Impact of climate change in Brazil: modeling for the prediction of displacements in the Brazilian southeast

doi:10.22167/2675-6528-202602701

Article

September 30, 2026

Impact of climate change in Brazil: modeling for the prediction of displacements in the Brazilian southeast

Impact of Climate Change in Brazil: Modeling for the Prediction of Displacements in Southeastern Brazil

Anne Caroline Charles Braga; Andressa Porto Castro

DOI: 10.22167/2675-6528-202602701

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

Climate change has intensified the frequency and severity of extreme weather events, significantly increasing their social impacts, including internal population displacement. The study analyzed the monthly occurrence of internal displacement in the southeastern region of Brazil between 2013 and 2024, evaluating the relationship between meteorological variables and forced displacement to identify patterns and develop a predictive model for count data. Public data from the Internal Displacement Monitoring Center (IDMC) and the National Institute of Meteorology were integrated. The analysis was performed in Python, combining exploratory statistics and generalized linear models for count data, including Poisson, Negative Binomial, Zero-Inflated Negative Binomial, and Hurdle models. The data revealed strong asymmetry, excess zeros, overdispersion, and seasonality, with a positive association between precipitation and displacement, and a non-linear relationship with temperature. Among the tested models, the Hurdle model without seasonality presented the best balance between fit, stability, and parsimony. Precipitation was the variable with the greatest influence on the magnitude of displacement, while temperature impacted the probability of its occurrence, with a non-linear behavior. It was concluded that count models, which represent overdispersion, excess zeros, and temporal structure, are effective tools for predicting climate-related displacement. The findings provide quantitative subsidies for improving disaster monitoring, prevention, and response strategies, as well as supporting the formulation of public policies for climate adaptation.

Keywords: count data; climate disasters; internal displacement; rare events.

1. Introduction

The climate crisis has been the subject of extensive discussion in recent decades, driven by the intensification of extreme events and the difficulty of mitigating its effects. With growing recognition of its impacts, it has been extrapolating the field of environmental concern and assuming relevance on the world stage in what can be characterized as a securitization of climate movement, a process in which certain political issues are discursively framed as security issues that may come to demand exceptional actions (Buzan et al., 1998).

Events such as sea-level rise, droughts, floods, and intensified storms are phenomena that have the potential to profoundly transform living conditions. Therefore, the climate crisis, associated with economic, political, and social factors, has come to be described as a risk multiplier (Zickgraf, 2020). The increase in the frequency with which natural disasters have occurred and the securitizing discourse adopted in relation to the subject, consequently, lead to an increased interest in the topic itself (Pelling et al., 2002; Sulaiman and Aledo, 2016).

Despite the devastating potential of natural disasters in themselves, they should not be treated in isolation, but rather connected with pre-existing and normalized vulnerabilities in daily life, such as structural poverty and precarious urbanization (Blaikie et al., 2004). This perspective is corroborated by studies that identify how extreme weather events exacerbate food insecurity, the spread of diseases, and social inequalities (Bellizzi et al., 2023). From an economic point of view, disasters negatively affect growth, productivity, competitiveness, and human capital formation, especially in countries considered to be developing (Klomp and Valckx, 2014). This intersection between environmental risks and socioeconomic fragilities reinforces the need for studies that support effective public policies and territorial adaptation strategies.

The present study focuses on one of the effects of the climate crisis and associated events that has gained increasing attention on the international humanitarian and economic agenda: the forced displacement of individuals. According to a report published in 2024 by the Internal Displacement Monitoring Centre (IDMC), out of the 46.9 million internal displacements recorded worldwide in 2023, 56% had natural disasters as the main cause, a number that exceeds that of displacements caused by armed conflicts and repression (Internal Displacement Monitoring Center, 2024).

In Brazil, the impacts are particularly significant. In 2022, the floods that affected the national territory led more than 700,000 people to leave their homes, positioning the country as the leader in the number of internal displacements due to natural disasters in South America and sixth globally (United Nations, 2024; Internal Displacement Monitoring Center, 2024). Corroborating the observations from studies published by Blaikie et al. (2004) and Sulaiman and Aledo (2016) on the occurrence of disasters and local socioeconomic characteristics, Ribeiro et al. (2022) conclude that Brazilian municipalities with a higher incidence of disasters tend to exhibit low levels of education, precarious infrastructure, and high poverty rates.

For these reasons, Brazil’s case becomes an interesting focus of study to better understand the patterns of disasters and the displacements that arise as a consequence of them. The Southeast region, formed by the states of Minas Gerais, São Paulo, Rio de Janeiro, and Espírito Santo, occupies 924,558,342 km² of the national territory (Brazilian Institute of Geography and Statistics, 2025) and presents one of the greatest climatic diversities in the country (Neto, 2005). With over 84 million inhabitants (Brazilian Institute of Geography and Statistics, 2023), this region is frequently affected by heavy rains, floods, flash floods, and mass movements that impact a large number of cities. Due to these conditions, there is a significant demand for strategies that can contribute to improving the adaptive capacity of the states and municipalities in the region.

Considering the presented context and the significant socioeconomic impacts associated with it, the present work uses climatic and displacement records that occurred between 2013 and 2024 in Brazil, more specifically in the states of Rio de Janeiro, Minas Gerais, Espírito Santo, and São Paulo, in order to answer the following research question: how many internal displacements caused by climatic events occur monthly in the Brazilian Southeast? By integrating meteorological data and displacement records, the study seeks to analyze the behavior of displacements that occurred between 2013 and 2024, identify patterns, and propose a predictive approach through the development and evaluation of statistical models that contribute to the planning and risk management associated with extreme events.

2. Material and Methods

This study was characterized as a quantitative research, with a methodological approach based on exploratory statistics and predictive modeling. The central objective was to analyze the behavior of internal displacements caused by extreme climatic events in the Southeast region of Brazil, identify temporal patterns, and develop statistical models for prediction. The research focused on records occurring between the years 2013 and 2024.

The unit of analysis consisted of the monthly internal displacements recorded in the states of Minas Gerais, São Paulo, Rio de Janeiro, and Espírito Santo. Data collection was carried out by consulting public databases. The first dataset, referring to displacements, was obtained from the website of the Internal Displacement Monitoring Center (IDMC), an institution that monitors cases of internal displacement globally.

The IDMC database provided information on the number of internal displacements caused by climate disasters in Brazil. For this study, the definition of internal displacement was adopted as the involuntary or forced movement of a person within the national borders of their country of habitual residence (Internal Displacement Monitoring Center, 2025). Initially, the database contained 867 observations of displacements recorded between 2008 and 2024 throughout the Brazilian territory.

The second dataset, of a meteorological nature, was accessed on the website of the National Institute of Meteorology (INMET). This database provided daily records of maximum temperature, in degrees Celsius, and total precipitation level, in millimeters, for the four states in the Southeast region. Only data from conventional meteorological stations were used, after the identification and treatment of inconsistent records, such as excessively high temperatures.

The IDMC data cleaning and preprocessing step involved filtering to include only displacements caused by weather events from 2013 onwards. Columns without records, redundant, or irrelevant, such as “Country” and “ISO3”, were eliminated. The “event” column was subdivided to create a “state” column, allowing for the exclusive selection of displacements that occurred in the Southeast region.

The displacement records, originally by event occurrence, were resampled to a monthly frequency using the “resample” method from the Pandas library. This allowed summing the values for each month and assigning zero to months without records, aligning the data to the desired temporal exposure for the research question on monthly displacements.

Similarly, daily meteorological data from INMET were resampled to monthly granularity, calculating the sum of precipitation and the average of maximum temperatures. The four resulting tables, one for each state, were joined into a single dataframe. Subsequently, the displacement and meteorological datasets were integrated into a final database.

Due to the absence of meteorological data for Espírito Santo in the months of September, October, November, and December 2024, these months were removed from the complete dataset. This measure ensured the correspondence between displacement, temperature, and precipitation data for all states, resulting in a balanced state-month panel with 140 observations per federative unit.

The entire process of data extraction, processing, exploratory analysis, visualization, and modeling was conducted in the Python language, using the PyCharm integrated development environment (IDE). The open-source libraries employed included Pandas v.2.1.4, Numpy v.1.26.4, Matplotlib v.3.8.0, Seaborn v.0.13.2, Statsmodels 0.14.0, Statstests 1.0.7, and SciPy 1.11.4.

Exploratory Data Analysis (EDA) was performed to investigate the dataset, identify patterns, underlying structures, and assess assumptions for model selection (Heckert and Filliben, 2003). The defined dependent variable was the number of internal displacements, characterized by integer and non-negative values, which guided the choice of count models.

In the modeling phase, generalized linear models (GLM) were estimated for count data. Initially, a Poisson model was developed as a benchmark. Subsequently, models more suited to the distribution of displacement data were estimated, including Negative Binomial, Zero-Inflated Negative Binomial (ZINB), and Hurdle.

Each of these models was developed considering the predictor variables precipitation and temperature. For precipitation, logarithmic transformation was chosen to represent nonlinear associations and reduce the influence of extreme values. For temperature, linear and quadratic terms, previously centered, were included to capture the observed curve in the data and reduce collinearity.

The comparison between the estimated models was based on the log-likelihood criteria and the Akaike Information Criterion (AIC), which evaluate model fit and parsimony. The seasonal Hurdle model was selected as the most robust, presenting complete convergence and numerical stability, justifying its choice as the final model for the interpretation of the results.

3. Results and Discussion

The analysis of internal displacements in the Southeast region of Brazil between 2013 and 2024 revealed complex patterns and a strong association with meteorological variables, indicating the need for robust statistical approaches for their prediction. The displacement data exhibited notable characteristics, such as strong right-skewness, excess zeros, and overdispersion, which are typical of rare and high-magnitude events. These particularities were crucial for the selection and evaluation of predictive models, guiding the choice towards more flexible methods than traditional linear regression models.

Exploratory Analysis

The exploratory data analysis phase provided an in-depth understanding of the nature of the dependent variable, internal displacements. Descriptive statistics revealed a mean of 996 displacements, with a standard deviation of 6,360.04, indicating substantial heterogeneity among events. The variance, which reached approximately 4.05 x 10^7, resulted in a variance/mean ratio of about 40,612, a value significantly higher than expected for a Poisson distribution, confirming the presence of overdispersion in the data. Furthermore, 75% of observations recorded zero displacements, evidencing a zero-inflation that demanded specific models.

The distribution of the displacement variable showed itself to be extremely right-skewed, with most observations concentrated in values close to zero and the occurrence of few extreme events, which were responsible for displacements of over 100,000 people. This behavior is characteristic of long-tail phenomena, where the severity of rare events has a disproportionate impact on the mean. The high heterogeneity and zero inflation were determining factors for the inadequacy of models such as Poisson, which assumes equidispersion, and justified the exploration of Negative Binomial and Hurdle models.

The geographical distribution of climatic disasters in the Southeast region between 2013 and 2024 was also investigated. A total of 182 events were recorded, with Minas Gerais concentrating the largest share (35.71%, with 65 events), followed by Espírito Santo (23.63%, with 43 events), Rio de Janeiro (23.08%, with 42 events), and São Paulo (17.58%, with 32 events). This heterogeneous distribution suggests that specific geographical and climatic factors of each state, such as the high physiographic heterogeneity of Minas Gerais, contribute to susceptibility to extreme hydrological events, as pointed out by Reboita et al. (2015) and Pazini et al. (2020).

Storms were identified as the main type of disaster, representing approximately 60 occurrences, a number that surpassed the sum of floods, mass movements, forest fires, and extreme temperature events. This finding reinforces that internal displacements in the Southeast are predominantly triggered by hydrometeorological processes. The analysis of the frequency of disaster occurrences compared to the number of displacements they cause revealed that, although storms are the most frequent, the magnitude of displacements they generate can vary widely, with some episodes resulting in over 100,000 people displaced.

Mass movements, despite their low frequency (approximately six events), demonstrated a high potential to produce significant displacements, with high variability in magnitude. This observation, along with the analysis of the logarithmic scale of displacements, which shows the concentration of most displacements in a few extreme events, reinforces the idea that the severity of the event has a greater influence on displacements than its absolute frequency. This underscores the importance of considering variables related to rainfall intensity and territorial vulnerability in statistical modeling.

The investigation of seasonality in the data confirmed a clear temporal pattern, where precipitation levels and average displacements tend to increase significantly during the period corresponding to summer in the southern hemisphere, between December and March. January, for example, showed an average displacement of over 5,000, about 50 times higher than the values observed in the dry months of June to September. The average monthly precipitation also exceeded 2,800 mm in December and January, contrasting with less than 400 mm in winter, a difference of over 600%.

This high temporal synchrony between precipitation and displacements suggests a strong association and justifies the inclusion of sine and cosine functions in the modeling to capture annual periodic oscillations, reducing biases associated with the temporal dependence of the data. The descriptive stratification of precipitation and temperature into quartiles revealed that precipitation has a positive and progressive association with the average displacements, increasing from approximately 200 to 2,200 displacements between the first and fourth quartile, an increase of over 1,000%. This result is consistent with the literature that associates intense precipitation events with higher risks of floods and landslides (IPCC, 2022).

In contrast, the temperature exhibited non-monotonic behavior, with the average displacements increasing up to the third quartile (approximately 2,400 displacements) and then decreasing in the fourth quartile (approximately 1,100 displacements). This non-linear relationship between temperature and displacements indicated the need for more flexible specifications in the model, such as the inclusion of a quadratic term, to avoid biases in the estimation of its effect, as suggested by Schuster et al. (2022). Although the quartile analysis is exploratory, it was fundamental in guiding the choice of variables and their transformations in the modeling.

Data Modeling

The data modeling stage began with the estimation of a Poisson model as a baseline, despite the data characteristics indicating its inadequacy. The Cameron and Trivedi (1990) overdispersion test confirmed overdispersion with a p-value of 0.009 for the alpha parameter, demonstrating that the Poisson model was not suitable for representing the dispersion structure of the response variable. Subsequently, a Negative Binomial model was estimated, which presented more stable results and a superior fit to the Poisson model, with a significant reduction in the Akaike Information Criterion (AIC).

Considering the high zero inflation in the data, Zero-Inflated Negative Binomial (ZINB) models were tested. Although ZINB showed a better fit in terms of AIC and the Vuong test (1989) indicated the presence of zero inflation, the model did not converge and produced many empty values, compromising its reliability and interpretability. The inclusion of seasonality variables (sin12 and cos12) in the seasonal ZINB model improved the fit and interpretability of standard errors and p-values, but non-convergence persisted, indicating instability.

Finally, Hurdle models were estimated, which separate the zero-generating process from the counting process. The non-seasonal Hurdle model converged in both steps and achieved a fit that generated the second-best log-likelihood and AIC values among all tested models. The seasonal version of the Hurdle model presented the best log-likelihood and AIC values, but its convergence was only partial, which required caution in interpretation. The comparison of fit, stability, and parsimony metrics among the models led to the choice of the non-seasonal Hurdle model as the most suitable specification for interpreting the results.

The selected Hurdle model demonstrated statistical adequacy to represent the dynamics of displacements, evidenced by the complete convergence of its two parts and a significant likelihood ratio test (p-value below 0.05). This result indicates that the set of explanatory variables contributes relevantly to the explanation of internal displacements. The alpha parameter, also statistically significant, confirmed the data’s overdispersion, reinforcing the relevance of choosing a more flexible model that accommodates this characteristic.

The results of the Hurdle model revealed that the predictor variables exert distinct effects on the binary and count components. In the binary component, which models the probability of occurrence of displacements, centered temperature (temp_c) and its quadratic term (temp_c2) were statistically significant, indicating a non-linear influence of temperature on the probability of an event occurring. Logarithmic precipitation (log_precipitação) was not statistically significant in this component. In the count component, which models the magnitude of displacements when they occur, logarithmic precipitation (log_precipitação) was the variable with the greatest influence, with a positive and statistically significant coefficient (0.645, p=0.001), while the temperature terms were not significant.

This distinction in the effects of the variables validates the choice of the Hurdle model, which allows for the separate analysis of the occurrence and intensity of displacements. Precipitation, therefore, is the most relevant factor in determining the magnitude of displacements once a climatic event occurs, while temperature, with its non-linear behavior, mainly influences the probability that such events result in displacements. These findings provide a quantitative basis for the development of early warning systems and for the improvement of disaster monitoring and response strategies.

In summary, the research identified that internal displacements in the Southeast region of Brazil are complex phenomena, characterized by overdispersion, zero inflation, and strong seasonality, predominantly triggered by storms and hydrometeorological events. The Hurdle model without seasonality, by balancing fit, stability, and parsimony, proved to be an effective tool for predicting both the probability and magnitude of these displacements, with precipitation influencing the magnitude and temperature, non-linearly, the probability of occurrence. These results are crucial for planning and managing risks associated with extreme climate events, supporting public adaptation policies and improving disaster response capacity.

4. Conclusion

This study aimed to analyze the monthly occurrence of internal displacements in the Southeast region of Brazil between 2013 and 2024, identifying patterns and developing a predictive approach through statistical models. It was found that displacement data exhibited characteristics of strong asymmetry, excess zeros, and overdispersion, in addition to clear seasonality, with a higher incidence of events during the summer period. The analysis revealed that storms were the main type of disaster, and that the severity of events, rather than their frequency, influenced the magnitude of displacements. A positive and progressive association was observed between precipitation and the magnitude of displacements, while temperature showed a non-linear relationship with the probability of their occurrence. Among the models tested, the Hurdle model without seasonality was selected as the most robust, balancing fit, stability, and parsimony, and proved to be statistically adequate to represent the complex dynamics of displacements.

The main contribution of this work lies in providing quantitative subsidies for the improvement of early warning systems, monitoring strategies, disaster prevention and response, in addition to supporting the formulation of public policies for climate adaptation. However, the study faced limitations related to the dimension and quality of the data, including underreporting of climate events and lack of standardization of records. For future studies, it is recommended to strengthen continuous monitoring and improve the quality of databases. It is also suggested to expand the spatial resolution of the analysis to the municipal level and incorporate sociodemographic and territorial variables, such as population density, Human Development Index, topography, and land use, into spatio-temporal models and machine learning techniques, aiming to improve predictive performance and the construction of decision support systems for integrated risk management.

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Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

Aggregate non-payment in the Brazilian financial system is a crucial indicator of credit risk, the prediction of which is regulated by CMN Resolutions n.° 4.557/2017 and 4.966/2021 for the prospective measurement of expected losses. Aggregate non-payment was modeled and predicted using the SARIMAX model, projecting its trajectory until December 2026 under optimistic, base, and pessimistic scenarios. 164 monthly observations (July/2012 to February/2026) and macroeconomic variables such as unemployment, Selic, IPCA, IBC-Br, and household debt were used. The specification with contemporaneous variables was selected based on information criteria and coefficient stability, and residual diagnostics confirmed statistical adequacy, with a MAPE of 3.12%. Historical non-payment ranged between 4.33% and 7.64%. The estimation identified unemployment (β = 0.2534; p < 0.001) and Selic (β = 0.2128; p < 0.001) as the main positive determinants, with strong temporal persistence (AR(1) = 0.8965). Selic showed increasing correlation with non-payment (r = 0.62 at lag 12), while unemployment demonstrated a contemporaneous effect (r = 0.33). Projections indicated non-payment between 6.30% and 7.98% in December 2026, with a range of 1.68 percentage points, highlighting direct implications for provisioning in financial institutions.

Keywords: Unemployment; Predictive modeling; Monetary policy; Loss provisioning; Time series.

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]