Circular chemistry has great relevance for the evolution of the circular economy. Its principles are based on reducing the consumption of raw materials and addtives through a lower complexity of production processes, greater reuse of solvents and additives, using waste as raw material. The chemical sector has a direct impact on waste generation, energy consumption and greenhouse gas release. However, the migration to circular business models depends on economic and political factors for

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... tions to be able to implement innovations and improvements that take them to another level from the point of view of circularity. Within this context, the present study aimed to calculate a circularity indicator (CEI) that allowed the aggregation of indicators of waste generation, atmospheric emissions and energy consumption of the Brazilian chemical sector, segmented to the first-and second-generation industries. Principal component analysis was used as part of a methodology for index calculation. The work started from the construction of a theoretical model supported by circularity and indicators. The data were provided by the digital platforms of IBAMA and IBGE. In addition to the calculation, the variations of the indicator were studied using as reference economic and political triggers to identify which factors would be more relevant for the Brazilian case at the level of organizations and at the state level. The period from 2017 to 2019 was used as the basis for the research. As a function of the work, two regression models were obtained. The first identifying net sales revenue, investments in improvements and people as relevant economic factors and the second identifying the growth potential of states, as well as their investments in social/environmental sustainability in addition to actions in innovation as relevant factors for cis variations. The study concluded that the segment of the first and second generation has room to advance in circularity by CEI oscillations throughout the studied period. Another conclusion is that the states that have greater competitiveness do not have the most favorable CEI values. The models were able to explain 41% and 61% of CEI variations showing that there is still research potential for a better understanding of how the chemical industry is moving to circular business environments.