Increase In CO2 Emissions Research Articles

The impact of globalisation on economic systems and social structures

The study's relevance is stipulated by the need to understand the impact of globalisation on economic systems and social structures in the context of countries' uneven development. This study aims to analyse the effect of globalisation on economic systems and social structures in ten countries with different levels of economic growth. The study uses three empirical methods: regression analysis, comparative data analysis, and correlation analysis to identify the relationships between economic indicators. The statistical data were obtained from the World Bank, IMF, and national statistical agencies. Regression analysis has shown that every 1% increase in the share of high-tech exports in the GDP of developed countries contributes to a 0.5% increase in their economic growth. In developed countries like the United States and Germany, high innovation and urbanisation stimulate sustainable economic development and reduce CO₂ emissions by 12% per capita. In developing countries like Ukraine and Kenya, insufficient innovation and weak urbanisation lead to limited economic progress and an 8% increase in CO₂ emissions. The study's findings highlighted the importance of investing in innovation and urban infrastructure for sustainable development. For example, introducing high-tech export support programmes in developing countries can increase economic efficiency and reduce environmental impact. Policy strategies should focus on fostering innovation clusters and improving urban infrastructure to balance social and environmental challenges. The study provides an essential basis for strategic decision-making aimed at achieving sustainable development, increasing competitiveness and reducing the environmental burden on a global scale.

Catalytic Hydrogenolysis of Lignin into Serviceable Products.

ConspectusLignin, a major component of lignocellulosic biomass, accounts for nearly 30% of organic carbon on Earth, making it the most abundant renewable source of aromatic carbon. The valorization of lignin beyond low-value heat and power has been one of the foremost challenges for a long time. On the other hand, aromatic compounds, constituting a substantial segment of the chemical industry and projected to reach a market value of $382 billion by 2030, are predominantly derived from fossil resources, contributing to increased CO2 emissions. Integrating lignin into the aromatic chemical supply chain will offer a promising strategy to reduce the carbon footprint and boost the economic viability of biorefineries. Thus, depolymerizing lignin biopolymers into aromatic chemicals suitable for downstream processing is an important starting point for its valorization. However, owing to lignin's complexity and heterogeneity, achieving efficient and selective depolymerization that yields desirable, isolable aromatic monomers remains a significant scientific challenge.The structure of lignins varies significantly in terms of subunits and linkages across plant species, leading to considerable differences in their reactivity, in the distribution of resulting monomers, and in their subsequent utilization. In this context, this Account highlights our recent studies on the catalytic hydrogenolysis of lignin into serviceable products for preparing valuable materials, fuels, and chemicals. First, we designed a series of catalytic systems for lignin hydrogenolysis specifically tailored to the structural features of lignin from wood, grass, and certain seed coats. To reduce reliance on expensive commercial catalysts like Pd/C, Ru/C, and Pt/C, we advanced heterogeneous metal catalysts by shifting from high-loaded nanostructured metals to low-loaded, atomically dispersed metals and replacing precious metals with nonprecious alternatives. This approach significantly reduces the cost of catalysts, enhances their atomic economy, and improves their catalytic activity and/or selectivity. Then, using the developed catalysts, phenolic monomers tethering a distinct side chain were selectively generated from the hydrogenolysis of lignin (from various plants), achieving yields close to the theoretical maximum. The high selectivity allowed the separation and purification of monomeric phenols from lignin reaction mixtures readily. To gain deeper insights into the cleavage of lignin C-O bonds, we designed deuterium-incorporated β-O-4 mimics (dimers and one polymer) for a mechanistic study, which excluded the pathways involving the loss of linkage protons and led to the proposal of a concerted hydrogenolysis process for β-O-4 cleavage. Finally, to enable the utilization of depolymerized lignin phenolic monomers, unconventional feedstocks in the current chemical industry, we developed a series of methods to transform them into valuable bioactive molecules, functional materials, and high-energy fuels. Overall, these contributions opened new avenues for converting lignin into serviceable products, encompassing upstream processing and downstream applications.

Comparative Evaluation of the Effect of Exhaust Gas Recirculation Usage on the Performance Characteristics and Emissions of a Natural Gas/Diesel Compression-Ignition Engine Operating at Part-Load Conditions

The use of natural gas as an alternative fuel in dual-fuel compression-ignition engines can lead to a substantial reduction in the majority of pollutant emissions compared to fossil fuels, while the thermal efficiency of the engine can be maintained at adequate levels. Its usage has increased widely in recent years, and significant efforts have been made to investigate the inherent physical and chemical processes that take place during this engine’s combustion, as well as the parameters that affect the operation of the engine and use natural gas as energy source. The scope of this study is to investigate the effect of EGR temperature (cold and hot) and rate (10% and 20%) on the performance characteristics and emissions of a dual-fuel compression-ignition engine operating at a specific engine operating point under dual-fuel (diesel–natural gas) conditions. For this reason, a phenomenological two-zone combustion model was developed. The results of the model were validated against the experimental data obtained from a single-cylinder direct-injection, turbocharged compression-ignition dual-fuel research engine operated under part-load conditions (IMEP = 0.52 Mpa and engine speed = 1500 rpm) and at various replacement percentages of diesel using methane (which was treated as a natural gas surrogate). The model results were in good agreement with the experimental results, revealing the ability of the model to be used in the aforementioned EGR analysis. The results of the study revealed that engine operation with 10% cold EGR does not significantly affect the engine performance characteristics, and combined with the addition of 80% gaseous fuel energy, can lead to a substantial reduction in NO and soot emissions, with a moderate increase in CO emissions. On the other hand, a significant finding of the present work is that engine operation with hot EGR under the investigated operating conditions, even though it had a beneficial effect on NO-specific emissions, led to a reduction in engine efficiency and may raise issues regarding the mechanical strength of the engine.

Technological innovation, trade openness, natural resources, clean energy on environmental sustainably: a competitive assessment between CO2 emission, ecological footprint, load capacity factor and inverted load capacity factor in BRICS+T

The study investigates the relationship between technological innovation, clean energy, trade openness, and natural resource rents on environmental sustainability within BRICS + T nations. Motivated by the urgent need to address escalating CO2 emissions—reaching 36.4 billion metric tons in 2022—the research aims to understand how these factors influence CO2 emissions, ecological footprint, load capacity factor, and its inverse, contributing to the Sustainable Development Goals (SDGs). The study uses panel data from BRICS + T countries spanning the period from 1990 to 2022. Employing advanced econometric techniques such as Dynamic Seemingly Unrelated Regression (DSUR), Cross-Sectionally Augmented Panel Unit Root (CUP-FM, CUP-BC), and nonlinear autoregressive distributed lag (ARDL) models, the research tests the Environmental Kuznets Curve (EKC) hypothesis and evaluates asymmetric effects of the variables. Key findings indicate that technological innovation consistently reduces CO2 emissions and ecological footprints, reinforcing its role in promoting sustainability through cleaner technologies and more efficient industrial processes. Clean energy adoption has also been shown to be a significant driver in reducing environmental degradation, with consistent negative effects on emissions and ecological footprint, while improving the load capacity factor. However, trade openness exhibits a dual effect. While it enhances resource use efficiency, it simultaneously increases CO2 emissions and the ecological footprint, likely due to heightened industrial activity. Natural resource rents display mixed results: in some cases, they exacerbate emissions, while in others, they contribute to sustainability by funding eco-friendly initiatives. The study recommends that BRICS + T nations prioritize investments in green technologies, strengthen environmental regulations, and enhance international collaboration to accelerate the transition to renewable energy. Policymakers should balance the benefits of trade openness with stricter environmental standards to mitigate its adverse effects on sustainability. These integrated strategies are essential for achieving the environmental targets outlined in the SDGs.

The Impact of Trade Openness on Carbon Emissions: Empirical Evidence from Emerging Countries

Emerging countries are the main source of new CO2 emissions and the major net carbon importers, and they have also become an important part of the global trade pattern. In this study, the impact of trade openness on CO2 emissions was investigated by approaches such as fully modified least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group-autoregressive distributive lag (PMG-ARDL) methods. Further estimations were conducted by employing methods such as DCCEMG (dynamic common-correlated effect mean group) and Driscoll–Kray to strengthen the robustness of the results. Moreover, the Granger causality between trade openness and CO2 emissions was tested by using the Dumitrescu–Hurlin method. Conclusions can be drawn as follows: First, economic growth, energy consumption, trade openness, and CO2 emissions are all interconnected in the long term. Specifically, higher levels of economic growth and trade openness are associated with lower CO2 emissions, whereas energy consumption contributes to higher emissions. However, in the short term, economic growth and energy consumption lead to an increase in CO2 emissions, while trade openness does not have a significant impact. Moreover, there is a two-way Granger causality between trade openness and CO2 emissions. Additionally, economic growth and energy consumption have an indirect effect on CO2 emissions by influencing trade openness. Given these findings, emerging market countries should focus on enhancing their service sectors, promoting technological advancements, and fostering international collaboration in green technologies. By actively engaging in efforts to combat climate change, these countries reach a point where trade expansion and carbon reduction are achieved.

Moderate grazing reduces while mowing increases greenhouse gas emissions from a steppe grassland: Key modulating function played by plant standing biomass.

Grassland represents one of the most expansive terrestrial ecosystems, exerting a profound influence on atmospheric greenhouse gas (GHG) levels within the broader context of global change. Both climate and land use changes play important roles in modulating grassland GHG emissions by directly or indirectly altering soil physical and chemical properties, especially soil temperature and inorganic nitrogen content. The optimal grassland management practices need to simultaneously meet the requirements of reducing GHG emissions, maintaining biological biodiversity, and ensuring productivity. However, the information on the management effects on GHG emissions from natural grasslands is still insufficient. Here we conducted a six-year grazing and mowing experiment in a semi-arid steppe grassland in central Inner Mongolia, and employed the static chamber method to investigate the effects of three major management measures, fencing, grazing and mowing, on ecosystem respiration (CO2 emission), methane uptake (CH4), and nitrous oxide emission (N2O) patterns in the experimental grassland. The results demonstrated that: (i) moderate grazing reduced plant aboveground standing biomass and CO2 emissions, but promoted belowground nutrient cycling and CH4 uptake; (ii) mowing enhanced plant biomass production, increased soil carbon and nitrogen content, and also increased CO2 emission; (iii) reducing grazing frequency reduced plant biomass loss and N2O emissions. We conclude that grazing at a moderate intensity and frequency is the best for mitigating GHG emissions while maintaining grassland production, and that mowing enhancement of plant production and GHG emissions should be considered in optimizing grassland management.

Triple bottom line analysis of the decline in foreign tourist demand in Japan due to the COVID‐19 pandemic: Counterfactual structural decomposition approach

AbstractThis study developed a consumption‐endogenous input–output analysis framework and a novel counterfactual structural decomposition analysis (SDA) framework to assess the economic, social, and environmental effects of the 2020 decline in foreign tourists due to the COVID‐19 pandemic. The results show a pandemic‐induced loss of 33 million tourists, resulting in 3.44 trillion JPY in value‐added losses, employment declines for 868,976 people, and environmental benefits of an 11.6 Mt‐CO2 emissions reduction. Sectors hit the hardest include hotels, eating and drinking services, and wholesale and retail trade. The deterioration revealed a 100% reduction in value added from foreign visitors, whereas shifts in production contributed to a 3.6% increase in CO2 emissions. This study recommends government intervention, focusing on stipends for sectors with the highest value‐added losses and employment declines, as well as support funds for sectors that reduce environmental impact.

Impacts of Mangrove Loss on Greenhouse Gas Emissions in the Niger Delta, Nigeria

The mangrove vegetation within the Niger Delta region of Nigeria is ravaged by anthropogenic practices including but not limited to rapid urbanization, aquaculture expansion and oil exploration which penultimately distorts the biodiversity of both the mangrove and marine environments, culminating in the loss of structural and functional integrity of these ecosystems, specifically their role in climate change regulation. The study aimed at assessing the changes in mangrove covers from 1987 to 2022 in the study area as well as examining the changes in GHGs emissions resulting from the mangrove changes. The methodology adopted a remote sensing-based research design utilizing satellite imagery to analyze temporal changes in mangrove cover and evaluated their association with climate variables such as CO2 emissions and LST of the study area. Each satellite image geo-referenced in ArcGIS 10.8 & LULC changes calculated using geometry module of ArcGIS 10.8. NDIR spectroscopy was used in examining the variation in GHGs emissions. The data obtained revealed mangrove reduction from 12,991 km2 in 1987 to 9,089km2 in 2022 resulting in the loss of 3,904.00 km2 of mangrove forest. The reduction resulted in increased CO₂ emissions from 370.70 ppm to 403.29 ppm between 1987 and 2022. These results illustrate a clear link between mangrove cover change and CO₂ emissions, highlighting the critical role mangroves play in regulating climate change. The study was able to show that significant losses in mangrove cover have been closely associated with increased CO₂ emissions, thus reflecting the vital role these ecosystems play in carbon sequestration which underscores the importance of preserving these vital ecosystems to mitigate local and global climate impacts.

Building a Sustainable Future: The Nexus Between Artificial Intelligence, Renewable Energy, Green Human Capital, Geopolitical Risk, and Carbon Emissions Through the Moderating Role of Institutional Quality

Countries worldwide are focusing on energy efficiency, economic sustainability, and responsible resource management to address climate change and meet sustainable development goals (SDGs). This study investigates how factors such as artificial intelligence, renewable energy, green human capital, geopolitical risk, natural resource rent, and information and communication technology influenced CO2 emissions in 36 countries between 2000 and 2021. The study also explores how institutional quality moderates these relationships. We employed advanced econometric techniques to address this gap, including panel-correlated standard errors (PCSE) and the Driscoll–Kraay estimations (DKSE) models. A two-step system GMM approach was also used to strengthen the robustness of our findings. The findings reveal that green human capital, renewable energy consumption, and institutional quality can significantly reduce CO2 emissions. Conversely, artificial intelligence, geopolitical risk, natural resource rent, and information communication technology contribute to increased CO2 emissions. Institutional quality enhances the positive impact of green human capital and renewable energy on emission reduction. However, it has the opposite effect on artificial intelligence, leading to an even greater increase in CO2 emissions. These findings underscore the importance of green policies in achieving sustainable development goals. We recommend that policymakers prioritize investing in clean energy and green human capital while strengthening institutional quality to effectively mitigate carbon emissions and meet SDGs. They also regulate AI and ICT carbon footprints and address geopolitical risks through energy diversification and international cooperation.

Climate change and migration dynamics in Somalia: a time series analysis of environmental displacement

Climate change is a significant driver of human migration, especially in vulnerable regions like Somalia. This study investigates the relationship between climate variables—average annual precipitation, temperature, and CO₂ emissions—and net migration in Somalia, using time series data from 1990 to 2020. Additionally, it examines the role of population growth as a factor influencing migration. Applying the Autoregressive Distributed Lag (ARDL) model, this research captures both short- and long-term dynamics, providing insights into how environmental and demographic factors impact migration in this climate-sensitive region. The results indicate that favorable rainfall conditions positively influence net migration, as improved agricultural productivity stabilizes livelihoods, reducing pressures to migrate. In contrast, increased CO₂ emissions, associated with environmental degradation, negatively impact migration by limiting financial capacity, creating a “trapped population” effect. Population growth also intensifies migration pressures by increasing competition for limited resources. Interestingly, temperature variations do not significantly influence migration, possibly due to adaptive strategies or resilience to temperature fluctuations in the region. These findings underscore the need for policies focused on enhancing agricultural resilience, restoring degraded environments, and creating economic opportunities to reduce migration pressures in Somalia. Investments in sustainable land use, climate adaptation, and population management strategies are essential to address the complex challenges of climate-induced migration.

Effect of Changes in Mains Voltage on the Operation of the Low-Power Pellet Boiler

Modern low-power boilers with automatic burners require electricity for proper operation. The electricity voltage in the network is not constant and is subject to fluctuations. Variations in voltage will have the most significant impact on the operation of electric motors since their speed is controlled by changing the voltage. The purpose of this research was to assess the impact of supply voltage deviations within the range allowed by the EN 60038:2012 standard (230 V ±10%, i.e., 207 V and 253 V) on boiler operation. This study analysed the effects of these variations on flue gas and dust emissions during boiler operation at full load, as well as on the boiler firing process. Tests were conducted on a boiler with a nominal output of 25 kW. Changes in voltage significantly influenced the blower fan speed. For the nominal boiler output, at 253 V the speed increased by 17.6%, and at 207 V it decreased by 20.4%. Variations in voltage affected the volume of air supplied to the combustion chamber, altering the excess air ratio (λ): 1.8 at 230 V, 2.1 at a higher voltage, and 1.4 at a lower voltage. Changes in voltage translated into changes in exhaust gas temperature and flue gas and dust emissions. Boiler operation at 253 V increased CO emissions by 77.2%, NOx by 31.2%, and dust by 12.5%. In contrast, at 207 V, emissions were lower, with CO decreasing by 17.3%, NOx by 11.7%, and dust by 18.8%. Fluctuations in voltage further influenced the boiler’s ignition time; the ignition process was four times longer at a higher voltage and twice as long at a lower voltage. The results of these studies underscore the necessity of adapting boiler designs to fluctuating voltage conditions.

Impacts of different intensities of commercial Sphagnum moss extraction on CO2 fluxes in a northern Patagonia peatland.

Peatlands are key ecosystems for global climate regulation because they provide the most efficient carbon sink on the planet. Despite this, they have been widely degraded by various anthropogenic disturbances, causing imbalances in their ecological functioning. A more recent type of disturbance corresponds to the commercial extraction of Sphagnum mosses, which has been carried out in temperate peatlands distributed in Australasia and Patagonia. In the extreme north of Chilean Patagonia, many peatlands have been subject to intensive commercial extraction of the moss Sphagnum magellanicum, causing their degradation and impacts on CO2 fluxes, a situation that has been barely studied. We conducted a field study on CO2 exchange at three sites, two of them representing different intensities of commercial extraction of the moss S. magellanicum and an undisturbed site in an anthropogenic peatland located in the northern Patagonian region of Chile. CO2 fluxes were measured in situ using a transparent and opaque closed chamber. Gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE) were modeled at high temporal resolution by determining daily and cumulative CO2 flux rates during the vegetation growing season. The NEE records showed significant differences between the three sites studied, attributed to differences in the GPP. Sites with high (<30% residual moss cover) and low (∼50% residual moss cover) extraction intensity were a source of CO2, with cumulative NEE values of 416.6±13.1 and 248.2±9.9g CO2-C m-2 season-1 respectively, while the undisturbed area was a moderate CO2 sink with a cumulative NEE of -86.4±2.8g CO2-C m-2 season-1. Our results provide evidence that increasing levels of Sphagnum moss commercial extraction generate increases in CO2 emissions from anthropogenic peatlands into the atmosphere, and could be used to advance the improvement of current regulations that regulate moss harvesting in Chile.

Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0

Abstract. The global annual mean contrail climate forcing may exceed that of aviation's cumulative CO2 emissions. As only 2 %–3 % of all flights are likely responsible for 80 % of the global annual contrail energy forcing (EFcontrail), re-routing these flights could reduce the occurrence of strongly warming contrails. Here, we develop a contrail forecasting tool that produces global maps of persistent contrail formation and their EFcontrail formatted to align with standard weather and turbulence forecasts for integration into existing flight planning and air traffic management workflows. This is achieved by extending the existing trajectory-based contrail cirrus prediction model (CoCiP), which simulates contrails formed along flight paths, to a grid-based approach that initializes an infinitesimal contrail segment at each point in a 4D spatiotemporal grid and tracks them until their end of life. Outputs are provided for N aircraft-engine groups, with groupings based on similarities in aircraft mass and engine particle number emissions: N=7 results in a 3 % mean error between the trajectory- and grid-based CoCiP, while N=3 facilitates operational simplicity but increases the mean error to 13 %. We use the grid-based CoCiP to simulate contrails globally using 2019 meteorology and compare its forecast patterns with those from previous studies. Two approaches are proposed to apply these forecasts for contrail mitigation: (i) monetizing EFcontrail and including it as an additional cost parameter within a flight trajectory optimizer or (ii) constructing polygons to avoid airspace volumes with strongly warming contrails. We also demonstrate a probabilistic formulation of the grid-based CoCiP by running it with ensemble meteorology and excluding grid cells with significant uncertainties in the simulated EFcontrail. This study establishes a working standard for incorporating contrail mitigation into flight management protocols and demonstrates how forecasting uncertainty can be incorporated to minimize unintended consequences associated with increased CO2 emissions from re-routes.

Experimental and Simulation Study on Reducing the Liquid Film and Improving the Performance of a Carbon-Neutral Methanol Engine

Methanol is a potential carbon-neutral fuel. It has a high latent heat of vaporization, making it difficult to achieve evaporation and mixing, and it is prone to forming a liquid film, which in turn affects engine performance. To reduce the liquid film and improve engine performance, this work investigates the influence mechanism of injection strategies on the generation of liquid films in the intake port and cylinder of an inline 6-cylinder port fuel injection (PFI) spark-ignition (SI) methanol engine and further explores the optimization scheme for improving engine performance. The results show that the end of injection (EOI) influences the methanol evaporation rate and the methanol–air mixing process, thereby determining the liquid film deposition, mixture distribution, and temperature distribution in the cylinder. As the EOI advances, the higher methanol evaporation rate during the intake process reduces the amount of methanol droplets and the deposition of a liquid film in the cylinder. The in-cylinder temperature is relatively high, while the mixture inhomogeneity slightly increases. As the EOI increases from 170 °CA to 360 °CA, the higher in-cylinder temperature and properly stratified mixture accelerate the early and middle stages of combustion, shorten the ignition delay, advance the center of combustion, and improve the brake thermal efficiency (BTE). However, further advancing the EOI results in the BTE remaining basically unchanged. Optimized injection timing can enhance the BTE by 1.4% to 2.4% under various load conditions. The increase in the EOI contributes to the reduction of HC emissions due to the weakening of the crevice effect with lower masses of methanol droplets and liquid film in the cylinder, while the increase in mixture inhomogeneity leads to an increase in CO emissions. In general, controlling the EOI at around 360 °CA can maintain relatively low CO emissions under various load conditions, while significantly reducing HC emissions by 71.2–76.4% and improving the BTE.

Exploring the Impacts of Economic Policies, Policy Uncertainty, and Politics on Carbon Emissions

Abstract Addressing climate change challenges through managing and mitigating CO2 emissions has taken center stage in shaping economic policies. This paper analyzes the intricate interplay among economic policies, their uncertainty, political dynamics, and CO2 emissions by utilizing the U.S. data from 1973 to 2024. Our empirical results derived from the autoregressive distributed lag (ARDL) model with dummy variables indicate that expansionary monetary policies and higher corporate tax rates may increase CO2 emissions. Political factors, including economic policy uncertainty and the political climate, yield long-term impacts on CO2 emissions. The dominant party in the legislative branch, but not the presidency, emerges as a key determinant in carbon dioxide emission control, underscoring the significance of consensus-building in democratic processes, yet with variation in effects across different chambers. The robustness of these key findings is reaffirmed through different models. The findings suggest that party affiliation does not always dictate environmental policy outcomes, emphasizing the complexity of policy formulation and its representation within legislative bodies, providing insight to decision-makers when economic policies are considered.

MLP Enhanced CO2 Emission Prediction Model with LWSSA Nature Inspired Optimization

Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor’s contribution to the rise of CO2. This study proposes a novel hybrid framework combining a Multi-Layer Perceptron (MLP) with an enhanced Locally Weighted Salp Swarm Algorithm (LWSSA) to address the limitations of traditional optimization techniques, such as premature convergence and stagnation in locally optimal solutions. The LWSSA improves the standard Salp Swarm Algorithm (SSA) by incorporating a Locally Weighted Mechanism (LWM) and a Mutation Mechanism (MM) for greater exploration and exploitation. The LWSSA-MLP framework achieved a prediction accuracy of 97% and outperformed traditional optimizer-based MLP models across several evaluation metrics. A permutation feature significance analysis identified global trade, coal energy, export levels, urbanization, and natural resources as the most influential factors in CO₂ emissions, offering valuable insights for targeted interventions. The study provides a reliable and scalable framework for CO₂ emission prediction, contributing to actionable strategies for sustainable development and environmental resilience.

Possible usage of five percent chloride ion by cement weight (2.56 M NaCl) in mixing water with pozzolanic materials emerging as sustainable construction materials

Abstract Using recycled waste brine or recycled seawater as mixing water in concrete manufacturing and the cement production have led to an increase in CO2 emission, and thus, another alternative needs to be sought. This work was the first in incorporating 5% $$\:{\text{C}\text{l}}^{-}$$ by cement weight (2.56 M NaCl) into mixing water blended with four different pozzolanic materials, examining its effect on fresh, 28-day hardened properties and the embedded reinforcing-steel (RS) corrosion. Results indicated that, with 5% $$\:{\text{C}\text{l}}^{-}$$ , the slump flows of Portland cement type I (CemI), 8% CemI replaced by fly ash (CemI + FA), 8% CemI by silica fume (CemI + SF), and Portland cement type III (CemIII(slag)) at water/binder 0.55 were 193–223 mm, the bending and the compressive strengths were 4.43–7.12 MPa and 33.47–42.66 MPa, respectively, the porosities were 11.44–17.86%, the pore diameters were 0.024–0.026 μm and the bulk densities were 2087–2151 kg/m3. The chloride binding capacity was higher in CemI + SF (0.75%) and CemIII(slag) (0.74%) than in CemI (0.62%) and CemI + FA (0.63%). 5% $$\:{\text{C}\text{l}}^{-}$$ encouraged the presence of an expansive gel sodium oxide (Na2O) in CemI (0.51%), CemI + FA (0.59%), CemI + SF (1.02%) and CemIII(slag) (1.27%) after 28-day curing. Due to better chloride binding in CemIII(slag), the RS mass loss was sequenced as CemIII(slag) (14.30 mg) &lt; CemI + SF (51.30 mg) &lt; CemI + FA (66.10 mg) &lt; CemI (78.33 mg) at 430 days and the RS corrosion rate was CemIII(slag) (5.92 μm/year) &lt; CemI + SF (6.54 μm/year) &lt; CemI + FA (14.89 μm/year) &lt; CemI (21.05 μm/year).

Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations

Data centers are poised for unprecedented growth due to a revolution in Artificial Intelligence (AI), rise in cryptocurrency mining, and increasing cloud demand for data storage. A sizable portion of the data centers’ growth will occur in the US, requiring a tremendous amount of power. Our hypothesis is that the expansion of data centers will contribute to an increase in US CO2 emissions. To estimate CO2 emissions, we applied three forecasted power demands for data centers and applied 56 NREL (National Renewable Energy Laboratory) power mixes and policy scenario cases using 11 AI models. Among these, the linear regression model yielded the most accurate predictions with the highest R-square. We found that overall CO2 emissions in the US could increase up to 0.4–1.9% due to expansion of data centers by 2030. This increase represents ~3–14% of CO2 emissions from the US power sector by 2030. Using the state-level power mix forecasts for 2030 among increasing CO2 emission scenarios, we predict that Virginia’s power mix will maintain emissions in line with the US average, while the Texas, Illinois, and Washington’s power mix are expected to reduce emissions due to greater renewables in their power mix in 2030. However, Illinois and Washington may face challenges due to their limited power resource availability. In contrast, New York and California’s power mix may increase CO2 emissions due to higher natural gas in their power mix in 2030. The highest variability in data center CO2 emissions stems from AI-driven demand and improvements in data center efficiency and is followed by the power mix. To reduce CO2 emissions from data centers, we offer pathways such as reducing power consumption, improving power mix with renewable sources, and using hydrogen in power plants. We propose focusing on New Mexico and Colorado for data centers to minimize CO2 emissions. Finally, we highlight a set of federal policies supplemented by states to facilitate CO2 emission reductions across energy, emissions, waste, R&amp;amp;D, and grid infrastructure.

Meta-analysis on the Effects of Organic Fertilizer Application on Global Greenhouse Gas Emissions from Agricultural Soils

To explore the direct and indirect effects of organic fertilizer application on greenhouse gas emissions from agricultural soils, a total of 1228 groups of data from 129 published studies were selected. Meta-analysis was used to analyze the effects of organic fertilizer on global greenhouse gas emissions from agricultural soils and their influencing factors. Meanwhile, a structural equation model （SEM） was further constructed to quantify and determine the causal relationships between the factors. The results showed that, overall, compared with those under no fertilization, organic fertilizer substitution increased CO2 emissions by 70.73% （lnR： 0.53, 95%CI： 0.45-0.62）, CH4 emissions by 52.58% （lnR： 0.42, 95%CI： 0.26-0.59）, and N2O emissions by 208.14% （lnR： 1.13, 95%CI： 1.04-1.21）, respectively. Compared with those under the application of inorganic fertilizers, the substitution of organic fertilizers increased CO2 emissions by 19.24% （lnR： 0.18, 95%CI： 0.13-0.22）, CH4 emissions by 27.72% （lnR： 0.24, 95%CI： 0.15-0.34）, and N2O emissions by -3.66% （lnR：-0.04, 95%CI：-0.11- 0.04）. Under the application of organic fertilizer, the dryland system had the greatest impact on both CO2 and N2O emissions, and the rice system had the most significant effect on CH4 emission effects. The average CO2 growth rate was highest in silty soils, and sandy soils had a greater effect on the average growth rates of N2O and CH4. The average growth rate of CO2 and N2O was extremely large under straw substitution compared to that under commercial organic fertilizer and biogas. The substitution of farmyard manure had significant CH4 emissions from soil promotion. Greenhouse gas emissions were significantly increased under the nitrogen fertilizer and organic fertilizer treatments. The SEM showed that mean annual temperature （MAT） was the most important environmental determinant of CO2 and N2O emissions, in which soil CO2 emissions were significantly positively correlated with MAT （P &lt; 0.01） and soil N2O emissions were negatively correlated with MAT. The main environmental determinants of CH4 emissions in dryland, grassland, and rice systems were annual precipitation （MAP） and mean annual temperature （MAT）, respectively, which were both significantly and positively correlated with each other （P &lt; 0.05）. This research can provide a scientific basis for optimizing fertilizer management and mitigating greenhouse gas emissions from farmland.

Exploring the Feasibility of Green Hydrogen Production Using Wind Energy in India

Abstract India heavily relies on coal for about 70 % of its energy demand, leading to a concerning 29 % increase in CO2 emissions from 2015 to 2022. The need for alternate fuels is evident with rising energy demand and the rapid depletion of fossil fuels. Green hydrogen emerges as a solution, leveraging renewable energy sources to mitigate environmental impact. This study assesses five high-speed wind locations in India for green hydrogen production, analysing wind resource potential using six different wind turbine (WT) models. A comprehensive techno-economic analysis was conducted to evaluate the feasibility of hydrogen production, considering factors like annual energy production (AEP), hydrogen production, levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). Furthermore, the study quantified the carbon emissions mitigated using wind energy for hydrogen production instead of conventional methods. Additionally, the study assessed the hydrogen storage capacity. Results indicated Mandvi (S2) as the most potential location, boasting a capacity factor of 50.51 % and AEP by WT4 of 13.273 GWh, yielding 221.21 tons of hydrogen annually. Cost analysis reveals an LCOE of 0.0225 $/kWh and an LCOH of 2.3103 $/kg. Furthermore, utilizing WT4 at the S2 location could mitigate 12.37 Mt/year of carbon emissions.