At the heart of technology transitions lie complex processes of technology choices. Understanding and planning sustainability transitions requires modelling work, which necessitates a theory of technology substitution. A theoretical model of technological change and turnover is presented, intended as a methodological paradigm shift from widely used conventional modelling approaches such as cost optimisation. It follows the tradition of evolutionary economics and evolutionary game theory, using ecological population growth dynamics to represent the evolution of technology populations in the marketplace, with substitutions taking place at the level of the decision-maker. Extended to use principles of human demography or the age structured evolution of species in interacting ecosystems, this theory is built from first principles, and through an appropriate approximation, reduces to a form identical to empirical models of technology diffusion common in the technology transitions literature. Using an age structure, it provides the appropriate groundwork and theoretical framework to understand interacting technologies, their birth, ageing and mutual substitution. This analysis provides insight in explaining the nature and origin of observed timescales of technology transitions , in terms of technology life expectancies, the dynamic process of production capacity expansion or collapse and its timescales, in what is termed a `demographic phase'. While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of technology diffusion in large scale models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
At the heart of technology transitions lie complex processes of social and industrial dynamics. The quantitative study of sustainability transitions requires modelling work, which necessitates a theory of technology substitution. Many, if not most, contemporary modelling approaches for future technology pathways overlook most aspects of transitions theory, for instance dimensions of investor choices, dynamic rates of diffusion and the profile of transitions. A significant body of literature however exists that demonstrates how transitions follow S-shaped diffusion curves or Lotka-Volterra systems of equations. This framework is used ex-post since timescales can only be reliably obtained in cases where the transitions have already occurred, precluding its use for studying cases of interest where nascent innovations in protective niches await favourable conditions for their diffusion. Scaling parameters of transitions can in principle be derived from industrial dynamics, technology turnover rates and technology characteristics. In this context, this paper presents a theory framework for calculating the parameterisation of S-shaped diffusion curves for use in simulation models of technology transitions without the involvement of historical data fitting, making use of standard demography theory applied to technology at the unit level. The classic Lotka-Volterra competition system emerges from first principles from demography theory, its timescales explained in terms of technology lifetimes and industrial dynamics. The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions .
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technology choices. Understanding and planning
social and industrial dynamics. The quantitative study of
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A theoretical model of technological change and turnover is presented, intended as a methodological paradigm shift from widely used conventional modelling approaches such as cost optimisation. It follows the tradition of evolutionary economics and evolutionary game theory, using ecological population growth dynamics to represent the evolution of technology populations in the marketplace, with substitutions taking place at the level of
Many, if not most, contemporary modelling approaches for future technology pathways overlook most aspects of transitions theory, for instance dimensions of investor choices, dynamic rates of diffusion and the profile of transitions. A significant body of literature however exists that demonstrates how transitions follow S-shaped diffusion curves or Lotka-Volterra systems of equations. This framework is used ex-post since timescales can only be reliably obtained in cases where the transitions have already occurred, precluding its use for studying cases of interest where nascent innovations in protective niches await favourable conditions for their diffusion. Scaling parameters of transitions can in principle be derived from industrial dynamics, technology turnover rates and technology characteristics. In this context, this paper presents a theory framework for calculating
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decision-maker. Extended to use principles of human demography or the age structured evolution of species in interacting ecosystems, this theory is built from first principles, and through an appropriate approximation, reduces to a form identical to empirical models of technology diffusion common in the technology transitions literature. Using an age structure, it provides the appropriate groundwork and theoretical framework to understand interacting technologies, their birth, ageing and mutual substitution. This analysis provides insight in explaining the nature and origin of observed timescales of technology transitions , in terms of technology life expectancies, the dynamic process of production capacity expansion or collapse and its timescales, in what is termed a `demographic phase'. While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the
R
technology diffusion in large scale
S-shaped diffusion curves for use in simulation
R
systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions
transitions without the involvement of historical data fitting, making use of standard demography theory applied to technology at the unit level. The classic Lotka-Volterra competition system emerges from first principles from demography theory, its timescales explained in terms of technology lifetimes and industrial dynamics. The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions