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The purpose of this paper is to analyze this phenomenon and identify its determinants using data from Brazilian higher education institutions.

Based on a data set comprehending 2,230 university students from 70 different institutions across the country, the authors develop five Probit models to assess impacts related to individual traits and systemic conditions on five dependent dimensions: entrepreneurial activity, potential entrepreneurs, high-impact entrepreneurship, serial entrepreneurship and innovation-driven entrepreneurship.

The lack of significance in many of the variables included in estimations suggests that student entrepreneurship seems to be a rather random phenomenon in Brazil.

Findings pose challenges for student entrepreneurship, as targets for intervention are not clear.

Over the past decades, universities have been receiving an increasing demand to go beyond their role of producing science and technology to explore its knowledge potential to produce novel commercial applications. However, while there is a growing interest in ways to foster scientific academic entrepreneurship, universities also serve as a positive environment for student entrepreneurship training, knowledge sharing, testing ideas and learning. So far, the importance of student entrepreneurship has received far less attention than it likely deserves.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.