Simultaneously protecting the environment and promoting the economy are two critical dimensions for sustainable development. Carbon productivity is popularly used in assessing the environmental and economic efficiency over time, and is deemed as the appropriate indicator of sustainable development. Given the prominent contribution of energy consumption to sustainable development, this study incorporates energy consumption into the Log Mean Divisia Index (LMDI) decomposition model to explore the main factors influencing carbon productivity change. Based on the data from 19 industries in Beijing from 2013 to 2016, this study then reports the carbon productivities and their changes. Energy productivity change is the main cause of carbon productivity changes, and its correlation with carbon productivity change is significantly positive, whereas there is a weak correlation and no significant difference in energy consumption per unit of carbon emissions. Although the average carbon productivities in all 19 industries increased year over year in Beijing, the average level could be further promoted by improving energy productivity. The carbon productivities of the primary and secondary industries are less than the average, and far below the tertiary industry level. For the primary industry, increasing economic levels is conducive to improving carbon productivity; for secondary industry, reducing energy consumption and enhancing energy efficiency are most effective; and for tertiary industry, maintaining an outstanding performance will guarantee sustainable development in Beijing. This study has defined carbon productivity change from the energy consumption perspective and for the first time, comprehensively measured it for all industries in Beijing. The results are expected to assist these industries to essentially improve productivity performance and thus improve development sustainability.

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.

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.

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.

Despite the popularity of online digital music and the broad application of digital music sampling, in the existing literature, there is a lack of substantial studies that examine online digital music sampling. This study uses a laboratory experiment to explore the determinants of the five effectiveness dimensions, i.e., evaluation, Willingness-to-Pay (WTP), perceived sampling usefulness, sampling cost and the likelihood of being a free rider, of online digital music sampling. Digital music samples with a higher quality and longer segments were found to increase the sampler's music evaluation and make the evaluation process more useful. Also, the sampler's music evaluation significantly determines his/her WTP. Higher music evaluations not only decrease the sampler's sampling cost during the sampling process, but also reduces the probability that the sampler will take the music sample as a substitute for the original music. This study also shows that the current practice of online digital music sampling is not ideal and music retailers could improve their music sampling strategies by providing digital music samples with longer segments and of higher quality. All of these findings have significant implications for music retailers to use digital music sampling strategies better.