Reduce CO2 Emissions Research Articles

Towards Carbon Neutrality: The Impact of Private AI Investment and Financial Development in the United States – An Empirical Study Using the STIRPAT Model

This investigation analyses the influence of private AI investment and financial development (FD) on CO2 emissions in the United States, using the STIRPAT framework to account for the functions of GDP, population, and foreign direct investment (FDI). The data's robustness was verified through the application of a variety of unit root tests, which confirmed that the variables are free of unit root issues and exhibit a varied order of integration. The ARDL bound test was used to investigate the cointegration among the variables and it found a long-run equilibrium relationship. The ARDL model results show that income, FDI, FD, and population significantly increase CO2 emissions in both the short and long term. In contrast, we found that private investment in AI led to a significant reduction in CO2 emissions over these time frames. Additional estimations were conducted using FMOLS, DOLS, and CCR methods to verify the ARDL results, all of which attested to the initial findings' robustness. In addition, the study implemented a pairwise Granger causality test to illustrate the directional relationships between the variables. There is a unidirectional causal link between GDP, private AI investment, FDI, population, and CO2 emissions, according to the findings. Most notably, we observed bidirectional causality between CO2 emissions and FD. Diagnostic tests further corroborated the validity of the study's conclusions, confirming that the model is free from specification errors, serial correlation, and heteroscedasticity.

Dynamic linkages between element input structure, global value chain, and carbon emissions: evidence from a heterogeneous panel cointegration model

ABSTRACT Industrial CO2 constitutes a dominant component of global CO2 emissions. Therefore, modifications in industrial production processes are crucial. We analyse global cross-national panel datasets from 1995 to 2018 using the panel autoregressive distributed lag (ARDL) model to provide comprehensive evidence of the combined effects of input-side element structures and production-side global value chain (GVC) divisions on output-side CO2 emissions. The results show that: (i) Renewable energy and service elements in production factors can reduce CO2 emissions, though their abatement effects vary. Both can produce sustained carbon mitigation effects in the long term, but only renewable energy is effective in the short term. (ii) The GVC division pattern can induce a CO2 emission increase in both the short and long term, though these effects are heterogeneous depending on the differences in forward-backward GVC divisions. (iii) A path dependence analysis shows that input-side element structures can change production-side GVC divisions, thereby affecting output-side emissions. This study extends our understanding of the environmental effects of various production links and helps coordinate high-quality economic development and impending environmental constraints.

Prediction of CO2 emissions to achieve net-zero objectives in the iron and steel sectors of North America

ABSTRACT Predictive models are widely used to create effective plans for reducing CO2 emissions in manufacturing. The Iron and Steel (I&S) industries play a crucial role in meeting international commitments to achieve Net-zero emissions by 2050. The objective of this study is to forecast carbon dioxide emissions from the I&S industries in North America through the utilization of a Multi-Objective Mathematical model. The proposed data-driven approach is integrated with various machine learning algorithms capable of accurately predicting future values with a small dataset. Additionally, sensitivity analyses under different scenarios are conducted to evaluate the impact of implementing proposed solutions by the research community. Results show a significant improvement in accuracy through the employment of the Whale Optimization Algorithm (WOA). Forecasts reveal a sustained increment of 0.7 MtCO2 every year spanning between 2022 and 2050. This study provides valuable information for stakeholders and policymakers as it allows a more precise evaluation to integrate new technologies to abate forthcoming CO2 emissions.

Synergistic Impact of Magnets and Fins in Solar Desalination: Energetic, Exergetic, Economic, and Environmental Analysis

This study investigates the effectiveness of combining magnets with parabolic and truncated fins in enhancing the distillation process of solar stills. The integration of magnets accelerated evaporation rates, while the fins increased the heat absorption area, resulting in improved output, vis-à-vis traditional solar stills. A comparative assessment revealed that the parabolic fin solar still (PFS) with magnets outperformed the truncated fin solar still (TCFS), producing 20%, 15%, and 16% more distillate at three different depths (1, 2, and 3 cm). The superior performance of the PFS is attributed to the magnetism of the water and the fins’ more extensive surface area for heat absorption. Efficiency measurements at a water depth of 1 cm showed that the PFS achieved the maximum energy and exergy efficiencies at 30.49% and 8.85%, respectively, compared with TCFS’s 25.23% and 6.22%. Economically, the PFS setup proved more feasible, with a 20.9% lower cost per liter of distilled water than TCFS. Additionally, the environmental impact assessment indicated a significant reduction in CO2 emissions, potentially generating revenues of approximately USD 1242.32 through carbon credits. These results reflect a considerable margin to enhance the efficiency of solar desalination through well-planned adjustments, which bodes well for the future of optimized solar distillation systems from an economic and environmental perspective.

Anticorrelation of net uptake of atmospheric CO2 by the world ocean and terrestrial biosphere in current carbon cycle models

Abstract. The rate at which atmospheric carbon dioxide (CO2) would decrease in response to a decrease in anthropogenic emissions or cessation (net zero emissions) is of great scientific and societal interest. Such a decrease in atmospheric CO2 on the centennial scale would be due essentially entirely to transfer of carbon into the world ocean (WO) and the terrestrial biosphere (TB), which are sink compartments on this timescale. The rate of decrease in excess atmospheric CO2 and the apportionment of this decrease into the two sink compartments have been examined in two prior model intercomparison studies, subsequent either to a pulse emission of CO2 or to abrupt cessation of anthropogenic CO2 emissions. The present study examines and quantifies inter-model anticorrelation in those studies in the net rate and extent of uptake of CO2 into the two sink compartments. Specifically, in each study the time-dependent coefficients characterizing the net transfer rate into the two sink compartments (evaluated as the net transfer rate normalized to excess atmospheric CO2 above the pre-pulse amount for the pulse experiment or as the net transfer rate divided by excess atmospheric CO2 above the preindustrial amount for the abrupt cessation experiment) were found to exhibit strong anticorrelation across the participating models. That is, models for which the normalized rate of uptake into the WO was high exhibited low uptake rate into the TB and vice versa. This anticorrelation in net transfer rate results in anticorrelation in net uptake extent into the two compartments that is substantially greater than would be expected simply from competition for excess CO2 between the two sink compartments. This anticorrelation, which is manifested in diminished inter-model diversity, can lead to artificially enhanced confidence in current understanding of the consequences of potential future reductions of CO2 emissions and in the global warming potentials of non-CO2 greenhouse gases relative to that of CO2.

Renewable realities: Charting a greener course for the world's high‐emitting nations through information technology insights

AbstractCarbon dioxide (CO₂) is the most abundant gas among all greenhouse gas emissions, severely impacting global warming. This study examines the impact of Information and Communication Technology (ICT), population dynamics, Per Capita Gross Domestic Product (PGDP), and Renewable Energy Consumption (REC) on CO₂ on a global scale, representing 38 countries selected using the Pareto principle. Results from the panel regression model indicate a significantly positive relationship between ICT, PGDP, and population on CO₂ emissions. In contrast, REC exhibits a negative relationship. The Multiple Linear Regression model shows that an increase in PGDP leads to higher CO₂ emissions, except in Uzbekistan. ICT increases emissions in the United States, Argentina, Australia, Canada, and Egypt. Population growth raises emissions, except in the United States, France, Germany, and Russia. REC reduces CO₂ emissions in most countries. Policymakers in individual countries can gain a precise understanding of how these variables impact CO₂ emissions, enabling them to mitigate the risks associated with global warming.

Measuring the Effectiveness of the ‘Batch Operations’ Energy Design Pattern to Mitigate the Carbon Footprint of Communication Peripherals on Mobile Devices

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.

Analysis of Energy-Related-CO2-Emission Decoupling from Economic Expansion and CO2 Drivers: The Tianjin Experience in China

Cities are key areas for carbon control and reduction. The study of the decoupling between CO2 emissions and gross domestic product (GDP) and the drivers of CO2 emissions in cities facilitates the reduction of CO2 emissions to safeguard the development of the economy. This paper first calculates the CO2 emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO2-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO2 emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO2 emissions, it is essential to harmonize the interplay between economic-development approach and CO2 emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO2 emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO2 emission factors and can also interest urban policymakers.

Non‐Destructive and Mechanical Characterization of the Bond Quality of Co‐Extruded Titanium‐Aluminum Profiles

The transportation industry aims to improve energy efficiency and reduce CO2 emissions, with a focus on reducing vehicle mass. A key method involves advanced lightweight construction techniques using materials like aluminum alloys. Research is concentrated on developing processes to combine different materials into reinforced hybrid components, such as aluminum and titanium. This study focuses on the lateral angular co‐extrusion (LACE) process to produce hybrid hollow profiles of EN AW‐6082 and Ti6Al4V, investigating the impact of the thermomechanical processing during extrusion and heat treatment (HT) on the resulting bond quality and material properties. Various HT routes are tested to see their impact on intermetallic phase formation, longitudinal weld seams, and bonding strength. Mechanical testing evaluates the tensile strength of the joining zone, while nondestructive ultrasonic testing (UT) assesses joining zone integrity and poor bonding detection. Results indicate that HT parameters significantly influence the bond quality and mechanical properties of hybrid profiles. UT data shows a strong correlation with tensile strength and intermetallic phase growth, providing a nondestructive way to evaluate bond quality. This study highlights the potential of LACE processes and optimized HT strategies to improve the performance and reliability of aluminum–titanium hybrid components.

Wastewater sample storage for physicochemical and microbiological analysis.

Wastewater-based epidemiology (WBE) is a crucial tool for health and environmental monitoring, providing real-time data on public health indicators by analysis of sewage samples. Ensuring the integrity of these samples from collection to analysis is paramount. This study investigates the effects of different cold-storage conditions on the integrity of wastewater samples, focusing on both microbiological markers (such as extractable nucleic acids, SARS-CoV-2, and crAssphage) and physicochemical parameters (including ammonium, orthophosphate, pH, conductivity, and turbidity). Composite samples from the raw wastewater influent from five wastewater treatment works in South Wales, UK, were stored at 4°C, -20°C, and -80°C, and subjected to up to six freeze-thaw cycles over one year. The study found significant effects of storage temperature on the preservation of certain WBE markers, with the best yield most frequently seen in samples stored at -80°C. However, the majority of WBE markers showed no significant difference between storage at -80°C or at 4°C, demonstrating that it may not always be necessary to archive wastewater samples at ultra-low temperatures, thus reducing CO2 emissions and laboratory energy costs. These findings underscore the importance of optimized storage conditions to maintain sample integrity, while ensuring accurate and reliable WBE data for public health and environmental monitoring.

Global Warming: Predictive Models and Correlation Analysis

In recent years, the Earth's rising temperatures and the shrinking Antarctic ice cover have become increasingly evident. Understanding the factors driving global warming and developing strategies to mitigate it are critical. By applying the Pettitt change-point detection method, we analyzed March temperatures from the past decade and identified a significant shift occurring in March 2015. Forecasting models, including ARIMA and LSTM, predict similar temperature trends, with the LSTM model demonstrating slightly higher accuracy. Through grey correlation analysis and multiple linear regression, we identified CO2 as the most influential factor affecting global temperatures. Therefore, reducing CO2 emissions is crucial to mitigating global warming.

Implementation of AI Transportation Routing in Reverse Logistics to Reduce CO2 Footprint

Purpose: This study explores the implementation of artificial intelligence (AI) in transportation routing within reverse logistics to reduce CO2 emissions. By optimizing routing processes, we aim to enhance the efficiency of logistics operations while minimizing environmental impact. Methodology: we analyze the implications of improved transportation strategies. Furthermore, we discuss the intersection of technological innovation and environmental economics in shaping sustainable practices. Findings: The findings underscore the importance of AI-driven decision-making frameworks (D81) and their role in advancing IT management in logistics. The study on AI-driven transportation routing in reverse logistics highlights significant implications for both businesses and the environment. By optimizing routes, AI reduces fuel consumption and CO₂ emissions, helping companies meet sustainability goals. This leads to cost savings, as AI improves route efficiency and fleet utilization, while also enhancing operational efficiency. AI’s ability to predict and adjust to real-time conditions ensures scalability and adaptability, making it easier to handle increasing returns volumes in e-commerce. Moreover, AI enables better inventory management and resource planning, reducing the need for urgent shipments. It also paves the way for innovations like autonomous vehicles, further transforming reverse logistics. Unique Contribution to Theory, Practice and Policy: The study shows how AI can help businesses comply with environmental regulations, providing a competitive edge. Ultimately, AI integration in reverse logistics contributes to both operational improvements and environmental sustainability, reinforcing corporate social responsibility.

Advances in Tandem Strategies for CO2 Electroreduction: from Electrocatalysts to Reaction System Design

The electrochemical reduction of CO2 (CO2RR) offers the opportunity to store renewable energy in the form of chemicals and fuels while simultaneously reducing CO2 emissions. Compared to low‐carbon products such as carbon monoxide and formic acid, multicarbon products have demonstrated greater value in terms of economic feasibility. To date, various strategies have been developed to enhance electrocatalytic performance, with tandem strategies emerging as a promising approach, particularly for the formation of multicarbon products. In this review, current tandem strategies regarding CO2RR are thoroughly discussed, covering electrocatalyst designs from atomic‐scale tandem electrocatalysts to macroscale electrode configurations as well as reaction system designs. Additionally, the internal reaction processes involving reduction product upgrades, the application of multi‐physical tandem fields, and the tandem reaction systems are further summarized. This review aims to provide more fundamental insights into tandem strategies for CO2RR applications and inspire more creative ideas in the research community of CO2RR in light of this promising approach with its wide versatility, diversity and flexibility.

Potential Reduction in Carbon Emissions in the Transport of Aggregates by Switching from Road-Only Transport to an Intermodal Rail/Road System

Aggregates are the second-most consumed product in the world after water. This geological resource is used as building and construction material, and its production in quarries and delivery to customers generates several environmental problems. Their transport from quarries to consumption points, almost entirely done by truck, also generates impacts such as an increase in traffic and noise and the emission of greenhouse gases and other pollutants. Transportation and storage of goods account for 15% of greenhouse gas emissions in Europe and will increase significantly by 2050. To mitigate this, the European Union suggested shifting 30% of long-distance road freight to cleaner alternatives, such as rail or waterborne transport. This approach neglects the enormous volume of short-distance freight movement and its impact on achieving the goal of reducing greenhouse gas emissions. In this study, the hypothesis to test is whether the use of an intermodal rail/road transport mode, instead of just roads, for the transport of some products can help reduce global CO2 emissions even for short distances. To test this, this study investigates the carbon emissions (and transport cost reduction) generated by rail/road intermodal aggregate transport for short distances in the Madrid region (Spain), rather than the currently used direct truck transport. An analysis of variables, such as aggregate supply, demand locations and amounts, and road and rail networks, using a geographical information system provides the associated carbon emissions of the different transport alternatives. To obtain a reduction in CO2 emissions, this study proposes the establishment of intermodal transfer facilities near consumption centers, where materials are primarily transported by rail, with road transport limited to the final delivery to consumption areas. The results anticipate a notable decrease in carbon emissions in aggregate transport and allow the establishment of more efficient and environmentally friendly rail/road intermodal transport that would help to meet the goals of reducing climate change while making the use of aggregates more environmentally friendly.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.

AN OVERVIEW OF ENERGY CONSUMPTION AND CO2 EMISSIONS IN IRAN'S RESIDENTIAL SECTOR: ENERGY TRANSITION AND SUBSIDY POLICIES

The transition from fossil fuels to renewable energy sources has become a critical global issue, particularly in response to climate change. This study aims to analyse the trends in energy consumption and CO2 emissions within Iran's household sector. The findings reveal a significant increase in energy consumption in Iran between 2001 and 2019. Despite the household sector's heavy reliance on fossil fuels, a shift in fuel consumption patterns can be observed, with a transition from oil to natural gas starting in 2008. Electricity generation in Iran is also predominantly fossil fuel-based, with natural gas playing a major role. This underscores the urgent need to reinforce renewable energy sources to mitigate CO2 emissions from the household sector. Another key factor influencing energy consumption in Iran is the country's extensive energy subsidies. The disparity in fuel prices compared to neighbouring countries has also led to fuel smuggling. However, the results show that the twophase subsidy reform plan implemented in 2010 and 2013 did not lead to a reduction in energy consumption. Therefore, further reforms are needed to improve energy equity and reduce CO2 emissions in the household sector.

Carbon dioxide capture in large-scale CCGT power plant from flue gases obtained from various fuel mixtures

In this study, the thermodynamic analysis of a combined cycle gas turbine integrated with post-combustion carbon capture and storage using the solvent method is performed. The syngas obtained from the gasification of sewage sludge is mixed with methane and nitrogen-rich natural gas fuels at different proportions, used in the gas turbine, and the properties of fuel and flue gases are analyzed. The flue gas obtained from the fuel mixture is passed through the post-combustion carbon capture and storage at various load conditions to assess the heat and electricity required for the carbon capture process. The solvent used for the carbon capture from flue gases enables CO2 capture with the high efficiency of 90%. With the calculated results, the load conditions of flue gas using fuel mixtures are identified, which reduces the heat and power demand of post-combustion carbon capture and storage and provides the possibility to achieve neutral emission. The impact of selected operating conditions of post-combustion carbon capture and storage on the CO2 emission reduction process and on the power plant performances is investigated. Considering the factors of electricity generation, energy efficiency, heat supply to the consumers, operating load of post-combustion carbon cap-ture and storage and CO2 emission, the 50% mixture of syngas with both fuels performs better. Also, the use of a mixture of 2-amino-2methyl-1-propanol and piperazine with reboiler duty 3.7 MJ/kgCO2 in post-combustion carbon capture and storage slightly enhanced the performance of the power plant compared to the use of monoethanolamine with reboiler duty 3.8 MJ/kgCO2.

A Cybernetic Delay Analysis of the Energy–Economy–Emission Nexus in India via a Bistage Operational Amplifier Network

In analyzing the decoupling of emissions from economic growth, current literature foregoes the nonlinear complexities of macroeconomic systems, leading to ineffective energy transition policies, specifically for developing countries. This study focuses on the Indian energy–economy–emission nexus to establish a control system that internalizes inflation, trade openness, and fossil fuel imports with economic growth and macro-emissions to visualize the complex pathways of decoupling. Through long-term cointegration and vector error correction modeling, it was found that GDP and energy affect capital, inflation and energy imports, which are locked in a long-run negative feedback loop that ultimately increases emissions. Capital growth enables decoupling at 0.7% CO2 emissions reduction for every 1% capital growth, while 1% inflation growth inhibits decoupling by increasing CO2 emissions by 0.8%. A cybernetic fractional circuit of R-C elements and operational amplifiers was utilized to examine the delay of pulses from GDP to the loop elements, which revealed that capital is periodic with GDP pulses. However, inflation, being aperiodic with the clock pulses of GDP, causes the pulse-width of capital to decrease and fossil fuel imports to increase. Through the circuital model, it was possible to determine the exact policy intervention schedule in business cycle growth and recession phases that could build clean energy capital and limit inflation-induced recoupling.

Evolution of CO2 Uptake Degree of Ordinary Portland Cement During Accelerated Aqueous Mineralisation

The utilisation of carbonation treatments to produce building materials is emerging as a valuable strategy to reduce CO2 emissions in the construction sector. It is of great importance to regulate the degree of carbonation when the mineralisation process is combined with hydration, as a high CO2 uptake may impede the development of adequate strength. A significant number of studies focus on attaining the maximum carbonation degree, with minimal attention paid to the examination of the evolution of CO2 uptake over the initial stages of the process. In this context, the present study aims to investigate the evolution of CO2 uptake over time during carbonation. Ordinary Portland Cement (OPC) is employed as material, with aqueous carbonation selected as the mineralisation process. This investigation encompasses a range of carbonation durations, spanning from 5 to 40 min. The analysis of the evolution of the mineral composition with time demonstrated that the rate of the carbonation reaction accelerates in the initial minutes, resulting in the conversion of all the portlandite produced during the hydration process in the initial 10 min. Quantitative analysis of the carbonation degree indicated that the CO2 uptake at 40 min is equal to 19.1%, which is estimated to be approximately 70% of the maximum achievable value. By contributing to the understanding of the early carbonation mechanisms in aqueous conditions of OPC, this study provides valuable support for further investigation focused on the use of cement mineralisation processes to produce building materials.

Drone-Assisted Last-Mile Delivery Under Windy Conditions: Zero Pollution Solutions

As cities expand and the global push for zero pollution intensifies, sustainable last-mile delivery (LMD) systems are essential to minimizing environmental and health impacts. This study addresses the need for more sustainable LMD by examining the integration of wind conditions into drone-assisted deliveries, focusing on their effects on air and noise pollution in urban areas. We extend the flying sidekick traveling salesman problem (FSTSP) by incorporating meteorological factors, specifically wind, to assess drone delivery efficiency in varying conditions. Our results show that while drones significantly reduce greenhouse gas emissions compared to traditional delivery vehicles, their contribution to noise pollution remains a concern. This research highlights the environmental advantages of using drones, particularly in reducing CO2 emissions, while also emphasizing the need for further investigation into mitigating their noise impact. By evaluating the trade-offs between air and noise pollution, this study provides insights into developing more sustainable, health-conscious delivery models that contribute to smart city initiatives. The findings inform policy, urban planning, and logistics strategies aimed at achieving zero pollution goals and improving urban livability.