This work presents a sector-level study exploring the growth–energy–pollution nexus within
the framework of the environmental Kuznets curve (EKC). The combined contribution to CO …

Artificial intelligence (AI) is gaining attention in energy economics due to its ability to process
large-scale data as well as to make non-linear predictions and is providing new …

With the vision of carbon peak by 2030, China's non-ferrous metal industry needs to achieve
low-carbon development as soon as possible. This study adopts the logarithmic mean …

Commercial banks are the main body of the finance industry in China. It is of great
significance to study the impact of commercial banks' spatial agglomeration on PM2. 5 for …

We propose a non-linear state-space model to examine the relationship between CO $ _2 $
emissions, energy sources, and macroeconomic activity, using data from 1971 to 2019. CO …

Neural networks excel in handling complex, non-linear relationships, making them suitable
for predictions where traditional linear models fall short. Applications include forecasting …

The decline in global environmental sustainability along with the expansion of economic
development necessitates understanding the economic growth-sustainability relationship for …

The paper studies the relationship between inequality and environment in the case of the
NMS of the European Union. We are interested in the sign of the effect in order to see …

Accurate tracking of anthropogenic Carbon Dioxide (CO2) emissions is essential for the
formulation of effective climate policies and meeting long-term international decarbonization …

In this paper, I investigate the use of machine learning methods for nowcasting the yearly
growth rate of United States carbon dioxide emissions over the period 2000-2019, using a …