A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5 °C temperature control target.

The clean and low-carbon transition of China's power sector is of great importance to the achievement of dual carbon targets and the control of global warming. This paper first estimates the remaining carbon budget of the power sector under a 1.5 °C temperature control target and on this basis constructs 1.

Urban configuration and PM concentrations: Evidence from 330 Chinese cities.

Preventing high concentrations of fine particulate (PM) to realize the goal of sustainable development is becoming a challenge for rapidly urbanized cities. Increasing vehicle emission due to inefficient urban form is thought to be the main cause of traffic congestion and increased PM concentrations. Previous efforts attributing PM concentrations to urban forms are yet to reach consistent conclusions on practical environmental protection strategies.

Carbon dioxide emissions, urbanization level, and industrial structure: empirical evidence from North China.

This paper aims to examine the nexus among carbon dioxide (CO) emissions, urbanization level and industrial structure in North China over the period 2004-2019, according to an expanded Cobb-Douglas production function. The panel econometric techniques are employed to complete the empirical analysis, including cross-sectional correlation test, panel unit root test, panel co-integration test, and panel Granger causality test. The empirical results support the long-term equilibrium relationship among CO emissions, urbanization level and industrial structure in North China, and the urbanization level contributes most to CO emissions, followed by fossil energy consumption.

Exploring the impact of determining factors behind CO emissions in China: A CGE appraisal.

Along with the arrival of the post-Kyoto Protocol era, the Chinese government faces ever greater pressure to reduce greenhouse gases (GHGs). Hence, this paper aims to discuss the drivers of carbon dioxide (CO) emissions and their impact on society as a whole. First, we analyzed the background and overall situations of CO emissions in China.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data.