Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration

This paper develops a stylized (or conceptual) system optimization model to analyze the adoption of an emerging infrastructure associated with uncertain technological learning and spatial reconfigurations. The model first assumes that the emerging infrastructure will be implemented for the entire system when it is adopted. With the model, this paper explores (1) how the emerging infrastructure's initial investment cost, technological learning and its uncertainty, market size, and efficiency influence the adoption of the emerging infrastructure and (2) how the efficiency and investment cost of the associated technology (which will be located in a different place with the adoption of the emerging infrastructure) influence the adoption of the emerging infrastructure. Then, this paper extends the model and explores whether it is a better solution to implement the emerging infrastructure for part of the distance from resource site to demand site if its efficiency is a function of the implemented distance. With optimizations under three types of efficiency dynamics, this paper finds that whether the emerging infrastructure should be implemented partly or entirely is not determined by the value of its efficiency but by the dynamics of its efficiency.

[1]  S. Messner,et al.  A stochastic version of the dynamic linear programming model MESSAGE III , 1996 .

[2]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[3]  Ad Seebregts,et al.  Energy/Environmental Modeling with the MARKAL Family of Models , 2002 .

[4]  M. Strubegger,et al.  The energy model MESSAGE III , 1994 .

[5]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[6]  Fred D. Davis,et al.  Development and Test of a Theory of Technological Learning and Usage , 1992 .

[7]  Tieju Ma,et al.  Spatial configuration and technology strategy of China’s green coal-electricity system , 2012 .

[8]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[9]  Tieju Ma Coping with Uncertainties in Technological Learning , 2010, Manag. Sci..

[10]  Yoshiteru Nakamori,et al.  Modeling technological change in energy systems – From optimization to agent-based modeling , 2009 .

[11]  Tieju Ma,et al.  Technology adoption with limited foresight and uncertain technological learning , 2014, Eur. J. Oper. Res..

[12]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .

[13]  K. Arrow The Economic Implications of Learning by Doing , 1962 .

[14]  F. Bass A new product growth model for consumer durables , 1976 .

[15]  W. Arthur,et al.  INCREASING RETURNS AND LOCK-IN BY HISTORICAL EVENTS , 1989 .

[16]  Tieju Ma,et al.  Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax , 2012 .

[17]  Yoshiteru Nakamori,et al.  Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents , 2009, Eur. J. Oper. Res..

[18]  K. Arrow The Economic Implications of Learning by Doing , 1962 .

[19]  D. North Competing Technologies , Increasing Returns , and Lock-In by Historical Events , 1994 .

[20]  Malte Schwoon,et al.  Learning by doing, learning spillovers and the diffusion of fuel cell vehicles , 2008, Simul. Model. Pract. Theory.