The vigorous development of the digital economy, alongside the collaborative promotion of enterprise digital transformation and low-carbon supply chains, has emerged as a critical pathway for achieving green and high-quality development in enterprises. In this paper, we utilize a mathematical model framework to empirically investigate the mechanisms and impacts of enterprise digital transformation on the low-carbon effect of supply chains, employing data from A-share-listed companies spanning 2011 to 2021. The findings indicate that (1) enhancing the degree of enterprise digital transformation can significantly decrease the carbon emission intensity of upstream suppliers, thereby promoting low-carbon supply chains. (2) “Innovation-driven” and “structural transformation” mechanisms are vital channels by which enterprise digital transformation promotes carbon reduction in supply chains. (3) The diffusion mechanism effect and demonstration effect exhibit heterogeneity in the process of enterprise digital transformation, driving low-carbon emission reductions in supply chains.

Taking the carbon emissions trading mechanism implemented in seven pilot provinces since June 2013 as a quasi-natural experiment,this paper establishes a difference in difference and an intermediary effect model with the listed enterprises of pilot industries as the balanced panel data from 2011 to2018 to study whether the carbon emissions trading mechanism improve the enterprise value by promoting enterprise technological innovation,so as to test whether the "strong Porter Hypothesis" is successful in China.It is found that carbon emissions trading mechanism cannot promote the listed enterprises in pilot areas to improve the enterprise value through technological innovation and thus achieve the porter effect.The regional test shows that the porter effect can be realized only in Beijing,but not in the whole country and other provinces and cities.The low carbon price caused by excessive initial quota allocation is the main reason.Enterprises should be encouraged to improve their value through green technology innovation.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

To prevent the spread of COVID-19 in China, many cities were locked down after January 23, 2020. Based on the panel data of the “2+26” cities from 10 January to 15 March 2020, this paper took the lockdown as a quasi-natural experiment and established a multi-phase DID model to investigate whether the lockdown measures significantly reduced air pollution in locked-down cities in the Beijing-Tianjin-Hebei (BTH) region. The core innovation of this paper is that we considered the urban immigration scale index as a mediating variable , which is rarely adopted in the existing literature, and we identified the relationships between the lockdown, the intracity migration index, the urban immigration scale index and air pollution. The results showed that compared with the non-locked-down cities, the lockdown significantly reduced air pollution. Furthermore, it was found that the lockdown reduced air pollution by reducing intracity migration and the urban scale of immigration. Moreover, compared with the corresponding period in 2019, air pollution was significantly reduced in the locked-down cities of the “2+26” cities. Air pollution is closely related to human activity, and green production and technological innovations are critical for reducing air pollution in the BTH region.

Most existing data synthesis methods are designed to tackle problems with dataset imbalance, data anonymization, and an insufficient sample size. There is a lack of effective synthesis methods in cases where the actual datasets have a limited number of data points but a large number of features and unknown noise. Thus, in this paper we propose a data synthesis method named Adaptive Subspace Interpolation for Data Synthesis (ASIDS). The idea is to divide the original data feature space into several subspaces with an equal number of data points, and then perform interpolation on the data points in the adjacent subspaces. This method can adaptively adjust the sample size of the synthetic dataset that contains unknown noise, and the generated sample data typically contain minimal errors. Moreover, it adjusts the feature composition of the data points, which can significantly reduce the proportion of the data points with large fitting errors. Furthermore, the hyperparameters of this method have an intuitive interpretation and usually require little calibration. Analysis results obtained using simulated original data and benchmark original datasets demonstrate that ASIDS is a robust and stable method for data synthesis.

With the improvement of China's carbon emissions trading market, more attention is paid to the prediction of trading price. Based on the average monthly transaction price of Beijing carbon market, this paper constructs ARIMA(Autoregressive Integrated Moving Average) model to make a three-month short-term prediction of carbon price, and evaluates the prediction effect of LSTM(Long Short-term Memory) model. By comparing the predicting accuracy of LSTM and ARIMA model, this paper finds that ARIMA model has higher predicting accuracy than LSTM model in short-term prediction of univariate time series data. Traditional statistical methods should be combined with machine learning, deep learning and other artificial intelligence algorithms to improve the prediction ability in practical application. Meanwhile, in order to prevent the price fluctuation risk of carbon emissions trading, government should strengthen the carbon emission quota regulation, control the drastic price fluctuation, integrate regional and national markets and improve the liquidity of carbon emissions trading markets.

Although the booming carbon markets provide additional incentives to reduce greenhouse gases, their impacts on the society and economy have attracted increasing attention. Based on 2014–2016 daily carbon market trading price data, this study estimates the direct and indirect carbon emissions cost incurred by Beijing carbon market and explores its impact on industrial competitiveness via an evaluation model. Our results show that the impact of the carbon emissions cost is negligible, and the proportion of the three most affected industries’ added values to Beijing’s gross domestic product is only 10%, indicating that the economic impact is limited. However, the impact on the production and supply of power, gas and water industry could reach as high as 3.02% in three years. Compared with the European carbon market, the trading price of Beijing’s carbon market is relatively low, and the price cap could possibly increase to 100 Yuan per ton. However, each 10-Yuan increment in the carbon price will increase the impact on industry competitiveness by 1.68%. This study provides a scientific basis for exploring the impact of China’s carbon market on industry competitiveness and will be of significant value to policy makers.

Competitiveness change after the establishment of carbon emissions trading mechanism is explored on the basis of Beijing industry energy consumption and carbon price data. It is demonstrated that additional costs incurred by carbon trading mechanism have less effect on the industrial competitiveness. Scenario analysis is also conducted to discuss changes under different price.

We assess the efficiency of the sovereign credit default swap (CDS) market by investigating how sovereign CDS spreads react to macroeconomic news announcements. Contrary to the vast majority of the existing literature, one of our main findings supports the hypothesis that news announcements reduce market uncertainty and, thus, that both better- and worse-than-expected news lower CDS prices during our sample period. In addition, we find that CDS spreads respond differently to the four macroindicators across the three different regions. Our findings might help investors in these areas to interpret the surprises of macronews announcements when making decisions in CDS markets.

Competitiveness change after the establishment of carbon emissions trading mechanism is explored on the basis of Beijing industry energy consumption and carbon emissions price data.It is demonstrated that additional costs incurred by carbon trading mechanism have less effect on the industrial competitiveness.Scenario analysis is also conducted to discuss changes under different price.

Many music retailers use online product sampling and online customer reviews to help potential buyers evaluate music on the Internet. In this study, we investigate the profiles of music consumers in the presence of the Internet, and explore how consumers use online sampling and/or online review for music evaluation. Some interesting insights into digital music evaluation are discovered and discussed in this on-going study.