In many reports scenarios for the circular economy are designed in order to evaluate the macroeconomic, environmental and societal impact of a transition towards a circular economy. We distinguish an opportunity based scenario approach as for example developed by the Ellen MacArthur foundation and a policy-based approach, as for example modelled for Turkey by Bouzaher et al. (2015). Such a policy-based scenario approach is developed further and it has been shown that changes in input coefficients and environmental parameters can also be used for the opportunity based approach.
The opportunity based approach starts with an explicit formulation of circular opportunities. The baseline consists of options that will be realised under current circumstances, while the circular scenario implements the options that become profitable or will be realized if government develops a package of circular policy measures. From the circular opportunities identified, opportunity-specific policy options are derived. The approach shows that a list of circular opportunities exist, but doesn’t show that this list is better from a GDP growth point of view than a more general list including both circular and linear opportunities.
The second approach starts from an analysis of market and government failures that cause the environmental, natural capital and resource use problems. Because from this perspective the main reason why the circular economy is insufficiently developing is external cost it seems logical to start with pricing of externalities through environmental taxation as the first option to tackle the barriers for the circular economy. Regulation that directly focuses on the targets of the circular economy is a second option, after which infrastructure development and research policies follow. Information and coordination activities that are at the start of the list used by the Ellen MacArthur Foundation may be the last from a narrow economic perspective.
In the macroeconomic evaluation of a circular-economy scenario, the first step is a welfare analysis of the environmental results of the scenario, because these are the primary targets of the circular policy. Furthermore, less imports of raw materials implies that geopolitical risks are also reduced, with potential consequences for economic stability. The second step is to investigate to what extent employment and economic activities are changed. Employment and welfare effects depend on the current situation of the economy. For example, in a situation with a negative output gap (i.e. aggregate demand is smaller than aggregate supply) cyclical employment exists that can be reduced by extra investment and other expenditures. If the circular policy generates an extra incentive for investment, this may generate extra employment and extra growth when the economy starts in a situation with excess macro-economic supply.
A fast transition towards a circular economy will generate adjustment costs. Stranded assets and qualitative structural unemployment are examples of these costs. The speed of adjustment and the stability in the policy environment is relevant from this perspective. Explicit policies for labour mobility may be required to adjust the qualitative characteristics of labour supply to the new demand pattern for labour.
The analysis in this report shows that in current macroeconomic evaluations of the circular economy the assumptions generating the outcomes remain hidden for the normal reader and even for specialist readers. Many results in such analyses are the consequence of policy adjustments, but it is not transparent what the empirical background is of the mechanisms that are in the models. In many analyses profitable circular opportunities are listed but it is not made explicit to what extent these opportunities are more or less profitable than other opportunities that may also exist. In order to get deeper into the fundamental issues around the macroeconomic evaluation of the circular economy more empirical analysis is required. Case studies, econometric studies and other studies that reveal plausible mechanisms and estimates of coefficients are needed for this. These empirical insights may be included in complex models, but it may be more useful to calculate the consequences of the empirical insights in a more transparent manner with simple calculation tools.