Assessment of Tunnel Lining Stability through Integrated Monitoring of Fiber Bragg Grating Strain and Structural Deformation.

Tunnel excavation induces the stress redistribution of the surrounding rock. Structural cracks may develop in the secondary lining due to this stress redistribution and bias pressure, consequently affecting the overall construction safety of the tunnel. This paper aims to achieve real-time monitoring of the excavation stability of the lining structure by integrating two monitoring technologies: structural deformation monitoring and fiber grating strain monitoring. Additionally, it proposes a method to simultaneously measure the thermal strain and applied stress-strain of the structure. By analyzing the displacement and deformation of the lining structure, its stability can be preliminarily evaluated in the short term. To achieve long-term real-time monitoring and a more accurate assessment of the tunnel structure's stability, the paper introduces fiber Bragg grating (FBG) strain sensor monitoring technology. First, based on the geological stratigraphy information obtained from the exploration, a simulation model of the tunnel under different section bias angles is established. The displacement and stress concentration areas of the lining structure are then analyzed to optimize the sensor deployment array and provide a theoretical basis for the sensor arrangement. FBG strain sensors are installed on the surface of the structure to measure thermal strain and loading stress-strain, whereas FBG temperature sensors measure local temperature. The findings indicate that following tunnel excavation, the maximum daily strain differences at K107+043 and K107+240 were 126.87 µÎµ and 209.38 µÎµ, respectively. After a period of rock disturbance, the average daily strain differences due to applied stress-strain were 16.8 µÎµ and 12.65 µÎµ, respectively. The thermal strain was close to the daily strain difference. Therefore, after the rock disturbance subsided, the strain fluctuations in the lining structure were mainly caused by local temperature changes, and the surrounding rock tended to stabilize. This offers a viable method for evaluating structural stability post-tunnel excavation.

Assessment of benthic ecological status and heavy metal contamination in an estuarine intertidal mudflat in the Northern Bohai Sea.

Evaluating the ecological quality and pollution status of coastal mudflats is crucial for environmental protection and management, particularly when these areas serve as major shellfish production hotspots. In this study, we assessed the benthic ecological quality and heavy metals pollution in Geligang, located in the Northern Bohai Sea using the macrobenthos diversity index and the heavy metal pollution index. The Shannon-Wiener index (H'), AZTI marine biotic index (AMBI), multivariate AMBI (M-AMBI) showed that the benthic ecological quality in Geligang is either good or high. The potential ecological risk index and geoaccumulation index highlighted that cadmium (Cd) and mercury (Hg) as the primary heavy metal pollutants in Geligang. Surprisingly, the biomass of the two dominant species other than these indices serve as reliable indicators of heavy metal pollution. This suggests that the biomass of Mactra veneriformis and Potamocorbula laevis could be used to assess heavy metal pollution levels in Geligang.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China.

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Comparing the Centers for Medicare and Medicaid Services (CMS) enrollment data death dates to the National Death Index (NDI).

PURPOSE: In the United States, the National Death Index (NDI) is the most complete source of death information, while epidemiologic studies with mortality outcomes often rely on U.S. Medicare data for outcome ascertainment. The purpose of this study was to assess the agreement of death information between the Centers for Medicare & Medicaid Services (CMS) Medicare enrolment data and NDI. METHODS: Using Medicare and NDI data from 1999 through 2016, we identified Medicare beneficiaries who were reported dead in the CMS Medicare enrolment database (EDB) and Common Medicare Environment (CME), linked these beneficiaries to the NDI using CMS Health Insurance Claim number, and compared death dates between the two data sources. To assess agreement between our data sources, we calculated kappa scores; where a kappa of 1 indicates perfect agreement and a kappa of 0 indicates agreement equivalent to chance. We also examined CMS to NDI linkage and death date matching for stability over time. RESULTS: Of the 36 785 640, Medicare beneficiaries reported dead in CMS enrollment data from 1999 to 2016, 97.5% were linked to the NDI. A kappa score of 0.98 showed a near perfect agreement between NDI and CMS reported deaths. The percentage of linked cases exactly matching on death dates increased from 94.8% in 1999 to 99.4% in 2016. CONCLUSIONS: Our findings suggest strong concordance between death dates as recorded by CMS enrollment data and the NDI in the entire Medicare population.

The selection of animal models influences the assessment of anti-tumor efficacy: promising sialic acid-conjugate modified liposomes demonstrate remarkable therapeutic effects in diverse mouse strains.

Mice as a crucial tool for preclinical assessment of antineoplastic agents. The impact of physiological differences among mouse strains on the in vivo efficacy of antitumor drugs, however, has been significantly overlooked. Mononuclear phagocyte system (MPS) is the major player in clearance in vivo, and differences in MPS among different strains may potentially impact the effectiveness of antitumor preparations. Therefore, in this study, we employed conventional liposomes (CL-EPI) and SA-ODA modified liposomes (SAL-EPI) as model preparations to investigate the comprehensive tumor therapeutic effects of CL-EPI and SAL-EPI in KM, BALB/c, and C57BL/6 tumor-bearing mice. The results demonstrated significant variability in the efficacy of CL-EPI for tumor treatment across different mouse strains. Therefore, we should pay attention to the selection of animal models in the study of antitumor agents. SAL-EPI effectively targeted tumor sites by binding to Siglec-1 on the surface of peripheral blood monocytes (PBMs), and achieved good therapeutic effect in different mouse strains with little difference in treatment. The SA modified preparation is therefore expected to achieve a favorable therapeutic effect in tumor patients with different immune states through PBMs delivery (Siglec-1 was expressed in both mice and humans), thereby possessing clinical translational value and promising development prospects.

Influence of interpersonal distance on collaborative performance in the joint Simon task-An fNIRS-based hyperscanning study.

Collaboration is a critical skill in everyday life. It has been suggested that collaborative performance may be influenced by social factors such as interpersonal distance, which is defined as the perceived psychological distance between individuals. Previous literature has reported that close interpersonal distance may promote the level of self-other integration between interacting members, and in turn, enhance collaborative performance. These studies mainly focused on interdependent collaboration, which requires high levels of shared representations and self-other integration. However, little is known about the effect of interpersonal distance on independent collaboration (e.g., the joint Simon task), in which individuals perform the task independently while the final outcome is determined by the parties. To address this issue, we simultaneously measured the frontal activations of ninety-four pairs of participants using a functional near-infrared spectroscopy (fNIRS)-based hyperscanning technique while they performed a joint Simon task. Behavioral results showed that the Joint Simon Effect (JSE), defined as the RT difference between incongruent and congruent conditions indicating the level of self-other integration between collaborators, was larger in the friend group than in the stranger group. Consistently, the inter-brain neural synchronization (INS) across the dorsolateral and medial parts of the prefrontal cortex was also stronger in the friend group. In addition, INS in the left dorsolateral prefrontal cortex negatively predicted JSE only in the friend group. These results suggest that close interpersonal distance may enhance the shared mental representation among collaborators, which in turn influences their collaborative performance.

Job preferences of Chinese primary health care workers: A discrete choice experiment.

OBJECTIVE: Primary health workers (PHWs) are a critical pillar of health systems but primary health care centers often struggle to attract and retain talented staff. To better understand why this is, we investigated the job preference of PHWs in a Chinese urban setting. METHODS: In a discrete choice experiment, PHWs from 15 primary health care centers in Guangzhou, China, made trade-offs between several hypothetical job scenario combinations of salary, type of health institution, bianzhi (permanent post), work years required for promotion, career development and training opportunities, educational opportunities for children, and community respect. Based on the estimate of the mixed logit model, willingness to pay and policy simulations were applied to estimate the utility of each attribute. RESULTS: Data were collected from 446 PHWs. The PHWs were willing to forgo Chinese Renminbi 2806.1 (US$ 438.5) per month to obtain better education opportunities for their children, making it the most important non-monetary factor. Their preferences were also influenced relatively more by salary, bianzhi, and community respect, than with the other attributes we tested for, work years required for promotion, career development and training opportunities, and type of health institution. CONCLUSION: Salary is a robust predictive factor, while three non-monetary factors (opportunities for children's education, bianzhi, and community respect) are essential in retaining health workers in primary care.

Cost-effectiveness analysis of atezolizumab in patients with non-small-cell lung cancer ineligible for treatment with a platinum-containing regimen: a United Kingdom health care perspective.

Background: Cost-effectiveness of atezolizumab, as a treatment for advanced non-small-cell lung cancer (NSCLC) patients who cannot receive a platinum-containing regimen,was still unknown. Our objective was to evaluate the cost-effectiveness of atezolizumab vs. chemotherapy in this indication from the perspective of UK healthcare system. Methods: From the global, randomised, open-label, phase III IPSOS trial, clinical inputs and patient characteristics were obtained. A partitioned survival model with three health states was built: Progression-free survival, progressed disease and death. A lifetime time horizon was applied, with an annual discount rate of 3.5%. Additionally, the willingness-to-pay threshold of £50,000/QALY was utilized. Primary outcomes were quality-adjusted life-year (QALY), costs, and incremental cost-effectiveness ratio (ICER). Sensitivity, scenario, and subgroup analyses were used to assess the reliability of base-case results. Price simulations were carried out in order to provide information for the pricing strategy at specific willingness-to-pay threshold. Results: In the base-case analysis, atezolizumab resulted in a gain of 0.28 QALYs and an ICER of £94,873/QALY compared to chemotherapy, demonstrating no cost-effectiveness. Price simulation results revealed that atezolizumab would be preferred at a price lower than £2,215 (a reduction of 41.8%) at the willingness-to-pay threshold of £50,000. Sensitivity, scenario and subgroup analyses revealed these conclusions were generally robust, the model was most sensitive to the price of atezolizumab and subsequent medication. Furthermore, atezolizumab was found to be more cost-effective for patients displaying a positive PD-L1 expression, with an ICER of £72,098/QALY as compared to chemotherapy. Conclusion: Atezolizumab is not cost-effective for patients with advanced NSCLC ineligible for platinum-containing regimen, potential price reduction is necessary.

[A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.

Environment and natural resources degradation under COVID-19 crises: Recovery post pandemic.

Environmental stability improved during the covid 19 pandemic when production and industrial activities, and natural resources depletion processes stopped during the lockdown environment worldwide; however, based on the judgment of COP26 and the recent COP27, environmental degradation increased in the world in post-pandemic; therefore, policymakers and researchers re-focused their attention on the determinants of CO2 in economies. Hence, this study investigates the nexus of natural resource rents, including oil rents, mineral rents, and coal rents, on the carbon emissions of upper-middle-income economies from 1984 to 2021. The study included economic growth and renewable energy as additional determinants. We have presented detailed time series methods that aid in examining the modeled variables characteristics in the current research, i.e., ADF and ADF-GLS for a unit root in the data variables and considering their stationarity, Johansen cointegration for long-term cointegration among variables, FMOLS, DOLS and CCR for the long run elasticities between dependent and independent variables and Granger causality test in our range of methods. Robustness checks analysis is done through a non-parametric approach by quantile regression and robust regression analysis. Our results exhibit that two natural resource rents that are oil rents and coal rents, have adverse impacts on carbon emissions, and both are positive and significant. In contrast, mineral rents have no statistical significance and role in the carbon emissions of upper-middle-income economies. Moreover, economic growth and renewable energy also positively and significantly impact carbon emissions. Granger causality analysis exerts that natural resources rents, except for mineral rents, economic growth, and renewable energy, all granger causes CO2 emissions, and the feedback is also true. The relevant findings are suitable for policymakers in upper-middle-income economies to ensure environmental sustainability in upper-middle-income economies.

Can Internet Construction Promote Urban Green Development? A Quasi-Natural Experiment from the "Broadband China".

Broadband, as a key element of Internet infrastructure, plays an important role in breaking down barriers to the flow of production factors and promoting green economic transformation. Using the "Broadband China" strategy as a quasi-natural experiment, this study examines the impact and mechanisms of Internet infrastructure on urban green development by constructing a multi-period Difference-in-Differences (DID) model based on panel data from 277 Chinese prefecture-level cities from 2009 to 2019. The results show that the "Broadband China" pilot policy significantly promotes urban green development, with green technological innovation and talent aggregation playing important moderating roles. However, there is a certain lag in the impact of the "Broadband China" pilot policy on urban green development. Furthermore, our heterogeneity analysis suggests that the promotion of the "Broadband China" pilot policy for urban green development mainly exists in central cities, large-scale cities, and resource-based cities, as opposed to surrounding cities, small-scale cities, and non-resource-based cities. The above findings clarify the impact of Internet construction on urban green development and provide a theoretical and practical exploration for achieving a win-win situation of high-quality urban development and environmental protection.

Development zone policy and urban carbon emissions: empirical evidence from the construction of national high-tech industrial development zones in China.

As a key strategy to promote system reform, improve the investment environment, and encourage industrial agglomeration, the national high-tech industrial development zone (NHTDZ) policy in China can not only reduce energy consumption through the scale effect but also improve energy efficiency by modernizing industrial structure and fostering technological innovation, thereby alleviating environmental pollution. Existing studies, however, focus solely on the effects of NHTDZ policy on social and economic development, ignoring their impact on the ecological environment, especially carbon (CO2) emissions that contribute to global warming. Thus, this article analyzes a panel data of 285 prefecture-level cities and above in China from 2003 to 2019 to assess the influence of NHTDZ policy on CO2 emissions, treating the NHTDZ construction since 1988 as a quasi-natural experiment. The results indicate that the NHTDZ policy would mitigate urban carbon emissions, particularly in middle, southeastern, medium-sized, resource-based (RB), non-key environmental protection (non-KEP), and non-two control zone (non-TCZ) cities. In addition, the mediation mechanism test demonstrates that the environmental benefits of the NHTDZ policy in China are attributable to the scale effect, the structural upgrading effect, and the technology innovation effect. The NHTDZ policy would lower per capita CO2 emissions by reducing energy consumption, upgrading industrial structure, and promoting green technology innovation.

Copula-based analysis of socio-economic impact on water quantity and quality: A case study of Yitong River, China.

Socio-economic development has a significant impact on both water quantity and quality. However, few studies have considered the complex relationship between water quantity and quality when evaluating such impact. In this study, three indicators based on copula model were proposed, namely, water quantity improvement degree (WQIDw), water quality improvement degree (WQIDq) and water quantity and quality joint improvement degree (WQJID). These indicators were used to assess the impact of social economy on water quantity and quality, and applied to a case study in Yitong River in Northeast China from 2021 to 2025. Four scenarios were set to explore the impact of socio-economic development and water resources protection on WQIDw, WQIDq and WQJID. The maximum WQIDw, WQIDq and WQJID were <1 under the business-as-usual scenario, which showed that the present socio-economic pattern caused great damage to river water quantity and quality. The combined effect of socio-economic development and water resources protection increased the WQJID of COD and NH3-N by 1.67 and 1.30. This showed that attention should be paid to water resources protection while developing social economy. Compared with comprehensive evaluation, separate evaluation of water quality will underestimate the impact of social economy on rivers, while separate evaluation of water quantity will overestimate the impact. The relationships between WQIDw, WQIDq and WQJID were quantified. Meanwhile, the uncertainty of the evaluation was controlled by the selection of water quality indicators. The WQIDq, WQIDw and WQJID proposed in this study provide a comprehensive assessment tool for guiding water resources management.

Development of a convolutional neural network (CNN) based assessment exercise recommendation system for individuals with chronic stroke: a feasibility study.

BACKGROUND: The use of artificial intelligence (AI) is revolutionizing nearly every aspect of healthcare, but the application of AI in rehabilitation is lagging behind. Clinically, gait parameters and patterns are used to evaluate stroke-specific impairment. We hypothesized that gait kinematics of individuals with stroke provide rich information for the deep-learning to predict the clinical decisions made by physiotherapist. OBJECTIVE: To investigate whether the results of clinical assessments and exercise recommendations by physiotherapists can be accurately predicted using a deep-learning algorithm with gait kinematics data. METHOD: In this cross-sectional study, 40 individuals with stroke were assessed by a physiotherapist using the lower-extremity subscale of the Fugl-Meyer Assessment (FMA-LE) and Berg Balance Scale (BBS). The physiotherapist also decided whether or not the single-leg-stance was an appropriate balance training for each participant. The participants were classified as having good mobility and a low fall risk based on the cutoff scores of the two clinical scales. A convolutional neural network (CNN) was trained using gait kinematics to predict the assessment results and exercise recommendations. RESULTS: The trained model accurately predicted the results of the clinical assessments and decisions with an average prediction accuracy of 0.84 for the FMA-LE, 0.66 for the BBS, and 0.78 for the recommendation of the single-leg-stance exercise. CONCLUSIONS: This CNN deep-learning model provided time-effective and accurate prediction of clinical assessment results and exercise recommendations. This study provides preliminary evidence to support the use of biomechanical data and AI to assist treatment planning and shorten the decision-making process in rehabilitation.

A nexus of social capital-based financing and farmers' scale operation, and its environmental impact.

The study constructs a theoretical model of social capital, farm household financing, and scale operation and their environmental effects, and conducts an empirical test based on data from China Family Panel Studies (CFPS) which is conducted in 2018 using causal mediation analysis. The results showed that farmers who spent more on human interaction have a higher probability of choosing scale operation by renting land, the mechanism of which is that the social capital accumulated by farmers based on human interaction facilitates their access to formal and informal financing, which in turn alleviates the financing constraint of scale operation. In addition, we found that the farmers with low education were more dependent on social capital to obtain informal financing to achieve scale operation. The environmental effects of scale operation found that the farmers significantly reduced the proportion of fertilizer and pesticide use, which could effectively mitigate possible pollution problems caused by excessive fertilizer and pesticide use. These findings confirm that social capital can alleviate the financing constraints of farmers to expand their operation scale through formal and informal financing, which in turn has a positive environmental effect.

Ongoing Clinical Trials in Aging-Related Tissue Fibrosis and New Findings Related to AhR Pathways.

Fibrosis is a pathological manifestation of wound healing that replaces dead/damaged tissue with collagen-rich scar tissue to maintain homeostasis, and complications from fibrosis contribute to nearly half of all deaths in the industrialized world. Ageing is closely associated with a progressive decline in organ function, and the prevalence of tissue fibrosis dramatically increases with age. Despite the heavy clinical and economic burden of organ fibrosis as the population ages, to date, there is a paucity of therapeutic strategies that are specifically designed to slow fibrosis. Aryl hydrocarbon receptor (AhR) is an environment-sensing transcription factor that exacerbates aging phenotypes in different tissues that has been brought back into the spotlight again with economic development since AhR could interact with persistent organic pollutants derived from incomplete waste combustion. In addition, gut microbiota dysbiosis plays a pivotal role in the pathogenesis of numerous diseases, and microbiota-associated tryptophan metabolites are dedicated contributors to fibrogenesis by acting as AhR ligands. Therefore, a better understanding of the effects of tryptophan metabolites on fibrosis modulation through AhR may facilitate the exploitation of new therapeutic avenues for patients with organ fibrosis. In this review, we primarily focus on how tryptophan-derived metabolites are involved in renal fibrosis, idiopathic pulmonary fibrosis, hepatic fibrosis and cardiac fibrosis. Moreover, a series of ongoing clinical trials are highlighted.

Decomposition and decoupling research of Chinese power sector carbon emissions through the consumption accounting principle.

The Logarithmic Mean Divisia Index (LMDI) model is applied to study Chinese national and regional power sector carbon emission changes through consumption side from 2003 to 2017, and regional power sector carbon emissions are estimated through the production and consumption accounting principle. The two-factor ANOVA and one-factor ANOVA are used to compare the differences of regional power sector carbon emissions through the two principles. In addition, the Tapio decoupling analysis model is used to investigate the decoupling state between carbon emissions of power sector and the corresponding driving forces through the consumption side. There are several results: (1) Through the two different principles, regional power sector carbon emissions are statistically significant, yet national power sector carbon emissions are not statistically significant; (2) the main factors contributing to the power sector carbon emission growth are economic scale effect and income level effect, and the main restraining factors are electricity consumption carbon intensity effect and production sector electricity intensity effect; (3) the highest contribution effect to the decoupling indexes between various influencing factors and power sector carbon emissions was scale effect, and technical effect had the second largest contribution value; (4) in 2003-2017, economic scale effect was the first significant factor causing the difference of regional power sector carbon emissions, followed by production sector electricity intensity effect and electricity consumption carbon intensity through the regional decomposition analysis. Finally, this paper gives some targeted suggestions for the low-carbon development of the power sector through national and regional perspectives.

Challenging the "old boys club" in academia: Gender and geographic representation in editorial boards of journals publishing in environmental sciences and public health.

In light of global environmental crises and the need for sustainable development, the fields of public health and environmental sciences have become increasingly interrelated. Both fields require interdisciplinary thinking and global solutions, which is largely directed by scientific progress documented in peer-reviewed journals. Journal editors play a critical role in coordinating and shaping what is accepted as scientific knowledge. Previous research has demonstrated a lack of diversity in the gender and geographic representation of editors across scientific disciplines. This study aimed to explore the diversity of journal editorial boards publishing in environmental science and public health. The Clarivate Journal Citation Reports database was used to identify journals classified as Public, Environmental, and Occupational (PEO) Health, Environmental Studies, or Environmental Sciences. Current EB members were identified from each journal's publicly available website between 1 March and 31 May 2021. Individuals' names, editorial board roles, institutional affiliations, geographic locations (city, country), and inferred gender were collected. Binomial 95% confidence intervals were calculated for the proportions of interest. Pearson correlations with false discovery rate adjustment were used to assess the correlation between journal-based indicators and editorial board characteristics. Linear regression and logistic regression models were fitted to further assess the relationship between gender presence, low- and middle-income country (LMIC) presence and several journal and editor-based indicators. After identifying 628 unique journals and excluding discontinued or unavailable journals, 615 journal editorial boards were included. In-depth analysis was conducted on 591 journals with complete gender and geographic data for their 27,772 editors. Overall, the majority of editors were men (65.9%), followed by women (32.9%) and non-binary/other gender minorities (0.05%). 75.5% journal editorial boards (n = 446) were composed of a majority of men (>55% men), whilst only 13.2% (n = 78) demonstrated gender parity (between 45-55% women/gender minorities). Journals categorized as PEO Health had the most gender diversity. Furthermore, 84% of editors (n = 23,280) were based in high-income countries and only 2.5% of journals (n = 15) demonstrated economic parity in their editorial boards (between 45-55% editors from LMICs). Geographically, the majority of editors' institutions were based in the United Nations (UN) Western Europe and Other region (76.9%), with 35.2% of editors (n = 9,761) coming solely from the United States and 8.6% (n = 2,373) solely from the United Kingdom. None of the editors-in-chief and only 27 editors in total were women based in low-income countries. Through the examination of journal editorial boards, this study exposes the glaring lack of diversity in editorial boards in environmental science and public health, explores the power dynamics affecting the creation and dissemination of knowledge, and proposes concrete actions to remedy these structural inequities in order to inform more equitable, just and impactful knowledge creation.

Driving Effect of Decoupling Provincial Industrial Economic Growth and Industrial Carbon Emissions in China.

China is facing the dual challenges of fostering economic growth and mounting an effective response to climate change, so it is vital to continue promoting industrial carbon emission reduction. This paper uses panel data from 1998 to 2019 to measure the industrial carbon emissions of 30 provinces in China. The Tapio decoupling and IPAT (Impact = Population × Affluence × Technology)-based decoupling models are used to analyze each province's velocity and quantity decoupling index for industrial carbon emissions. The fixed effect model analyzes the influencing factors for carbon decoupling. The results show that the industrial carbon emissions of various provinces in China are increasing yearly, but there are significant differences among provinces. The carbon decoupling of the industrial economy in most provinces is weak, and the quantitative decoupling index is better than the velocity decoupling index. The cleanliness of energy, balance, and labor productivity significantly affect the velocity decoupling index. The cleanliness of energy, the industry's structure, and the population significantly affect the quantity decoupling index. Based on empirical results, the study puts forward some policies to promote the efficient carbon decoupling of the industrial economy.

Decoupling analysis between carbon dioxide emissions and the corresponding driving forces by Chinese power industry.

This study applied the Logarithmic Mean Divisia Index (LMDI) model to analyze changes in carbon dioxide emissions by Chinese power industry from 2003 to 2017. Besides, the Tapio decoupling analysis model is applied to explore the decoupling states between power industry carbon dioxide emissions and the corresponding influence factors. Several conclusions were obtained: (1) the power industry carbon dioxide emissions only displayed a slight downward trend during 2011-2012, 2013-2014, and 2014-2015; (2) the factors promoting the growth in power industry carbon dioxide emissions are energy consumption structure effect and total power generation effect. Power generation structural effect and fossil energy conversion efficiency effect inhibit the power industry carbon dioxide emissions from increasing, but they were far from offsetting the positive contribution value brought by total power generation effect; (3) changes in carbon dioxide emissions by the power industry were not sensitive to the change of fossil energy conversion efficiency and power production structure but were sensitive to the change of total power generation; (4) the contributions of technical effect were higher than those of structural effect on the decoupling index between impact factors and power industry carbon dioxide emissions.