Heterogeneous relations among environmental regulation, technological innovation, and environmental pollution.

In this study, interprovincial panel data of China from 2011 to 2020 are selected and empirically examined to determine the effects of three types of environmental regulation tools: command-and-control, market-incentive, and public-participation types. Then, hierarchical regression analysis and instrumental variables are used to analyze and verify the mediating role of technological innovation. Results show no significant relationship between command-and-control regulation and environmental pollution, while market-incentive and public-participation regulations have a significantly negative inhibitory effect on environmental pollution and contribute to pollution reduction. In addition, product and process innovations play partially mediating roles between market-incentive regulation and environmental pollution and between public-participation regulation and environmental pollution, respectively, thus indicating that technological innovation is an effective way to reduce pollutant emissions. Compared with product innovation, process innovation has a better effect on pollution emission reduction but a smaller incentive effect under environmental regulation. This finding indicates that enterprise technology innovation and environmental regulation fail to achieve a suitable match for maximizing environmental benefits. Further analysis shows that the effects of the three types of environmental regulation tools on reducing pollution emissions vary in different periods and show significant changes around 2010. The effects of command-based regulation weaken, while those of the pollution abatement of market-incentive and public-participation regulations increase.

Study on the evolution of collaborative innovation in marine economy considering the participation of financial institutions and two types of cooperation.

In the realm of significant technological research and innovation within the marine economy, enterprises and academic research institutions often grapple with a lack of innovation motivation due to financial constraints. This paper introduces the factor of "capital constraints" into the marine innovation chain, establishing a technological innovation chain within the marine economy. Utilizing a three-party evolutionary game model, the study delves into the strategy selection and evolution of financial institutions, marine enterprises, and academic research institutions. In contrast to previous studies, this paper categorizes technological innovation cooperation into two types: "cooperative tackling type" and "market-oriented promotion type." Additionally, it posits that collaboration between academic research institutions and marine enterprises establishes an implicit guarantee relationship, facilitating access to higher loan amounts for both parties. The research reveals that the behavior of governments and marine enterprises is influenced by the initial willingness of participants. Higher basic benefits of cooperation and innovation between academic research institutions and marine enterprises lead to a quicker attainment of an evolutionary stable state. Moreover, in collaborations between marine enterprises and research institutions, an excessively high proportion of funds occupied by marine enterprises proves disadvantageous. The paper suggests that pure market-oriented promotion innovation cooperation could serve as a supplementary approach to traditional cooperation and innovation. Finally, numerical examples are presented to elucidate the outcomes of the theoretical model, accompanied by policy suggestions.

Dynamic coopetition game between private label and national brand under carbon trading policy.

The increasing environmental concerns have changed the coopetition behavior between private-label (PL) retailers and national-brand (NB) manufacturers, which needs firms to reconsider their competitive and cooperative strategies to cope with this change. To address this need, we incorporate carbon trading policy into dynamic coopetition game models between a PL retailer and an NB manufacturer. We examine their dynamic evolution trajectories of state variables, decision variables, and profit value functions by using numerical simulation. The sensitivity analysis shows that: (i) some key parameters affect the steady-state values of variables and profit value functions; (ii) the relative strengths of the NB manufacturer and the PL retailer affect decision variables and profit value functions. The results indicate that the carbon trading policy significantly affects dynamic coopetition between the PL retailer and the NB manufacturer.

Research on the Evolution of the Express Packaging Recycling Strategy, Considering Government Subsidies and Synergy Benefits.

With the year-on-year growth of e-commerce transactions and the increasing popularity of the concept of ecological civilization, the waste and recycling of express packages have aroused widespread discussion and attention. On the issue of express package recycling, how consumers, e-commerce enterprises, and e-commerce platforms choose their own strategies, how to better promote the recycling of express packages, and what is the effect mechanism of government subsidies on different players. These are the questions that this article wants to answer. Since this article involves many stakeholders, in order to better identify the strategic choice and evolution of different entities and to better study the influence of government subsidies on the strategic choice of game players, this article uses two triparty evolutionary game models. The results show that without subsidies, changes in the rate of return and the initial probability will affect the evolution of the equilibrium strategy, while the synergistic benefits will have a corrective effect in some cases; when government subsidies are included and the probability of the three parties choosing "green strategies" is relatively low, subsidies should be paid to e-commerce companies mainly; lower subsidies can only provide incentives for e-commerce platforms. This article can provide certain references and value for government policymakers.

Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme.

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.