https://repositorio.ufpb.br/jspui/handle/123456789/11120 2026-09-09T09:37:31Z 2026-09-09T09:37:31Z Pires, Carlos Henrique da Silva https://repositorio.ufpb.br/jspui/handle/123456789/38874 2026-09-04T06:12:07Z 2026-08-03T00:00:00Z

Título: Os padrões de gestão de riscos de desastres ambientais hidrológicos X a execução orçamentária da locação de máquinas para prevenção em João Pessoa (2022-2025): uma análise sob ótica da NBR ISO 31000:2018, COSO ERM 2017 e PNPDEC Autor(es): Pires, Carlos Henrique da Silva Orientador: Santos, Samara Lauar Abstract: The occurrence of hydrological environmental disasters in urban areas poses complex challenges to public administration, requiring a transition from reactive response models to proactive risk governance. The general objective of this study was to analyze how the budget execution for leasing machinery for river and stream cleaning in João Pessoa, from 2022 to 2025, complies with the risk management standards established by NBR ISO 31000:2018, COSO ERM 2017, and Federal Law No. 12,608/2012 (National Protection and Civil Defense Policy). Methodologically, this research is characterized as a descriptive case study with a mixed-methods approach. It utilizes raw budget data extracted from the municipal Transparency Portal to calculate the Budget Execution Index (BEI); maps the quadrimestral seasonality of expenditures over the analyzed four-year period; and develops a Strategic Compliance Matrix aligned with risk management standards. The results indicate an average BEI of 65% over the four-year period, highlighting a disparity between budget authorization and the financial settlement capacity of the executing agency. A specific concentration of emergency spending was observed in 2023, driven by atypical rainfall events, whereas the 2024–2025 biennium marked a transition toward continuous, preventive resource allocation through permanent service contracts. It is concluded that the effectiveness of hydrological environmental disaster mitigation policies requires constant convergence among formal budget forecasting, cash flow timeliness, and the operational capacity of the municipal public administration. Editor: Universidade Federal da Paraíba Tipo: TCC

2026-08-03T00:00:00Z Silva, Cristiane Siqueira da https://repositorio.ufpb.br/jspui/handle/123456789/38873 2026-09-04T06:12:08Z 2026-08-03T00:00:00Z

Título: Comparação entre modelos de regressão estatísticos e modelos de aprendizado de maquina interpretáveis na modelagem de risco no seguro agropecuário Autor(es): Silva, Cristiane Siqueira da Orientador: Duarte, Filipe Coelho de Lima Abstract: Agricultural insurance is a relevant instrument for mitigating climatic and production risk in the Brazilian agribusiness sector, with claim severity modeling being a central element for insurers' pricing and risk management. This study aimed to compare regression models and interpretable machine learning models in predicting the severity of rural insurance claims, evaluating both predictive performance and the interpretability of the approaches. A database of 190,423 policies with claim occurrence was used, drawn from the Rural Insurance Premium Subsidy System (SISSER), covering the period from 2016 to 2024. Four models were estimated: Multiple Linear Regression (MLR), Generalized Linear Model (GLM) with Gamma distribution, Random Forest, and XGBoost, evaluated through cross-validation and compared using the MAE, RMSE, and sMAPE metrics. The interpretability of the machine learning models was assessed using the SHAP (SHapley Additive exPlanations) technique. Results showed that the machine learning models outperformed the statistical ones in predictive performance, with an approximate 12% reduction in RMSE compared to the Gamma GLM, albeit with greater relative overfitting between training and testing; among them, XGBoost achieved the lowest MAE and RMSE, and Random Forest the lowest sMAPE. The guarantee limit and the year 2021, marked by severe drought, stood out as the main determinants of severity across all models, a result that converged between the statistical and machine learning approaches. Segmenting the database between agricultural and livestock activities produced no performance gain, given the predominance of agricultural policies in the sample. It was concluded that combining machine learning models with explainability techniques constitutes a viable alternative for reconciling predictive accuracy and transparency in agricultural risk modeling. Editor: Universidade Federal da Paraíba Tipo: TCC

2026-08-03T00:00:00Z Silva, Janailson Coelho da https://repositorio.ufpb.br/jspui/handle/123456789/38858 2026-09-03T06:12:21Z 2026-08-03T00:00:00Z

Título: Situação financeira e atuarial dos regimes próprios de previdência social das mesorregiões paraibanas entre 2020-2024 Autor(es): Silva, Janailson Coelho da Orientador: Kataoka, Sheila Sayuri Abstract: Public Servants’ Social Security Regimes (RPPS) are responsible for providing social security protection to permanent public employees. Monitoring their financial and actuarial results is therefore essential for assessing their short- and long-term conditions. This study aimed to analyze and compare the financial and actuarial situation of municipal RPPS schemes in the state of Paraíba, considering their distribution across the Agreste Paraibano, Borborema, Mata Paraibana, and Sertão Paraibano mesoregions between 2020 and 2024. The research was descriptive, documentary, and quantitative, using secondary data obtained from the Brazilian Social Security Statistical Yearbook (AEPS) and information provided by the Brazilian Institute of Geography and Statistics (IBGE). The analysis covered 70 municipal RPPS schemes and considered the number of active public employees, retirees, and pension beneficiaries, as well as financial and actuarial results. Descriptive statistics, the Shapiro– Wilk test, the Kruskal–Wallis test, and, when applicable, Dunn’s multiple comparison test were employed. Overall, the results indicated a decrease in the number of active public employees and an increase in the number of retirees and pension beneficiaries, revealing changes in the composition of the insured population. Financial results showed fluctuations, high dispersion, and the occurrence of extreme values. However, the Kruskal–Wallis test did not identify a statistically significant difference among the mesoregions for this indicator. Regarding actuarial results, the averages of all four mesoregions remained negative throughout the period, indicating the existence of long-term social security obligations. Statistically significant differences among the mesoregions were found for this indicator. Dunn’s test showed that these differences involved Mata Paraibana in its comparisons with Agreste Paraibano, Borborema, and Sertão Paraibano, with a greater concentration of negative actuarial results in Mata Paraibana. It was concluded that grouping the municipalities by mesoregion did not significantly differentiate their financial results, although it allowed differences in the distribution of actuarial results to be identified. The findings provide an overview of the situation of municipal RPPS schemes in Paraíba and may assist public managers and oversight bodies in monitoring the financial and actuarial conditions of these regimes. Editor: Universidade Federal da Paraíba Tipo: TCC

2026-08-03T00:00:00Z Felismino, João Antonio da Silva https://repositorio.ufpb.br/jspui/handle/123456789/38857 2026-09-03T06:12:25Z 2026-08-03T00:00:00Z

Título: As desigualdades regionais geográficas nas concessões de benefícios no Regime Geral de Previdência Social Autor(es): Felismino, João Antonio da Silva Orientador: Cruz, Vera Lúcia Abstract: This study analyzed regional inequalities in the granting of benefits under the General Social Security Regime (RGPS) in Brazil from 2012 to 2022. The objective was to analyze how demographic and economic differences among Brazilian macro- regions are related to the distribution of benefit grants. The research is characterized as quantitative, descriptive, and explanatory, using data from the Federal Government and the Brazilian Institute of Geography and Statistics (IBGE), organized by macro-region, age group, sex, and benefit amount. The results showed a higher concentration of benefit grants in the Southeast region. When considering the rate of grants per 100,000 inhabitants, the Central-West and South regions stood out, with 40,328.86 and 35,976.31 grants, respectively, while the North and Northeast regions presented the lowest rates, with 15,855.94 and 21,952.58 grants, respectively. Regarding age groups, the highest concentration of grants was observed among individuals aged 40 to 59, followed by a reduction in their relative share from the age of 60 onward. The results indicate that the regional distribution of benefit grants is related to demographic differences and labor market characteristics across Brazilian macro-regions. It is concluded that understanding these inequalities is relevant for planning public policies aimed at reducing regional disparities and strengthening social security protection. Editor: Universidade Federal da Paraíba Tipo: TCC

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