Logistic model-based forecast of sales and generation of obsolete computers in the US

被引:73
|
作者
Yang, Yan [1 ]
Williams, Eric [1 ,2 ]
机构
[1] Arizona State Univ, Dept Civil Environm & Sustainable Engn, Tempe, AZ 85287 USA
[2] Arizona State Univ, Sch Sustainabil, Tempe, AZ 85287 USA
基金
美国国家科学基金会;
关键词
Computer penetration rate; End-of-life computers; e-waste; Logistic model; Bounding analysis; CARRYING-CAPACITY; DIFFUSION; GROWTH; INNOVATIONS; ENERGY;
D O I
10.1016/j.techfore.2009.03.004
中图分类号
F [经济];
学科分类号
02 ;
摘要
Our goal is to characterize future trends in the generation of obsolete computers in the U.S. Starting from historical sales data on new computers and assuming a plausible first lifespan distribution, we extrapolate the historical sales trend to the future using a logistic model. The major challenge is that the personal computer is still in an early stage of its technology adoption life cycle and thus early for statistical fits to yield a reasonable estimation of carrying capacity (or saturation adoption level). Our approach is to use a bounding analysis which characterizes a range based on plausible upper and lower bounds on the future carrying capacity (1.3 and 1.0 computers per capita respectively). These lower and upper bounds yield a generation of 92 and 107 million obsolete computers in 2020 respectively. The growth rates of adoption over the next decade are very different for lower versus upper bound, however by 2020 the adoption will be at most 8% away from the long-term carrying capacity in both cases. Assuming computer adoption follows logistic behavior we assert that the saturation level of generation of obsolete computers is not much more than a decade away. The current recycling level of computers is 65 million units, thus if the U.S. expects to recycle computers domestically significant growth of recycling facilities will be required. Note however that this analysis does not address how long obsolete computers are stored nor the distribution of obsolete computers to reuse, recycling, landfill options. This is an important issue to resolve in future work. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:1105 / 1114
页数:10
相关论文
共 50 条
  • [21] Parallel Model-Based Diagnosis on Multi-Core Computers
    Jannach, Dietmar
    Schmitz, Thomas
    Shchekotykhin, Kostyantyn
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2016, 55
  • [22] Statistical model-based forecast of minimum and maximum temperatures at Manali
    Dimri, AP
    Mohanty, UC
    Madan, OP
    Ravi, N
    CURRENT SCIENCE, 2002, 82 (08): : 997 - 1003
  • [23] Model-based optimization of crop management for climate forecast applications
    Royce, FS
    Jones, JW
    Hansen, JW
    TRANSACTIONS OF THE ASAE, 2001, 44 (05): : 1319 - 1327
  • [24] Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
    Hwang, Taehyun
    Oh, Min-Hwan
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 7, 2023, : 7971 - 7979
  • [25] An Abstraction for Reusable MDD Components - Model-based Generation of Model-based Code Generators
    Kulkarni, Vinay
    Reddy, Sreedhar
    GPCE'08: PROCEEDINGS OF THE ACM SIGPLAN SEVENTH INTERNATIONAL CONFERENCE ON GENERATIVE PROGRAMMING AND COMPONENT ENGINEERING, 2008, : 181 - 184
  • [26] Corporate Financial Forecast Warnings Based on the Logistic Regress Model and the Probit Regress Model
    Sun, Xiaoli
    Bai, Chengbiao
    2011 INTERNATIONAL CONFERENCE ON EDUCATION SCIENCE AND MANAGEMENT ENGINEERING (ESME 2011), VOLS 1-5, 2011, : 1891 - 1894
  • [27] Improving Model-Based Test Generation by Model Decomposition
    Arcaini, Paolo
    Gargantini, Angelo
    Riccobene, Elvinia
    2015 10TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE 2015) PROCEEDINGS, 2015, : 119 - 130
  • [28] Forecast of Hydroelectricity Generation in Guangxi Based on ARIMA Model
    Lu, ZhiXiang
    Chen, JiChang
    Ma, ShiMing
    MODERN INDUSTRIAL IOT, BIG DATA AND SUPPLY CHAIN, IIOTBDSC 2020, 2021, 218 : 179 - 186
  • [29] Mathematical model based on the product sales market forecast of Markov forecasting and application
    Li, Lihong
    Sun, Jie
    Li, Yan
    Xuan, Hai
    Journal of Chemical and Pharmaceutical Research, 2014, 6 (06) : 1359 - 1365
  • [30] A Generalized Model-based Test Generation Method
    Bonifacio, Adilson Luiz
    Moura, Arnaldo Vieira
    Simao, Adenilso da Silva
    SEFM 2008: SIXTH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND FORMAL METHODS, PROCEEDINGS, 2008, : 139 - +