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Mitigating climate change requires a portfolio of strategies and the use of carbon dioxide removal techniques or negative emissions technologies (NETs) will be necessary to achieve this goal. However, the high implementation costs of advanced NETs lead to expensive carbon credits, hindering their broad acceptance and use. One potential solution involves governmental support through subsidies, aiming to boost the availability of NET-derived carbon credits. This research uses a graphical technique based on an extension of pinch analysis to identify the ideal subsidy level for carbon dioxide removal, taking into account factors such as carbon pricing, supply, and demand. The proposed approach modifies the limiting composite curve (LCC) methodology to accurately determine the optimal subsidy and establish the baseline amount of subsidized carbon dioxide removal needed. The approach enables the convenient and efficient construction of the LCC using a composite table algorithm. To illustrate the proposed methodology, two case studies composed of different NETs and demand sectors are investigated. The results show the most advantageous subsidy levels for these technologies, providing valuable insights to guide policymakers and investors in their decarbonization efforts. This work contributes to the development of effective governance and investment strategies by optimizing NET subsidy allocation. Such optimization is crucial for facilitating the widespread implementation of these technologies, which are in-line with the global efforts to mitigate climate change.

Abstract Quantifying the effect of external forcings like seismicity or rain on slope destabilization is a long-standing and challenging issue. To investigate the respective roles of these forcings, we analyze an unprecedented 10-year long catalog of rockfalls occurring in the crater of the Piton de la Fournaise volcano (La Reunion Island), using statistical tools originally developed for earthquakes. Our analysis reveals the predominant effect of low amplitude repetitive seismicity in the triggering of rockfalls located at a few kilometers from the source, due to progressive damaging of the slope. Moreover, we show that the efficiency and time-delay of this dynamic triggering is controlled by the stability state of the slope, i.e. its closeness to the failure, as observed with lab-experiments on metastable granular slopes. Our results show the need to account for long-term swarm-type seismic activity that can affect the stability of geological structures like slopes and faults, but also buildings.

The demand for renewable alternatives to natural gas as a fuel for industries and residential consumers has been growing fast. Bio-natural gas is expected to play an important role in green energy transition and climate change. Large amounts of high organic load wastewater are discharged from coking, pharmaceutical, papermaking, printing and dyeing, petrochemical, and food processing industries. Bio-natural gas could be produced from high organic load wastewater using anaerobic digestion or membrane bio-reactor and biogas upgrading technologies. This study performs a comprehensive analysis of bio-natural gas produced from high organic load wastewater (BNGWW) in China. The status quo is compared with the potential use of this alternative, coupled with a techno-economic analysis of production technologies, energy policies, and opportunities and challenges. A model is proposed to estimate the provincial BNGWW demand in China from 2016 to 2020 and the decomposed demand across 42 sectors from 2016 to 2019. Factors influencing BNGWW potential are analyzed under the changes of time, region, and sectors. The result shows that BNGWW demand is affected mainly by natural gas prices, industrial scale, and chemical oxygen demand of organic wastewater. Some provinces in East China, Central and Southern China, and Xinjiang have high BNGWW potential. The agricultural and food processing industry has the highest production potential. Challenges and barriers to the development of BNGWW have also been highlighted.

A fire risk analysis was conducted to evaluate and manage the potential for accidents in the petrochemical industry. From the perspective of systems and control theory, this study proposes a fire risk assessment and management method for chemical plants based on system-theory accident models and processes. An integrated framework was proposed to assess and manage fire risk in chemical plants. Hazard and operability analyses were conducted to identify the deviations and contributing factors leading to fires in a chemical plant. For fire risk points, a hierarchical control structure model of the system production process was integrated with the internal production and external safety management interactive feedback unit to clarify the safety constraints and controls. Chemical plant operation scenarios were developed to focus on coordination and feedback between multiple organizations in the system. A decision-making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling (ISM) were combined with an analysis of constraint defects. A case study of a fatty alcohol polyoxyethylene ether plant was conducted. The results show that the DEMATEL–ISM model describes the potential cross-level control process and can comprehensively analyze the relationship between contributing factors to improve the system’s overall safety and prevent accidents.

The increasing Municipal Solid Waste (MSW) poses great pressure on cities, especially for emerging countries. This paper presents a mathematical programming model to optimise MSW management. A disjunctive fuzzy optimisation approach is used in this work for several MSW treatment technologies, resulting in multi-objective optimization. The proposed model is used to choose the suitable treatment technologies and optimal distribution of MSW between sources and sinks to minimise the total cost of MSW management and greenhouse gas emission. The optimisation problem is formulated as a mixed-integer linear programming model using generalised disjunctive programming to select suitable MSW treatment technologies and optimal distribution networks. A case study of Hefei, China is presented to illustrate the proposed approach. The multiple objectives of total cost and GHG emissions for MSW management are analysed based on fuzzy optimisation yielding a compromise solution of 192.99 M$/y for total cost and 1,120.48 t/y for emissions. A compromised solution suggested in this study can effectively minimize both total cost and carbon emission and properly deal with the MSW management problem. This work provides a decision tool for the selection of MSW technologies during MSW management.

Bio-natural Gas (BNG) from livestock manure (BNGLM) is a potential alternative energy source that can yield significant resource and environmental benefits. Energy potentials and carbon intensity differences in livestock manure utilization may exist in provincial areas. The spatial and temporal distribution characteristics for the BNG potentials are analyzed. A life-cycle carbon emission accounting model for carbon intensity was proposed. The results show that with the increase of scale and intensification of animal husbandry, the BNG potential is increasing. The potentials vary greatly among provinces. The annual carbon emission reduction potential from BNGLM reached 1.25 Gt. The proposed energy potential, its spatial characteristics, and carbon emission reduction measurement are expected to provide better support for enhancing specific regional plans for waste manure utilization and development.

The development of Bio-Natural Gas (BNG) is of great strategic significance in alleviating the shortage of fossil natural gas and fostering low-carbon energy transition. This work shows the economic and environmental analysis of the straw-based BNG supply chain using a Mixed Integer Linear Programming (MILP) model. Considering a sustainable supply chain framework, including straw collection, transportation, pre-treatment and product, and technologies (e.g., Anaerobic Digestion and Gasification), the optimal alternative is determined. Different straw harvesting rates and carbon emission reduction targets are designed to evaluate the cost and carbon emissions of each routine. The proposed model is applied to a country case study in China to illustrate the optimization of a regional straw-based BNG supply chain. The results show that the minimal cost ranges from 6.02 to 8.55 RMB/m3, and the emission reduction potential is from 9.82 to 12.74 kg/m³. The use of straw-based BNG can generate environmental benefits and estimate costs.

Objective To construct and validate predictive models based on socioeconomic factors for predicting overall survival (OS) in cervical cancer and compare them with the American Joint Council on Cancer (AJCC) staging system.Design Retrospective cohort study.Setting and participants We extracted data from 5954 patients who were diagnosed with cervical cancer between 2007 and 2011 from the Surveillance, Epidemiology, and End Results Database. This database holds data related to cancer incidence from 18 population-based cancer registries in the USA.Outcome measures 1-year and 5-year OS.Results Of the total 5954 patients, 5820 patients had 1-year mortality and 5460 patients had 5-year mortality. Lower local education level [Hazard ratios (HR): 1.15, 95% confidence interval (CI): 1.04 to 1.27, p= 0.005] and being widowed (HR 1.28, 95% CI 1.06 to 1.55, p=0.009) were associated with a worse OS for patients with cervical cancer. Having insurance (HR 0.75, 95% CI 0.62 to 0.90, p=0.002), earning a local median annual income of ≥US$56 270 (HR 0.83, 95% CI 0.75 to 0.92, p

Aim: To explore the clinical application of laparoendosopic single-site surgery (LESS) in the treatment of benign ovarian masses of different sizes. Methods: We reviewed 117 patients with benign ovarian mass. 57 underwent LESS with self-made approach, and 60 underwent conventional laparoscopic surgery (CLS). Results: For the group with the maximum diameter of mass 0.05). Conclusion: LESS is feasible in the surgery of benign ovarian mass. Especially, it can fully demonstrate the advantages of LESS in large benign ovarian mass (maximum diameter of ≥10 cm).

Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the learned features for identifying cracks in the network are not evaluated, it may lead to safety risks. In this study, to evaluate the recognition basis of different crack detection networks; several crack detection CNNs are trained using the same training conditions. Afterwards, several crack images are used to construct a dataset, which are used to interpret and analyze the trained networks and obtain the learned features for identifying cracks. Additionally, a crack identification performance criterion based on interpretability analysis is proposed. Finally, a training framework is introduced based on the issues reflected in the interpretability analysis.

Renewable energy and carbon dioxide capture and storage can cut carbon dioxide emissions, and negative emissions technologies are effective methods of carbon dioxide removal. The integration of the three is an important approach to mitigate surface temperature rise and achieve the climate change vision. This paper presents an improved algebraic targeting approach for multi-period energy planning integrating fossil energy, renewable energy, carbon capture and storage, and negative emission technologies. In this work, the risk hedging effect of negative emission technologies and the operational lifetime of carbon capture and storage are considered to reduce the amount of carbon capture and storage deployed. The approach can reduce the economic costs, environmental costs, and likelihood of stranded assets in low-carbon energy planning. This multi-period algebraic targeting approach is demonstrated through a case study. The results show that multi-period low-carbon energy planning can achieve better deployment of resources and technologies and reduce the pressure to reduce carbon emissions in the early stages of planning.

Deep reduction in industrial greenhouse gas emissions can be achieved through engineering measures such as energy efficiency enhancement, fuel switching, and carbon dioxide removal (CDR). Integrated energy systems like polygeneration plants are inherently efficient, while partial or total replacement of fossil fuels with renewables allows further cuts to be realized. Novel CDR technologies can also be used to generate negative emissions. In this work, we develop an optimization model for optimizing a novel polygeneration system which integrates CDR based on ex situ enhanced rock weathering. The latter relies on exposing pulverized rock to accelerate naturally occurring geochemical reactions of minerals with ambient carbon dioxide and water, and results in the sequestration of carbon as bicarbonate ions in water. Integration with a polygeneration plant allows surplus electricity to be utilized for the energy-intensive rock grinding process, as an alternative to direct energy storage. A mixed-integer linear programming enterprise input-output (MILP-EIO) model is developed and then applied to a case study on design and operation problem. The objective is to determine the optimal design of a zero emissions polygeneration system which is economically feasible. Results indicate that such a result is only possible once CO2 price reaches at least US$ 50 /t.

A large fraction of anthropogenic CO2 emissions comes from point sources such as power plants, petroleum refineries, and other large industrial facilities. Carbon capture, utilization, and storage (CCUS) is one of various strategies for carbon management that captures carbon dioxide emissions from point sources and either reuses or stores it. It is also an important component technology for regional low-carbon development. A mixed integer nonlinear programming (MINLP) model for carbon management network of regional CCUS system is proposed here for planning regional CCUS networks. The optimization objective is to minimize the total annual cost of the carbon management network (CMN). According to carbon life cycle metabolism analysis, region-wide source-sink models are developed. Various emissions point sources are considered. Alternative capture methods (pre-combustion capture, post-combustion capture, and oxy-combustion) and transportation using pipelines are included in the model. Four utilization sinks (greenhouse, urea production, methanol production, and enhanced oil recovery) are also considered. With the consideration of different regional carbon neutrality goals, potential utilization sites and CMN alternatives are investigated. The case in an assumed city in China is used to illustrate the optimization of regional CCUS schemes. For the given case study, the results show that the CMN and costs vary with CO2 reduction targets.

Carbon capture, utilization, and sequestration (CCUS) is an important option for meeting national carbon neutrality targets. However, the application of CCUS on an industrial scale and the extension of supply chain networks inevitably increase the exchange of information and material flows, creating a "media shifting" from environmental to safety risk issues. This work proposed system modeling and safety risk assessment for integrated regional CCUS systems. Considering CCUS modules, Aspen Plus simulation-based regional CCUS system is constructed. An integrated inherent safety index system is proposed to evaluate the risk characteristics. A CCUS system in Shandong Province is illustrated as a case study to analyze its safety risk. The results show that in terms of the overall inherent safety index, the emission source module has the highest risk, which is 2.67 times higher than that of the biological/geological utilization module.

In this study, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was employed to investigate the polymorphism of GHR gene and the correlations of polymorphic sites with growth traits. The results showed that GHR detected CC, CD and DD genotypes in the five sheep populations. GHR gene had the highest DD genotype frequencies in Yuxi fatty-tailed sheep, Dorper sheep, Henan large-tailed han sheep, Hu sheep and small-tailed han sheep, being 0.889, 0.784, 0.785, 0.529 and 0.750, respectively. The correlation analysis indicated that GHR gene had significant correlations with chest circumference and abdomen circumference of sheep

Sectoral contributions of greenhouse gas (GHG) emissions are at the heart of the climate debate. Carbon drawdown solutions from different sectors (e.g. energy, land, urban infrastructure, and industrial systems) will play significant roles in mitigating climate change in the coming decades. This work identifies the feasible carbon drawdown solutions to support GHG emissions reduction paths globally. These solutions should meet both economic constraints and carbon emissions targets. This work develops a novel approach that integrates Carbon Emission Pinch Analysis (CEPA), the Best-Worst Method (BWM), and Cost-Benefit Analysis (CBA). For each sector, BWM is used to evaluate the performance of reduction solutions and identify their priority weights. Then, CBA is used to rank the options further. Finally, CEPA combines with the Marginal Emission Reduction Cost Curve to find the final mix to meet carbon reduction targets and investment costs. The results prioritize global carbon drawdown potentials for the power (186.4 Gt CO2-eq), construction (97.9 Gt CO2-eq), industry (23.8 Gt CO2-eq), transportation (20.2 Gt CO2-eq), food, agriculture, and land use (2.5 Gt CO2-eq) sectors. Based on these results, this work presents an optimal path for carbon drawdown.

Park-wide water management is an important way to conserve water resources. However, the enhancement of inter-plant connection will weaken the system stability and strengthen the cascade effect of disaster losses. In this work, a dynamic inoperability input-output model is applied to park-wide water network to predict the impact of disturbances. The inoperability is expressed as the degree of water loss. The dependence of different water networks on freshwater is presented. This work analyzes the impact of freshwater supply disturbance on the water volume. By simulating the time-varying trajectory of inoperability, the indicators (i.e., resistance to disturbance, adaptability, and recoverability) are determined to evaluate the vulnerability of the water network in each scenario. The results show that under the same freshwater disturbance, water networks that are less dependent on freshwater are more robust but weaker in recoverability. In the case of freshwater disturbance, the possibility of disruptions in the participating plants of the without integrated optimization water network is about 1.5 times that of the integrated optimization water network.

This paper provides a literature review of the impact of the COVID-19 spread-prevention measures on the water sectors, including general water network management, water quality and quantity, and wastewater treatment. Fifty-four papers are selected for the analytical review, and the results showed that the pandemic poses both positive and potential long term negative impacts to the water sector. In the short term, the limitations in mobility and industrial activities lead to the water use patterns shifts between especially the industrial and residential sector, and reduction in aquatic pollution discharge. But in the long run, the changes in the industrial development patterns and people’s lifestyle caused by the pandemic might also require further adaption and update of the current water networks. Understanding the interactions between the pandemic and water-related aspects is essential to ensure the urban water supply system is resilient in pandemic situations. As a response, this resilience can help to facilitate controlling and mitigating the spreading of the virus.

Municipal solid waste (MSW) management is one of the most important issues in urban environments, especially in developing countries. In this work, a comprehensive assessment framework for MSW management is proposed to evaluate and screen the optimal scenario. The best-worst method (BWM) is utilized to determine the optimal weight of each criterion for each disposal scenario. However, the original BWM model is difficult to be solved globally. A linear model is presented to solve the model and an interval model is employed to verify the optimality of the linear model. The results indicate that the results of the linear model and interval model are consistent. A case study of MSW disposal in Qingdao City is used to demonstrate the application of the proposed method. The results indicate that a combination of landfilling, incineration technology with energy recovery facility is preferred for the current MSW management in Qingdao from the chosen criteria. The framework proposed in this work can be assisted to help the decision-makers to identify the priority sequence of MSW management scenarios.

The tobacco industry is an important contributor to realizing the carbon reduction goal. Less attention is paid to the carbon emissions of the tobacco industry. The tobacco production system is generally a carbon sink, where carbon sequestration by photosynthesis in tobacco planting and by soil are sufficient to offset the carbon emissions of the tobacco production system. This work proposed an integrated framework of life-cycle assessment (LCA) and Source-Sink Model to determine the optimal allocation of carbon sources to sinks with the objective of maximizing the profitable external benefits. From an economic perspective, internal carbon sources could be offset by the internal carbon sink of the tobacco production system. The additional internal carbon sinks can be transferred in the form of carbon trading, increasing external revenue. A case-study tobacco manufacturing plant in Sichuan, China, was chosen to demonstrate the feasibility of the proposed work. This study assesses the carbon footprint and economic benefits of a tobacco industry supply chain case (from tobacco cultivation to finished product) and analyzes the energy restructuring of different percentages of renewable energy to replace thermal power. The objective of the study is to maximise the offsetting of carbon emissions from the tobacco production system, while achieving optimal internal costs and profitable external benefits.

Sustainable water management is one of the key measures to achieve Sustainable Development Goals. Increasing water consumption prompts industrial plants to implement water-saving projects and improve the utilization efficiency of water resources. This paper develops an integrated graphical method for designing an implementation path of water conservation projects that considers both water-saving targets and investment costs with or without subsidies. First, the cost-benefit analysis of each project is carried out. The unit water savings cost diagram is used to rank the priority of the implementation of each project. Then, the project mix which meets the expected water saving goal is determined by pinch analysis. Finally, the water conservation potential and cost-benefit of the optimal project mix are shown by the marginal cost curve diagram. This work presents two scenarios (i.e., with and without government subsidies). A chlor-alkali/ polyvinyl chloride complex is used as an illustrative case study. The complex achieves annual water savings of 3,580.4 kt by implementing the selected water conservation projects under the budget and subsidy constraints.

Increasingly frequent disturbances such as droughts, earthquakes and terrorist attacks have severely affected the interdependent infrastructure of industrial parks. It is necessary to explore the relationship between the interdependence degree and stability of the infrastructure and ensure that the infrastructure systems can withstand the impact of emergencies from natural disasters, industrial accidents, or malicious attacks. The water system of an industrial park is investigated in this work, which proposes a method combining Process Integration with the inoperability input-output model (IIM) for risk analysis. First, Process Integration is used to generate optimal water network alternatives under different optimisation conditions. The IIM was next established to investigate the inoperability, elasticity, and flexibility of each alternative after a disturbance occurs. Finally, practical risk mitigation measures are discussed based on the analysis. Results showed that when PI is used to help the park save water, it also leads to the high interdependency, which makes the park difficult to withstand the shock of external disturbances and causes cascading damage.

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

Daya Bay is a shallow, semi-en closed bay in the northern section of the South China Sea. The present study analyzed variations of water temperature in Daya Bay over the past 21 years (1985 - 2005) using Advanced Very High Resolution Radiometer (AVHRR) satellite remote sensing data and in situ observations. Results showed that AVHRR readings of sea surface temperature (SST) increased by 0.07°C y-1. Linear regression anal y sis for monthly SST anomalies (SSTA) showed a shift from negative to positive from 1995 - 1996, when the Daya Bay nuclear power station commenced operations in 1994. The slope of linear regression analysis for SSTA nearly doubled from 0.05 (1985 - 1993) to 0.09 (1994 - 2005). Monthly AVHRR images showed a thermal plume from the power station and revealed the in crease of SST over 21 years. In situ observations in water temperature also showed an in creasing trend for the same period (1985 - 2005). Variations in water temperature in Daya Bay were connected with climatic perturbations and in creasing human activity including thermal discharge from nuclear power stations and the rapid economic development around the bay area.

Fast and slow earthquakes are two modes of energy release by the slip in tectonic fault rupture. Although fast and slow slips were observed in the laboratory stick-slip experiments, due to the sampling rate limitation, the details of the fault thickness variation were poorly understood. Especially, why a single fault would show different modes of slip remains elusive. Herein, we report on ring shear experiments with an ultrahigh sampling rate (10 MHz) that illuminate the different physical processes between fast and slow slip events. We show that the duration of slips ranged from dozens to hundreds of milliseconds. Fast slip events are characterized by continuous large-amplitude AE (acoustic emission) and somewhat intricate variation of the sample thickness: A short compaction pulse during the rapid release of stress is followed by dilation and vibrations of the sample thickness. As the slip ends, the thickness of the sample first recovers by slow compaction and then dilates again before nucleation of the following slip event. In contrast, during slow slip events, the shear stress reduction is accompanied by intermittent bursts of low-amplitude AE and sample dilation. We observed the detailed thickness variation during slips and found that dilation occurs during both fast and slow slips, which is consistent with natural observations of coseismic dilatation. This study may be used to reveal the mechanism of fault slips during fast and slow earthquakes, which explain the potential effect of fast and slow slips on stress redistribution and structural rearrangement in faults. [ABSTRACT FROM AUTHOR]

Abstract Background Microsatellites are increasingly realized to have biological significance in human genome and health in past decades, the assembled complete reference sequence of human genome T2T-CHM13 brought great help for a comprehensive study of short tandem repeats in the human genome. Results Microsatellites density landscapes of all 24 chromosomes were built here for the first complete reference sequence of human genome T2T-CHM13. These landscapes showed that short tandem repeats (STRs) are prone to aggregate characteristically to form a large number of STRs density peaks. We classified 8,823 High Microsatellites Density Peaks (HMDPs), 35,257 Middle Microsatellites Density Peaks (MMDPs) and 199, 649 Low Microsatellites Density Peaks (LMDPs) on the 24 chromosomes; and also classified the motif types of every microsatellites density peak. These STRs density aggregation peaks are mainly composing of a single motif, and AT is the most dominant motif, followed by AATGG and CCATT motifs. And 514 genomic regions were characterized by microsatellite density feature in the full T2T-CHM13 genome. Conclusions These landscape maps exhibited that microsatellites aggregate in many genomic positions to form a large number of microsatellite density peaks with composing of mainly single motif type in the complete reference genome, indicating that the local microsatellites density varies enormously along the every chromosome of T2T-CHM13.