Flue gas treatment by power-to-gas integration for methane and ammonia synthesis – Energy and environmental analysis

In this research, U.S. manufacturing activities' life cycle-based carbon and energy footprint impacts have been quantified, taking international trade linkages with the rest of the world into account. The U.S economy has been integrated into a multi-region input-output (MRIO) life cycle assessment framework where total of 40 major economies, including the USA, China, Russia, and others, plus the rest of the world (ROW) were modelled to assess global energy and carbon footprint impacts. Each country's economy is assumed to compromise 35 major industries based on the WIOD database classification. A total of 1435 (41 × 35 = 1435) industries has therefore been taken to represent the global structure of the world economy. The novelty of the approach is that the MRIO model has been developed in a stochastic fashion, plus global trade-linked uncertainties have also been taken into consideration. Top carbon emitting and energy consumer industries and countries have been analysed using data analytics and statistical modelling methods. The results show that the USA is the largest contributor to the total carbon footprint (CFP) and the total energy footprint (EFP) with 81.73% and 84%, respectively. Moreover, the agriculture/hunting forestry/fishing sector and the electricity/gas/water supply sectors dominate the overall U.S. carbon footprint, contributing 22% and 21.28%, respectively. The coke/refined petroleum/nuclear fuel sector has the largest share of the total energy footprint, with 47.9% of the total impacts.

Effects of soluble and particulate substrate on the carbon and energy footprint of wastewater treatment processes

Carbon and energy footprints of high-value food trays and lidding films made of common bio-based and conventional packaging materials

Carbon and energy footprint analysis of Hungarian transportation activities using a multi-region input-output model

The environmental sustainability of bitcoin is making waves in the empirical literature, yet, no study has thus far examined the financial determinants of bitcoin energy consumption and carbon footprint. Here, we use novel estimation methods comprising dynamic ARDL simulations and general-to-specific VAR to examine steady-state effects, cumulative impulse-response, and counterfactual shocks of bitcoin trade volume on bitcoin energy and carbon footprint to ensure genuine causal inferences. We observed an increase in bitcoin trade volume spur both carbon and energy footprint by 24% in the long-run, whereas a dynamic shock in trade volume escalates bitcoin energy and carbon footprint by 46.54%.

Rice cultivation, as a cornerstone of global food security, holds significant environmental implications due to its carbon, water, and energy footprints. Energy, carbon and water footprint assessments can be powerful tools to guide sustainable food production systems. Due to higher water losses in conventional rice culture, the irrigation water footprint associated with rice cultivation increases, thereby elevating the energy and carbon footprint. Improper use of resources like fertilizers, pesticides, labour and fuel may lead to higher energy consumption. Several alternative rice production systems like Direct Seeded Rice (DSR), Alternate Wetting and Drying (AWD), System of Rice Intensification (SRI) as well as better nutrient management practices have been developed and refined to reduce energy, carbon and water footprint associated with rice cultivation. This review presents a comprehensive analysis of the intricate interplay between these footprints, highlighting potential trade-offs and synergies that warrant attention within the context of rice cultivation. Moreover, this review discusses in detail the significance of selecting appropriate rice cultivation techniques, such as direct seeded rice, SRI and alternate wetting and drying suitable for different ecologies in comparison to transplanted method of rice cultivation.

Energy-carbon-water footprint of sugarcane bioenergy: A district-level life cycle assessment in the state of Maharashtra, India

Energy, carbon and water footprints on agricultural machinery

“New normal” characteristics show in China's energy footprints and carbon footprints

Carbon and energy footprints of electric delivery trucks: A hybrid multi-regional input-output life cycle assessment

It is urgent for the wastewater treatment sector to respond to global climate change. Although studies related to the water–energy–carbon (WEC) nexus have been widely conducted, the application of the coupling coordination indicator is still limited in the wastewater treatment sector. This study fills such a research gap by linking water footprint (WF), energy footprint (EF), and carbon footprint (CF) together and testing these indicators in 140 wastewater treatment plants (WWTPs) in Shandong province, China. Both the EF and CF of these WWTPs were calculated by conducting hybrid life cycle assessments, while WF was calculated by using a WF method. The results show that gray WF generated from 1 m3 of wastewater ranged from 9.58 to 12.90 m3, while EF generated from 1 m3 of wastewater ranged from 9.42 × 10−2 to 0.22 kg oil eq and CF generated from 1 m3 of wastewater ranged from 0.58 to 1.27 kg CO2 eq. Also, the total WF, EF, and CF of these WWTPs in Shandong were 4.26 × 1010 m3, 5.32 × 108 kg oil, and 3.35 × 109 CO2 eq in 2021, respectively. Key factors contributing to the overall greenhouse gas (GHG) emissions were the on-site GHG emissions and off-site electricity-based GHG emissions. Meanwhile, total nitrogen was the dominant contributor to the gray WF. In addition, the coupling coordination indicators of WF, EF, and CF ranged from 0.7571 to 0.9293. Finally, this study proposed several policy recommendations to improve the overall sustainability of this wastewater treatment sector by considering local realities, including adopting multi-dimensional indicators, decarbonizing current electricity grids, promoting the utilization of renewable energy, and initiating various capacity building efforts.

Energy resource consumption, and particularly consumption of electrical energy, influences the increase of greenhouse gas – that is, carbon dioxide in the atmosphere – which results in global warming. One of the strategies to overcome this issue is to implement energy saving sustainability practices. Thus, the purpose of this article is to identify the level of carbon foot print from electricity consumption as well as the relationship and effects of sustainability knowledge, green knowledge, and sustainability practices towards electricity consumption (carbon footprint analysis) in primary schools. This study involved 423 students from ten primary schools within the district of Batang Padang, Perak, Malaysia. A cluster sampling method was used during the first stage of selecting the schools and groups of students. Then, the respondents were chosen by a simple random method from among students between the ages of 10 to 12 years old who were able to read. Results show that the carbon footprint emissions in the studied school areas are still at a low level. The relationship analysis shows a weak significant correlation between (i) sustainability practices and sustainability knowledge, (ii) sustainability practices and carbon footprint, and (iii) green environment and carbon footprint analysis. Similarly, the study shows a moderate relationship between 3R practice variables and carbon footprint analysis in schools. Regression analysis shows that sustainability practices contribute to carbon footprint when compared with sustainability knowledge and green environment. Thus, this shows that sustainability knowledge has a direct relationship with sustainability practices and electricity consumption. The results clearly prove that primary school students show positive elements of sustainability practices. These findings can help schools to identify weak variables, such as green practices knowledge, that need to be improved in order to reduce carbon emissions in schools.

A systematic analysis of green-house gases emission (carbon footprint) and primary energy consumption (energy footprint) of prefabricated industrial buildings during their entire life cycle is presented. The life cycle assessment (LCA) study was performed in a cradle-to grave approach: site-specific data from an Italian company, directly involved in all the phases from raw material manufacturing to in-situ assembly, were used to analyze the impacts as a function of different design choices. Four buildings were analyzed and results were used to setup a parameterized model that was used to study the impacts of industrial prefabricated buildings over the input parameter space. The model vs. data agreement is within 4% for both carbon and energy footprint. The functional unit is 1 m3 of prefabricated building, considering a 50-year lifetime. The results of the four buildings decrease from 144.6 kgCO2eq/m3 and 649.5 kWh/m3 down to 123.5 kgCO2eq/m3 and 556.8 kWh/m3 as the building floor area increases from 1048 m2 to 21,910 m2. The use phase accounts for the major impact (approximate 76%). It is found that the carbon footprint is proportional to the energy footprint, the proportional factor being 0.222 kgCO2eq/kWh within 0.5% accuracy. Finally, a systematic study of the sensitivity of input parameters (insulation, lifetime, foundation type) is presented.

Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint.

The product environmental assessment quantifies possible further improvements during the life cycle of a product, from flue gas extraction, through adsorption, carbonation, rinsing, activation and utilization. In this study, the carbon footprint, water footprint and energy footprint of magnesium carbonate product (MCC) has been estimated, following the life cycle assessment method. Though the analysis, the species of alkali, efficiency of alkali using, seawater desalination technology and substitution ratio have strongly influenced on the overall CO2 footprint, water footprint and energy footprint balance of the CO2 mineralization process. Several scenarios using ammonia or waste alkali would produce a negative carbon footprint MCC material, to substitute plasterboard. Energy consumption for scenarios with 100% substitution is -2 MJ to 14 MJ per kg of final MCC for scenarios using waste alkali.