Research On The Factors Influencing And Predicting The Price Of Carbon Trading In China

In recent years,the frequent occurrence of extreme weather and serious damage to the ecological environment have attracted increasing attention from the international community.China is a major global emitter of greenhouse gases and is under great pressure to save energy and reduce emissions.As a result,in 2013,carbon trading pilots in various provinces and cities across the country began trading online one after another,and in 2017,a unified national carbon trading system was officially opened.As the price of carbon trading is the key to China’s government policies and enterprises’ emission reduction decisions,studying the influencing factors of the price of carbon trading and finding a more accurate method to predict the price of carbon trading is an urgent problem to be solved at present.Firstly,this paper defines the concepts of carbon emission rights,carbon trading and the price of carbon trading,and briefly explains the externality theory,equilibrium value theory,fractal market theory and chaos theory.The current status of China’s carbon emissions trading policies,regulations,trading volumes and trading prices are discussed and analyzed.Secondly,this paper divides the factors affecting the price of carbon trading in China into two categories: extrinsic factors-energy prices,macroeconomics,climate environment and exchange rate movements and intrinsic influences-the historical price of carbon trading,the mechanism of the factors influencing the price of carbon trading is analyzed at a theoretical level.Taking the price of carbon trading in Shanghai as an example,the relationship between each influencing factor and the price of carbon trading is quantitatively analyzed through a vector autoregressive(VAR)model.In the long and short term,the direction of influence shows a relatively obvious alternation of positive and negative effects.The degree of influence is characterized by continuity and volatility,with the price of carbon trading being more influenced by intrinsic factors,and the impact of each extrinsic factor indicator on the price of carbon trading in Shanghai being delayed for one period,with energy price indicators having the strongest impact,followed by macroeconomic and exchange rate changes,and finally the climate environment.Thirdly,in this paper,a combined model of influencing factors based on particle swarm optimization algorithm for least squares support vector machines(PSO-LSSVM)is constructed.On the model side,the LSSVM model parameters(kernel parameters σ,regularization parameters γ)are optimized using the PSO algorithm,and on the data side,intrinsic and extrinsic factors are incorporated into the model for prediction.The price of carbon trading in Shanghai was selected as a sample for empirical analysis,and the results of the study showed that the indicators chosen were reasonable and valid,and the prediction results had a higher fitting accuracy.Finally,the factors influencing and forecasting the price of carbon trading in China are analyzed.Combined with the actual operation of carbon trading market in China,measures are formulated in terms of comprehensive consideration of carbon pricing factors,maintaining the stability of the carbon trading market,and improving the price of carbon trading forecasting methods,to provide corresponding countermeasure suggestions for achieving a reasonable and predictable the price of carbon trading.