Emission Footprint Research Articles

Modeling of Carbon Footprint Emissions in Sugarcane Production Using System Dynamics

Objective: This study proposes a model aimed at estimating and reducing carbon emissions in sugarcane cultivation and juice extraction, designed for potential application in real-world systems. Method: The model grounded in a hypothetical case study of a 60-hectare sugarcane plantation in a warm climate with a 7-month growth cycle and a three-year simulation period, focuses on estimate and evaluate mitigation scenarios to decrease emissions from fuel and electricity usage in activities such as plowing, sowing, harvesting, and irrigation. Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation. Results and Discussion: Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation estimated a 3060.81 tons CO2 equivalent carbon footprint. Two scenarios aimed at footprint reduction were tested: substituting electrical energy with solar power reduced the footprint by 86% to 450.092 tons, demonstrating clean energy's efficacy. Conversely, replacing an industrial mill with the traditional trapiche technique for juice extraction yielded a mere 1% reduction, indicating its ineffectiveness for real-world application. Research Implications: This study emphasizes the development of a simulation model based on system dynamics to estimate the carbon footprint emissions in sugar cane production considering its cultivation and juice extraction. The results support that through simulations, it is possible to determine the factors that influence the generation of carbon dioxide, offering a broader vision to establish and implement mitigation measures such as adopting clean energies and reducing fuel consumption. Originality/Value: This research contributes to the understanding of the application of system dynamics by using software such as Vensim PLE to estimate the carbon footprint emissions in sugar cane production, considering its cultivation and juice extraction. The results found through simulations suggest the implementation of mitigation measures such as the adoption of clean energies and the reduction of fuel consumption.

Global Freight Transport Emissions Responsibility.

The transportation of freight by land, sea and air underpins the complex network of global trade in physical commodities. Greenhouse gas emissions from freight transportation are a significant component of global emissions and are predicted to grow in coming decades. However, the inclusion of freight transport in emissions accounts and environmental impact studies is often incomplete. Both data availability and difficulties in allocating freight emissions to specific commodity trades contributes to this. In this study, international freight movements by transport mode are estimated from the bottom-up by imputing global freight transport routes. Emissions are estimated from these freight movements and integrated with a global multiregional input-output model. This enables the calculation of carbon footprints that are complete with respect to freight emissions. We estimate that global freight transport emissions contributed 2.8 Gt CO2-equiv in 2012, or about 41% of total transport emissions. In general, freight footprints contribute about 9% to national emissions footprints. While trade in physical commodities (such as construction materials, food and fossil fuels) are associated with the largest embodied freight emissions, services (such as public administration, education and health) also require significant freight transport. Using a consumption-based allocation of freight transport emissions allows the decarbonisation of other sectors to be complementary to the decarbonisation of transport through reduction in demand, for example through material efficiency strategies. To drive decarbonisation in maritime transport it is critical to include bunker emissions in national emissions inventories, thereby completing the system boundary.

A step toward the attainment of carbon neutrality and SDG-13: Role of financial depth and green technology innovation

Following the recommendations of COP28 and Sustainable Development Goal -13 (Climate Action), the present study examines the role of financial depth and green technology innovation in carbon neutrality and climate change. This study measures the relationship between green innovation, technological innovation, ICT, financial depth, economic growth, carbon emission, and ecological footprint from 1990-2021 in the USA. The autoregressive distributed lag (ARDL) model examines the relationship between the above-mentioned variables and their impacts on each other. Results reveal that green innovation, technological innovation, ICT, and financial depth significantly negatively impact ecological footprint and carbon emissions in both the short and long run. In contrast, economic growth positively impacts carbon emissions and ecological footprint. Green and financial innovation-centric policies are suggested to the USA to attain net zero emission and SDG-13.

What is the role of environmental stress on public health? Asymmetric evidence on carbon emissions, ecological footprint, and load capacity factor

The aim of this paper is to analyze the effects of carbon emission, ecological footprint, which takes into account the demand side of the environment, and load capacity factor, which takes into account both the supply and demand sides of the environment, on health expenditures with conventional and quantile methods. According to the conventional co-integration approach, there is no relationship between the environment and health expenditures. The other side, the findings obtained from the quantile co-integration method, which can give robust results in the presence of tailed distributions and possible endogeneity problems and consider the asymmetric structure in the data set, show the existence of a long-term relationship between the variables. According to the coefficient estimates, while carbon emission and ecological footprint increase health expenditures, the load capacity factor decreases.

Region specific economic model for solid sorbent direct air capture using geothermal energy resources (S-DAC-GT)

Low temperature geothermal resources, ranging from 80° to 120 °C, may substantially lower both the cost and the CO2 emissions footprint of CO2 direct air capture (DAC) systems. This paper provides a model for determining a region-specific economic analysis for DAC with solid sorbent (S-DAC) using geothermal resources (S-DAC-GT).This paper provides a methodology and program for calculating estimated cost and carbon emissions for potential S-DAC facilities on a region-specific basis. The paper outlines the necessary region-specific characteristics required as parameters for the techno-economic model. The region-specific characteristics are then applied to an S-DAC energy and cost model based on existing literature to calculate the levelized cost per tonne of CO2 captured and stored. Further, the model provides a reasonable approximation of the carbon intensity of the S-DAC-GT system. These calculations allow selecting and prioritizing regions appropriate for potential S-DAC-GT facilities operating at a scale of ∼1 Mt CO2 captured and stored per year.This paper presents a novel approach that addresses several gaps in current DAC techno-economic analyses found in literature. First, the model is highly customizable, allowing the user to apply their own proprietary sorbent properties and region specific parameters to determine precise costs and carbon emissions for their hypothetical S-DAC-GT facility. Second, the overwhelming majority of DAC techno-economic analyses in literature assume away variability of ambient conditions, presuming stable temperature and humidity during operations. This paper directly integrates these large local ambient effects on sorbent performance and evaluates S-DAC-GT costs and carbon emissions based on specific and variable conditions dictated by the user. Third, the flexibility of the model allows the user to rapidly compare S-DAC-GT costs and carbon emissions in different user selected regions, with custom sorbent characteristics. The model provides visualizations of the sensitivity of S-DAC-GT costs and carbon emissions to different parameters and provides visualizations of the cost and carbon emissions due to variability in ambient conditions.Existing techno-economic analyses are either region agnostic or constrain their results to a specific region. The novelty of this paper and its model is the deeper technical and economic analysis of using geothermal energy as the thermal resource, coupled with customizability and visualizations that enable accelerated differentiation of S-DAC-GT costs and carbon intensity by region.

Reduction of the Emission Footprint of Gas Turbines in Future Energy System Scenarios Through Optimized Hydrogen Admixture Strategies

Abstract Due to the inherent volatility of renewable power generation technologies, dispatchable components, such as gas turbines (GT) will have to be used increasingly for residual load balancing. In addition, GTs are expected to operate fewer hours per year, more flexibly, and at lower capacities. However, fuel utilization in GT is inherently linked to emissions. As a potentially CO2-free energy carrier, hydrogen is a promising fuel for GTs and manufacturers are working on suitable combustor technologies. However, the availability of large quantities of CO2-free H2 remains unclear in the near future. In this study, a physical-based gas turbine performance model and an emission calculation tool are used to derive an optimized H2 admixture strategy for different load profiles. Characteristic load demand scenarios are derived from actual load profiles of gas power plants and the emission footprints are comprehensively evaluated by different environmental impact categories. In general, the emission footprint is increased significantly and moderately for capacity reduction and flexibility increase of GT operation. The availability of H2 in the near future is derived from forecasts for Germany, and the corresponding quantities are allocated to the partial loads according to the optimized strategy. In most scenarios, the addition of H2 is associated with a reduction in emissions compared to conventional fossil fuel operation. The greatest leverage of H2 admixture in reducing the environmental footprint is found when applied from the lowest load up, thus assisting in the start-up and shut-down process.

Opinion on the prospects of emerging food processing technologies to achieve sustainability in the industry by reduced energy consumption, waste reduction and valorisation, and improved food nutrition

SummaryConventional food processing technologies cannot meet the demands of sustainability in the face of societal concerns about climate change, resource scarcity, and food insecurity. Therefore, the food industry must embrace emerging food processing technologies such as ohmic heating, microwave heating, cold plasma, pulsed electric field, and ultrasound to achieve sustainable food production. Although these technologies have been researched worldwide, they have yet to be widely adopted in the industry as concerns such as high capital investments, regulatory issues, technological limitations, and scalability limit their practical applications. This manuscript discusses key contributions of emerging technologies in enhancing sustainability, including reducing energy consumption, waste reduction and valorisation, and enhancing nutrition. Despite these promising capabilities, fully benefiting from emerging technologies in the industry requires further technological advancements, optimisation, economic viability, consumer acceptance, regulatory compliance, and sustainable workforce education. These could be achieved only by close collaborations between academia, industry, and technology developers. Such considerations and actions to resolve the concerns could facilitate the industrial application of emerging technologies to achieve a more sustainable future with reduced carbon emission, environmental impact, and carbon footprint, along with the capability of improved resource efficiency and further contributions to food security and food nutrition improvement.

371 Applying precision rangeland grazing management systems in western South Dakota

Abstract Western rangelands represent approximately 58% of the total arable land in the U.S. and are used primarily for cow-calf production, which has the largest greenhouse gas (GHG) emission footprint of all beef production phases. Further, beef production sustainability concerns involve climate mitigation (reducing GHG output) and adaptation (climate-resilient soil-plant-animals). Precision livestock farming (PLF) may help address sustainability concerns by providing innovative solutions and new opportunities for extensive rangeland production. The use of precision measurement and management tools with precision system models can connect measurement data to inform management capabilities. For example, real-time individual weighing and remote sensing (precision measurement tools) can be used to inform and implement dynamic rotational grazing management using virtual fencing technology (precision management; Menendez et al., 2022, 2023). Recent USDA investment in climate-smart agriculture (CSA) commodities has provided over $3 billion in funding to implement practices to reduce GHG emissions in agriculture production, for which PLF may be one approach to accomplish these goals. The overall purpose is to develop commodities produced using NRCS climate-smart practices (e.g., prescribed grazing, 528) and document reductions to provide market opportunities associated with inset GHG. Currently, South Dakota State University is leading two simultaneous programs, which include precision ranching (virtual fencing, precision weighing, and GHG evaluation) and a beef and bison CSA program (GHG evaluation; 7 producer-owned research ranches, representing more than 1.09 million ha). The scale of the CSA program has revealed that current PLF research has only been a prelude to providing the precision tools necessary to successfully implement CSA practices and document their impact through monitoring, measuring, reporting, and verification (MMRV). This presentation will include rangeland grazing case studies that cover the application of virtual fencing, animal location, behavior tracking, remote sensing, precision weighing, feeding, supplementation, and enteric emissions and soil moisture monitoring on extensive rangelands. Case studies will include an overview of big data processing and precision system model development methods. Increasing awareness of available PLF tools for optimizing grazing management, animal performance, productivity, and associated challenges (maintenance, costs) is essential for meeting livestock and sustainability goals. PLF and data-driven approaches aid in the creation of scalable, cost-effective MMRV protocols and models (soil carbon and enteric GHG) for extensive rangelands (20 to 70,000 ha areas) that allow producers to realize potential CSA market incentives. Further, MMRV will likely help guide management decisions by identifying CSA strategies that build climate-resilient landscapes for sustainable livestock production and other environmental synergies (soil microbiome, bird and insect habit, water retention).

Adoption of Multi-Modal Transportation for Configuring Sustainable Agri-Food Supply Chains in Constrained Environments

Agri-food supply chains have the potential to make a significant contribution to achieving sustainable development goals through ongoing improvements in their configurations. A range of strategic, tactical, and operational level decisions pertaining to the design and operation of sustainable supply chains have been studied in the extant literature. However, investigations into the adoption of multi-modal transportation as a strategic decision in the context of agri-food supply chains operating in constrained environments are limited. As such, in this study, the adoption of bi-modal transportation for the domestic vegetable supply chain in a developing country context under certain constraints was examined. A mixed-integer linear programming model was developed to determine the volume and direction of the product flow to achieve the minimum total food-miles and smallest emissions footprint. As a case study, a Sri Lankan mainstream vegetable supply chain was used to investigate the applicability of a combination of truck and railway modes to transport vegetables from farms to retailer locations via economic (consolidation) centers. The adoption of a bi-modal transportation structure demonstrated the potential to reduce food miles by 32%, transportation costs by 36%, contributions to global warming potential by 35%, and empty truck hauls by 38%, compared to a structure with truck-based, uni-modal transportation.

A 4E Analysis of a Solar Organic Rankine Cycle Applied to a Paint Shop in the Automotive Industry

In a conventional automotive manufacturing plant, the paint shop alone can represent 36% of the total energy consumption, making it the most demanding area in terms of electricity and fossil fuel energy consumption. This study explores the possibility of decentralizing the production of electrical power and heat simultaneously, using an Organic Rankine Cycle (ORC) system integrated with a Parabolic Trough Collector (PTC) in a paint shop. To date, no similar system has been explored or implemented by the automotive industry. To increase the efficiency of the integrated system, wasted heat generated during the paint manufacturing process is recovered and used to pre-heat the organic fluid in the ORC system. A 4E analysis (Energy, Exergy, Economic, and Environmental) is conducted to determine the practical viability of the proposed system. When applied to the southern region of the USA, this system’s installed capacity is projected to be 11 times higher than the two unique SORC pieces of equipment currently running in Louisiana and Florida. The goals are to reduce the reliance on external primary energy sources and decrease the carbon emission footprint from production activity. The system is evaluated for a location in Alabama, USA. The designed SORC, using toluene, can produce 712.2 kWel net and 13,132 kg/h of hot water, with an overall energy efficiency of 31.02%; exergy efficiency of 34.23; and ORC efficiency of 27.70%. This leads to an electrical energy saving of 5.9% for the manufacturing plant. The regenerative thermal oxidizer (RTO) heat exchanger, the secondary heat source of the system, has the highest exergy destruction—3583 kW. The system avoids the emission of 4521 tCO2 per year. A payback period of 10.16 years for the proposed system is estimated. Considering a planning horizon of 10 years, the investment in the system is also justified by a benefit–cost analysis.

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and contributing to a substantial carbon footprint. This paper addresses this challenge by proposing a novel method named Binary Particle Swarm Optimization Layer Pruner (BPSO-LPruner), aimed at achieving substantial computational reduction and mitigating environmental impact during CNN inference. BPSO-LPruner utilizes a constrained Binary Particle Swarm Optimization for CNN layer pruning, integrating a masked-bit strategy and a new population initialization strategy to enhance search performance. We illustrate the effectiveness of our method in reducing model computational costs and carbon footprint emissions while improving performance across multiple models (VGG16, VGG19, DenseNet-40, ResNet18, ResNet20, ResNet34, ResNet44, ResNet56, ResNet110, ResNet50, and MobileNetv2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, COVID-19 X-ray dataset). Promising results underscore the performance of the proposed method. Additionally, we demonstrate that layer pruning yields benefits beyond enhanced computational performance. Our experimentation reveals that BPSO-LPruner enhances the model’s reliability and robustness by effectively addressing variations in input data, inherent ambiguity in model parameters, and adversarial images.

One–third substitution of nitrogen with cow manure or biochar greatly reduced N2O emission and carbon footprint in saline–alkali soils

The rapid expansion of farmland and long−term excessive nitrogen (N) application have caused huge environmental risks in fragile ecosystems facing global warming. Partially substituting N fertilizer with organic fertilizers offers an alternative field management strategy to alleviate the pressure of the ecological environment. To this end, the influence of one−third substitution of N fertilizer with cow manure or biochar field experiment was conducted under maize in Tarim River Basin since 2019. Five treatments with three replications were applied: CK (Fallow); no fertilization (0 N); conventional N fertilizer (N; N: 300 kg N ha−1, organic fertilizer: 0 kg N ha−1); one−third substitution of N with biochar (NB; N: 200 kg N ha−1, Biochar: 100 kg N ha−1) and one−third substitution of N with cow manure (NM; N: 200 kg N ha−1, Cow manure: 100 kg N ha−1) under maize season in saline−alkali soils. The greenhouse gas (GHG) emissions, net ecosystem carbon budget (NECB), soil organic carbon (SOC), maize yield, carbon footprint (CF), and yield carbon footprint (CFy) were analyzed from 2021 to 2022. The results showed that NB treatment decreased the average cumulative CO2 emissions by 21 %, while NM treatment showed no difference compared to N treatment. NB and NM treatments reduced the average cumulative N2O emissions (−61 %, −49 %), CF (−68 %, −10 %), CFy (−66 %, −19 %) and increased maize yield (+3 %, +2 %), SOC storage (+43 %, +6 %), NECB (+80 %, +24 %), and agronomic N use efficiency (ANUE) (+5 %, +3 %), compared to N treatment. NB treatment had the lowest emission factors (EF) (0.19 %) and the highest sustainability index (1.58) compared to NM treatment (0.26 %, 0.61) and N treatment (0.53 %, 0.84). To sum up, substituting one−third of N fertilizer with biochar or manure in saline−alkali soils was proved to be a multi−benefit strategy to increase yields and reduce GHG emissions and CF.

Developing the green operating room: exploring barriers and opportunities to reducing operating room waste.

The Australian health care system contributes 7% of the national greenhouse gas emission footprint and generates massive waste streams annually. Operating rooms are a particular hotspot, generating at least 20% of the total hospital waste. A systematic search of several global academic databases was conducted in mid-2022 (articles from 1992 to 2022) for peer-reviewed research relevant to waste management in the operating rooms. We then used thematic analysis to enumerate and characterise the strategies and barriers to sustainable waste management in the operating room. The waste reduction strategies focused on avoidance of high carbon products; correct waste segregation and reduced overage; reusing, reprocessing, and repurposing devices; and improved recycling. The first barrier identified was a constrained interpretation of the concept of "first do not harm", ingrained in surgeons' practices, in prioritising single-use surgical products. The second barrier was ineffective or insufficient waste education. The third barrier was the immediate cost of implementing waste management compared with the long term realisation of environmental and economic benefits. The last barrier to implementing institutional practice change was the lack of policies and regulations at the local hospital, federal and international levels. We also evaluated the knowledge gaps in current surgical waste research, including lack of benchmarking data and standardised regulations concerning reusable or reprocessed devices, as well as the methods used to promote pro-sustainability behavioural change.

Healthcare procurement in the race to net-zero: Practical steps for healthcare leadership.

Although it is challenging to assess the greenhouse gas emission footprint associated with individual products and services, health leaders can play a pivotal role in emissions reduction by understanding and utilizing available tools and certifications that measure suppliers' operational environmental performance. Integrating environmental standards into procurement and supplier selection has the potential to greatly impact emissions production across the healthcare landscape as it will pressure suppliers to improve their operations in order to be selected. The purpose of this article is to emphasize the importance of the supply chain in addressing healthcare-related greenhouse gas emissions. We provide an overview of the types of tools available that can be used to evaluate the carbon footprints of individual companies and rate their performances, as well as certifications that formally recognize companies' sustainability practices and commitments.

A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification

Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a range of computer vision tasks. However, these deep CNNs typically demand significant computational resources, which not only hinders their practical deployment but also contributes to a considerable carbon footprint. To tackle this issue, several filter pruning methods based on evolutionary algorithms have been proposed to provide significant memory and energy savings during CNN inference. However, due to the curse of high dimensionality in the structure of deep CNNs, the search space expands dramatically, presenting significant challenges for these methods. This paper proposes a novel algorithm called BPSO-FPruner for CNN filter pruning. BPSO-FPruner utilizes a constrained binary particle swarm optimization algorithm for filter pruning, incorporating a new initialization strategy based on filter weighting information and a reduced search space strategy. Extensive validation using VGG, ResNet, DenseNet, and MobileNetv2 architectures on the CIFAR-10, CIFAR-100, and Tiny ImageNet datasets demonstrates the effectiveness of BPSO-FPruner in reducing model computational costs and carbon footprint emissions while maintaining or improving performance.

Guidline to Asses Geometrical Intolerance of Thin-Walled Blanks After Burnishing Process

AbstractApplication of lightweight material like aluminum alloy is increasing its importance in various industries due to effective reduction of structure weight and sequential advantages like reduction of greenhouse gas emission and carbon footprint. However, deflection of aluminum thin-walled blank during production by machining is a challenge that merits further studies. Burnishing as a non-metal removal finish-machining process is usually used as a final treatment in the production chain of samples. However, in burnishing of thin-walled structure, machining-induced residual stress causes dimensional and geometrical distortion followed by problems in manufacturing accuracy and mismatch in assembly. Therefore, to minimize the consequence of the abovementioned errors, the source of the distortion should be identified and minimized during machining since usually no further operation is placed in the production chain after burnishing. To effectively tackle this challenge, in the present study an analytical model is developed to find how the burnishing process factors i.e. pass number and static force together with initial blank size impact the distortion of thin-walled 6061-T6 plates. The curvatures which were derived from analytical model were compared to those of burnished samples measured by coordinate measuring machine. It was found from the results that the burnishing pass number because of its impact on work hardening and regeneration of stress together with blank size play crucial role on determining the sample’s distortion. It was obtained that with 2 pass burnishing results in minimizing the distortion of material. Moreover, the blank’s length to width ratio due to its impact on material stiffness in corresponding direction significantly impacts the deformation after unclamping. The results which were derived from analytical model were compatible well with experimental values in term of final distribution of residual stress and maximum height of distorted parts.

Synergizing AI and Blockchain: Innovations in Decentralized Carbon Markets for Emission Reduction through Intelligent Carbon Credit Trading

This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.

Multi-Parametric Methodology for the Feasibility Assessment of Alternative-Fuelled Ships

The shipping industry significantly influences global greenhouse gas emissions through a predominant fossil fuel-based fleet. Regulating bodies are continuously developing rules to reduce the shipping carbon footprint. Adopting low-carbon fuels is considered a step toward achieving the Paris Agreement’s goals; however, it represents a significant paradigm shift in ship design. This work aims to illustrate a methodology for the feasibility assessment of alternative-fuelled vessels considering technical, environmental, and economic perspectives. The technical feasibility focuses on ship propulsion, fuel system safety, and design parameters. The environmental impact evaluation is based on the Tank-to-Wake and the Well-to-Wake approaches. The cost assessment is performed by estimating capital and operational expenditures, considering only the modifications required by the new fuel. The methodology addresses new-building and retrofit solutions, and can be used as a decision support tool for selecting the best strategy. A key output of the methodology is the cargo emission footprint, expressed in equivalent carbon dioxide per cargo unit. Using a handysize bulk carrier as a case study, this work points out the effects of using methanol as an alternative fuel, highlighting its impact on market and transport strategies in a sector evolving towards Eco-Delivery services.

Unlocking the path to environmental sustainability: navigating economic policy uncertainty, ICT, and environmental taxes for a sustainable future.

The study aims to gauge the impact of economic policy uncertainty, ICT, and environmental tax on environmental sustainability, which is measured by carbon emission and ecological footprint in a panel of 22 nations from 1997 to 2021. The present study has implemented the advanced panel data estimation techniques, including continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), dynamic seemingly unrelated regressions (DSUR), and nonlinear autoregressive distributed lagged (NARDL) in documenting the elasticities of target variables. Moreover, the directional causality has been tested through the D-H causality test. Study findings documented a positive and statistically significant linkage between EPU and environmental degradation. That is, EPU amplifies the emission of CO2 and ecological instability. The effects of ET and ICT are positively associated with environmental sustainability; that is, ET and ICT control the emission of CO2 and bring ecological improvement. This study contributes to the existing body of literature by conducting a thorough analysis of the relationship between various factors and their impact on environmental degradation. The study emphasizes the significance of every factor in influencing environmental outcomes. It provides policy suggestions to reduce CO2 emissions and promote ecological sustainability. The findings add valuable insights to the ongoing conversation about how to tackle environmental challenges in our constantly evolving world.

Phytoremediation of laundry wastewater using Pistia stratiotes and recycling of spent plant biomass for sustainable biomethane production

This study investigates the decontamination of laundry wastewater (LWW) using Pistia stratiotes via phytoremediation and recyclability of residual macrophyte biomass for enriched bio-CH4 production. Phytoremediation was performed using a hydroponic system. The optimum phytoremediation factors were LWW concentration = 79.4 %, plant density = 4 plants/reactor, and retention time = 12.9 days. This condition offered maximum removal efficiencies of 82.32 ± 3.59 %, 67.19 ± 2.90 %, and 70.34 ± 3.56 % for NH4+, COD, and PO43−, respectively. The residual P. stratiotes biomass (RPSB) was anaerobically co-digested with buffalo dung (BD) at 1:1 (w/w, wet basis) for enriched bio-CH4 production, achieving a maximum cumulative bio-CH4 yield of 696.84 ± 35.82 mL/g-VS. The modified Gompertz kinetic model fitted well to the anaerobic co-digestion experimental data with R2-value = 94.4 %. The integrated phytoremediation/co-digestion scheme succeeded in promoting social well-being through improved water quality, clean energy recovery, and benefiting the environment through carbon footprint emission reduction. These outputs achieved multiple targets of sustainable development goals.