Since energy is one of the basic inputs for development, emerging economies should make an effort to investigate the environmental impacts of their fast economic growth. However, large emerging economies present significant regional heterogeneity that is usually uncounted for. This study uses the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and regional data on the 27 Brazilian states to investigate the growth-CO nexus under distinct development stages. To perform this analysis, we divided the states into three groups according to their average annual GDP (i.e., richer, intermediate, and poorer regions). The results suggest that richer and poorer regions, particularly, present economic and demographic developments that are environmentally costly. Also, population and per capita GDP have the largest influences on CO emissions. The roles of the industrial sector and the ascending service sector are also subject to criticism. Moreover, Brazil arguably suffers from technological stagnation as its energy intensity is growing and boosting CO emissions. We discuss the policy implications of these findings and suggest a future research agenda.
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http://dx.doi.org/10.1007/s11356-021-14097-w | DOI Listing |
J Environ Manage
January 2025
School of Management, Hefei University of Technology, Hefei 230009, China; Data Science and Smart Social Governance Philosophy and Social Sciences Laboratory of the Ministry of Education, Hefei University of Technology, Hefei 230009, China; Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Smart Management of Energy & Environment and Green & Low Carbon Development, Hefei University of Technology, Hefei 230009, China. Electronic address:
The information and communication technology (ICT) industry plays a vital role in high-quality development process but contributes significantly to carbon emissions due to its high energy consumption. Therefore, it is crucial to identify the factors influencing carbon emissions in the ICT industry to achieve carbon neutrality goal in China. Here, this study calculates the carbon emissions of ICT industry from 2000 to 2021 in China and analyzes factors influencing carbon emissions in the ICT industry by extending the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Bishan District of Chongqing Modern Service Industry Development Promotion Centre, Chongqing, China.
The rapid development of China's economy and the acceleration of the urbanization process have led to a significant increase in carbon emissions, and more effective policies are urgently needed. As the first city in China to be approved for the overall master plan of territorial space, Chongqing is facing new opportunities in urban construction. This research constructed a classification system of the territorial space functional areas in Chongqing (CQ-TSFA) and matched the corresponding carbon emission and carbon sequestration projects.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Management, Wuhan University of Technology, 430070, Wuhan, China.
The global situation of carbon reduction is very severe, and the coupling of digital and green technology innovation is one of the most significant approaches to promoting regional low-carbon transformation. A coupling evaluation model is employed to assess the coupling index between digital technology innovation and green technology innovation in China's 30 provinces from 2011 to 2021. The STIRPAT model is used to examine the impact of the increasing coupling index on carbon emissions, as well as its spatial effects, and heterogeneity.
View Article and Find Full Text PDFHuan Jing Ke Xue
January 2025
School of Public Administration & Law, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Carbon peaking is of great significance for China to achieve the "dual carbon" target goal and promote the green transformation of the economy and society. Based on the improved STIRPAT model, to analyze the main factors affecting carbon emissions in Fujian Province, we set up three scenarios and predicted the carbon emissions in Fujian Province from 2022 to 2035 using the hybrid CNN-LSTM neural network model. The results showed that ① Population, GDP per capita, and industrial structure positively drove carbon emissions in Fujian Province, while energy intensity, energy structure, and foreign trade degree negatively drove them.
View Article and Find Full Text PDFSci Total Environ
January 2025
Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China.
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