Modeling And Analyzing Technology Adoption From The Perspective Of System Optimization

Human society as a system, needs to consider new technologies’adoption for the sustainable development of the system. Existing system optimization models of technology adoption always assume that there’s a global decision maker with perfect foresight who make decisions of technology adoptions for the economic system. They always neglect the factors that may have great influence of technology adoption in reality, e.g.. the uncertainty of technology development, the time limit of the planning cycle, the coexisting of different planning subjects and the interaction among them, and the spatial reconfiguration of the system along with a new technology’s adoption. Based on a simplified energy system, this paper builds a system optimization model of technology adoption with these neglected factors mentioned above, and analyzes how these factors influence the technology adoption.Most previous optimization models on technology adoption assume perfect foresight over the long-term. Decision-makers in reality do not have perfect foresight and the endogenous driving force for technology adoption is uncertain. With a stylized optimization model, in chapter 3, this paper explores the adoption of a new technology, its associated cost dynamics, and technological bifurcations with limited foresight and uncertain technological learning. The study shows that:when modeling with limited foresight and technological learning, (1) the longer the length of the decision period is, the earlier the adoption of a new technology, and the value of a foresight can be amplified with a high learning rate. (2)With limited foresight, decisions aiming at minimizing the total cost of each decision period commonly will result in a non-optimal solution from the perspective of the whole decision horizon; and (3) the range of technological bifurcation is much larger than that with perfect foresight, but uncertainty in technological learning tends to reduce the range by removing the early adoption paths of a new technology.The traditional operational optimization models of systematic technology adoption commonly assume the existence of a global social planner and ignore the existence of heterogeneous decision makers who interact with each other. In chapter 4, this paper develops a stylized (or conceptual) optimization model of systematic technology adoption with heterogeneous agents (i.e., decision makers) and uncertain technological learning. Each agent attempts to identify optimal solutions to adopting technologies for a portion of the entire system. The agents in the model have different foresight and different risk attitudes and interact with one another in terms of technological spillover. Using the model, this paper explores how the agents’heterogeneities and interactions affect the optimal solutions of systematic technology adoption. The main findings of the study are that (1) the existence of multiple agents implies a slower adoption of advanced technologies in the entire system than assuming the existence of a global social planner, (2) with homogeneous agents, technological spillover tends to enhance the lock-in effect on previous technologies, and (3) with heterogeneous agents, even a small technological spillover rate can significantly accelerate the adoption of the advanced technology.In chapter 5, this paper analyzes 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 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.