RESUMEN
Tax evasion is the illegal withholding or underpayment of taxes, typically accomplished by intentionally providing false or no evidence to tax authorities. Tax evasion has had a severe detrimental influence on the economy of the Amhara National Regional State, Ethiopia. The Amhara Regional State lost tax revenue in recent years due to tax evasion. The objective of this study was to see how tax evasion, taxpayers' psychological egoism, and other relevant factors affect tax revenue collection performance in the Amhara Region, Ethiopia. Data were collected from 395 VAT-registered taxpayers through a structured questionnaire. The structural equation model and multiple regression analysis method were utilized for empirical test based on the softwares of SPSS and AMOS. This research revealed that tax evasion and psychological egoism negatively affect tax revenue collection performance. Tax education and technology significantly and positively affected tax revenue collection performance. Meanwhile, the relationships between the above factors (tax evasion, tax education, and technology) and the tax revenue collection performance are reliably mediated by taxpayers' psychological egoism. Those findings can give clues to researchers, tax experts, and policymakers for improving the tax revenue collection performance in Amhara Region. The government can enhance public education to reduce tax evasion and such misbehavior caused by taxpayers' psychological egoism. Meanwhile, the most up-to-date tax invoicing technologies, like artificial intelligence and machine learning technology should be adopted.
RESUMEN
The achievement of China's low-carbon development and carbon neutrality depends heavily on the decrease of manufacturing carbon emissions. From coagglomeration's dynamic evolution perspective, by using panel-threshold-STIRPAT and mediation-STIRPAT models, this study examines the relationships among industrial coagglomeration, green innovation, and manufacturing carbon emissions and explores the direct and indirect function mechanisms. Panel data of China's 30 provinces from 2010 to 2019 are employed. The results imply that, first, the impact of industrial coagglomeration on manufacturing carbon emissions is nonlinear and has significant threshold effects. Industrial coagglomeration negatively affects manufacturing carbon emissions, and as the coagglomeration level deepens, the negative effect has a diminishing trend in marginal utility. Once the coagglomeration degree exceeds a certain threshold, the negative impact becomes insignificant. At present, for 90% of China's regions, an increase in industrial coagglomeration level can help reduce manufacturing carbon emissions. Second, green innovation is a vital intermediary between industrial coagglomeration and manufacturing carbon emissions. It is a partial intermediary when industrial coagglomeration is at a relatively lower-level stage and a complete intermediary when industrial coagglomeration is at a relatively higher-level stage. These findings reveal the significance of optimizing industrial coagglomeration and the level and efficiency of green innovation to decrease carbon emissions.