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China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

To effectively solve the environmental pollution caused by greenhouse gases, countries around the world have successively set up carbon emission trading markets in recent years. At present, China's carbon trading market is only in its infancy, making various mechanisms and policies insufficient. As the core issue of carbon trading market, the fluctuations of carbon trading price are related to the investment decisions of market participants, the formulation of enterprise production and operation plans, and the realization of global emission reduction targets. Therefore, it is of great practical significance to study the influencing factors of carbon price fluctuations and to predict future carbon prices. In this paper, we take the carbon trading week data of the Beijing carbon trading market in China from May to September 2020 as the research object. Firstly, the gray correlation technique is employed to measure the rationality of the selected factors affecting carbon price fluctuations. Secondly, through the principal component analysis method to analyze the various influencing factors, it is found that energy prices and macroeconomic development are the main factors affecting carbon prices, and weather conditions will also cause carbon price fluctuations. Then, four different models are proposed to predict carbon trading prices, and the forecast results are evaluated through the performance evaluation index system. The results show that the GA-ELM model has the best prognosis effect. Finally, according to the analysis results, it provides a useful theoretical reference for carbon market decision makers and participants. [ABSTRACT FROM AUTHOR]