Revelando a economia não observada dos produtos florestais não madeireiros no estado do Pará: os caminhos e desenvolvimento no processo de construção da metodologia das contas sociais alfa CSª

Abstract

The general objective of this research was to estimate the Agroextractive Social Accounting, consisting of the observed and unobserved economy of the NTFPs of seven integration regions of the State of Pará. For this purpose, the regional input-output system generated by the methodology of the Alpha Social Ascending Accounts (CSα) was adopted. The data used to carry out the research are from the study of NTFPs in the seven integration regions of Pará by Idesp - Institute for Economic, Social and Environmental Development of Pará and in partnership with Ipea - Institute for Applied Economic Research estimated for 2019, within the scope of the Bioeconomy of Sociobiodiversity project developed by an initiative of The Nature Conservancy (TNC), with support from the Inter-American Development Bank (IDB) and Natura. To meet the general objective, the thesis was structured in three articles. Article 1 presents the applicability of the Alpha Social Ascending Accounts methodology to estimate an economy not observed by the Regional Accounts System adopted in Brazil. Article 2 presents the importance of PNFM in the state of Pará, using the Social Accounts Alpha (CSα) method to give visibility to the observed and unobserved economy of PNFM, through the Regional Accounts System adopted in Brazil, in 2019. Then, Article 3 analyzes the intersectoral relations of the observed and unobserved economy of PNFM in conditions of limited information of seven Pará Integration Regions, in 2019, in order to evaluate the applicability of the CSα methodology. It is concluded that the proposed approach made it possible to build a basic regional system capable of capturing the peculiarities of the economies of PNFM in seven Pará Integration Regions. Contribute to the methodological discussion about the estimation of regional input-output systems in conditions of limited information.