Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.

The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.

Impact of different geopolitical factors on the energy transition: The role of geopolitical threats, geopolitical acts, and geopolitical risks.

In order to better understand the impact of different geopolitical factors on energy transition, the impact of geopolitical threats (war threats, peace threats, military buildups, nuclear threats and terror threats), geopolitical acts (beginning of war, escalation of war and terror acts), and geopolitical risks on energy transition were systematically investigated. Green technologies, natural resource rents and trade openness were incorporated into the analytical framework, and a dynamic panel threshold model was utilized to explore the impact of geopolitical risks on energy transition across different income levels. To this end, data on geopolitical threats, geopolitical acts, geopolitical risks, energy transitions and other key social economic factors for 38 countries from 2000 to 2022 were collected. The heterogeneity simulation results show that there is a negative correlation between geopolitical threats, geopolitical acts, geopolitical risks and energy transition. Moreover, geopolitical threats have more significant hindrance to the energy transition than geopolitical acts. The results of the nonlinear panel simulation show that there is a double threshold effect of geopolitical risks on energy transition. When geopolitical risk crosses the threshold (0.5197), the coefficient decreases to -0.29, which means that the rising geopolitical risk increases the inhibition on energy transition, and the inhibitory effect is slightly weakened after a certain level. Finally, policy implications are offered.

Cost-effectiveness analysis of hepatitis E vaccination strategies among patients with chronic hepatitis B in China.

AIM: This study aimed to evaluate the cost-effectiveness of hepatitis E vaccination strategies in chronic hepatitis B (CHB) patients. METHODS: Based on the societal perspective, the cost-effectiveness of three hepatitis E vaccination strategies-vaccination without screening, screening-based vaccination, and no vaccination-among CHB patients was evaluated using a decision tree-Markov model, and incremental cost-effectiveness ratios (ICERs) were calculated. Values for treatment costs and health utilities were estimated from a prior investigation on disease burden, and values for transition probabilities and vaccination-related costs were obtained from previous studies and government agencies. Sensitivity analyses were undertaken for assessing model uncertainties. RESULTS: It was estimated that CHB patients superinfected with hepatitis E virus (HEV) incurred significantly longer disease course, higher economic burden, and more health loss compared to those with HEV infection alone (all p < 0.05). The ICERs of vaccination without screening and screening-based vaccination compared to no vaccination were 41,843.01 yuan/quality-adjusted life year (QALY) and 29,147.32 yuan/QALY, respectively, both lower than China's per-capita gross domestic product (GDP) in 2018. The screening-based vaccination reduced the cost and gained more QALYs than vaccination without screening. One-way sensitivity analyses revealed that vaccine price, vaccine protection rate, and decay rate of vaccine protection had the greatest impact on the cost-effectiveness analysis. Probabilistic sensitivity analyses confirmed the base-case results, and if the willingness-to-pay value reached per-capita GDP, the probability that screening-based vaccination would be cost-effective was approaching 100%. CONCLUSIONS: The disease burden in CHB patients superinfected with HEV is relatively heavy in China, and the screening-based hepatitis E vaccination strategy for CHB patients is the most cost-effective option.

Revisiting the Environmental Kuznets Curve (EKC) Hypothesis of Carbon Emissions: Exploring the Impact of Geopolitical Risks, Natural Resource Rents, Corrupt Governance, and Energy Intensity.

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions.

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS: We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChunyueFeng/Kidney-DataSet.

An integrated economic, energy, and environmental analysis to optimize evaluation of carbon reduction strategies at the regional level: A case study in Zhejiang, China.

China plays a crucial role in responding to global climate change. Provinces are the main sources of energy consumption and greenhouse gas emissions in China's economic and social development. However, it is still unclear how to achieve dual-carbon goals by formulating and implementing local policies to adapt to climate change. In this study, we take Zhejiang Province in China as the research object, based on the LEAP (Low Emissions Analysis Platform) model to construct four social scenarios under different policies, comprehensively considering regional economic characteristics, population, and energy consumption patterns. The results show that to achieve Zhejiang Province's goal of carbon peaking by 2030 while maintaining steady economic growth, additional measures are required to reduce energy consumption intensity or improve the power generation structure. Otherwise, energy demand will increase to 228.06 million tonnes of coal equivalent and carbon emissions will be 487.76 million tonnes in 2050. Moreover, developing clean energy and promoting CCUS technology can continuously reduce carbon emissions to 293.59 and 210.76 million tonnes respectively. The economic viability of CCUS power generation is contingent upon the development of carbon taxes in the future. Once the growth rate reaches 7.2%, power cost will be 167.77 billion RMB and CCUS will become economically advantageous in 2050.

Geopolitical risk and ecological efficiency: A combination approach based on super-efficiency-DEA and extended-STIRPAT models.

Increased geopolitical risks are impacting the sustainable development of the ecological environment. To better understand the impact of geopolitical risk on ecological sustainability, this study develops a research framework for the impact of geopolitical risk on ecological efficiency. (i) Measuring ecological efficiency by data envelopment analysis. (ii) Examining the relationship between geopolitical risks and ecological efficiency using the extended STIRPAT. (iii) Heterogeneity analysis and mediation test were used to further explore the impact mechanism of geopolitical risks. The research results show that: (i) There are obvious differences in the ecological efficiency of countries with different income levels. The ecological efficiency of countries with higher income levels is generally higher, while the ecological efficiency of countries with lower income levels is lower. (ii) Geopolitical risks reduce ecological efficiency, which is bad for ecosystem sustainability. (iii) The magnitude of the adverse impact of geopolitical risks on ecological efficiency is different among different income groups. The negative impact of geopolitical risk on eco-efficiency is worse in high-income countries than in low-income countries.

Impact of income inequality on carbon emissions: a matter of corruption governance.

Corruption is often linked with income inequality and its impact on carbon emissions. This study investigates the moderating effect of corruption governance on the relationship between income inequality and carbon emissions. Panel data for 62 countries from 2012 to 2020 were used. We employed a threshold panel regression approach, considering income inequality as the explanatory variable and carbon dioxide emissions as the dependent variable, with corruption governance as the threshold variable. Our findings suggest that enhancing the level of corruption governance can mitigate the CO2 emissions driven by income inequality. Specifically, we found a shift in the impact on CO2 emissions when corruption governance crosses a certain threshold. This study provides insights into how improving corruption governance can help in managing the environmental effects of income inequality.

In vivo burn scar assessment with speckle decorrelation and joint spectral and time domain optical coherence tomography.

Significance: Post-burn scars and scar contractures present significant challenges in burn injury management, necessitating accurate evaluation of the wound healing process to prevent or minimize complications. Non-invasive and accurate assessment of burn scar vascularity can offer valuable insights for evaluations of wound healing. Optical coherence tomography (OCT) and OCT angiography (OCTA) are promising imaging techniques that may enhance patient-centered care and satisfaction by providing detailed analyses of the healing process. Aim: Our study investigates the capabilities of OCT and OCTA for acquiring information on blood vessels in burn scars and evaluates the feasibility of utilizing this information to assess burn scars. Approach: Healthy skin and neighboring scar data from nine burn patients were obtained using OCT and processed with speckle decorrelation, Doppler OCT, and an enhanced technique based on joint spectral and time domain OCT. These methods facilitated the assessment of vascular structure and blood flow velocity in both healthy skin and scar tissues. Analyzing these parameters allowed for objective comparisons between normal skin and burn scars. Results: Our study found that blood vessel distribution in burn scars significantly differs from that in healthy skin. Burn scars exhibit increased vascularization, featuring less uniformity and lacking the intricate branching network found in healthy tissue. Specifically, the density of the vessels in burn scars is 67% higher than in healthy tissue, while axial flow velocity in burn scar vessels is 25% faster than in healthy tissue. Conclusions: Our research demonstrates the feasibility of OCT and OCTA as burn scar assessment tools. By implementing these technologies, we can distinguish between scar and healthy tissue based on its vascular structure, providing evidence of their practicality in evaluating burn scar severity and progression.

Exploring interactions between socioeconomic context and natural hazards on human population displacement.

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.

Does artificial intelligence (AI) reduce ecological footprint? The role of globalization.

This article explores the impact of artificial intelligence (AI) on global ecological footprints, which has important implications for global sustainability in the digital age. Using the comprehensive evaluation index of AI constructed by the entropy method and the dataset at the global national level, we find that from 2010 to 2019, the overall level of global AI shows an upward trend, in which the growth rate of AI in developed countries is more pronounced and exhibits a stable growth trend, while the growth rate of AI in developing countries displays a trend of instability. The research results show that AI has a significant inhibitory effect on ecological footprints. This conclusion holds even after endogeneity and robustness tests. In addition, under the effect of globalization, the impact of AI on ecological footprints shows nonlinear characteristics. As globalization deepens, the marginal effect of AI in reducing the ecological footprint shows an increasing trend. These findings emphasize the important role of AI in environmental governance and provide a new and comprehensive perspective for policymakers. Therefore, the government should continue to support the research and application of AI, promote the cross-industry integration of AI, and play a positive role in the process of globalization to promote global sustainable development.

Cost-effectiveness of seasonal influenza vaccination of children in China: a modeling analysis.

BACKGROUND: China has a high burden of influenza-associated illness among children. We aimed to evaluate the cost-effectiveness of introducing government-funded influenza vaccination to children in China (fully-funded policy) compared with the status quo (self-paid policy). METHODS: A decision tree model was developed to calculate the economic and health outcomes, from a societal perspective, using national- and provincial-level data. The incremental cost-effectiveness ratio (ICER) [incremental costs per quality-adjusted life year (QALY) gained] was used to compare the fully-funded policy with the self-paid policy under the willingness-to-pay threshold equivalent to national and provincial GDP per capita. Sensitivity analyses were performed and various scenarios were explored based on real-world conditions, including incorporating indirect effect into the analysis. RESULTS: Compared to the self-paid policy, implementation of a fully-funded policy could prevent 1,444,768 [95% uncertainty range (UR): 1,203,446-1,719,761] symptomatic cases, 92,110 (95% UR: 66,953-122,226) influenza-related hospitalizations, and 6494 (95% UR: 4590-8962) influenza-related death per season. The fully-funded policy was cost-effective nationally (7964 USD per QALY gained) and provincially for 13 of 31 provincial-level administrative divisions (PLADs). The probability of a funded vaccination policy being cost-effective was 56.5% nationally, and the probability in 9 of 31 PLADs was above 75%. The result was most sensitive to the symptomatic influenza rate among children under 5 years [ICER ranging from - 25,612 (cost-saving) to 14,532 USD per QALY gained]. The ICER of the fully-funded policy was substantially lower (becoming cost-saving) if the indirect effects of vaccination were considered. CONCLUSIONS: Introducing a government-funded influenza policy for children is cost-effective in China nationally and in many PLADs. PLADs with high symptomatic influenza rate and influenza-associated mortality would benefit the most from a government-funded influenza vaccination program.

Prolonged war reverses carbon emissions from an early decline to a late increase - Evidence from Syria.

Given that war can have a serious impact on the climate, this article is aimed to discuss the impact of warfare on carbon emissions by examining changes in CO2 before and during the war in Syria based on the kaya constant equation and the LMDI decomposition method. In the decade before the war, population was the largest contributor, making up 32.64% of the total 51.02% increase in carbon emissions. The only factor that offsetting carbon emissions was energy intensity, making a 22.30% curbing effect. In the early stage of the war, carbon emissions decreased by 56.38%, in which per capita GDP contributed 37.55% of the total CO2 decline. Carbon intensive of energy was the only factor promoting the carbon increase with a 4.67% contribution. In the late war, carbon emissions start to resume slow increase with energy intensity and economy turning negative to positive. It can be speculated that the impact of the war on CO2 emissions: (i) in the first years of the war, CO2 would drop significantly at the cost of significant population decline and economic recession, the least desirable and the worst way to reduce carbon emissions. (ii) if evolves into a prolonged war, it would reverse carbon emissions from decline to increase, although the population and the economy are both falling. This research, therefore contends that once war is triggered, there is no other solution to prevent this worst-case scenario of Population Decline - Economic Recession - Increased Carbon Emissions from happening, unless the war is stopped immediately.

Do industrial robots reduce carbon intensity? The role of natural resource rents and corruption control.

Although the research on the impact of robotics on carbon emissions is increasing, there are still relatively few studies on the impact of robots on carbon intensity from the perspective of natural resources and corruption. In order to fill in the research gaps, panel data from 66 countries between 1993 and 2018 are collected, and linear and nonlinear panel regression approaches are developed. Natural resource rent and corruption control are used as threshold variables, robot penetration is used as explanatory variables, and carbon emission intensity is the explained variable. The results of the linear model show that robot penetration is negatively correlated with carbon emission intensity, which means that robot penetration reduces carbon emission intensity. The results of the nonlinear model show that when natural resource rents and corruption control are used as thresholds, the relationship between robot penetration and carbon emission intensity presents a U shape and an inverted U shape, respectively. Specifically, the threshold for natural resource rents is 4.7%. When the natural resource rent is lower than this threshold, the robot penetration rate reduces the carbon emission intensity, but when the natural resource rent is higher than this threshold, the robot penetration rate increases the carbon emission intensity. The threshold value of corruption control is -0.4349. When the corruption control is lower than this threshold, the robot penetration rate increases the carbon emission intensity. If the corruption control is higher than this threshold, the robot reduces the carbon emission intensity. Finally, policy recommendations for better use of robotics to reduce carbon emission intensity are put forward from the perspective of natural resource rent and corruption control.

Molecular basis and evolutionary cost of a novel macrolides/lincosamides resistance phenotype in Staphylococcus haemolyticus.

Staphylococcus haemolyticus (S. haemolyticus) is a coagulase-negative Staphylococcus that has become one of the primary causes of nosocomial infection. After a long period of antibiotic use, S. haemolyticus has developed multiple resistance phenotypes for macrolides and lincosamides. Herein, we evaluated four S. haemolyticus clinical isolates, of which three had antibiotic resistance patterns reported previously. The fourth isolate was resistant to both erythromycin and clindamycin in the absence of erythromycin induction. This novel phenotype, known as constitutive macrolides-lincosamides-streptogramins resistance, has been reported in other bacteria but has not been previously reported in S. haemolyticus. Investigation of the isolate demonstrated a deletion in the methyltransferase gene ermC, upstream leader peptide. This deletion resulted in constitutive MLS resistance based on whole-genome sequencing and experimental verification. Continuous expression of ermC was shown to inhibit the growth of S. haemolyticus, which turned out to be the fitness cost with no MLS pressure. In summary, this study is the first to report constitutive MLS resistance in S. haemolyticus, which provides a better understanding of MLS resistance in clinical medicine. IMPORTANCE This study identified a novel phenotype of macrolides/lincosamides resistance in Staphylococcus haemolyticus which improved a better guidance for clinical treatment. It also clarified the mechanistic basis for this form of antibiotic resistance that supplemented the drug resistance mechanism of Staphylococcus. In addition, this study elaborated on a possibility that continuous expression of some resistance genes was shown to inhibit the growth of bacteria themselves, which turned out to be the fitness cost in the absence of antibiotic pressure.

Review of Wireless RFID Strain Sensing Technology in Structural Health Monitoring.

Strain-based condition evaluation has garnered as a crucial method for the structural health monitoring (SHM) of large-scale engineering structures. The use of traditional wired strain sensors becomes tedious and time-consuming due to their complex wiring operation, more workload, and instrumentation cost to collect sufficient data for condition state evaluation, especially for large-scale engineering structures. The advent of wireless and passive RFID technologies with high efficiency and inexpensive hardware equipment has brought a new era of next-generation intelligent strain monitoring systems for engineering structures. Thus, this study systematically summarizes the recent research progress of cutting-edge RFID strain sensing technologies. Firstly, this study introduces the importance of structural health monitoring and strain sensing. Then, RFID technology is demonstrated including RFID technology's basic working principle and system component composition. Further, the design and application of various kinds of RFID strain sensors in SHM are presented including passive RFID strain sensing technology, active RFID strain sensing technology, semi-passive RFID strain sensing technology, Ultra High-frequency RFID strain sensing technology, chipless RFID strain sensing technology, and wireless strain sensing based on multi-sensory RFID system, etc., expounding their advantages, disadvantages, and application status. To the authors' knowledge, the study initially provides a systematic comprehensive review of a suite of RFID strain sensing technology that has been developed in recent years within the context of structural health monitoring.

Radiomics models for preoperative prediction of the histopathological grade of hepatocellular carcinoma: A systematic review and radiomics quality score assessment.

OBJECTIVE: To systematically review the efficacy of radiomics models derived from computed tomography (CT) or magnetic resonance imaging (MRI) in preoperative prediction of the histopathological grade of hepatocellular carcinoma (HCC). METHODS: Systematic literature search was performed at databases of PubMed, Web of Science, Embase, and Cochrane Library up to 30 December 2022. Studies that developed a radiomics model using preoperative CT/MRI for predicting the histopathological grade of HCC were regarded as eligible. A pre-defined table was used to extract the data related to study and patient characteristics, characteristics of radiomics modelling workflow, and the model performance metrics. Radiomics quality score and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were applied for research quality evaluation. RESULTS: Eleven eligible studies were included in this review, consisting of 2245 patients (range 53-494, median 165). No studies were prospectively designed and only two studies had an external test cohort. Half of the studies (five) used CT images and the other half MRI. The median number of extracted radiomics features was 328 (range: 40-1688), which was reduced to 11 (range: 1-50) after feature selection. The commonly used classifiers were logistic regression and support vector machine (both 4/11). When evaluated on the two external test cohorts, the area under the curve of the radiomics models was 0.70 and 0.77. The median radiomics quality score was 10 (range 2-13), corresponding to 28% (range 6-36%) of the full scale. Most studies showed an unclear risk of bias as evaluated by QUADAS-2. CONCLUSION: Radiomics models based on preoperative CT or MRI have the potential to be used as an imaging biomarker for prediction of HCC histopathological grade. However, improved research and reporting quality is required to ensure sufficient reliability and reproducibility prior to implementation into clinical practice.

Supply chain finance and innovation efficiency: An empirical analysis based on manufacturing SMEs.

This paper firstly demonstrates the positive and negative effects of supply chain finance on the innovation efficiency of China's small and medium-sized enterprises (SMEs) in the manufacturing industry from the theoretical point of view. Based on the data of 267 manufacturing companies in China Growth Enterprise Market from 2015 to 2019, the DEA-SBM method was used to measure the comprehensive innovation efficiency of different companies, and it was further decomposed into technological innovation efficiency and organizational innovation efficiency. Afterwards, it conducts an empirical analysis through the double fixed effect model, and explores the difference in the impact of supply chain finance on innovation efficiency in enterprises with different industries and different property rights. The results show that supply chain financial services have a strong positive impact on the comprehensive innovation efficiency, technological innovation efficiency and organizational innovation efficiency of manufacturing SMEs. Further, supply chain finance has the most significant improvement on the technological innovation efficiency of the sample of private traditional enterprises, but it has a significant inhibitory effect on the organizational innovation efficiency of the sample of state-owned high-tech enterprises. Therefore, this paper suggests that the development of supply chain financial services should increase support for traditional manufacturing industries; appropriately tilt resources to private enterprises; improve relevant supply chain financial laws and regulations, establish and improve corresponding institutional arrangements, and encourage state-owned enterprises to participate in market competition.

Evolution and driving factors of ocean carbon emission efficiency: A novel perspective on regional differences.

Existing studies on carbon emission efficiency seldom discuss ocean carbon emission efficiency, and few studies on ocean carbon emission efficiency hardly discuss its regional differences. To fill this research gap, this paper innovatively measures and evaluates the ocean carbon emission efficiency of 11 Chinese coastal provinces from 2001 to 2019 using the super-efficiency SBM-GML model, and empirically analyzes the dynamic link between ocean carbon emission efficiency, trade openness and financial development by constructing a PVAR model based on an endogeneity perspective. Meanwhile, another major innovation of this study is to divide China's 11 coastal provinces into two coastal areas, north and south, with the Huaihe River as the boundary, in order to investigate the regional heterogeneity of ocean carbon emission efficiency and its influencing factors. The results show that (i) China's average ocean carbon emission efficiency has improved significantly, which is mainly due to the driving effect of technological progress. (ii) China's ocean carbon emission efficiency generally presents a spatial pattern that is higher in the south and lower in the north. Technological progress is the main source of the improvement in ocean carbon emission efficiency in the two regions. (iii) Significant regional heterogeneity exists in the impact of trade openness and financial development on ocean carbon emission efficiency, that is, trade openness and financial development both promote and hinder ocean carbon emission efficiency in the southern region than in the northern region. Finally, targeted policy recommendations are proposed.

Could the digital economy increase renewable energy and reduce carbon emissions? Empirical research on EKC in 67 countries.

The digital economy is considered important to achieve carbon peaking and carbon neutrality. This paper explores the impact of the digital economy on carbon emissions and renewable energy development using panel data for 67 countries from 2005-2019. The results show that there is an inverted U-shaped relationship between the digital economy and carbon emissions, which is consistent with the Environmental Kuznets Curve (EKC) hypothesis, and a U-shaped relationship with renewable energy consumption, which is consistent with the Renewable energy Kuznets Curve (RKC) hypothesis. Compared with gross domestic product (GDP), the digital economy is more likely to accelerate the process of energy transition and carbon reduction, which is a key factor for carbon peaking. In addition, it is also found that the turning point of the RKC precedes the EKC, which means that the RKC reaching its turning point is a prerequisite for the corresponding EKC to reach its peak.Therefore, the digital economy should be accelerated to push RKC to cross the turning point as soon as possible, thereby accelerating EKC to cross the turning point.