Mitigation from a cross-sectoral perspective

Sustainable innovation, business models and economic performance: an overview

The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at this http URL An analysis of interdisciplinary relations is pursued at three levels of aggregation using the newly added ISI subject category of "Nanoscience & nanotechnology". The journal level provides the finer grained perspective. Errors in the attribution of journals to the ISI subject categories are averaged out so that the factor analysis can reveal the main structures. The mapping of science can, therefore, be comprehensive at the level of ISI subject categories.

A Global Map of Science Based on the ISI Subject Categories

Within the field of environmental economics, the role of technological change has received much attention The long-term nature of many environmental problems, such as climate change, makes understanding the evolution of technology an important part of projecting future impacts Moreover, in many cases, environmental problems cannot be addressed, or can only be addressed at great cost, using existing technologies Providing incentives to develop new environmentally friendly technologies then becomes a focus of environmental policy This chapter reviews the literature on technological change and the environment Our goals are to introduce technological change economists to how the lessons of the economics of technological change have been applied in the field of environmental economics, and suggest ways in which scholars of technological change could contribute to the field of environmental economics

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Energy, the Environment, and Technological Change

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.

Agent-based simulation of innovation diffusion: A review

The impact of induced technological change (ITC) in energy/climate models on the timing of optimal CO2-abatement depends on whether R&D or learning-by-doing (LBD) is the driving force. Bottom-up energy system models employing LBD suggest strong increases in optimal early abatement. In this paper we extend an existing top-down model supporting this view according to the notion that socio-economic inertia interferes with rapid technological change. We derive analytical results concerning the impact of inertia and ITC on optimal initial abatement and show a wide range of numerical simulations to illustrate magnitudes. Inertia now dominates the timing decision on early abatement, such that LBD might even have a negative effect on early abatement and the impact of R&D is limited. However, ITC still reduces costs of stabilizing atmospheric CO2-concentrations considerably. Copyright

Optimal CO2-abatement with socio-economic inertia and induced technological change

We present the Canberra-Hamburg Integrated Model for Population (CHIMP), a new global population model for long-term projections. Distinguishing features of this model, compared to other model for secular population projections, are that (a) mortality, fertility, and migration are partly driven by per capita income; (b) large parts of the model have been estimated rather than calibrated; and (c) the model is in the public domain. Scenario experiments show similarities but also differences with other models. Similarities include rapid aging of the population and an eventual reversal of global population growth. The main difference is that CHIMP projects substantially higher populations, particularly in Africa, primarily because our data indicate a slower fertility decline than assumed elsewhere. Model runs show a strong interaction between population growth and economic growth, and a weak feedback of climate change on population growth.

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CHIMP: A simple population model for use in integrated assessment of global environmental change

There is a general sense of urgency that major technological transitions are required for sustainable development. Such transitions are best perceived as involving multiple transition steps along a transition path. Due to the path dependent and irreversible nature of innovation in complex technologies, an initial transition step along some preferred path may cut off paths that later may turn out to be more desirable. For these reasons, initial transition steps should allow for future flexibility, where we define flexibility as robustness regarding changing evidence and changing preferences. We propose a technology assessment methodology based on rugged fitness landscapes, which identifies the flexibility of initial transition steps in complex technologies. We illustrate our methodology by an empirical application to 2,646 possible future car systems.

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A complex systems methodology to transition management

Supply security and environmental concerns associated with oil call for an introduction of hydrogen as a transport fuel. To date, scenario studies of infrastructure build up and sales of fuel cell vehicles (FCVs) are driven by cost estimates and technological feasibility assumptions, indicating that there is a "chicken and egg problem": Car producers do not offer FCVs as long as there are no hydrogen filling stations, and infrastructure will not be set up unless there is a significant number of FCVs on the road. This diffusion barrier is often used as an argument for a major (public) infrastructure program, neglecting that the automobile market is highly competitive and car producers, consumers, and filling station operators form an interdependent dynamic system, where taxes influence technology choice. In this paper, an agent-based model is used that captures the main interdependencies to simulate possible diffusion paths of FCVs. The results suggest that a tax on conventional cars can successfully promote diffusion even without a major infrastructure program. However, consumers and individual producers are affected differently by the tax, indicating that differently strong resistance towards such a policy can be anticipated. Moreover, there is evidence that some producers might benefit from cooperation with filling station operators to generate a faster build up of infrastructure.

Simulating the Adoption of Fuel Cell Vehicles

Fuel cell vehicles (FCVs) running on hydrogen do not cause local air pollution. Depending on the energy sources used to produce the hydrogen they may also reduce greenhouse gases in the long-term. Besides problems related to the necessary investments into hydrogen infrastructure, there is a general notion that current fuel cells costs are too high to be competitive with conventional engines, creating an insurmountable barrier to introduction. But given historical evidence from many other technologies it is highly likely that learning by doing (LBD) would lead to substantial cost reductions. In this study we implement potential cost reductions from LBD into an existing agent based model that captures the main dynamics of the introduction of the new technology together with hydrogen infrastructure build up. Assumptions about the learning rate turn out to have a critical impact on the projected diffusion of the FCVs. Moreover, LBD could imply a substantial first mover advantage. We also address the impact of learning spillovers between producers and find that a government might face a policy trade off between fostering diffusion by facilitating learning spillovers and protecting the relative advantage of a national technological leader.

Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles

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