Carbon Footprint Model Research Articles

Towards Application Centric Carbon Emission Management

Carbon emissions are due to application execution on a target system (operational emissions) and the production, transportation, and disposal of the system itself (embodied emissions). This paper investigates the impacts of different resource configurations in terms of available DRAM memory on the overall carbon emission of individual application executions. We first propose an application-centric carbon footprint model that considers DRAM and CPU. We then study the model using a widely-used key-value store (RocksDB) and Graph500 applications. The results for RocksDB indicate that the minimal emission configuration is application dependent and can lead to significant emission reductions compared to application-oblivious configurations that use higher DRAM capacity without improving performance. For some applications, small performance degradation may lead to substantial additional emission reductions.

Carbon Safety Assessment based on Tourism Carbon Footprint and Tourism Carbon Capacity Model in Gansu Province

Based on Carbon Footprint model, we calculated the tourism carbon footprint and tourism carbon capacity of Gansu Province in 2019 and 2020. The results indicate that: 1. Tourism carbon deficit is in 2019 and in 2020, which indicates a negative impact on ecosystem. 2.The number of tourists in 2020 is lower than 2019. Therefore, the tourism carbon footprint in 2020 was reduced compared to 2019. 3.From the information in this study, indicate the tourism industry in Gansu Province is unsustainable.

Exploring the Synergistic Governance of Water Conservation and Carbon Reduction in Thermal Power Plants

In the industrial sector, thermal power plants not only use a lot of water, but also cause more carbon emissions. In today's era of carbon neutral carbon peaking and water scarcity in China, thermal power plants should not only do a good job of water conservation, but also reduce carbon emissions. But water-saving behaviours mostly consume more energy and cause more carbon emissions. This study explores how thermal power plants can save water and reduce carbon synergistically, and introduces a carbon footprint model to study the synergistic benefits of water saving and carbon reduction in thermal power plants, and takes a thermal power plant in the south as a typical case. Considering the environmental benefits of water conservation from a more systematic perspective helps to promote the synergistic effect of water conservation and carbon reduction.

An urban emergy footprint: Comparing supply- and use-extended input-output models for the case of Vienna, Austria

Urban activities currently consume 75% of global final energy demand, which is expected to increase given absolute and relative population growth in cities. Assessments of both producer (upstream) and consumer (downstream) ecological and socioeconomic impacts of urban inter-industry exchanges are needed to reduce energy consumption and resource use behind the industrial footprints of cities. Environmental extensions in the input-output analysis are designed from the user side perspective, focusing only on commercial energy supply and use. This study introduced emergy-evaluated supply-extended and use-extended carbon footprint models for Vienna and compared their empirical and conceptual implications. Emergy-evaluated footprints of Vienna's urban consumption were estimated by combining industrial and systems ecology approaches as per the research question, based on previous investigations of GHG emissions and energy supply- and use-extensions. Results showed that the ranking of footprints of final product categories is sensitive to the evaluation method, with products of extractive and manufacturing industries differing by more than 10% depending on whether emergy or carbon evaluation is chosen. The emergy-based comparison further reveals that for products of extractive industries, the difference between use and supply extension results can be more than 20% as opposed to carbon-based comparison with the difference between supply and use extension results for services not even amounting to 5%. Future studies could address the over-estimation of direct energy supply to the economy, under-estimation of product and service, inconsistency in standard use-extension design, and challenges in assembling emergy-evaluated supply and use extensions. Fundings are relevant for unified responsibility assessment of upstream and downstream sectors without prioritising structural features.

Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption

The carbon footprint (CF) linked to electricity consumption in buildings has become a significant environmental issue because of its significant role in greenhouse gas emissions. This study seeks to assess and examine the CF of electricity consumption in buildings across various building types. Additionally, this paper aims to investigate the impact of the COVID-19 pandemic on the CF of buildings. The investigation involves a comparative analysis between the CF values observed and predicted during the years affected by the pandemic. Additionally, the study evaluates the influence of the pandemic on the accuracy of CF model predictions by employing three distinct machine-learning models. Spatial analyses were conducted to identify clustering patterns of CF and identify areas of both high and low CF concentrations within the study area. The findings demonstrate significant disparities in the CF of electricity consumption across distinct building types, with residential buildings emerging as the largest contributors to carbon emissions. Moreover, the pandemic has had a notable impact on CF patterns, leading to alterations in the areas identified as hotspots and cold spots during the pandemic years compared to the pre-pandemic period, based on building types.

Developing a carbon footprint model and environmental impact analysis of municipal solid waste transportation: A case study of Tehran, Iran

ABSTRACT The greenhouse gas emitted due to transportation is the third greatest emitter globally, and its impact has become a threat to the environment, public health, and economic development. Waste transportation is excluded in studies of waste management despite its significant environmental impacts such as global warming and human toxicity. The objective of this study is to develop a quantification model to estimate the carbon footprint of waste transportation and environmental impact assessments in three categories applied in Tehran using IPCC guidelines. In Tehran, light and heavy vehicles ran on diesel fuel. Data on fuel and waste characteristics were provided by Tehran’s department of transportation and municipality, respectively. In this study, transport-related emissions are 8.47 k tonCO2eq/y, and the carbon footprint of waste transportation is 93.57 g of CO2 eq per ton of waste transported (t.km), which is relevant to three main parameters: the amount of waste transported annually, the freight shipped from the temporary station to the disposal landfill site, and fossil fuels consumed. Also, an environmental impact assessment in three categories – human health (global warming, abiotic depletion, and ozone layer depletion), resources (fossil fuels), and ecosystem quality (acidification and eutrophication) – using SimaPro, a Life Cycle Assessment (LCA) tool is presented. Global warming (3.49 kg CO2 eq/t MSW), human toxicity (0.95 kg 1,4-DB eq/t MSW), and freshwater aquatic eco-toxicity (0.04 kg 1,4-DB eq/t MSW) have the greatest impact among categories. Sensitivity analysis of the effective parameters allows us to conclude one of the potential implications of this study would be the introduction of natural gas or biogas-based trucks replacing diesel fuel vehicles to improve air quality and mitigate the greenhouse gas emission. Implications: This paper addresses the significant issue of global warming, particularly in Iran, a developing country that ranks among the top contributors to greenhouse gas emissions. The study emphasizes the importance of evaluating emissions across various sectors such as electricity, waste, etc., Specifically, in this paper we focus on developing a model to quantify the environmental impact resulting from the combustion of fossil fuels in vehicles, focus on the metropolitan city of Tehran as a case study. By examining the waste transportation process, we aim to provide decision-makers with effective strategies to mitigate the environmental consequences. In this paper, we develop a simple quantification term of Carbon Footprint to calculate total greenhouse gas emission of waste transportation process. Carbon Footprint is a fraction which, its numerator is total greenhouse gas emission and its denominator is total waste transported in traveled distance. Effective parameters have been investigated and based on parameters and emission factors taken out of IPPC, the carbon footprint model have been developed. The total greenhouse gas emission of this study and the carbon footprint has estimated at 8.47 k tonCO2eq/y and 93.57 g CO2eq/t.km respectively. Furthermore, the paper explores additional environmental impacts beyond global warming, including abiotic depletion, ozone layer depletion, acidification, eutrophication, human toxicity, photochemical oxidation, and freshwater aquatic eco-toxicity. Using SimaPro software these eight impact categories have been estimated. in this study we identify fossil fuel consumption, traveled distance, and mass transported are the primary parameters influencing greenhouse gas emissions and the carbon footprint. To reduce emissions in the waste transportation system, we suggest promoting renewable biofuels, highlighting Iran as a suitable candidate due to its high percentage of biodegradable material in municipal solid waste. Additionally, the study assesses nonrenewable energy and mineral extraction using the IMPACT 2002+ V2.15/IMPACT 2002+ method, revealing that global warming (100 years), human toxicity (100 years), freshwater aquatic eco-toxicity, nonrenewable energy, and mineral extraction have the most significant impacts on the municipal solid waste transportation system. Overall, this research underscores the need for quantifying environmental impacts and recommends strategies to mitigate them in waste transportation processes, particularly in developing countries like Iran.

Determining the optimal biomass-gasification-based fuel cell trigeneration system in exergy-based cost and carbon footprint method considering energy level

The accompanying exergy-cost-carbon nexus in trigeneration systems brings challenges to distinguishing the formation mechanisms of costs and carbon footprints of power, cold, and heat. In this paper, a fuel cell trigeneration system based on biomass gasification is constructed, and seven optional system configurations are designed according to the waste heat utilization methods including organic Rankine cycle, absorption cycle, and heat exchange. A novel exergy-based cost and carbon footprint model based on the energy levels of energy streams is proposed to obtain the allocation rules of multiple products of the trigeneration system, in which carbon cost with exergy cost is integrated into the exergo-economic method. Using this method, the coupling relationships of exergy-cost-carbon are illustrated and the unit exergy-carbon costs and carbon footprints of electricity, cold, and heat are determined. To select the optimum configuration, a multi-criteria decision-making method, considering energetic, economic, and environmental aspects, is employed to calculate their compositive scores, in which the integrated weights of indicators combine the experts’ knowledge and opinions with the entropy information of numerical performances of alternative trigeneration configurations. The sensitivities of exergy-cost and exergy-carbon performances on operation time, carbon footprint of biomass, and interest rate are implemented to discuss their influences. The consideration of energy level increases the cost and carbon footprint of power from fuel cells by around 30%. The carbon cost of system power, chilled water, and hot water account for 5.18%, 6.89%, and 5.25% of their corresponding total exergy-carbon costs. The integration of biomass gasification, fuel cell, gas turbine, and heat exchanger achieves the best comprehensive performance.

A multisource data‐driven approach for carbon footprint analysis of remanufacturing systems

AbstractThe remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems.

Decoupling effect and driving factors of carbon footprint in megacity Wuhan, Central China

BackgroundChina’s 35 largest cities, including Wuhan, are inhabited by approximately 18% of the Chinese population, and account for 40% energy consumption and greenhouse gas emissions. Wuhan is the only sub-provincial city in Central China and, as the eighth largest economy nationwide, has experienced a notable increase in energy consumption. However, major knowledge gaps exist in understanding the nexus of economic development and carbon footprint and their drivers in Wuhan.MethodsWe studied Wuhan for the evolutionary characteristics of its carbon footprint (CF), the decoupling relationship between economic development and CF, and the essential drivers of CF. Based on the CF model, we quantified the dynamic trends of CF, carbon carrying capacity, carbon deficit, and carbon deficit pressure index from 2001 to 2020. We also adopted a decoupling model to clarify the coupled dynamics among total CF, its accounts, and economic development. We used the partial least squares method to analyze the influencing factors of Wuhan’s CF and determine the main drivers.ResultsThe CF of Wuhan increased from 36.01 million t CO2eq in 2001 to 70.07 million t CO2eq in 2020, a growth rate of 94.61%, which was much faster than that of the carbon carrying capacity. The energy consumption account (84.15%) far exceeded other accounts, and was mostly contributed by raw coal, coke, and crude oil. The carbon deficit pressure index fluctuated in the range of 8.44–6.74%, indicating that Wuhan was in the relief zone and the mild enhancement zone during 2001–2020. Around the same time, Wuhan was in a transition stage between weak and strong CF decoupling and economic growth. The main driving factor of CF growth was the urban per capita residential building area, while energy consumption per unit of GDP was responsible for the CF decline.ConclusionsOur research highlights the interaction of urban ecological and economic systems, and that Wuhan’s CF changes were mainly affected by four factors: city size, economic development, social consumption, and technological progress. The findings are of realistic significance in promoting low-carbon urban development and improving the city’s sustainability, and the related policies can offer an excellent benchmark for other cities with similar challenges.

Modelling carbon footprint, emission, and sequestration of kenaf cultivation and fiber processing and utilization into automotive components

This study aimed to model carbon footprint, carbon dioxide sequestration, and emissions from kenaf cultivation as well as utilization of core and bast as biomaterials in the automotive industries and their environmental implications. It also considers the selection of suitable processing methods to improve the environmental performance of automotive parts. The data were obtained from three areas, and each area was divided into three cultivated sub areas for the carbon footprint model. Data regarding the conversion of kenaf to fibre when using kenaf high decorticator machines were provided from Kenaf Processing and Marketing Centre (CMPC) in Malaysia. The results showed that the total estimated quantity of carbon footprint of kenaf cultivation in the studied areas as well as the farms was about 0.750 tonne CO2/tonne. In addition, the total estimated quantity of carbon sequestration of automotive components in the studied states as well as the district was approximately 180,000 tonne CO2/tonne/hectare/year in addition to carbon sequestered by soil in hectare of cultivated kenaf. The practices of kenaf cultivation, processing, and utilization into automotive components is essential for the environment and will assist in the mitigation of climate change risks.

Sustainability measurement in a logistics transportation company

We present a preliminary study to the Decision Support System for Sustainable Logistics Transportation, using as case study, the Portuguese logistics transportation company Santos e Vale. In the last five years, it has reduced its fleet carbon footprint by 20%. Our research presents a systematic literature review of indicators, carbon footprint models, and reduction measures associated to the sector, using the PRISMA method. Moreover, we considered the application of methodologies and analytical models to measure the logistic transportation carbon footprint, such as in Greenhouse Gas Protocol, ISO 14064 and EN 16258. Our aim is twofold:(1) provision of standard methodologies and analytical models targeting the reduction of carbon footprint in logistics transportation, to a more sustainable operation;(2) future development of a Decision Support System, to assist the monitoring over time.

Carbon footprint model and low–carbon pathway of inland shipping based on micro–macro analysis

China's inland shipping industry is under urgent pressure to reduce its emissions. Identifying carbon emission sources and estimating carbon emissions from multiple perspectives form the basis for scientifically proposing low-carbon pathways for inland shipping. Carbon footprint assessment models for inland ships were developed from both micro and macro perspectives to assess the carbon emissions of individual ships and fleets. An inland diesel-powered ship was taken as an example to analyse the carbon footprint of LNG (liquid nature gas) and methanol as alternative fuels, and the uncertainty and economy were analysed. The carbon emissions from China's inland shipping in 2019–2020 were studied using an improved turnover method, and the carbon intensities of inland passenger and freight transportation were analysed. Finally, low-carbon pathways for inland shipping were proposed. The results show that the CO2-eq of the LNG scheme was 6.83% and 53.99% less than that of diesel and coal-based methanol, respectively, making it a suitable fuel alternative for ships. CO2 emissions from inland shipping reached 20.924 million tons in 2019, followed by a 0.98% reduction by 2020. The carbon intensity of freight transportation was less than that of passenger transportation; however, the energy consumption carbon intensities of both were approximately equal.

Impacts of shared mobility on vehicle lifetimes and on the carbon footprint of electric vehicles

Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles—the additional distance traveled to pick up passengers—may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

Coupling life cycle assessment and global sensitivity analysis to evaluate the uncertainty and key processes associated with carbon footprint of rice production in Eastern China.

An accurate and objective evaluation of the carbon footprint of rice production is crucial for mitigating greenhouse gas (GHG) emissions from global food production. Sensitivity and uncertainty analysis of the carbon footprint evaluation model can help improve the efficiency and credibility of the evaluation. In this study, we combined a farm-scaled model consisting of widely used carbon footprint evaluation methods with a typical East Asian rice production system comprising two fertilization strategies. Furthermore, we used Morris and Sobol' global sensitivity analysis methods to evaluate the sensitivity and uncertainty of the carbon footprint model. Results showed that the carbon footprint evaluation model exhibits a certain nonlinearity, and it is the most sensitive to model parameters related to CH4 emission estimation, including EFc (baseline emission factor for continuously flooded fields without organic amendments), SFw (scaling factor to account for the differences in water regime during the cultivation period), and t (cultivation period of rice), but is not sensitive to activity data and its emission factors. The main sensitivity parameters of the model obtained using the two global sensitivity methods were essentially identical. Uncertainty analysis showed that the carbon footprint of organic rice production was 1271.7 ± 388.5kg CO2eq t-1 year-1 (95% confidence interval was 663.9-2175.8 kg CO2eq t-1 year-1), which was significantly higher than that of conventional rice production (926.0 ± 213.6kg CO2eq t-1 year-1, 95% confidence interval 582.5-1429.7kg CO2eq t-1 year-1) (p<0.0001). The carbon footprint for organic rice had a wider range and greater uncertainty, mainly due to the greater impact of CH4 emissions (79.8% for organic rice versus 53.8% for conventional rice). EFc , t, Y, and SFw contributed the most to the uncertainty of carbon footprint of the two rice production modes, wherein their correlation coefficients were between 0.34 and 0.55 (p<0.01). The analytical framework presented in this study provides insights into future on-farm advice related to GHG mitigation of rice production.

Multi-objective optimization of a small-scale solar-hot-spring-geothermal brackish water direct contact membrane distillation system

A small-scale solar-hot-spring-geothermal brackish water membrane distillation (MD) system is proposed by combining hot-spring and solar components to reduce fossil energy consumptions. A performance parameter framework and carbon footprint model is developed to perform an analysis on whole life cycle of the proposed system. The results show that the solar subsystem improved heat recovery with the water generation ratio increased by 22–42 %. As the feed-side inlet temperature increases, the total Specific Energy Consumption (SEC) contribution rate of renewable geothermal and solar energies increases from 12.34 % to 20.45 %. The cold and hot sources are responsible for the largest exergic losses at 1.79 kJ/S and 0.48 kJ/S. The unit exergy cost of the solar subsystem is the highest of 0.037 $/h in terms of economic benefits. One of effective ways to improve the carbon emission reduction rate is to develop a cogeneration process for the water purification. A multi-software co-simulation methodology is proposed to explore innovative MD systems of optimal configurations, improve desalination efficiency and reduce the energy consumptions. The Non-dominated Sorting Genetic Algorithm (NSGAII) and improved NSGAII genetic algorithms (NSGAII-A and NSGAII-B) are utilized for multi-objective optimization of the MD system. Finally, the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method based on entropy weight is employed to obtain the Pareto frontier solution set. It is indicated that NSGAII-B based on the four objective-optimal scheme is the best for the Multi-objective optimization of the MD system.

A dynamic calculation model of the carbon footprint in the life cycle of hospital building: a case study in China

PurposeThe calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.Design/methodology/approachOn the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.FindingsThe results applied to the case show that the CO2 emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO2 emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO2 reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.Originality/valueThis research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Comparison of carbon footprint and water scarcity footprint of milk protein produced by cellular agriculture and the dairy industry

PurposeThis paper studies the carbon footprint and water scarcity footprint (WSF) of a milk protein, beta-lactoglobulin, produced by cellular agriculture and compares this to extracted dairy protein from milk. The calculations of the microbially produced proteins were based on a model of a hypothetical industrial-scale facility. The purpose of the study is to examine the role relative to dairy of microbially produced milk proteins in meeting future demand for more sustainably produced protein of high nutritional quality.MethodsThe evaluated process considers beta-lactoglobulin production in bioreactor cultivation with filamentous fungi T. reesei and downstream processing for product purification. The model considers four production scenarios in four different locations (New Zealand, Germany, US, and Australia) with a cradle-to-gate system boundary. The scenarios consider different sources of carbon (glucose and sucrose), different options for the fungal biomass treatment (waste or animal feed) and for the purification of the product. Allocation to biomass was avoided by considering it substituting the production of general protein feed. The carbon footprint and WSF (based on AWaRe factors) modelling is compared to calculations and actual data on extracted dairy protein production in NZ. The uncertainties of modelled process were addressed with a sensitivity analysis.Results and discussionThe carbon footprint of microbially produced protein varied depending on the location (energy profile) and source of carbon used. The lowest carbon footprint (5.5 t CO2e/t protein) was found with sucrose-based production in NZ and the highest (17.6 t CO2e/t protein) in Australia with the glucose and chromatography step. The WSF results varied between 88–5030 m3 world eq./t protein, depending on the location, type of sugar and purification method used. The avoided feed production had a bigger impact on the WSF than on the carbon footprint. Both footprints were sensitive to process parameters of final titre and protein yield from sugar. The results for milk protein were of similar magnitude, c.10 t CO2e/t protein and 290–11,300 m3 world eq./t protein.ConclusionsThe environmental impacts of microbially produced milk protein were of the same magnitude as for extracted dairy protein. The main contributions were sugar and electricity production. The carbon footprints of proteins produced by cellular agriculture have potential for significant reduction when renewable energy and more sustainable carbon sources are used and combined with evolving knowledge and technology in microbial production. Similarly, the carbon footprint of milk proteins can potentially be reduced through methane reduction technologies.

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner.

Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH4+ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH4+ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).

Carbon footprint modelling of national health systems: Opportunities, challenges and recommendations.

Health care presents significant contributions towards climate change. An awareness of a health systems carbon footprint provides a quantification of its environmental impact, an understanding of carbon intensive areas to target with reduction measures and a means of mapping trends in emissions over time. Attempts at calculating the carbon footprint of national health systems are few, predominantly of developed nations, and are limited by data availability and methodological inadequacies. There is a need to mobilise countries to understand the role of health care in contributing towards climate change and for them to start engaging in ongoing calculations of their national health system carbon footprints. There is also a need to improve data availability and information systems to allow for such calculations, especially in developing countries where there may be differences in carbon hotspots. Finally, there is a need for continued improvements in the carbon footprint modelling methodology of health systems as data collection and available emission factors, especially of health care specific products and supply chain emissions, improves. Health systems need to join the global fight against climate change.

Study on carbon footprint model and its parameter optimisation of wave soldering process based on response surface method

Study on carbon footprint model and its parameter optimisation of wave soldering process based on response surface method