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This document discusses the importance of carbon reduction engineering in combating global climate change. It highlights the role of production and supply chain management in achieving carbon neutrality and reducing carbon emissions. The document presents 20 selected research papers that cover various aspects of production optimization in carbon reduction engineering, including carbon reduction in the production process, low-carbon and sustainable supply chains, assessment and optimization methodologies, and policy instruments. The papers provide insights into topics such as scheduling optimization, renewable energy production, carbon emission efficiency, sustainable consumption, government subsidizing arrangements, and the impact of policy instruments on carbon reduction. The document expresses gratitude to the authors and reviewers for their contributions and acknowledges the support of the International Journal of Production Research. [Extracted from the article]

We study a newsvendor problem with greenhouse gas (GHG) emissions at the disposal stage regulated by a cap-and-trade policy. We first consider a synchronous version of the problem where the replenishment period and the emission assessment period are the same. For this problem, we propose simple algorithms to derive the optimal quantity and cost for both continuous and discrete demand distributions. We show that ignoring the carbon tax may lead to large overcost, especially when the GHG quota is small. For a normally distributed demand, we also show that the demand standard deviation has a non-linear and non-monotonic effect on the order quantity. We then investigate an asynchronous version of the problem where replenishment decisions occur at a higher frequency than the quota allocation. We formulate this lot sizing problem as a Markov decision process and solve it with a dynamic programming algorithm whose complexity is assessed. Through a numerical experimentation and theoretical results, we demonstrate how and when the flexibility of the optimal dynamic replenishment decisions allows to reduce costs. When approaching the end of the time horizon, the uncertainty reduction allows the decision maker to adopt a riskier policy and order larger quantities. [ABSTRACT FROM AUTHOR]

Agriculture is a major contributor to greenhouse gas emissions and is vulnerable to climate change. Regenerative agriculture, which focuses on increasing soil carbon, has been recognized as a potential climate solution. A recent study found that seven regenerative practices, including planting cover crops and using non-chemical fertilizers, all increased carbon sequestration. However, the study also highlights that regenerative agriculture has other benefits beyond carbon sequestration, and the focus should not solely be on carbon credits. The popularity of regenerative agriculture is growing, but there is a risk of it being co-opted and losing its essence. [Extracted from the article]

In recent years, recognition has grown of the significant role that food systems play in driving climate change, constituting about one-third of global greenhouse gas emissions, while also playing a key role in sequestering carbon and adapting to climate changes. The COP28 climate conference signaled a pivotal shift, spotlighting food and agriculture in discourse. However, despite pledges and declarations, concrete plans and targets to address emissions from food chains remain elusive. Urgent action is imperative to transform food systems and avert surpassing critical warming thresholds. [ABSTRACT FROM AUTHOR]

Catherine Bertini, a leader in addressing global hunger, discusses the challenges of achieving zero hunger by 2030. Currently, 800 million people are food-insecure due to poverty, conflicts, climate issues, and the COVID-19 pandemic. The goal of reaching net-zero greenhouse gas emissions by 2050 poses a challenge, as agriculture contributes a quarter of all emissions. Efforts are being made to develop climate-resilient African crops and reduce negative impacts from the agricultural industry. The text emphasizes the importance of proactive measures to mitigate the effects of climate change on food security, investing in pre-mitigation efforts, and supporting local food systems. It also highlights the role of women in food and climate issues and the recent recognition of food's importance in climate discussions. The text concludes by discussing the food situation in Gaza and the need for an end to war to ensure food delivery to the region. [Extracted from the article]

Recent California regulatory efforts, United States goals, and industry roadmaps all target net-zero greenhouse gas (GHG) emissions from the cement and concrete industries within a few decades. While changes in production of cement and concrete, including varying constituents, can greatly reduce GHG emissions, carbon dioxide removal (CDR) will be needed to meet this net-zero goal. Hydrated cement in concrete can carbonate (i.e., form carbon-based minerals with atmospheric CO2) and thus act as a CDR mechanism. This process occurs faster with a large surface area, such as crushed concrete at its end-of-life (EoL), which can be uniquely leveraged by transportation infrastructure projects. In this work, a literature review of key parameters that can facilitate desired CO2 uptake for transportation projects at their end of life is conducted and an initial meta-analyses of data from the literature to inform CO2 uptake for individual projects is performed. Initial considerations for what concomitant impacts may arise from this process are presented. Finally, experiments to fill a key gap in understanding how thin crushed concrete must be spread to maximize uptake reactions are conducted. Cumulatively, findings will inform whether carbonation can be implementedin a way that would support policies that include carbonation as a route for reducing emissions from cement-based materials in transportation applicationsView the NCST Project Webpage

There is growing interest in the use of hydrogen as a transportation fuel but the environmental benefits of using hydrogen depend critically on how it is produced and distributed. Leading alternatives to using fossil natural gas to make hydrogen through the conventional method of steam methane reforming include using electrolyzers to split water into hydrogen and oxygen, and the use of biogas as an alternative feedstock to fossil natural gas. This report examines the latest carbon intensity (CI) estimates for these and various other hydrogen production processes, adding important nuances to the general “colors of hydrogen” scheme that has been used in recent years. CI values for hydrogen production can vary widely both within and across hydrogen production pathways. The lowest CI pathways use biomass or biogas as a feedstock, and solar or wind power. The report also analyses jobs creation from new hydrogen production facilities and shows that these benefits can be significant for large-scale facilities based on either future biomass/biogas-to-hydrogen or solar-hydrogen production technologies. Recommendations include setting stricter goals for the state’s Low Carbon Fuel Standard (LCFS) program to continue to reduce the carbon footprint of California’s transportation fuels.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Methane (CH4) is a potent greenhouse gas emitted by archaea in anaerobic environments such as wetland soils. Tidal freshwater wetlands are predicted to become increasingly saline as sea levels rise due to climate change. Previous work has shown that increases in salinity generally decrease CH4 emissions, but with considerable variation, including instances where salinization increased CH4 flux. We measured microbial community composition, biogeochemistry, and CH4 flux from field samples and lab experiments from four different sites across a wide geographic range. We sought to assess how site differences and microbial ecology affect how CH4 emissions are influenced by salinization. CH4 flux was generally, but not always, positively correlated with CO2 flux, soil carbon, ammonium, phosphate, and pH. Methanogen guilds were positively correlated with CH4 flux across all sites, while methanotroph guilds were both positively and negatively correlated with CH4 depending on site. There was mixed support for negative relationships between CH4 fluxes and concentrations of alternative electron acceptors and abundances of taxa that reduce them. CH4/salinity relationships ranged from negative, to neutral, to positive and appeared to be influenced by site characteristics such as pH and plant composition, which also likely contributed to site differences in microbial communities. The activity of site-specific microbes that may respond differently to low-level salinity increases is likely an important driver of CH4/salinity relationships. Our results suggest several factors that make it difficult to generalize CH4/salinity relationships and highlight the need for paired microbial and flux measurements across a broader range of sites.

Contemporary resource management is doubly burdened by high rates of organic material disposal in landfills, generating potent greenhouse gases (GHG), and globally degraded soils, which threaten future food security. Expansion of composting can provide a resilient alternative, by avoiding landfill GHG emissions, returning valuable nutrients to the soil to ensure continued agricultural production, and sequestering carbon while supporting local communities. Recognizing this opportunity, California has set ambitious organics diversion targets in the Short-Lived Climate Pollutant Law (SB1383) which will require significant increases (5 to 8 million tonnes per year) in organic material processing capacity. This paper develops a spatial optimization model to consider how to handle this flow of additional material while achieving myriad social and ecological benefits through compost production. We consider community-based and on-farm facilities alongside centralized, large-scale infrastructure to explore decentralized and diversified alternative futures of composting infrastructure in the state of California. We find using a diversity of facilities would provide opportunity for cost savings while achieving significant emissions reductions of approximately 3.4 ± 1 MMT CO2e and demonstrate that it is possible to incorporate community protection into compost infrastructure planning while meeting economic and environmental objectives.

"Worn Out: How Retailers Surveil and Exploit Workers in the Digital Age and How Workers Are Fighting Back" by Madison Van Oort is a book that examines the social forces shaping the lives of fast-fashion workers and explores ways to create better jobs and reduce inequalities. The book focuses on two fast-fashion stores in New York City and uses qualitative research methods such as participant observation and interviews with workers and activists. It discusses topics such as digital scheduling, the loss of customer interaction, technological control, and collective action initiatives. The author argues for legislative change and closer cooperation between labor and citizenship organizations to ensure technology is used in favor of workers. While the book does not fully explore the impact of demographics on workers' experiences, it provides valuable insights into the challenges faced by fast-fashion workers and the role of technology in their work. [Extracted from the article]

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

PURPOSE: We aimed to examine the impact of different conference formats (in-person, virtual, and hybrid) of the ASCO conference on greenhouse gas (GHG) emissions and to recommend sustainable options for future conferences. MATERIALS AND METHODS: This study used data on the number of attendees, their departure locations, and the type of attendance (in-person v virtual) provided by ASCO between 2019 and 2022. The GHG emissions resulting from air and ground travel, remote connectivity, conference space utilization, hotel stays, distributed conference materials, and electricity use were estimated for each year. Emissions were stratified by attendee country of origin, type of attendance, and year. Simulations were conducted to evaluate how changes in conference size, location, and format impact emissions, as well as estimate the resulting mitigations from adopting the proposed changes. RESULTS: The highest estimated GHG emissions, calculated in carbon dioxide equivalents (CO2e), were associated with the 2019 in-person conference (37,251 metric tons of CO2e). Although international attendees had the largest contribution to emissions in all years (>50%), location optimization models, which selected conference locations that most minimized GHG emissions, yielded only minimal reductions (approximately 3%). Simulations examining changes to the conference format, location, and attendance percentage suggested that hub-and-spoke, where multiple conference locations are selected by global region, or hybrid models, with both in-person and virtual components, are likely to cause the largest drops in emissions (up to 86%). CONCLUSION: Using historical conference data, this study identifies key aspects that can be modified to reduce emissions and consequently promote more sustainable and equitable conference attendance. Hybrid conferences may be the best solution to maintain the networking opportunities provided by conferences while balancing out their environmental footprint.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Methane (CH4) and nitrous oxide (N2O) are major greenhouse gases that are predominantly generated by microbial activities in anoxic environments. N2O inhibition of methanogenesis has been reported, but comprehensive efforts to obtain kinetic information are lacking. Using the model methanogen Methanosarcina barkeri strain Fusaro and digester sludge-derived methanogenic enrichment cultures, we conducted growth yield and kinetic measurements and showed that micromolar concentrations of N2O suppress the growth of methanogens and CH4 production from major methanogenic substrate classes. Acetoclastic methanogenesis, estimated to account for two-thirds of the annual 1 billion metric tons of biogenic CH4, was most sensitive to N2O, with inhibitory constants (KI) in the range of 18-25 μM, followed by hydrogenotrophic (KI, 60-90 μM) and methylotrophic (KI, 110-130 μM) methanogenesis. Dissolved N2O concentrations exceeding these KI values are not uncommon in managed (i.e. fertilized soils and wastewater treatment plants) and unmanaged ecosystems. Future greenhouse gas emissions remain uncertain, particularly from critical zone environments (e.g. thawing permafrost) with large amounts of stored nitrogenous and carbonaceous materials that are experiencing unprecedented warming. Incorporating relevant feedback effects, such as the significant N2O inhibition on methanogenesis, can refine climate models and improve predictive capabilities.

To control the emission of SO2 from flue gas into the atmosphere while considering the capture and collection of greenhouse gas CO2, the adsorption behavior of a binary mixture of SO2 and other gases in flue gas (O2/N2/H2O/CO 2) in (10, 10) carbon nanotube (CNT) was simulated using grand canonical Monte Carlo (GCMC) simulation. The adsorption and separation performance of SO2 in CNTs with a diameter range of 0.81–1.63 nm for the five-component mixture gas was also analyzed. The findings suggest that the adsorption and separation of SO2 are primarily influenced by CO2 (reduction of adsorption capacity by about 50%, separation coefficient of SO2/CO2 is lowest) with this effect being more pronounced under high pressure. Meanwhile, it was observed that CNTs with larger pipe diameters exhibit higher SO2 adsorption capacity, but relatively lower SO2/CO2 selectivity and lower stability. On the other hand, CNTs with smaller diameters have relatively lower adsorption capacity for SO2, but exhibit good selectivity and stability (under different pressure) for SO2/CO2. Based on the statistical analysis of SO2 adsorption capacity and SO2/CO2 selectivity, it was determined that (6, 6) CNT with a diameter of 0.81 nm can exhibit excellent SO2 adsorption and separation performance at atmospheric pressure, while appropriate large diameter CNTs should be selected for flue gas treatment under high pressure. [ABSTRACT FROM AUTHOR]

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. [ABSTRACT FROM AUTHOR]

Summary: Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure.We used a dynamic vegetation model with plant hydrodynamics (FATES‐HYDRO) to simulate the stand‐level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086–2100: SSP2‐45, SSP5‐85) and two CO2 levels (contemporary, anticipated).The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1–11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate.Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo‐tropics accompanied by certain trait plant assemblages becoming nonviable. See also the Commentary on this article by Mackay, 244: 2115–2117. [ABSTRACT FROM AUTHOR]

[Display omitted] Sustainable waste management (SWM) practices are crucial for tackling environmental concerns under a circular economy (CE) scope. Thirty-one OECD countries have been evaluated on their national SWM performance via Malmquist data envelopment analysis (DEA) methodology from 2000 to 2021. The Malmquist (MPI) and Malmquist-Luenberger (MLPI) productivity indices are compared based on their total factor productivity (TFP) and its elements (i.e., efficiency and technological changes). Both models have the gross domestic product (GDP) as a desirable output, but MLPI also incorporates municipal solid waste (MSW) generation and greenhouse gases (GHGs, i.e. CO 2 and CH 4) as undesirable outputs. MLPI has a lower average performance (1.74%) than MPI (4.42%), meaning that the incorporation of waste and GHGs has diminished an OECD country's SWM performance by 2.5 times on average. In essence, it is innovation that drives TFP in the selected countries. Overall, the synergistic implementation of SWM practices, Malmquist DEA-based methodology, and CE principles is crucial for advancing sustainable development and achieving a circular and resilient future. The manuscript also offers policy implications regarding the need of financial incentives, vocational training, and fostering stakeholder encouragement in the waste sectors to implement CE solutions. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements. Design/methodology/approach: The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations. Findings: The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions. Originality/value: This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures. [ABSTRACT FROM AUTHOR]

The need for deep decarbonization of the transport sector cannot be understated, as it accounts for about the 25% of greenhouse gas emissions in Europe. Developing hydrogen-based trucks is one of the viable solutions for exploiting green hydrogen and reaching climate neutrality. This work presents an optimization framework to optimally place Hydrogen Refueling Stations (HRS) for hydrogen-based trucks under technical, policy and regulatory constraints. It relies on an EU heavy-duty road freight transport database adapted to the latest publicly available statistics to update the demand intensity. A revised Node Capacitated Flow Refueling Location Model is proposed to minimize the number of HRS to be sited on the highway network. The node capacity constraint considers standard sized HRS with a maximum daily capacity ranging from 500 (S-sized) to 4000 kg (XL-sized). The framework can be a useful evaluation tool to strategically site HRS, both for policymakers and stakeholders. To this end, the Italian highway network was evaluated as a case study, finding that at least 78 HRS nodes are required across the road network if a 10% share of hydrogen vehicles is considered, as planned in the Italian National Recovery and Resilience Plan. The median utilization factor of the refueling stations is 67.5%, ranging from 49% for the S-sized to 86% for the XL-sized, which are located mainly in northern Italian regions. To effectively reduce emissions in road freight transport, results show that at least 368 MW of additional equivalent photovoltaic capacity is needed to produce entirely green hydrogen, reducing the greenhouse gases emissions associated to the road freight transport by 6.5%. • GIS-based framework for optimal hydrogen refueling station (HRS) placement. • Node-capacitated flow refueling location model (NC-FRLM) used for HRS optimization. • Energy demand from ETIS-BASE O-D matrix adapted for Italian NUTS-3 road freight transport. • 78 HRS nodes needed for 10% hydrogen vehicle share by 2030, requiring 183 t H 2 daily. • +368 MW photovoltaic capacity needed to cut road freight transport emissions by 6.5%. [ABSTRACT FROM AUTHOR]

Energy storage technologies play a crucial role in addressing the intermittent characteristics of renewable energy sources, improving the stability of electrical grids, and decreasing the release of greenhouse gas emissions. In this study, we presented nickel indium oxalate (Ni 1-x In x C 2 O 4) as a promising material with potential applications in the field of electrochemical energy storage. The as-prepared Ni-In- oxalate sample was subjected to different physical characterizations, including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Regarding the electrochemical energy storage capability, the Ni 1-x In x C 2 O 4 electrode material exhibited a specific capacitance of 835 F g−1 (417.5 C g−1) at 1 A g−1. The incorporation of nickel (Ni) into the indium (In) oxalate nanoplates enhances their electrochemical performance. The presence of Ni in the nanoplates generated from substrate, improving the overall conductivity of the material and enhances its electrochemical reactions, thus leading to improved energy storage capabilities. [ABSTRACT FROM AUTHOR]

Accelerated anthropogenic emission of greenhouse gases due to increasing energy demands has created a negative impact on our planet. Therefore, the replacement of fossil by renewable energy resources has become of paramount interest, both societally and scientifically. It is within this setting that organic photocatalysts have emerged as a new generation of earth‐abundant catalysts for the conversion of solar radiation into chemical energy. In 2014, the first example of a covalent organic framework (COF) photocatalyst for the hydrogen evolution reaction was reported by our group, which has not only marked the beginning of COF photocatalysis for solar fuel production but also helped to accelerate research into "soft photocatalysis" based on porous polymers in general. In the last decade, significant progress has been made toward developing COFs as robust, molecularly precise platforms emulating artificial photosynthesis. This mini‐review commemorates the 10th anniversary of COF photocatalysis and gives a brief historical overview of the milestones in the field since its inception in 2014. We review milestones in the development of COFs for solar fuel production and related photocatalytic transformations, including hydrogen evolution, oxygen evolution, overall water splitting, CO2 reduction, N2 fixation, oxygen reduction, and alcohol oxidation. We discuss lessons learned for the design of structure‐property‐function relationships in COF photocatalysts, and future perspectives and challenges for the field of "soft photocatalysis" are given. [ABSTRACT FROM AUTHOR]

Enriching gasoline with onboard-produced HHO presents a promising solution to enhance motorcycle engine performance and mitigate greenhouse gas emissions. However, flashback, arising from residual HHO in the intake manifold, poses a significant challenge. This study examines the distribution of the hydrogen-air equivalence ratio in the intake manifold using various HHO supplying methods: continuous induction method (CIM), discontinuous induction method (DIM), and venturi injection method (VIM). The results identify the maximum HHO flow rate for each method to prevent flashback occurrence. Supplying HHO through CIM with the nozzle located at the butterfly valve is suitable for HHO flow rates below 8 LPM, corresponding to a gasoline substitution of 7.3%. When supplying HHO through the nozzle positioned at the venturi throat using CIM and DIM methods, the maximum flow rates are 2 LPM and 5.33 LPM, respectively, corresponding to gasoline substitutions of 1.6% and 2.5%. HHO can be safely supplied at higher flow rates through the vacuum valve by controlling the pressure at the retrofitted venturi throat. With an injection pressure of 0.7 bar and an injection duration angle of 80°CA, the gasoline substitution can reach 17.7% without the risk of flashback. The CIM with the nozzle located at the butterfly valve is an appropriate solution for supplying the minimum HHO flow rate to enhance combustion characteristics. The VIM is advantageous for supplying high HHO flow rates, significantly improving engine performance, increasing gasoline substitution, and reducing greenhouse gas emissions. • Hydrogen concentration distribution near intake port mainly affects flashback occurrence. • HHO can replace safely 2.5% gasoline by discontinuous induction method at venturi. • 7.3% gasoline substitution without flashback by continuous induction HHO at butterfly valve. • Gasoline substitution can reach 17.7% with HHO injection at venturi throat. • Venturi injection method with throat pressure control is suitable for high flow rate HHO supplying. [ABSTRACT FROM AUTHOR]

This study develops a framework for designing hydrogen supply chains (HSC) in island territories using Mixed Integer Linear Programming (MILP) with a multi-period approach. The framework minimizes system costs, greenhouse gas emissions, and a risk-based index. Corsica is used as a case study, with a Geographic Information System (GIS) identifying hydrogen demand regions and potential sites for production, storage, and distribution. The results provide an optimal HSC configuration for 2050, specifying the size, location, and technology while accounting for techno-economic factors. This work integrates the unique geographical characteristics of islands using a GIS-based approach, incorporates technology readiness levels, and utilizes renewable electricity from neighboring regions. The model proposes decentralized configurations that avoid hydrogen transport between grids, achieving a levelized cost of hydrogen (LCOH) of €8.54/kg. This approach offers insight into future options and incentive mechanisms to support the development of hydrogen economies in isolated territories. [Display omitted] • A framework for optimal hydrogen supply chain design in island territories is defined. • The multi-period problem is modelled using Mixed Integer Linear Programming. • The framework aims to minimize cost, emissions, and a risk-based index. • Island-specific geography is integrated using a Geographic Information System. • The hydrogen produced meets the transportation demand of Corsica from 2025 to 2050. [ABSTRACT FROM AUTHOR]

Global production of steel and aluminium is a major driver of greenhouse gas emissions. Various processes might allow continued primary production of the two metals, but all depend on emissions-free electricity or carbon storage, and global capacity of these two key resources will be below demand for decades to come. As a result, zero-emissions steel and aluminium will mainly come from recycling, but supply will be lower than demand. This motivates demand reduction, and for the first time, this article estimates the inefficiency in current metal use by component type. The results demonstrate that around 80% of steel and 90% of aluminium liquid metal produced today may be unnecessary. Around 40% of liquid steel and 60% of liquid aluminium are never used in final components as they are removed along the supply chain of manufacturing. Of the metal that enters final service, approximately one-third could be saved by avoiding component over-specification. A further third could be saved, where the properties of metal are not used to their limits. These results point to specific opportunities for innovation in design and manufacturing technology, of which the highest priority is to re-think the use of sheet metal in construction. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'. [ABSTRACT FROM AUTHOR]

The suitability of waste cement concrete dust (CD) and conventional hydrated lime (HL) as fillers in bituminous concrete was investigated. Bituminous mixes containing CD or HL in different proportions were designed. Their performance was studied using the Marshall quotient, indirect tensile strength, tensile strength ratio (TSR), passive and active adhesion and Cantabro tests. The cost of the various mixes and their greenhouse gas (GHG) emissions were also compared. Finally, the optimum filler content for both mixes was determined. The mixes containing CD displayed rutting, cracking and ravelling resistance similar to those of conventional mixes. CD offered improved affinity towards bitumen and ensured excellent moisture resistance (TSR > 80%). Compared with a traditional mix (4% HL), the use of CD marginally lowered the cost by around 3–8% and reduced the GHG emissions by more than 50%. Overall, the addition of CD ensured satisfactory engineering performance at a relatively lower cost and significantly lower GHG emissions. [ABSTRACT FROM AUTHOR]

Plant traits which vary both within and between species often drive biogeochemical cycling. Understanding the relative role of within‐ and between‐species trait variability in driving carbon cycling is essential to scaling site measurements to global carbon budgets. In seagrass meadows, carbon and nitrogen mineralization rates and associated greenhouse gas emissions are highly variable, impeding our ability to reliably predict whether meadows are net carbon sinks. Evaluating the influence of within‐ and between‐species trait variability on greenhouse gas fluxes will improve our understanding of local‐scale drivers of greenhouse gas production and consumption in seagrass meadows.To test the effects of plant traits on dissolved greenhouse gas fluxes, we performed mesocosm incubations with live, intact seagrass plants. We compared methane (CH4) and nitrous oxide (N2O) fluxes under dark and light conditions from sediments dominated by Halodule wrightii and Thalassia testudinum across dormant, early and peak growing seasons in a subtropical meadow along the west coast of peninsular Florida. We also measured oxygen (O2) fluxes to interpret greenhouse gas fluxes within the context of community metabolism. We measured several seagrass traits, such as above‐ and below‐ground biomass and leaf and root area and assessed their impact as well as the impact of species identity on dissolved gas fluxes.We found that abiotic factors linked to metabolism (i.e. light and temperature) influenced greenhouse gas fluxes across seasons. In addition to light conditions and sampling month, plant size (a composite trait variable) was a significant predictor of O2 consumption and CH4 production under dark conditions, and better predicted fluxes than individual plant traits. CH4 production was slightly higher in H. wrightii‐dominated sediments, but species identity was less important than plant size in driving CH4 production. N2O fluxes were low and not influenced by plant traits or species identity.Synthesis: Our results indicate that within‐species more so than between‐species trait variability drives the direction and magnitude of CH4 fluxes in seagrass meadows. We identified a trade‐off where seagrass biomass is often associated with enhanced sediment carbon storage, but in our study, plant size promoted CH4 production, potentially offsetting the benefits of long‐term storage. [ABSTRACT FROM AUTHOR]

With the increasing awareness of environmental and social impacts associated with conventional petrochemical materials and air pollution, the automotive industry is experiencing accelerated transformations. The emergence of electric vehicles (EVs) and urban air mobility (UAM) signifies a pivotal shift towards sustainable transportation solutions. Consequently, the focus has shifted towards lightweight and eco-friendly materials, driving the development of sustainable composite materials and recycling techniques. This study reviews the utilization of eco-friendly materials in the automotive industry, particularly focusing on biopolymers, natural fibers, and bioplasticizers. It examines manufacturing processes for bio-based composites and explores current trends and market prospects. Green composites, known for their lightweight properties and capacity to reduce vehicle fuel consumption and greenhouse gas emissions, offer significant advantages, including reduced production costs and enhanced sustainability. The market for green composites is projected to experience substantial growth, with a forecasted Compound Annual Growth Rate (CAGR) of 15.30%, reaching USD 86.43 billion from 2022 to 2030. Similarly, the global recycled plastic market, valued at USD 69.40 billion in 2023, is expected to reach USD 120.00 billion by 2030, driven by increasing demand in high-value-added sectors such as the automobile industry. This comprehensive review aims to analyze research trends in eco-friendly composites and their applications within the automotive sector, emphasizing significant contributions to sustainable mobility and environmental stewardship. [ABSTRACT FROM AUTHOR]

Silage maize (Zea mays L.) intensification to maximise biomass production increases greenhouse gas emissions, accelerates climate change and intensifies the search for alternative bioenergy crops with high carbon (C) sequestration capacity. The perennial cup‐plant (Silphium perfoliatum L.) not only serves as a viable bioenergy source but may also be a promising soil C conservator. However, the dynamics of soil organic C (SOC) under the C3 cup‐plant, exposed to moderate drought conditions, that reduces growth rate without causing crop failure, compared with the drought‐tolerant C4 maize, remains unexplored. Here, we investigated in a lysimeter experiment the effects of moderate drought stress on crop growth and soil CO2 efflux under cup‐plant and silage maize compared with well‐watered conditions. Soil CO2 efflux along with root and shoot biomass, soil moisture and temperature as well as SOC and nitrogen (N) were measured over three consecutive years. Irrespective of the watering regime, cup‐plant induced a greater soil CO2 efflux (16% and 23% for 2020 and 2021, respectively), which was associated with higher root and shoot biomass compared with silage maize suggesting a substantial contribution of the roots to total soil CO2 efflux. In addition, soil CO2 efflux correlated negatively with soil dissolved N and positively with microbial C:N imbalance suggesting that low soil N availability influences soil CO2 efflux through processes related to N‐limitation such as N‐mining. Strikingly, moderate drought had no effect on soil CO2 efflux and C content and microbial biomass C, but increased dissolved organic C and microbial biomass N in both crops suggesting a complex interplay between C availability, N‐limitation and microbial adaptation under these conditions. Although cup‐plant increased soil CO2 efflux, the observed higher root and shoot biomass even under moderate drought conditions suggests a similar soil C management as silage maize; however, this still requires longer‐term investigation. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Dairy farmers face increasing pressure to reduce GHG emissions (i.e., carbon dioxide, CO 2 ; methane, CH 4 ; and nitrous oxide, N 2 O), but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate these emissions. However, current models tend to be not user-friendly, difficult to access, or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal-related variables (i.e., DMI, milk yield, manure excretion, and enteric CH 4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade Shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard freestall system with 1,000 dairy cows averaging 40 kg/d of milk production. These factors included 2 NDF-ADF levels in the diet (28%–22.8% and 24%–19.5%), the presence or absence of 3-nitrooxypropanol (3-NOP) dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying (once yearly, only in fall; or twice a year, in fall and spring), and the use or nonuse of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kg CO 2 eq/kg of milk, corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kg CO 2 eq/kg) involved a combination of factors, including a lower NDF-ADF level in the diet in addition to incorporation of 3-NOP, use of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both fall and spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kg CO 2 eq/kg) involved a combination of a higher NDF-ADF level in the diet and excluded incorporation of 3-NOP, use of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in fall. All these scenarios can easily be simulated in the DairyPrint model, with results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) are significant users of energy and contributors to greenhouse gas emissions. Energy recovery strategies (ERSs) have potential for switching WDNs to a renewable energy source, thereby assisting with the transition to net-zero emissions and reducing the dependency of water utilities on power-grids. To address the current lack of understanding of the potential for ERSs to achieve these goals, this work presents a holistic review, which considers all available devices, i.e. turbines, pumps as turbines, and other alternative strategies, such as the GreenValves, as well as all the kinds of application in the literature. Besides describing the latest most significant achievements in the context of ERSs, the review also identifies the most common issues and proposes several future research directions, including the need for a better test and comparison of the various available solutions in field applications, and more thorough analyses of the socio/political problems hampering their diffusion. [ABSTRACT FROM AUTHOR]

This study aims to create a vision of the future hospital to help healthcare leaders understand how changes in society and the healthcare system, compounded by climate change, could affect future hospital estate. The study is part of a larger project based on participatory backcasting aimed at providing integrated strategies for transitioning to a zero-carbon future and adapting to existing climate change through improved asset management. The data presented in this paper were collected during a full-day workshop to construct the vision of the future hospital in 2050. A multidisciplinary team of 19 participants participated in the discussions. A six-phase thematic analysis was applied to the data to develop the narrative vision and graphic recording. The healthcare system is undergoing transformative changes due to evolving healthcare delivery, patient expectations, emerging technologies, climate change, and sustainability. However, current hospital strategies often fail to consider the interrelationship between the hospital estate and its socio-environmental context. Policymakers, healthcare system leaders, and hospital leaders need a clear vision of the hospital of the future to implement transformational strategies. Healthcare transformations require shifting from traditional centralised hospitals to a more flexible, distributed model. Healthcare leaders need to proactively assess how hospitals respond to current and future hazards and consider the impacts within the context of integrated and dispersed healthcare delivery. To address this, a systematic approach to modelling hazards and evaluating design or upgrading options is essential to mitigate the transfer of climate-related risks within healthcare systems. [ABSTRACT FROM AUTHOR]

Coming to terms with climate change by understanding climate processes and their policy implications is essential not only for climate scientists and policymakers, but also for the general public, for industrial practitioners, and for engineers, including process system engineers. We have developed a simplified linear climate model (SLCM) to enable non‐specialists to explore the essential physical and chemical processes caused by greenhouse gas emissions. Trading‐off simplicity and accuracy, achieved by calibrating model parameters using established simple climate models, the SLCM allows (i) determining the climate impact of given past and future emissions, (ii) determining the amounts of CO2 removal needed to compensate for any unavoidable emissions of CH4 and N2O from agriculture, and (iii) back‐calculating emission pathways to meet specified global warming targets. [ABSTRACT FROM AUTHOR]

With the acceleration of industrialization and population growth, there has been a notable surge in municipal solid waste (MSW) production globally. This necessitates the development of efficient waste management strategies that align with sustainable economic, environmental, and social goals. Implementing sorting and source separation policies serves as foundational steps toward optimizing material flow and resource utilization. In industrial areas, waste generation around a primary source may improve policy implementation and participation. Given the unique strategic context of port environments, Bahregan Port in Iran was selected as a case study to evaluate the proposed methodology. Three integrated scenarios were formulated and prioritized using the ELECTRE-I model: currently practiced collection method, and segregated collection followed by source separation of MSW into two or three groups. The analysis identified the third scenario as the most effective approach. Remarkably, when evaluating the results under optimistic and pessimistic conditions, the ranking of preferences remained consistent across scenarios in the optimal state. Under pessimistic conditions, all scenarios received identical scores, indicating significant uncertainty in criteria weights. Furthermore, sensitivity analysis revealed that criteria such as the raw material sources protection, emissions in the waste separation sector, and greenhouse gas emissions were highly influential. Interestingly, criteria with the highest weighting did not always dictate the final scenario selection. External factors, such as expert opinions on specific criteria, played a pivotal role in scenario prioritization. This research serves as a foundational study for source separation initiatives, offering practical and technically viable strategies to enhance environmental sustainability within industrial settings. Implications: Rising industrialization and population growth have led to increased municipal solid waste (MSW) production worldwide. To address this, efficient waste management strategies are essential, focusing on sustainability and resource optimization through sorting and source separation policies. By prioritizing the scenario of segregated collection followed by source separation – industrial areas can optimize material flow and resource utilization. The study highlights the influence of criteria like raw material protection, emissions, and greenhouse gases, making this research foundational for enhancing environmental sustainability in industrial settings. This study provides practical strategies for enhancing environmental sustainability in industrial settings through source separation initiatives. [ABSTRACT FROM AUTHOR]

Climate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is accentuated by the scarcity of subsurface thermal data in remote Arctic locales. To alleviate this lack of soil thermal data we set out to combine in situ observed soil temperatures with MERRA-2 reanalysis data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. First, reanalysis features are selected based on surface energy budget physics. Coupled with Julian calendar dates, reanalysis features are preprocessed and then fed to the ML prediction model. We conducted a series of computational experiments and tested the results against performance benchmarks to determine the optimal prediction models, length of look-back period of model inputs, and the training set size. Six conventional ML models (e.g., GBDT, RF, SVR) and five statistical baselines (e.g. ARIMA) using in situ time series of soil temperature data ranging from 0 - 1 m depth are considered for the prediction task. The models are trained using in situ soil temperatures at depths between 0 and 1 m, spanning 16+ years, from field sites at Deadhorse and Toolik Lake, Alaska. All prediction performances are assessed using root mean squared error (RMSE) and mean absolute error (MAE). Results show that locally trained ML models can estimate shallow soil temperatures with an average error of R M S E = 1. 308 ∘ C. Plain English Summary: Climate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is made more difficult by the lack of subsurface temperature data in remote Arctic regions. To reduce the problem of this lack of soil temperature data we set out to combine observed soil temperatures with atmospheric computer model data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. We conducted a series of computational experiments and tested the results of the experiments against other non-ML models to ascertain the effectiveness of ML in predicting soil temperature, to determine how much soil temperature data is required to develop a soil temperature prediction ML model, and the extent of atmospheric model data necessary for the ML model. All models were trained leveraging soil temperature data from Deadhorse or Toolik Lake, Alaska, and the results of the ML models show that soil temperatures can be predicted with an average error of 1. 308 ∘ C. [ABSTRACT FROM AUTHOR]

This cross-sectional study was carried out to determine the relationship between nutrition according to chronotype and the Mediterranean diet and environmental footprints. "Morningness Eveningness Questionnaire" was used to determine the chronotype classification, and "Mediterranean Diet Adherence Screener" was used to evaluate adherence to the Mediterranean diet. In addition, the food consumption record of the participants was taken and the carbon and water footprints were calculated. A total of 472 adults with a mean age of 22.7 ± 5.0 years participated in the study. Accordingly, 61.0% of the participants were morning type, 15.7% were intermediate type, and 23.3% were evening type. Individuals with higher chronotype values (morning) tended to be more adherent to the Mediterranean diet and had lower environmental (carbon and water) footprints (p < 0.05). The results of this study; showed that diet according to chronotype affects adherence to the Mediterranean diet and environmental footprints. [ABSTRACT FROM AUTHOR]

Traditional rice and wheat cropping system (RWCS) of the western Indo-Gangetic Plains (IGP) is not only less productive, but also unsustainable owing to its elevated energy demands and environmental carbon footprint. Transition towards the long-term adoption of conservation agriculture (CA) technologies can possibility overcomes these constraints and making it a crucial component of modern farming systems. Therefore, the effects of conservation tillage and residue retention on wheat cultivation were evaluated from 2015–2016 to 2019–2020 under RWCS on CA fields maintained for twenty one years. Five tillage treatments viz., zero tillage without residue retention (ZT-R), zero tillage with residue retention (ZT+R), permanent bed planting without residue retention (PBP-R), rotary tillage without residue retention (RT-R) and conventional tillage without residue retention (CT-R) were evaluated in four times replicated randomised complete block design. The CT-R recorded 28%, 25%, 24%, and 16% higher energy inputs than those of the ZT+R, ZT-R, PRB-R, and RT-R, respectively. Nevertheless, the lowest grain energy output was recorded in RT-R (86,769 MJ ha−1) and CT-R (86,926 MJ ha−1). Under CT-R, greenhouse gas (GHG) emissions were approximately 20%, 19%, 17%, and 10% greater than those under ZT-R, ZT+R, PRB-R, and RT-R, respectively. Compared to ZT-R, ZT+R, PRB-R, and RT-R plots, CT-R exhibited significantly lower carbon efficiency ratio and carbon sustainability index. The long-term study revealed that ZT+R represent a promising step towards sustainability, characterized by low global warming potential and high energy use efficiency. This makes it an appealing agricultural technique for wheat production in the sub-tropical IGP regions under irrigated RWCS. [ABSTRACT FROM AUTHOR]

Higher energy inputs, greenhouse gases (GHG) emission, and higher costs are associated with fertilizer use which necessitates the exploration of alternative sources of plant nutrients. Keeping this in view, modified indigenous mineral product (MIMP) along with nanofertilizers were evaluated in wheat crop. Strip plot design was followed with sources of phosphorus (P) and potassium (K) as horizontal factors viz. i) recommended phosphorus (P) and potassium (K) through fertilizers (P1) ii) MIMP with Nano DAP (P2), iii) MIMP with Nano K (P3); and Nitrogen (N) management options as vertical factors viz., i) No nitrogen (N1), ii) Three sprays of Nano Urea (NU) (N2) iii) 50 kg N ha−1 at sowing with two spray of NU (N3), iv) 150 kg N ha−1(N4). Growth and yield parameters were comparable for the horizontal factors; whereas, N3 and N4 recorded at par values among the vertical factors. N uptake in P2 was significantly more compared to rest. Sulfur (S) & silicon (Si) uptake were significantly higher with MIMP application. Increasing the dose of N increased the nutrient uptake. The alternative sources of NPK led to significant increase in net returns under unsubsidized supplies. Reduced input energy and increased energy ratio (each by 32%) were observed with N3 compared to N4. Lower GHG emissions was reported with P2 and P3 (74–77%) compared to P1; and with N3 (67%) compared to N4. The study provides an insight for alternative nutrient management in place of existing practices for sustainable agriculture without reducing the crop productivity. [ABSTRACT FROM AUTHOR]

The iron and steelmaking industries play a significant role in the manufacturing sector but result in significant greenhouse gas emissions. Biochar has recently gained attention as a potential substitute for coal in metallurgical processes due to its carbon capture potential. This review explores the potential of biochar as a sustainable substitute for coal in steelmaking industries. Notable research works have shown that substituting biochar in amounts ranging from as low as 5 % to as high as 50 % can be feasible and beneficial in processes such as coke making, iron sintering, blast furnaces, and electric furnaces. The information presented in this review can be applied to create sustainable and competitive alternatives to fossil fuels to help decarbonize metallurgical industries. [ABSTRACT FROM AUTHOR]

Decarbonizing the transportation sector is one of the key challenges to reduce greenhouse gas (GHG) emissions and meet EU Green deal 2030 targets. Road transport decarbonization requires a holistic approach encompassing various strategies and interventions aimed at fostering the sustainable transition of this sector. This challenge is crucial given that road is responsible for over 90% of the overall transport GHG emissions. Within this framework, this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU " fit for 55 " target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios. Due to the current "deep uncertainty" regarding key economic and social variables in this period, two forecasting scenarios – named "moderate decarbo" and "high decarbo" – were designed and based on different and somewhat opposite hypotheses on exogenous variables (e. g., GDP) and penetration rates of measures and policies promoting sustainable transportation (e. g., subsidies for electric vehicles) already in place. Although the current European legislation refers only to Tank-to-Wheel (TtW) objectives, this study included also the Well-to-Wheel (WtW) evaluation, which is more accurate in assessing the GHG impact of different policies, energy carriers, and technologies. As for the past, results suggest that GHG reductions over the period 2005–2022 are mostly due to crises and stagnation of the Italian economy. "Moderate decarbo" forecasts for 2030 lead to a 12% reduction of TtW (and 15% for WtW) GHG emissions compared to 2005 levels, with ASI policies compensating for the assumed economic growth. In any case these values are far from the EU "Fit for 55" goal setting a 43.7% reduction of TtW emissions. Even under very favourable hypotheses ("high decarbo" scenario), GHG emissions might be reduced by only 28% for TtW and 33% for WtW, still well below EU objectives. Furthermore, passengers and freight show very different behaviour, with the latter contributing to emissions more than proportionally, and "shift-to-rail" policies, although very useful in some market segments, contribute moderately at the overall national level. The main conclusion of this study is that the pathway towards road transport decarbonization in Italy (and possibly in other EU Countries) is very uncertain, and the ambitious 2030 target is unlikely to be met with currently planned policies alone. The transportation system needs to be constantly monitored, and new policies have to be implemented to reach higher emissions reduction goals. • Road transport is widely acknowledged as a hard-to-abate sector, contributing over 90% of total transport GHG emissions. • The study estimates Italian road transport demand (2005, 2019, 2022 and 2030) and evaluates GHG emissions. • Following the Avoid-Shift-Improve (ASI) approach, two forecasting scenarios were designed. • Even under very favourable hypotheses, WtW GHG emissions might be reduced by only 33%, still well below EU objectives. • Constant monitoring and new policies are required to meet higher emissions reduction targets. [ABSTRACT FROM AUTHOR]

Given the negative health impacts of climate change, clinicians have a fundamental responsibility to take an active role in mitigating the environmental impact of their practices. Inhaled anaesthetics are potent greenhouse gases, including nitrous oxide (N 2 O), with their long atmospheric lifetime, high global warming potential, and ozone-depleting properties. However, few clinicians realise that losses from central N 2 O supply systems account for the vast majority of overall N 2 O consumption in healthcare. Central N 2 O supply systems are standard in most facilities, compounding the impact of these under-recognised, unnecessary greenhouse gas emissions. We review the environmental impact of N 2 O in healthcare, offer N 2 O utilisation data from 47 hospitals in the USA, and provide clinician-targeted guidance for mitigating these widespread N 2 O emissions. Consistent with findings from the UK and Australia, data from two large US healthcare systems reveal significant nonclinical N 2 O losses of 47.2–99.8% of total procured N 2 O. As illustrated in one quaternary medical centre, the transition from central to portable supply systems reduced overall N 2 O consumption by 97.6%. To date, this mitigation initiative has been successfully implemented at over 25 hospitals in our system. Raising awareness of this considerable source of healthcare-specific N 2 O emissions empowers clinicians to spearhead facility-level engagement and action. As healthcare leaders, clinicians should advocate for decarbonisation of clinical practices and systems while ensuring high-quality patient care. [ABSTRACT FROM AUTHOR]