In tourism and recreation management it is still common practice to apply traditional input-output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input-output (NLIO) model, in which price-induced input substitution is accounted for. For large changes in final demand, a NLIO model is more useful than a traditional IO model, leading to higher or lower impacts. For small changes in final demand input substitution is less likely. In that case the application of the NLIO may lead to the same results as a traditional IO model. To analyse changes of subsidies, a traditional IO model is not an option. A more flexible model, such as the NLIO, is required. The NLIO model forces researchers to make choices about capacity constraints, factor mobility and the substitution elasticity, which can be difficult but create flexibility and allow for more realism.
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In tourism management, traditional input-output models are often applied to calculate economic impacts, including employment impacts. These models imply that increases in output are translated into proportional increases in labour, indicating constant labour productivity. In non-linear input- output (NLIO) models, final demand changes lead to substitution. This causes changes in labour productivity, even though one unit of labour ceteris paribus still produces the same output. Final demand changes can, however, also lead to employees working longer, harder and/or more efficiently. The goal of this article is to include this type of 'real' labour productivity change into an NLIO model. To do this, the authors introduce factor augmenting technical change (FATC) and a differentiation between core and peripheral labour. An NLIO model with and without FATC is used to calculate the regional economic impacts of a 10% final demand increase in tourism in the province of Zeeland in the Netherlands. Accounting for real productivity changes leads to smaller increase in the use of labour, as productivity increases allow output to be produced using fewer inputs.
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Deployment and management of environmental infrastructures, such as charging infrastructure for Electric Vehicles (EV), is a challenging task. For policy makers, it is particularly difficult to estimate the capacity of current deployed public charging infrastructure for a given EV user population. While data analysis of charging data has shown added value for monitoring EV systems, it is not valid to linearly extrapolate charging infrastructure performance when increasing population size.We developed a data-driven agent-based model that can explore future scenarios to identify non-trivial dynamics that may be caused by EV user interaction, such as competition or collaboration, and that may affect performance metrics. We validated the model by comparing EV user activity patterns in time and space.We performed stress tests on the 4 largest cities the Netherlands to explore the capacity of the existing charging network. Our results demonstrate that (i) a non-linear relation exists between system utilization and inconvenience even at the base case; (ii) from 2.5x current population, the occupancy of non-habitual charging increases at the expense of habitual users, leading to an expected decline of occupancy for habitual users; and (iii) from a ratio of 0.6 non-habitual users to habitual users competition effects intensify. For the infrastructure to which the stress test is applied, a ratio of approximately 0.6 may indicate a maximum allowed ratio that balances performance with inconvenience. For policy makers, this implies that when they see diminishing marginal performance of KPIs in their monitoring reports, they should be aware of potential exponential increase of inconvenience for EV users.
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