LOGISTIC REGRESSION MODELS OF FACTORS INFLUENCING THE LOCATION OF BIOENERGY AND BIOFUELS PLANTS

被引:0
|
作者
Young, Timothy M. [1 ]
Zaretzki, Russell L. [2 ]
Perdue, James H. [3 ]
Guess, Frank M. [2 ]
Liu, Xu [2 ]
机构
[1] Univ Tennessee, Ctr Renewable Carbon, Knoxville, TN 37996 USA
[2] Univ Tennessee, Dept Stat, Stokely Management Ctr 335, Knoxville, TN 37996 USA
[3] US Forest Serv, USDA, So Res Stn, Knoxville, TN 37996 USA
来源
BIORESOURCES | 2011年 / 6卷 / 01期
关键词
Bioenergy; Biofuels; Optimal siting; Logistic regression models; SOUTHEASTERN UNITED-STATES; BIOMASS; INDUSTRY; FEEDSTOCK; FUTURE; WOOD;
D O I
暂无
中图分类号
TB3 [工程材料学]; TS [轻工业、手工业、生活服务业];
学科分类号
0805 ; 080502 ; 0822 ;
摘要
Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m(2)/ha," "availability of unused mill residues," and "high density of railroad availability" had positive significant influences on the location of all wood-using faciities. "Median family income," "population," "low density of railroad availability," and "harvesting costs for logging residues" had negative significant influences on the location of all woodusing faciities. For larger woody biomass-using mills (e. g., biopower) availability of "thinnings to a basal area of 79.2m(2)/ha," "number of primary and secondary wood-using mills within an 128.8km haul distance," and "amount of total mill residues," had positive significant influences on the location of larger wood-using faciities. "Population" and " harvesting costs for logging residues" have negative significant influences on the location of larger wood-using faciities. Based on the logistic models, 25 locations were predicted for bioenergy or biofuels plants for a 13-state study region in the Southern United States.
引用
收藏
页码:329 / 343
页数:15
相关论文
共 50 条
  • [21] Factors affecting the location of sphenoid sinus ostium: a binary logistic regression analysis
    Kim, Dong-Hyun
    Koo, Hyung-Bon
    Son, Sang-Jun
    Lee, Jae-Hoon
    EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, 2023, 280 (04) : 1757 - 1764
  • [22] On the Complexity of Logistic Regression Models
    Bulso, Nicola
    Marsili, Matteo
    Roudi, Yasser
    NEURAL COMPUTATION, 2019, 31 (08) : 1592 - 1623
  • [23] Endogeneity in logistic regression models
    Avery, G
    EMERGING INFECTIOUS DISEASES, 2005, 11 (03) : 503 - 504
  • [24] Multicollinearity in Logistic Regression Models
    Bayman, Emine Ozgur
    Dexter, Franklin
    ANESTHESIA AND ANALGESIA, 2021, 133 (02): : 362 - 365
  • [25] Uncertain logistic regression models
    Gao, Jinling
    Gong, Zengtai
    AIMS MATHEMATICS, 2024, 9 (05): : 10478 - 10493
  • [26] Diagnostics in logistic regression models
    Sen Roy, Sugata
    Guria, Sibnarayan
    JOURNAL OF THE KOREAN STATISTICAL SOCIETY, 2008, 37 (02) : 89 - 94
  • [27] Diagnostics in logistic regression models
    Sugata Sen Roy
    Sibnarayan Guri
    Journal of the Korean Statistical Society, 2008, 37 : 89 - 94
  • [28] Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors
    Maulana, Yufri Isnaini Rochmat
    Badriyah, Tessy
    Syarif, Iwan
    EMITTER-INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY, 2018, 6 (01) : 151 - 167
  • [29] Influencing ethical decisions: A logistic regression analysis
    Cronan, TP
    Kreie, J
    DECISION SCIENCES INSTITUTE 1998 PROCEEDINGS, VOLS 1-3, 1998, : 711 - 713
  • [30] Logistic regression analysis of risk factors influencing childhood enuresis: a comprehensive survey study
    Li, Daolong
    Zhu, Ziqiang
    Zhang, Yin
    Zhang, Ye
    Chao, Min
    AMERICAN JOURNAL OF TRANSLATIONAL RESEARCH, 2024, 16 (07): : 3157 - 3163