Interface Engineering of the Cu1.5Mn1.5O4/CeO2 Heterostructure for Highly Efficient Electrocatalytic Nitrate Reduction to Ammonia.

The electrochemical nitrate reduction reaction (NO3RR) is considered a sustainable technology to convert the nitrate pollutants to ammonia. However, developing highly efficient electrocatalysts is necessary and challenging given the slow kinetics of the NO3RR with an eight-electron transfer process. Here, a Cu1.5Mn1.5O4 (CMO)/CeO2 heterostructure with rich interfaces is designed and fabricated through an electrospinning and postprocessing technique. Benefiting from the strong coupling between CMO and CeO2, the optimized CMO/CeO2-2 catalyst presents excellent NO3RR performance, with NH3 Faraday efficiency (FE) up to 93.07 ± 1.45% at -0.481 V vs reversible hydrogen electrode (RHE) and NH3 yield rate up to 48.06 ± 1.32 mg cm-2 h-1 at -0.681 V vs RHE. Theoretical calculations demonstrate that the integration of CeO2 with CMO modulates the adsorption/desorption process of the reactants and intermediates, showing a reduced energy barrier in the rate determination step of NO* to N* and achieving an outstanding NO3RR performance.

Dynamic 3D Point Cloud Sequences as 2D Videos.

Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing complex spatio-temporal point neighbor grouping and feature aggregation schemes, often resulting in methods lacking effectiveness, efficiency, and expressive power. In this paper, we propose a novel generic representation called Structured Point Cloud Videos (SPCVs). Intuitively, by leveraging the fact that 3D geometric shapes are essentially 2D manifolds, SPCV re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points. The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences. To achieve such re-organization, we design a self-supervised learning pipeline that is geometrically regularized and driven by self-reconstructive and deformation field learning objectives. Additionally, we construct SPCV-based frameworks for both low-level and high-level 3D point cloud sequence processing and analysis tasks, including action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the versatility and superiority of the proposed SPCV, which has the potential to offer new possibilities for deep learning on unstructured 3D point cloud sequences. Code will be released at https://github.com/ZENGYIMING-EAMON/SPCV.

Correlation between serum phosphate and all-cause mortality in critically ill patients with coronary heart disease accompanied by chronic kidney disease: a retrospective study using the MIMIC-IV database.

Background: The adverse clinical endpoints of cardiovascular and kidney diseases are correlated with increased serum phosphate levels. However, in critically ill patients with coronary heart disease (CHD) accompanied by chronic kidney disease (CKD), the prognostic value of serum phosphate remains unclear. Methods: Patients' medical records from the Medical Information Mart for Intensive Care IV database who had concomitant CKD and CHD were classified into four distinct groups in this large retrospective observational cohort study based on the quartiles of serum phosphate levels. Vital status and the duration of hospital and ICU stays within the short-term follow-up periods of 30 and 90 days constituted the primary outcomes. All-cause mortality in the intensive care unit (ICU) and hospital constituted the secondary outcomes. Further, the Cox proportional hazard and restricted cubic spline (RCS) regression models were employed to ascertain how serum phosphate levels correlated with the primary outcomes. In addition, the occurrence rate of the secondary outcomes across the four quartiles was determined utilizing the Kaplan-Meier method. Results: Among the total 3,557 patients (67.6% male) included, the hospital and ICU all-cause mortality rates were 14.6% and 10%, separately. Higher quartiles of serum phosphate concentrations were associated with shorter short-term survival rates, as shown by the Kaplan-Meier curves. Additionally, the Cox proportional hazards analysis illustrated that serum phosphate was independently linked to a higher death risk in the hospital [HR, 1.10 (95% CI: 1.03-1.18), P = 0.007] and ICU [HR, 1.14 (95% CI: 1.07-1.22), P < 0.001]. Lastly, the RCS regression models suggested a robust non-linear correlation between serum phosphate concentrations and death risk in the ICU and hospital (both P for non-linearity <0.001). Conclusions: The prognostic value of serum phosphate is significant in critically ill patients with CHD accompanied by CKD. Furthermore, serum phosphate is potentially valuable for identifying patients with this concomitant condition.

Perfluorooctane sulfonate induces ferroptosis-dependent non-alcoholic steatohepatitis via autophagy-MCU-caused mitochondrial calcium overload and MCU-ACSL4 interaction.

The incidence of nonalcoholic steatohepatitis (NASH) is related with perfluorooctane sulfonate (PFOS), yet the mechanism remains ill-defined. Mounting evidence suggests that ferroptosis plays a crucial role in the initiation of NASH. In this study, we used mice and human hepatocytes L-02 to investigate the role of ferroptosis in PFOS-induced NASH and the effect and molecular mechanism of PFOS on liver ferroptosis. We found here that PFOS caused NASH in mice, and lipid accumulation and inflammatory response in the L-02 cells. PFOS induced hepatic ferroptosis in vivo and in vitro, as evidenced by the decrease in glutathione peroxidase 4 (GPX4), and the increases in cytosolic iron, acyl-CoA synthetase long-chain family member 4 (ACSL4) and lipid peroxidation. In the PFOS-treated cells, the increases in the inflammatory factors and lipid contents were reversed by ferroptosis inhibitor. PFOS-induced ferroptosis was relieved by autophagy inhibitor. The expression of mitochondrial calcium uniporter (MCU) was accelerated by PFOS, leading to subsequent mitochondrial calcium accumulation, and inhibiting autophagy reversed the increase in MCU. Inhibiting mitochondrial calcium reversed the variations in GPX4 and cytosolic iron, without influencing the change in ACSL4, induced by PFOS. MCU interacted with ACSL4 and the siRNA against MCU reversed the changes in ACSL4，GPX4 and cytosolic iron systemically. This study put forward the involvement of hepatic ferroptosis in PFOS-induced NASH and identified MCU as the mediator of the autophagy-dependent ferroptosis.

A genome-wide association study of escitalopram treatment outcomes in patients with major depressive disorder.

Major depressive disorder (MDD) is a common psychological condition, the consequences of which, such as suicide, can be severe. Escitalopram, a selective serotonin reuptake inhibitor, is a commonly used antidepressant in clinics. However, more than one-third of patients with MDD do not respond to this drug. Gene polymorphism may affect the efficacy of escitalopram, but the genetic architecture of the antidepressant response in patients with MDD remains unclear. We perform a genome-wide association study (GWAS) of the genetic effect on the outcome of escitalopram in patients with MDD. A total of 203 patients with MDD and 176 healthy control (HC) adults were recruited from Beijing Anding Hospital. Patients received 12 weeks of antidepressant treatment with escitalopram. The Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) or Hamilton depression scale (HAMD) were used to evaluate the severity of depression symptoms at the baseline and the end of 2 and 12 weeks of treatment. A total of 140 variants in MDD patients were identified by GWAS to have genome-wide significance (p < 5e - 8) compared with HCs. Similarly, 189 and 18 variants were identified to be associated with QIDS-SR and HAMD score changes in patients after antidepressant treatment (p < 1e - 5), including rs12602361, rs72799048, rs16842235, and rs2518256. In the two weeks QIDS-SR score study, the gene-level association for these variants and gene set enrichment analyses implicate the enrichment of genes involved in the synaptic plasticity process and nervous system development. Our results implicate the predictive capacity of the effect of escitalopram treatment, supporting a link between genetic basis and remission of depression.

A novel phage putative depolymerase, Depo16, has specific activity against K1 capsular-type Klebsiella pneumoniae.

Klebsiella pneumoniae, especially hypervirulent K. pneumoniae (hvKP), is a common opportunistic pathogen that often causes hospital- and community-acquired infections. Capsular polysaccharide (CPS) is an important virulence factor of K. pneumoniae. Some phages encode depolymerases that can recognize and degrade bacterial polysaccharides. In this study, the lytic bacteriophage vB_KpnP_ZK1 (abbreviated as ZK1) was isolated using serotype K1 hvKP as the host. Although amino acid sequence BLAST analysis indicated that the tail fiber protein Depo16 of phage ZK1 showed no significant similarity to any reported phage depolymerases, it displayed enzymatic activities that are characteristic of phage depolymerases. After expression and purification, Depo16 could efficiently remove the capsular polysaccharide layer that surrounds the surface of serotype K1 K. pneumoniae. Although no bactericidal activity was detected, Depo16 makes serotype K1 K. pneumoniae sensitive to peritoneal macrophages (PMs). In addition, in a mouse bacteremia model of serotype K1 K. pneumoniae, 25 µg of Depo16 was effective in significantly prolonging survival. Depo16 treatment can reduce the bacterial load in blood and major tissues and alleviate tissue damage in mice. This indicates that the putative depolymerase Depo16 is a potential antibacterial agent against serotype K1 K. pneumoniae infections.IMPORTANCEKlebsiella pneumoniae often causes hospital-acquired infections and community-acquired infections. Capsular polysaccharide (CPS) is one of the crucial virulence factors of K. pneumoniae. K1 and K2 capsular-type K. pneumoniae strains are the most prevalent serotypes of hypervirulent K. pneumoniae (hvKP). In this study, a novel K. pneumoniae phage named vB_KpnP_ZK1 was isolated, and its putative depolymerase Depo16 showed low homology with other reported phage depolymerases. Depo16 can specifically degrade the K. pneumoniae K1 capsule making this serotype sensitive to peritoneal macrophages. More importantly, Depo16 showed a significant therapeutic effect in a mouse bacteremia model caused by serotype K1 K. pneumoniae. Thus, Depo16 is a potential antibacterial agent to combat serotype K1 K. pneumoniae infections.

Enhanced trajectory tracking for quadrotors: disturbance observer state feedback control.

This study investigates the dynamics and trajectory tracking of quadcopters by utilizing the Disturbance Observer-based Control (DOBC) algorithm. The quadcopter's dynamic model, which consists of six degrees of freedom, includes both disturbances and uncertainties in the model. The DOBC approach incorporates the disturbance model into the system by introducing it at the input variables. It then compensates for disturbances to achieve accurate tracking of different reference trajectories. The simulated trajectories span a range of motion, varying from simple straight paths to complex spiral paths. In order to verify and evaluate the efficacy of the suggested control technique, simulations are performed using MATLAB. The simulations conclusively show that the disturbance observer-based method effectively achieves the tracking of specified reference trajectories in three-dimensional space. The study highlights the effectiveness of the DOBC algorithm in reducing the effects of disturbances and uncertainties, thereby improving the quadcopter's capacity to accurately track various trajectories.

Iridium nanoparticles supported on polyaniline nanotubes for peroxidase mimicking towards total antioxidant capacity assay of fruits and vegetables.

In this study, a straightforward approach is presented for the first time to anchor Ir nanoparticles on the surface of uniform polyaniline (PANi) nanotubes (NTs), which can be used as an efficient peroxidase (POD)-like catalyst. The morphology and chemical structure of the PANi-Ir nanocomposite are characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), X-ray diffractometer (XRD), Raman and X-ray photoelectron spectroscopy (XPS) measurements. Owing to the strong interaction between Ir nanoparticles and PANi, a remarkable catalytic enhancement is achieved compared to the bare Ir black catalyst and individual PANi NTs, dominating withan electron transfer mechanism. Furthermore, an efficient colorimetric sensor for ascorbic acid (AA) is developed with a low detection limit of 1.0 µM (S/N = 3), and a total antioxidant capacity (TAC) sensing platform is also constructed for the rigorous detection and analysis of fruits and vegetables.

Improvement of astrocytic gap junction involves the anti-depressive effect of celecoxib through inhibition of NF-κB.

CONTEXT: Celecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, has been shown to exhibit anti-depressive effects in clinical trials. However, the direct mechanism underlying its effect on neuroinflammation remains unclear. Neuroinflammatory reaction from astrocytes leads to depression, and our previous study found that gap junction disorder between astrocytes aggravated neuroinflammatory reaction in depressed mice. OBJECTIVE: To investigate the potential mechanism of celecoxib's effects on astrocytic gap junctions during the central nervous inflammation-induced depression. MATERIALS & METHODS: Stereotaxic injection of lipopolysaccharide (LPS) into the prefrontal cortex (PFC) to establish a model of major depressive disorder (MDD). Celecoxib was administrated into PFC 15 min after LPS injection. The depressive performance was tested by tail suspension test and forced swimming test, and the levels of proinflammation cytokines were determined at mRNA and protein levels. Resting-state functional connection (rsFC) was employed to assess changes in the default mode network (DMN). Additionally, astrocytic gap junctions were also determined by lucifer yellow (LY) diffusion and transmission electron microscope (TEM), and the expression of connexin 43 (Cx43) was measured by western blotting, quantitative polymerase chain reaction, and immunofluorescence. RESULTS: LPS injection induced significant depressive performance, which was ameliorated by celecoxib treatment. Celecoxib also improved rsFC in the DMN. Furthermore, celecoxib improved astrocytic gap junctions as evidenced by increased LY diffusion, shortened gap junction width, and normalized levels of phosphorylated Cx43. Celecoxib also blocked the phosphorylation of p65, and inhibition of p65 abolished the improvement of Cx43. DISCUSSION & CONCLUSION: Anti-depressive effects of celecoxib are mediated, at least in part, by the inhibition of nuclear factor- kappa B (NF-κB) and the subsequent improvement of astrocytic gap junction function.

Significantly Enhanced Energy-Saving H2 Production Coupled with Urea Oxidation by Low- and Non-Pt Anchored on NiS-Based Conductive Nanofibers.

Rational designing electrocatalysts is of great significance for realizing high-efficiency H2 production in the water splitting process. Generally, reducing the usage of precious metals and developing low-potential nucleophiles oxidation reaction to replace anodic oxygen evolution reaction (OER) are efficient strategies to promote H2 generation. Here, NiS-coated nickel-carbon nanofibers (NiS@Ni-CNFs) are prepared for low-content Pt deposition (Pt-NiS@Ni-CNFs) to attain the alkaline HER catalyst. Due to the reconfiguration of NiS phase and synergistic effect between Pt and nickel sulfides, the Pt-NiS@Ni-CNFs catalyst shows a high mass activity of 2.74-fold of benchmark Pt/C sample. In addition, the NiS@Ni-CNFs catalyst performs a superior urea oxidation reaction (UOR) activity with the potential of 1.366 V versus reversible hydrogen electrode (RHE) at 10 mA cm-2 , which demonstrates the great potential in the replacement of OER. Thus, a urea-assisted water splitting electrolyzer of Pt-NiS@Ni-CNFs (cathode)||NiS@Ni-CNFs (anode) is constructed to exhibit small voltages of 1.44 and 1.65 V to reach 10 and 100 mA cm-2 , which is much lower than its overall water splitting process, and presents a 6.5-fold hydrogen production rate enhancement. This work offers great opportunity to design new catalysts toward urea-assisted water splitting with significantly promoted hydrogen productivity and reduced energy consumption.

Rationally Construction of Mn-Doped RuO2 Nanofibers for High-Activity and Stable Alkaline Ampere-Level Current Density Overall Water Splitting.

Nowadays, highly active and stable alkaline bifunctional electrocatalysts toward water electrolysis that can work at high current density (≥1000 mA cm-2) are urgently needed. Herein, Mn-doped RuO2 (MnxRu1-xO2) nanofibers (NFs) are constructed to achieve this object, presenting wonderful hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performances with the overpotentials of only 269 and 461 mV at 1 A cm-2 in 1 m KOH solution, and remarkably stability under industrial demand with 1 A cm-2, significantly better than the benchmark Pt/C and commercial RuO2 electrocatalysts, respectively. More importantly, the assembled Mn0.05Ru0.95O2 NFs||Mn0.05Ru0.95O2 NFs electrolyzer toward overall water splitting reaches the current density of 10 mA cm-2 with a cell voltage of 1.52 V and also delivers an outstanding stability over 150 h of continuous operation, far surpassing commercial Pt/C||commercial RuO2, RuO2 NFs||RuO2 NFs and most previously reported exceptional electrolyzers. Theoretical calculations indicate that Mn-doping into RuO2 can significantly optimize the electronic structure and weaken the strength of O─H bond to achieve the near-zero hydrogen adsorption free energy (ΔGH*) value for HER, and can also effectively weaken the adsorption strength of intermediate O* at the relevant sites, achieving the higher OER catalytic activity, since the overlapping center of p-d orbitals is closer to the Fermi level.

Rational design of bimetal phosphide embedded in carbon nanofibers for boosting oxygen evolution.

The development of non-precious metal electrocatalysts for oxygen evolution reaction (OER) is crucial for generating large-scale hydrogen through water electrolysis. In this work, bimetal phosphides embedded in electrospun carbon nanofibers (P-FeNi/CNFs) were fabricated through a reliable electrospinning-carbonization-phosphidation strategy. The incorporation of P-FeNi nanoparticles within CNFs prevented them from forming aggregation and further improved their electron transfer property. The bimetal phosphides helped to weaken the adsorption of O intermediate, promoting the OER activity, which was confirmed by the theoretical results. The as-prepared optimized P-Fe1Ni2/CNFs catalyst exhibited very high OER electrocatalytic performance, which required very low overpotentials of just 239 and 303 mV to reach 10 and 1000 mA cm-2, respectively. It is superior to the commercial RuO2 and many other related OER electrocatalysts reported so far. In addition, the constructed alkaline electrolyzer based on the P-Fe1Ni2/CNFs catalyst and Pt/C delivered a cell voltage of 1.52 V at 10 mA cm-2, surpassing the commercial RuO2||Pt/C (1.61 V) electrolyzer. It also offered excellent alkaline OER performance in simulated seawater electrolyte. This demonstrated its potential for practical applications across a broad range of environmental conditions. Our work provides new ideas for the ration design of highly efficient non-precious metal-based OER catalysts for water electrolysis.

Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder.

BACKGROUND: There is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood. RESULTS: To address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71. CONCLUSION: This study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.

Does green credit really increase green technology innovation?

Considering China's green credit policy (GCP) as a quasi-natural experiment, this study discusses the effect of GCP on enterprise green innovation (GI) using a difference-in-difference method based on data from Chinese listed companies from 2009 to 2020. The results indicate that green credit enhances the strategic GI of heavy polluters while significantly inhibiting essential GI, thus suggesting the nonexistence of the Porter effect. In addition, the inhibition effect is attributed to an increase in financing constraints and a reduction in government subsidies, firm research and development investment, and employment scale. This disincentive effect is particularly pronounced in privately owned firms, small cities, and capital-intensive low-profitability firms. Resource misallocation caused by the GCP fails to stimulate the green transformation of heavily polluting industries through the Porter effect. Hence, governments should establish a diversified green financial system, integrate green venture capital and GI elements, and guide the flow of social capital toward green industries.

Assessing the impact of economic growth target constraints on environmental pollution: Does environmental decentralization matter?

In the context of the goal of "carbon neutrality", an economic development model that achieves emission reduction goals and ensures stable economic growth is currently being advocated by China. Based on provincial panel data in China from 2005 to 2016, we analyse the impact of economic growth target (EGT) constraints on environmental pollution using a spatial econometric method. The results indicate that EGT constraints significantly exacerbate environmental pollution in local regions and adjacent areas. Local governments are motivated to achieve economic growth goals at the expense of the ecological environment. The positive effects are attributed to a reduction in environmental regulation (ER), industrial structure upgrading and technological innovation and an increase in foreign direct investment (FDI). Moreover, environmental decentralization (ED) plays a positive regulatory role and can weaken the adverse influences of EGT constraints on environmental pollution. Interestingly, the nonlinear impact of EGT constraints on environmental pollution relies on different types of ED. Environmental administration decentralization (EDA) and environmental supervision decentralization (EDS) can reduce the positive effect of EGT constraints on environmental pollution, while an improvement in environmental monitoring decentralization (EDM) can increase the promoting influences of the constraints of economic growth goals on environmental pollution. The above conclusions still hold under a series of robustness tests. Based on the above findings, we suggest that local governments set scientific growth targets, establish scientific assessment indicators for officials, and optimize the ED management institution.

The use of biochemical indexes in hair for clinical studies of psychiatric diseases: What can we learn about mental disease from hair?

Analysis of hair samples provides unique advantages, including non-invasive sampling, sample stability, and the possibility of additional optimization of high sensitivity detection methods. Hair sample analysis is often used in psychiatric disease research to evaluate previous periods of stress encountered by patients. Glucocorticoid analysis is the most frequently tested indicator of stress. Furthermore, the hypothalamus-pituitary-gonad axis and endocannabinoid system also are involved in the occurrence and development of mental disorders. The endocannabinoid and sex hormone levels in patients experiencing mental illness are considerably different from levels observed in healthy individuals. Nevertheless, due to the different methods used to assess the degree of disease and the range of analytical methods involved in clinical research, the trends in changes for these biomarkers are not uniform. The correlations between changes in biomarker concentrations and illness severity also are not clear. The observed alterations suggest these biochemical substances in hair have potential as biomarkers for diagnosis or predictive treatment. However, the variable results obtained thus far could hamper further development of hair samples for clinical assessment in psychiatric disorders. This article summarizes the published reports documenting the changes in the content of relevant substances in hair in individuals experiencing mental illness and the degree of correlation. In the discussion section, we proposed several issues that should be considered in future studies of hair samples obtained from patients with mental disorders to promote the use of hair sample assessment as an aid in diagnosis or predictive treatment.

Pathways to carbon neutrality: how do government corruption and resource misallocation affect carbon emissions?

The government corruption and resource misallocation are important factors that inhibit economic transformation and environmental governance. Using a study sample of 30 provinces in China, this study employs the spatial econometric methods and threshold model to investigate the relationship between regional corruption, resource misallocation, and CO2 emission. The results show that there is a clear spatial dependence and correlation of CO2 emissions. Government corruption is a neglected contributor to carbon emissions, and this promotion effects are attributed to a promotion in resource misallocation. Moreover, the CO2 emissions are significantly promoted by local labor misallocation and capital misallocation, and the increasing effect is more pronounced in areas with high corruption. For spatial spillover effects, there is also no evidence that local CO2 emissions are significantly promoted by capital misallocation of surrounding areas. Additionally, the promoting effect of corruption on CO2 emissions is more pronounced in regions with weak environmental regulation, low marketization process, and the central and western of China. This work elucidates the mechanism of the impact of corruption on environmental pollution from the perspective of resource allocation, and provides an important reference for the effective management of environmental pollution.

Does economic growth target constraint put pressure on green energy efficiency? Evidence from China.

Building a sustainable energy system and achieving sustainable economic growth has always been China's development goal. However, under the promotion incentive and information asymmetry, local governments may choose to sacrifice the ecological environment to maintain expected economic growth goals. This paper analyzes the relationship between economic growth targets and green energy efficiency (GEE) by using panel data models and spatial econometric techniques. Moreover, the mediation effect model and threshold effect model are used to study their influence mechanism and the possible nonlinear relationship. The results indicate that economic growth targets reduce the efficiency of GEE. The impact of environmental regulation on GEE is "U" shaped. Environmental regulation plays a positive moderation role between economic growth targets and GEE. Additionally, higher levels of technological innovation, industrial upgrading, and environmental regulation result in a reduction in the negative effects of economic growth targets on the GEE. However, the improvement in the foreign direct investment can increase the negative influences of economic growth targets on the GEE.

The role of digitalization on green economic growth: Does industrial structure optimization and green innovation matter?

The digital economy has demonstrated strong resilience and great potential, under the interwoven influence of the global pandemic and severe environmental concerns across the world. Therefore, there is a need to focus on the value of green economic growth in the digital economy. This paper constructs an evaluation index system and adopts the SEEA (System of Environmental and Economic Accounting) method to measure the digitalization level (Digi) and green economy growth level (GEG) of China. The internal mechanism and linear relationship between digitalization and green economy growth are examined based on the panel data from 2013 to 2019. Moreover, this study explores the spatial spillover effect. The major study findings are as follows: (1) Digitalization and green economy growth represent a steady growth trend, and the former as a whole significantly promotes the latter, with a marginal effect of 1.648. (2) The mechanism analysis indicates the intermediary effects' size of three crucial intermediaries: green technology innovation > advanced industrial structure > the rationalization of industrial structure. (3) Both the "local effect" (0.556; 0.574) and "neighboring effect" (1.382; 1.415) of digitalization on green economy growth are positive under the two weight matrices and display "simultaneous resonance" characteristics based on the spatial perspective. (4) There exists obvious regional spatial heterogeneity and resource endowment heterogeneity. Finally, this study put forward corresponding policy implications, such as construction of new digital infrastructures and guiding green-energy consumption.