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This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction. Nonlinear analysis indicates several key insights: (i) a higher proportion of the industrial sector diminishes the inhibitory effect of AI on ecological footprints and carbon emissions but enhances its positive impact on energy transitions; (ii) increased trade openness significantly amplifies AI’s ability to reduce carbon emissions and promote energy transitions; (iii) the environmental benefits of AI are more pronounced at higher levels of AI development, enhancing its ability to reduce ecological footprints and carbon emissions and promote energy transitions; (iv) as the energy transition process deepens, AI’s effectiveness in reducing ecological footprints and carbon emissions increases, while its role in promoting further energy transitions decreases. This study enriches the existing literature by providing a nuanced understanding of AI’s environmental impact and offers a robust scientific foundation for global policymakers to develop sustainable AI management frameworks.

Environmental degradation has profoundly impacted both human society and ecosystems. The environmental Kuznets curve (EKC) illuminates the intricate relationship between economic growth and environmental decline. However, the recent surge in trade protectionism has heightened global economic uncertainties, posing a severe threat to global environmental sustainability. This research aims to investigate the intricate pathways through which trade protection, assessed by available trade openness data, influences the nexus between economic growth and environmental degradation. Leveraging comprehensive global panel data spanning 147 countries from 1995 to 2018, this study meticulously examines the non-linear dynamics among trade, economy, and the environment, with a particular emphasis on validating the EKC hypothesis. This study encompasses exhaustive global and panel data regressions categorized across four income groups. The research substantiates the validity of the EKC hypothesis within the confines of this investigation. As income levels rise, the impact of economic growth on environmental degradation initially intensifies before displaying a diminishing trend. Additionally, trade protection manifests as a detriment to improving global environmental quality. The ramifications of trade protectionism display nuanced variations across income strata. In high-income nations, trade protection appears to contribute to mitigating environmental degradation. Conversely, within other income brackets, the stimulating effect of trade protection on environmental pressure is more conspicuous. In other words, trade protectionism exacerbates environmental degradation, particularly affecting lower-income countries, aligning with the concept of pollution havens. The study’s results illuminate nuanced thresholds in the relationship between trade, economic growth, and environmental degradation across income groups, emphasizing the heterogeneous impact and underlying mechanisms. These findings provide valuable insights for policymakers, urging collaborative efforts among nations to achieve a harmonious balance between economic advancement and environmental preservation on a global scale.

Research over the past three decades has provided rich empirical evidence for the inverted U-shaped EKC theory, but current problems facing advancing climate mitigation actions require us to re-examine the shape of global EKC rigorously. This paper examined the N-shaped EKC in a panel of 214 countries with 12 traditional and emerging variables, including institutions and risks, information and communication technology (ICT), artificial intelligence(AI), resource and energy use, and selected social factors. The two-dimensional Tapio decoupling model based on N-shaped EKC to group homogeneous countries is developed to explore the inter-group heterogeneous carbon emission effects of each variable. Global research results show that the linear and cubic terms of GDP per capita are significantly positive, while the quadratic term is significantly negative, regardless of whether additional variables are added. This means the robust existence of an N-shaped EKC. Geopolitical risk, ICT, and food security are confirmed to positively impact per capita carbon emissions, while the impact of composite risk, institutional quality, digital economy, energy transition, and population aging are significantly negative. The impact of AI, natural resource rents, trade openness, and income inequality are insignificant. The inflection points of the N-shaped EKC considering all additional variables are 45.08 and 73.44 thousand US dollars, respectively. Combining the turning points and the calculated decoupling coefficients, all countries are categorized into six groups based on the two-dimensional decoupling model. The subsequent group regression results show heterogeneity in the direction and magnitude of the carbon emission impacts of most variables. Finally, differentiated carbon emission reduction strategies for countries in six two-dimensional decoupling stages are proposed.

Effective management of natural resources is crucial for diminishing carbon emissions. This research explores how economic, financial, and political risks influence the relationship between natural resources rents and carbon emissions. Analyzing data from 66 countries, this study utilizes methods such as quantile regression and dynamic threshold regression to thoroughly assess the data. The findings reveal: (i) Natural resources rents tend to increase carbon emissions consistently across different quantiles (0.1 to 0.9). The fact is confirmed by robustness checks, illustrating that increased natural resources rents lead to higher emissions. (ii) Economic, financial, and political risks affect how natural resources rents impact carbon emissions. Notably, reduced economic and financial risks lessen the propensity of natural resources rents to boost emissions at higher quantiles, while a decline in political risk decreases the exacerbating effect of natural resources rents on emissions from the 0.1 to 0.9 quantiles. (iii) This analysis uncovers threshold effects where economic, financial, and political risks act as threshold factors. Specifically, when economic and political risks are low, a rise in natural resources rents actually leads to a decline in carbon emissions. The findings underscore the importance of considering these risks in the formulation of policies aimed at reducing carbon emissions from natural resource exploitation.

Research on the impact of foreign direct investment (FDI) on environmental quality has not reached consensus. This paper examines the potential structural break in the relationship between FDI and the environment from the perspective of economic scale. The results of the panel threshold estimation for 67 countries of different income groups show that the impact of FDI on carbon emissions shifts from positive to negative at different income level stages, using GDP as the threshold. This conclusion is further verified by the group regression results of the robustness test. When the GDP per capita is below$541.87, FDI shows a significant positive impact on carbon emissions, and this interval corresponds to a wide range of low-income economies today, however, when the GDP per capita exceeds $ 541.87, this positive impact almost disappears. The negative impact of FDI on carbon emissions manifests itself once the GDP per capita reaches $46515, and the sample countries corresponding to this interval since 2014 are mainly Switzerland, Iceland, Denmark, Sweden, the United States, Singapore, and Australia. Therefore, we call on countries to raise their income levels so that they can cross the lower threshold and thus take advantage of the emission reduction effect provided by FDI.

This study was conducted to examine the effects of Lactobacillus plantarum (LP) and sucrose (S) on clostridial community dynamics and correlation between clostridia and other bacteria in alfalfa silage during ensiling. Fresh alfalfa was directly ensiled without (CK) or with additives (LP, S, LP + S) for 7, 14, 28 and 56 days. Clostridial and bacterial communities were evaluated by next-generation sequencing. Severe clostridial fermentation occurred in CK, as evidenced by the high contents of butyric acid, ammonia nitrogen, and clostridia counts, whereas all additives, particularly LP + S, decreased silage pH and restrained clostridial fermentation. Clostridium perfringens and Clostridium butyricum might act as the main initiators of clostridial fermentation, with Clostridium tyrobutyricum functioning as the promoters of fermentation until the end of ensiling. Clostridium tyrobutyricum (33.5 to 98.0%) dominated the clostridial community in CK from 14 to 56 days, whereas it was below 17.7% in LP + S. Clostridium was negatively correlated with the genus Lactobacillus , but positively correlated with the genera Enterococcus, Lactococcus and Leuconostoc . Insufficient acidification promoted the vigorous growth of C. tyrobutyricum of silage in later stages, which was mainly responsible for the clostridial fermentation of alfalfa silage.

The intersection of geopolitics and energy security is a critical area of study that has garnered increasing interest from scholars around the globe. This paper employs bibliometric theory and methodologies to explore the research trajectory concerning the influence of geopolitical dynamics on energy security. Our findings, derived from both quantitative and qualitative analysis of relevant literature, reveal several key insights. Firstly, there is a notable upward trend in publications on this topic, reflecting a widespread recognition of the intricate link between geopolitics and energy security. This growing body of research aligns with the exponential growth law observed in scientific literature, showcasing a novel pattern of geographical distribution centered around energy issues. Secondly, an examination of collaboration networks at the national, institutional, and individual levels identifies China as the leading country in terms of research partnerships, positioning Chinese institutions and scholars at the forefront of this field. Lastly, our analysis delineates the research evolution within this domain through three distinct phases—pre-, mid-, and post-development stages. It highlights the shifting focus of global researchers towards the energy transition process, energy policy formulation, the stability of energy markets, and the environmental impacts of energy production and consumption. This study not only maps the current landscape of research on geopolitics and energy security but also signals the critical areas of interest and collaboration that shape this vital field of inquiry.

The development of polymer materials for water decontamination makes a significant contribution to environmental protection and public health. Herein, we report the preparation of metallacage-crosslinked free-standing supramolecular networks by photo-induced copolymerization of acrylate metallacages and butyl methacrylate for water decontamination. The integration of metallacages into polymer networks endows the networks good capability for generating singlet oxygen via photosensitization, making them serve as a type of decontamination materials that can effectively eliminate diverse organic pollutants and bacterial contaminants. This study not only provides a mild and effective strategy for the preparation of metallacage-cored supramolecular networks via photo-induced copolymerization but also explores their applications for photocatalytic dye degradation and bacterial killing, which will promote the future development of metallacage-based supramolecular materials for photocatalytic applications. The development of polymer materials for water decontamination is important for environmental protection and public health. Herein, the authors report the preparation of metallacage-crosslinked free-standing supramolecular networks by photo-induced copolymerization of acrylate metallacages and butyl methacrylate for water decontamination.

In the past studies, a road accident message was often broadcast by forming a Vehicular Ad Hoc Network (VANET), in which the safety message broadcasting methods often lead to broadcast storms. This study presented a method to broadcast accident messages in a travel guidance system (TGS) that avoids broadcast storms that avoids the need for vehicles to form an ad hoc network. Unlike most current methods, which disseminate messages to as many vehicles as possible, this method sends messages only to the exact vehicles that will be influenced by an accident. In the TGS, the transport center of a city sends messages of the optimal routes and alternative routes to travelers when they start their journey. In normal situations, vehicles run on the optimal routes determined by the transport center. When an accident happens in a street, the accidental vehicle sends a message to the transport center through 5G communication. The transport center looks up the vehicles with their original routes passing the accident location during the lasting time of the accident and sends a safety message to them. The influenced vehicles then select the alternative routes which are stored in their On-Board Unit (OBU) to avoid the accident. Vehicles not influenced by the accident receive no message. Thus the redundant and unnecessary dissemination of messages will be removed. The method presented in this paper is the only one that can thoroughly remove the unnecessary and redundant rebroadcast.

Insulin resistance (IR) and hypertension have a pivotal part in the pathophysiology of cardiovascular disease. The triglyceride–glucose (TyG) index and its derivatives—TyG–body mass index (BMI), TyG–waist circumference (WC), and TyG–WC-to-height ratio (WtHR)—have appeared as simple, cost-effective substitutes for evaluating IR. This research seeks to explore the associations between TyG combined with these obesity indicators and hypertension and pre-hypertension. Data of 9,132 participants in Bengbu (Anhui Province, China) 2019 was collected. The dose–response relationship between these parameters and hypertension or pre-hypertension were analyzed utilizing restricted cubic spline models with multiplex logistic regression (LR). The performance of these parameters in predicting hypertension or pre-hypertension was evaluated using LR and XGBoost model. LR analysis revealed that the TyG index and its derivatives were significantly associated with an increased risk of hypertension and pre-hypertension ( P  < 0.05). Notably, the TyG-WtHR index demonstrated superior potential predictive capacity (AUC = 0.631, OR = 3.133, 95% CI = 2.688–3.652). Dose–response relationships were predominantly nonlinear ( P for nonlinear < 0.05), with some linear trends and sex-based differences. The XGBoost model outperformed the traditional LR model (AUC: LR—0.617, XGBoost—0.662), identifying the TyG–WC and TyG–WtHR indices as potential risk factors of hypertension. Exploratory interaction analysis shows that, high meat consumption was associated with higher hypertension likelihood in individuals with elevated TyG values. The TyG index and its derivatives, are contributive, sex-specific factors of hypertension in middle-aged and elder participants. Our study finds the potential of metabolic monitoring plus a personalized diet for intervening hypertension in at-risk individuals, the parameters can be integrated into machine learning models to augment facilitating early detection.