Min Lu

Our research investigated whether the carbon emissions trading pilot policy (CET), while mitigating environmental pollution externalities and fostering green economic and social transformation, can also enhance China’s new quality productivity (NQP) as a key driver of economic growth. This study addresses a research gap by examining the CET from an integrated perspective of economic development and environmental protection. We have developed an NQP evaluation indicator system based on three productivity factors, revealing that the CET can elevate NQP levels in pilot provinces through the advancement of green finance (GF) and industrial structure upgrading (ISU). Furthermore, we analyzed the relationship between the CET and NQP from the perspective of low-carbon energy consumption (LCEC), demonstrating that the level of LCEC can reinforce the CET’s positive impact on NQP and moderate the path before and after the mediating process. Our findings offer valuable insights into leveraging market-based environmental regulation tools to support NQP development, thereby facilitating its cultivation and enhancement.

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.

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.

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.

When using machine learning methods to make predictions, the problem of small sample sizes or highly noisy observation samples is common. Current mainstream sample expansion methods cannot handle the data noise problem well. We propose a multipath sample expansion method (AMLI) based on the idea of linear interpolation, which mainly solves the problem of insufficient prediction sample size or large error between the observed sample and the actual distribution. The rationale of the AMLI method is to divide the original feature space into several subspaces with equal samples, randomly extract a sample from each subspace as a class, and then perform linear interpolation on the samples in the same class (i.e., K-path linear interpolation). After the AMLI processing, valid samples are greatly expanded, the sample structure is adjusted, and the average noise of the samples is reduced so that the prediction effect of the machine learning model is improved. The hyperparameters of this method have an intuitive explanation and usually require little calibration. We compared the proposed method with a variety of machine learning prediction methods and demonstrated that the AMLI method can significantly improve the prediction result. We also propose an AMLI plus method based on the linear interpolation between classes by combining the idea of AMLI with the clustering method and present theoretical proofs of the effectiveness of the AMLI and AMLI plus methods.

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.

This paper examines price bubbles in the relatively new carbon emission trading scheme of Beijing carbon market by employing a recently proposed econometric test which can stamp the occurrence and burst of financial bubbles. We find multiple bubbles in Beijing carbon market over the sample period between January 2014 to April 2018, and that the occurrences of carbon price bubbles are closely related to the announcements of environmental policies by the Chinese government. Comparing our results to the EU ETS, we find that the volatility of carbon price in Beijing market is higher than EU, and interestingly, the bubbles in Beijing market occur when the price volatility is relatively low, while in EU market the bubbles correspond to the peaks of volatility. Our empirical results provide insightful policy implications in the context of the actual China’s carbon market reform. To achieve effective stabilization of carbon price, policymakers should publicize alert notifications of the price fluctuations, and strengthen the carbon markets supervision and promote its improvement.