Environmental protection tax and green innovation of heavily polluting enterprises: A quasi-natural experiment based on the implementation of China's environmental protection tax law.

Environmental protection tax is an important tool for directing environmentally friendly growth in heavily polluting enterprises, but existing research has yet to provide consistent conclusions on whether and how environmental protection tax can promote green innovation in heavily polluting industries. The paper uses a double difference model based on data from Chinese listed companies in heavily polluting industries from 2012 to 2021 to empirically investigate whether environmental protection tax drives green innovation behavior of heavily polluting enterprises. The findings show that the environmental protection tax increases the degree of green innovation in heavily polluting enterprises, primarily through the anti-driving effect, in which an increase in environmental management expenses forces firms to increase their R&D investment, which improves the degree of green technical innovation. Furthermore, the environmental protection tax has a strong promotion effect on heavy polluters' green innovation for state-owned enterprises and those in growing period or located in high marketization regions. However, this promotion effect is insignificant for non-state-owned enterprises and those in recession period, and environmental protection tax hinders green innovation of enterprises in mature period and those located in low marketization regions. Accordingly, it is suggested to improve preferential tax policies, increase investment in corporate green innovation and strengthen the supervision of environmental tax.

Multiscale Mesh Deformation Component Analysis With Attention-Based Autoencoders.

Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this article, we propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales. Quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods. Furthermore, with the multiscale deformation components extracted by our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.

Variational Autoencoders for Localized Mesh Deformation Component Analysis.

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

A genetically engineered Escherichia coli that senses and degrades tetracycline antibiotic residue.

Due to the abuse of antibiotics, antibiotic residues can be detected in both natural environment and various industrial products, posing threat to the environment and human health. Here we describe the design and implementation of an engineered Escherichia coli capable of degrading tetracycline (Tc)-one of the commonly used antibiotics once on humans and now on poultry, cattle and fisheries. A Tc-degrading enzyme, TetX, from the obligate anaerobe Bacteroides fragilis was cloned and recombinantly expressed in E. coli and fully characterized, including its K m and k cat value. We quantitatively evaluated its activity both in vitro and in vivo by UV-Vis spectrometer and LC-MS. Moreover, we used a tetracycline inducible amplification circuit including T7 RNA polymerase and its specific promoter PT7 to enhance the expression level of TetX, and studied the dose-response of TetX under different inducer concentrations. Since the deployment of genetically modified organisms (GMOs) outside laboratory brings about safety concerns, it is necessary to explore the possibility of integrating a kill-switch. Toxin-Antitoxin (TA) systems were used to construct a mutually dependent host-plasmid platform and biocontainment systems in various academic and industrious situations. We selected nine TA systems from various bacteria strains and measured the toxicity of toxins (T) and the detoxifying activity of cognate antitoxins (A) to validate their potential to be used to build a kill-switch. These results prove the possibility of using engineered microorganisms to tackle antibiotic residues in environment efficiently and safely.