the base model for further analysis. To account for interspecific variability, a nonlinear mixed-effects model was developed using the Weibull function with species as a random effect. The mixed-effects model performed better than the fixed-effects global models, with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> = 0.79 and RMSE = 3.34 m. Furthermore, volume validation analysis revealed that mixed-effects model estimates were more consistent with the observed data. In contrast, global model estimates tended to underestimate volume in larger diameter classes (DBH &amp;gt; 50 cm). Additionally, applying response calibration to a subsample of trees localized these predictions and consistently reduced prediction errors relative to the uncalibrated population-average model. These findings suggest that the mixed-effects Weibull model is a robust and precise tool for predicting tree height and estimating volume for oak species in BLH forests of this region, providing a baseline for predicting biomass stock of these oak species and contributing to the development of biometric models to guide forest inventory, silvicultural practices, and forest management.

  PublisherDOI

• Landscape-level effectiveness of fuel treatments in a forest-dominated ecosystem in the Southern United States

  PLoS ONE · 2026-02-13

  articleOpen accessSenior author

  Wildfire has become an increasing threat to natural ecosystems and human livelihood alike in many parts of the world. Vegetation fuel treatments are considered a viable option for mitigating wildfire risk and damage; yet existing studies have yielded mixed or inconclusive results on fuel treatment effectiveness especially at the landscape level. Using fire behavior simulations and statistical analysis of simulation outputs, we assessed landscape-level effectiveness of prescribed burning (PB) and thinning from below (TFB) relative to their site-level effectiveness in terms of area burned (AB) and total cost of treatment and timber loss (TC) in a forest-dominated ecosystem in the southern United States. We found that effectiveness of a treatment varied with measurement metrics and extent, vegetation characteristics and dynamics, and their interactions with the treatment. PB and TFB were less effective at the landscape level than at the site level where fires burned only inside the treatment area. At both site and landscape levels, the effectiveness of PB and TFB in reducing AB and TC largely depended on the quantity of biomass and fire ignition location. TFB outperformed PB in mitigating both AB and TC with a larger timber volume, a longer delay in fire occurrence after treatment, or a higher uncertainty of fire ignition location. TFB was also more effective than PB in reducing TC at the landscape level. By clarifying the conditions under which a fuel treatment can mitigate the area burned and the total cost, this study advanced knowledge of fuel treatment effectiveness especially at the landscape level. Such knowledge can aid in developing and deploying treatment strategies to minimize fire extent and adverse economic consequences in the study region and beyond.

  PublisherDOI

• Disentangling the drivers of wildfires

  Science · 2025-01-02 · 4 citations

  letter1st authorCorresponding

  The risk of wildfires varies across regions with different vegetation.

  PublisherDOI

• Deep insights into the effects of polystyrene nanoplastics on methanogenic microbial communities in food waste anaerobic digestion under meso-thermophilic temperatures

  Journal of Cleaner Production · 2025-12-06

  article
  PublisherDOI

• Economic impacts of oil and gas clusters: Analyzing the influence of oil and gas revenues in Brazil and Texas

  The Extractive Industries and Society · 2024-11-16

  article
  PublisherDOI

• Bilevel optimization approach for fuel treatment planning

  European Journal of Operational Research · 2024-07-14 · 8 citations

  articleOpen access

  Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against large-scale wildfires. In this study, we formulate and solve a bilevel integer programming model, where the fuel treatment planner (modeled as the leader) determines appropriate locations and types of treatments to minimize expected losses from wildfires. The follower (i.e., the lower-level decision-maker) corresponds to nature, which is adversarial to the leader and designs a wildfire attack (i.e., locations and time periods, where and when, respectively, wildfires occur) to disrupt the leader’s objective function, e.g., the total expected area burnt. Both levels in the model involve integrality restrictions for decision variables; hence, we explore the model’s difficulty from the computational complexity perspective. Then, we design specialized solution methods for general and some special cases. We perform experiments with semi-synthetic and real-life instances to illustrate the performance of our approaches. We also explore numerically the fundamental differences in the structural properties of solutions arising from bilevel model and its single-level counterpart. These disparities encompass factors like the types of treatments applied and the choice of treated areas. Additionally, we conduct various types of sensitivity analysis on the performance of the obtained policies and illustrate the value of the bilevel solutions.

  PublisherDOI

• Private forest landowners’ perspectives on feral hog management and control in Arkansas, Louisiana, and east Texas

  2024-01-01

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• C. Tattersall Smith

  12 shared

• John W. Taylor

  11 shared

• Hsiao‐Hsuan Wang

  Texas A&M University

  11 shared

• Nana Tian

  11 shared

• James H. Miller

  University of Rhode Island

  11 shared

• Stephen H. Kolison

  Tennessee State University

  9 shared

• James H. Miller

  US Forest Service

  7 shared

• Larry D. Teeter

  6 shared

Education

• B.S., Forest Engineering

  Fujian Agriculture and Forestry University

• M.S., Forestry (Economics and Marketing)

  Iowa State University

• Ph.D., Forestry (Economics)

  Iowa State University