To create an evidence base for a circular economy we employ Computable General Equilibrium (CGE) Modelling as one of our services.

 

Circular economy policies and strategies require comprehensive data analytics to understand the various nuances involved in decision-making at various stages. 

 

A few examples of how analytics can support circular economy policy stakeholders include:

• the identification and calibration of appropriate incentives to promote a circular economy; 

• the design and development of Pareto optimal circular economy policies that consider all socio-economic agents; 

• the increased effectiveness in the regulation of circular economy stakeholders while ensuring a fine balance between competition and scale-dependent innovation; and 

• the examination of complementarities and trade-offs between other economic and climate change strategies and the circular economy.

 

Similar examples and use cases also exist in the context of business strategies. Using both granular private data from stakeholder organizations and aggregated public datasets allow examining a diversity of circular economy challenges and decision-making issues.

 

Therefore, data science and analytics can be deployed as a tool to promote circular economy innovation. In particular, stakeholders may be able to examine the macro-level impact of different circular economy interventions based on forward and backward linkages in sectoral changes. This is where CGE is valuable to evaluate alternative circular economy policy and strategy scenarios, based on their macro level and sectoral implications. Using various assumptions on future forecasts, the implications of different circular economy paths can be examined with specific timelines while factoring in the complexities of the real world.