Adopting different wind-assisted ship propulsion technologies as fleet retrofit: An agent-based modeling approach

The maritime shipping industry will increasingly switch to low carbon fuels and adopt energy saving technologies (ESTs) to achieve the industry target of decarbonization. Among ESTs, deck equipment, including those based on wind propulsion technologies (WPTs), represents the largest potential fuel savings and a source of increasing innovation initiatives by industry actors. Previous contributions to WPT innovation have addressed barriers and drivers for increased adoption in the industry but failed to consider the specific aspects of the fleet retrofitting market. Through an agent-based simulation model, this work studies the effects of different policy and market scenarios (subsidies, fuel prices, and networking) on the adoption of WPT retrofitting solutions. The proposed model incorporates two decision steps for each vessel to adopt the technology (acquiring awareness of the technology, and a utility decision process to determine the WPT option). The study also expands on previous knowledge by modeling three WPT options and by integrating real world data of technology costs and their fuel savings as well as vessel features. Insights from simulations allow to identify the most convenient policies as well as the potential of alternative models to reduce introduction barriers (e.g., product-service business models).Interreg North Sea Region project WASP: Wind Assisted Ship Propulsion, "Run Wind Propulsion Technology real life trials on sea going ships in operation, showcase proven concepts, market adaptation, green sea transport" 38-2-6-19Spanish Ministry of Science, Andalusian GovernmentEuropean Commission RYC-2016-19800ERDF under CONFIA PID2021-122916NB-I00ERDF under SIMARK P18-TP-447

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support

Prospects for large-scale financial systems simulation

As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research

Evolutionary macroeconomic assessment of employment and innovation impacts of climate policy packages

Climate policy has been mainly studied with economic models that assume representative, rational agents. Such policy aims, though, at changing carbon-intensive consumption and production patterns driven by bounded rationality and other-regarding preferences, such as status and imitation. To examine climate policy under such alternative behavioral assumptions, we develop a model tool by adapting an existing general-purpose macroeconomic multi-agent model. The resulting tool allows testing various climate policies in terms of combined climate and economic performance. The model is particularly suitable to address the distributional impacts of climate policies, not only because populations of many agents are included, but also as these are composed of different classes of households. The approach accounts for two types of innovations, which improve either the carbon or labor intensity of production. We simulate policy scenarios with distinct combinations of carbon taxation, a reduction of labor taxes, subsidies for green innovation, a price subsidy to consumers for less carbon-intensive products, and green government procurement. The results show pronounced differences with those obtained by rational-agent model studies. It turns out that a supply-oriented subsidy for green innovation, funded by the revenues of a carbon tax, results in a significant reduction of carbon emissions without causing negative effects on em ployment. On the contrary, demand-oriented subsidies for adopting greener technologies, funded in the same manner, result in either none or considerably less re- duction of carbon emissions and may even lead to higher unemployment. Our study also contributes insight on a potential double dividend of shifting taxes from labor to carbon