Carbon Gas Emissions Research Articles

Using Microalgae to Convert Brewery Carbon Gas Emissions into Valuable Bioproducts

Using Microalgae to Convert Brewery Carbon Gas Emissions into Valuable Bioproducts

Measuring the extent of trees outside of forests: a nature-based solution for net zero emissions in South Asia

Abstract To reduce emissions of carbon and other greenhouse gases on a pathway that does not overshoot and keeps global average temperature increase to below the 1.5 °C target stipulated by the Paris Agreement, it shall be necessary to rely on nature-based solutions with atmospheric removals. Without activities that create removals by carbon sequestration it will not be possible to balance residual emissions. Policies that focus solely on reducing deforestation will only lower future emissions. On the other hand, activities that include regeneration or regrowth of tree biomass can be used to create net-zero emissions through carbon sequestration and atmospheric removals now. New methods demonstrated here using high resolution remote sensing and deep machine learning enable analyses of carbon stocks of individual trees outside of forests (TOF). Allometric scaling models based on tree crowns at very high spatial resolution (<0.5 m) can map carbon stocks across large landscapes of millions of trees outside of forests. In addition to carbon removals, these landscapes are also important to livelihoods for millions of rural farmers and most TOF activities have the capacity to bring more countries into climate mitigation while also providing adaptation benefits. Here were present a multi-resolution, multi-sensor method that provides a way to measure carbon at the individual tree level in TOF landscapes in India. The results of this analysis show the effectiveness of mapping trees outside of forest across a range of satellite data resolution from 0.5 m to 10 m and for measuring carbon across large landscapes at the individual tree scale.

Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques

One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.

Analisis Sentimen Terhadap Kendaraan Listrik di Indonesia Menggunakan Metode Klasifikasi Naïve Bayes

Indonesians' awareness of environmental issues and carbon emission reduction is driving interest in electric vehicles as eco-friendly transportation. The Indonesian government supports the transition from conventional vehicles to electric vehicles to reduce carbon and greenhouse gas emissions, as seen by the significant increase in the use of electric vehicles from 2021 to 2023. Ministry of Industry data shows this increase, supported by Presidential Regulation No. 79 of 2023. While electric vehicles offer benefits such as zero emissions and high efficiency, challenges such as limited charging infrastructure still exist. Public response is mixed, influenced by environmental awareness, government incentives, and technology. To understand public sentiment, this study uses sentiment analysis from social media X (Twitter) with the Naïve Bayes method. The test results show an accuracy of 84.13%, with a recall of 92.70% positive sentiment and 50.00% negative sentiment. Precision of positive sentiment reaches 88.06%, while negative sentiment is 63.27%. With a total of 2,468 datasets, 2,198 data were predicted as positive sentiment and 298 as negative sentiment. This research aims to provide an understanding of public sentiment towards electric vehicles, helping the government and the automotive industry in designing policies and promotions.

Effects of Different Straw Return Modes on Soil Carbon, Nitrogen, and Greenhouse Gas Emissions in the Semiarid Maize Field.

Returning straw to the field is a crucial practice for enhancing soil quality and increasing efficient use of secondary crop products. However, maize straw has a higher carbon-to-nitrogen ratio compared to other crops. This can result in crop nitrogen loss when the straw is returned to the field. Therefore, it is crucial to explore how different methods of straw return affect maize (Zea mays L.) farmland. In this study, a field experiment was performed with three treatments (I, no straw returned, CK; II, direct straw return, SR; and III, straw returned in deep furrows, ISR) to explore the effects of the different straw return modes on soil carbon and nitrogen content and greenhouse gas emissions. The results indicated that the SR and ISR treatments increased the dissolved organic carbon (DOC) content in the topsoil (0-15 cm). Additionally, the ISR treatment boosted the contents of total nitrogen (TN), nitrate nitrogen (NO3--N), ammonium nitrogen (NH4+-N), dissolved organic nitrogen (DON), and DOC in the subsurface soil (15-30 cm) compared with CK. When it comes to greenhouse gas emissions, the ISR treatment led to an increase in CO2 emissions. However, SR and ISR reduced N2O emissions, with ISR showing a more pronounced reduction. The ISR treatment significantly increased leaf and grain biomass compared to CK and SR. The correlation analyses showed that the yield was positively correlated with soil DOC, and soil greenhouse gas emission was correlated with soil NO3--N. The ISR technology has great potential in sequestering soil organic matter, improving soil fertility, and realizing sustainable agricultural development.

Cost-effective options to reduce emissions from residential firewood heating in south-central Chile

This study simulates various environmental regulation scenarios for fine particulate matter (PM2.5) caused by the massive use of firewood for residential heating. Specifically, an optimization model calibrated with microdata is proposed to minimize the total costs of meeting an aggregate emissions reduction target, considering all urban areas of central-southern Chile where an Atmospheric Decontamination Plan (PDA) has been implemented. The results show that the total compliance cost in each urban area with PDA grows at increasing rates when the targets are more demanding. The cost-effectiveness indicators vary between 1.1 and 1.8 thousand dollars for a target of 20 % but rise to values between 3.7 and 7.6 thousand dollars for a target of 99 %. The lower indicators are observed in areas with greater firewood consumption. The preferred options for households to replace their current firewood heaters are pellet heaters and hot/cold inverter air conditioning units. Surprisingly, thermal improvement on dwellings is not economically attractive. Finally, it is concluded that achieving reduction targets close to 80 % would be possible if only 56 % of households replace their firewood heaters based on the cost-effectiveness criteria, also obtaining co-benefits from lower black carbon and greenhouse gas emissions.

Data-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS

Due to the rapidly developing technologies, fast and practical solutions are offered to the problems encountered in daily life. Metropolitan cities are greatly affected by the ever-increasing population and migrations to big cities, the increase in production with the economy and job opportunities. At this point, with the introduction of smart transportation systems, fast and effortless solutions can be produced by saving time and space. City life can be facilitated by applying more efficient and rational solutions with smart transportation systems. In this study, it is aimed to investigate information about the Intelligent Transportation Systems and one of its applications, park and ride, which has created a significant agenda within the scope of transportation engineering in the recent past, and to provide information about the investments made by examining the application for Istanbul along with its various applications in the world. Some suggestions will be made by emphasizing the importance of the park and Ride smart city application for Istanbul. In conclusion, predictions of P + R application and energy consumption in periods of 1–24 months were made through machine learning. By obtaining energy consumption data thanks to machine learning, carbon gas emissions and its effects on greenhouse gases were also examined. It can be thought that by obtaining energy consumption data for the long term thanks to machine learning, it can make significant contributions to future investments, green environment-green world, and climate change studies.

Assessing the Impact of Land Conversion on Carbon Stocks and GHG Emissions

With the recent thrust to convert forests in Ontario’s Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon and GHG emissions within a study area in Northern Ontario, Canada, during the next two decades under different land management schemes. The study established a framework to conduct simulations with the DNDC model for agricultural lands and the CBM for forested areas. The methodology involves a unique change detection method for models’ land cover and disturbance inputs. The work highlights the improvement in carbon simulation accuracy from better inputs to carbon models. Furthermore, it addresses modalities to ensure fewer uncertainties are introduced while merging data from multiple geospatial data sources. The simulations demonstrated that the carbon sequestration potential in the forests was almost double the soil organic carbon accumulation in the agricultural lands. Validations done for the estimation of carbon sequestered included comparisons of the carbon model outputs from field survey data from 2018–2021. In most sites, the carbon amounts from the computer models compared to those from the field survey, within limits of error. The average uncertainties in GHG emissions ranged from ~0.5% to 12.8%.

Alkali-assisted hydrothermal preparation of artificial humic acid from litchi wood and electrochemical performance of its hydrochar

Utilizing soluble alkalis such as KOH and NaOH, hydrothermal humification technology (HTH) facilitates the conversion of biomass into artificial humic acid-like substances. However, the impact of less soluble bases like Ca(OH)2 has been underexplored. In this study, we investigates the use of Ca(OH)2 as an alternative to KOH in converting waste litchi wood into artificial humic acid (AHA) under hydrothermal conditions, and examined the potential of residual hydrochar in the field of electrochemistry. Under optimal conditions (20 % Ca(OH)2 addition, 225 ℃, and 4 h), 16.77 ± 0.49 % AHA was produced, with a yield and structure comparable to those obtained with KOH. During the hydrothermal process, Ca(OH)2 promotes the formation of small molecules that contribute to AHA formation, which then complex with Ca2+ and incorporate into the solid phase. This process significantly reduces carbon gas emissions by decreasing the loss of soluble sugars and absorbing CO2 (5.22 % with Ca(OH)2vs. 18.24 % with KOH). Additionally, after simple pyrolysis, the hydrochar exhibited a specific capacitance of 88.15 F−1 at 0.5 A g−1 and maintained over 97 % of its capacitance at 10 A g−1 after 10,000 cycles. This study highlights the potential of Ca(OH)2 in the hydrothermal humification and offers a novel approach for the sustainable utilization and high-value application of litchi wood and other biomass.

Evolution mechanism of microbial community structure and metabolic activity in aquatic nutrient-poor sedimentary environments driven by 17β-estradiol pollution.

17β-Estradiol (E2) is a novel micro-pollutant that is widely distributed in aquatic sediments and has a universal toxicological effect on aquatic organisms. However, its ecological impact on aquatic microorganisms is not yet clear. In this study, we designed a simulation system for oligotrophic water deposition in the laboratory, analyzed the impact of different concentrations of E2 pollution on the carbon metabolism activity (carbon gas emission rate) of water microorganisms. Based on high-throughput sequencing results, we revealed the impact of E2 pollution on the community structure succession and metabolic function of bacteria, archaea, and methanogens in the simulated system, explored the impact mechanism of E2 pollution on microbial carbon metabolism in water bodies. Our results suggested that E2 significantly impacts the bacterial and archaeal community rather than the methanogen community, thereby indirectly inhibiting methane production. The achievements will bridge the theoretical gap between estrogen metabolism and carbon metabolism in sedimentary environments and contribute to enriching the ecological toxicology theory of steroid estrogen.

Causality of Environmental Degradation and Economic Growth in Indonesia

Nowdays, the issue of global warming is being widely discussed in all countries, both developed and developing countries, because of its negative impact on the environment. This study aims to develop an integrated analytical framework of causality between energy consumption, carbon emissions, financial development, trade openness, and economic growth in Indonesia, 1990-2021. This study provides research benefits in the form of a set of empirical evidence to assist in developing a series of key strategies that can improve environmental quality through sustainable development. With granger causality analysis and vector autoregression model, this study provides results that high economic growth and energy consumption can cause environmental degradation through increased carbon gas emissions.

An Implementation of Quasi-Newton Algorithm for Fast-charging Lithium-Ion Battery (LIB) Optimization in Electric Vehicle Application

Lithium-Ion Battery (LIB) is still an effective alternative technology in maximizing the efficiency of electric vehicles (EV). The application of EVs has had a significant impact in order to reduce the issue of global problems - reducing carbon gas emissions. The LIB charging mechanism with the fast-charging method is an alternative to the application of EVs on a more massive scale. However, the dynamics of the battery where the battery work function can decrease over time will affect battery performance. In addition, fast-charging efforts at LIB with maximum speed have the impact of increasing the risk of battery temperature and the existence of a larger gap in battery degradation. This paper proposes the application of Limited-Memory-Broyden-Fletcher-Goldfarb-ShannoBound Constrained (L-BFGS-B) algorithm for Lithium-Ion Battery (LIB) fast-charging optimization as an innovative solution approach in dealing with the complex LIB fastcharging dynamics. The results show that this approach is able to improve fast-charging speed and efficiency.

The LTAR Grazing Land Common Experiment at Archbold Biological Station-University of Florida.

The Archbold Biological Station-University of Florida (ABS-UF) Long-term Agroecosystem Research (LTAR) site lies in the heart of south-central Florida, representing subtropical humid grazing lands in North America and globally. Beef producers in this region face challenges due to climate variability, limited nutritive value of forages, poor soils, public concerns about water quality and greenhouse gas emissions, management trade-offs, economic uncertainty, and increasing urban encroachment. The ABS-UF Common Experiment, co-designed with stakeholders, will assess innovative management systems in comparison to prevailing management systems on key indicators of sustainability. Innovative management systems being tested are alternative fire (frequency and spatial extent) and grazing practices (stocking rate and system). The common experiment framework was implemented across a management intensity gradient spanning from native rangeland to cultivated pastures, including embedded wetlands. Issues that have arisen to date include difficulties in implementing prescribed fire and reduced productivity in cultivated pastures associated with innovative management, which led to an adjustment of the experimental treatment. A stakeholder advisory council will codesign future alternative treatments and guide experimental changes in this long-term experiment. Stakeholder engagement efforts revealed research priorities centered on financial strength, carbon (C) and greenhouse gas emissions, and water quality. Stakeholders are also interested in testing emerging technology such as the utility of virtual fencing. Results from ABS-UF provide a unique perspective from subtropical humid grazing lands for continental-scale cross-site synthesis on sustainable agroecosystems across LTAR.

Hydrogen as Fuel of Tomorrow

As well know that carbon-based fossil fuels are a non- renewable resource and these are depleting at an alarming rate. The unmonitored explicit usage of fossil fuels certainly demands for an alternate type of fuel which could replace it in the future because not today but in the near future there will come a time when we will be short on carbon based fuels and need for an alternative type of fuel will the demand of the day. The increasing global demand for sustainable and clean energy sources has finally demanded the exploration of alternative fuels. Hydrogen, with its high energy density and zero carbon and greenhouse gas emissions during combustion, has emerged as a promising fuel of the future. Hydrogen is a potential fuel which can change our fossil fuel based dependency for energy generation to hydrogen economy. The various properties of hydrogen make it excellent alternative and soon to be primary source of fuel in the future. The various techniques of hydrogen production such as electrolysis and coal-based extraction will be discussed as well as the storage methods. Hydrogen is a better fuel in every term. There will be comparative study as well which compares the versatility, the combustion properties, electrical generation efficiency, thermal usage efficiency and also that it’s extraction methods are certainly more eco friendly as compared to other fossils fuels. Researchers are still going to make hydrogen as feasible as fossil fuels are today and soon in the upcoming years, we will see hydrogen economy booming. Keywords—Hydrogen, electrolysis, fuel, efficiency, fuel cell

Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.

SYNCHRONISING SOLAR PHOTOVOLTAIC AND GRID SYSTEMS IN RESIDENTIAL ELECTRICAL ENERGY SUPPLY USING THE DIODE BLOCK METHOD

The Indonesian Government’s programs to reduce carbon gas emissions include reducing fossil energy sources and switching to renewable energy such as solar photovoltaic (PV). Solar PV applications are now common not only in the industry but also in the community. Solar energy is typically strongly influenced by weather in tropical countries like Indonesia. Therefore, it is necessary to propose an energy supply to synchronise two supplies from solar photovoltaic and the state electricity company grid. This concept can use some special inverters. However, due to the high cost, it is rarely applied to residential users. The diode-blocking method aims to increase the stability of the supply system so that the load is not disturbed due to erratic weather conditions. Solar PV is used as the main power source, while the grid becomes a backup resource. The results of the diode block showed that in the synchronisation for 120 minutes of the solar PV, the energy is supplied from the grid, indicating a stable load supply. Thus, synchronising solar PV and the grid can provide continuous electrical power without interruption and optimally save grid electricity in any weather.

Improving Decision Support Tools for Quantifying GHG Emissions from Organic Production Systems

AbstractAs food companies have adopted sustainability metrics to quantify the environmental impacts of supply chains, we need data-driven decision support tools that represent organic management practices. Decision support tools such as COMET-Farm and the Cool Farm Tool have been developed to estimate management practice impacts on soil carbon and greenhouse gas emissions from agricultural systems, but these tools have primarily been developed and used to evaluate conventional management systems. We provide an overview of the research, outreach, and educational activities used to improve these tools to better integrate organic management practices with a focus on cover crops. We summarize our previously published findings from a meta-analysis of the average potential soil carbon benefits of cover crops in temperate climates that identified planting window, biomass production, and soil texture as important predictors of cover crop soil carbon outcomes. We demonstrate how these findings were applied to improvements in process-based models and the parameterization of empirical models. In addition, we solicited feedback from organic community members on the utility of these tools and identified barriers to adoption. Finally, we evaluated both tools as resources for teaching undergraduate students about organic management systems and their impacts on greenhouse gas emissions. While both tools contain a range of customizable, organic amendment options, grazing management options still need further improvement. These improved decision support systems can help identify opportunities for enhancing the sustainability of organic systems.

The Role of Investment Management Institutions in Carbon Trading in Industrial Areas in Indonesia

Life always evolves with the times, including the country. Indonesia is developing by shifting from an agrarian to an industrial sector that is growing rapidly. The existence and development of the industry are instrumental in economic growth in Indonesia, but as the industry develops, the greenhouse gas emissions produced by Indonesia are also increasing. Therefore, a solution is needed to lower greenhouse gas emissions, and one of the solutions is carbon trading. Besides reducing carbon gas emissions, carbon trading can also encourage the domestic carbon market through its buying and selling system, opening up investment opportunities. In relation to investment, since the inauguration of the Job Creation Law, there has been an institution specifically regulating investment, namely the Investment Management Institution, representing the central government with significant authority and privileges. This study aims to analyze how the regulation of carbon trading has changed after the inauguration of the Job Creation regulation as a law and the position of the Indonesia Investment Authority in carbon trading within the industrial sector in Indonesia.

Recent advances in separator design for lithium metal batteries without dendrite formation: Implications for electric vehicles

Electric vehicle (EV) technology addresses the challenge of reducing carbon and greenhouse gas emissions. The power battery, which serves as the energy source for EVs, directly impacts their driving range, maximum speed, and service life. Considering the high energy density requirements for future EVs, lithium metal anodes possess several advantages such as high theoretical capacity, high energy and power density, and low electrochemical reduction potential which enable them to be a promising material for next-generation batteries. However, lithium metal anodes suffer from short cycle life and safety concerns due to the formation of dendritic and moss-like metal deposits that impede battery performance and reliability. This review will feature the recent advancement of functional separators to tackle these challenges. Firstly, this review presents a comprehensive review of the growth mechanism of lithium dendrites and delineates the underlying processes leading to battery failure. This aims to deepen understanding, which serves as a fundamental basis for classifying separators. Then, according to the growth of lithium dendrites and the failure process of lithium metal batteries, namely lithium-ion nucleation, growth of lithium dendrites, penetration of lithium dendrites into the separator, thermal runaway and even failure of the battery, four types of functional separators for different stages are proposed. The functions of these types of separators are to prevent the nucleation of lithium ions and regulate the uniform deposition of lithium ions, detect and eliminate dendrites, increase the mechanical strength of the separator and enhance the thermal stability and flame-retardancy of the separators, respectively. Finally, the recent advances of the above strategies are reviewed and discussed, existing critical problems are identified, and the future perspective of functional separators for the safety of lithium metal batteries is also discussed.

Efektivitas Pajak Karbon sebagai Strategi Mitigasi Perubahan Iklim Perspektif Hakim Pajak di Indonesia

This research analyzes the passing of regulations imposing a tax on carbon, discusses the effectiveness of implementing a carbon tax as well as the knowledge and opinions of tax judges regarding these regulations. Carbon emissions have a big influence on climate change, the more carbon emissions increase, the worse the environment becomes and is detrimental to all creatures on earth, therefore it is important to immediately reduce carbon emissions and control climate change. Seeing that Finland, Sweden and other countries have succeeded in reducing carbon gas emissions by implementing a carbon tax, the government decided to implement a carbon tax in Indonesia in the hope that it will reach the point of net zero emissions in accordance with the agreement in the Paris Agreement. In implementing a policy there must be regulations, for carbon taxes, the law enforcement will be enforced by the tax court. Tax judges must understand about Carbon Tax. By conducting this research, it is known that the differences in opinion of each judge mean that not all judges agree with the implementation of the carbon tax.