Future CO2 Research Articles

Quantifying CO2 Emissions From Smaller Anthropogenic Point Sources Using OCO‐2 Target and OCO‐3 Snapshot Area Mapping Mode Observations

AbstractWe quantify CO2 emissions from smaller anthropogenic point sources compared with earlier satellite studies, which have mostly focused on mid‐sized (∼10 MtCO2/year) and larger fossil fuel burning power plants. Two types of Orbiting Carbon Observatory (OCO) observation modes are used: OCO‐2 Target mode and OCO‐3 Snapshot Area Mapping (SAM) mode. Methods previously used with OCO‐3 SAMs are adapted to quantify CO2 emissions with OCO‐2 Targets for the first time, demonstrating a similar capability to track emission changes at the Bełchatów Power Station. SAMs and Targets are then applied to quantify emissions from smaller sources in Canada: the Boundary Dam and Poplar River Power Stations in Saskatchewan, and the Suncor and Syncrude Mildred Lake mined oil sands processing facilities in northern Alberta. We verify our method on the nearby Colstrip Power Station in Montana by comparison with hourly reported values. For Canadian sources, only annual emissions are reported, to which our emission estimates cannot be directly compared. Emissions derived from a single satellite overpass correspond to daily or finer temporal scales and thus do not account for source intermittency or variability, which requires multiple revisits to reliably estimate annual emissions. Finally, we average OCO‐3 SAMs on repeated revisits to improve weak enhancement signals above background noise. Averaging SAMs yields mixed results, with improvements achieved only under certain conditions. These studies help to clarify the capabilities and limitations of CO2 point source emission quantification with current satellites in advance of plans for operational monitoring with future CO2 satellite missions.

The effect of water motion and elevated carbon on two green algae Ulva intestinalis and Cladophora glomerata DIC acquisition and DOC release in the brackish Baltic Sea

ABSTRACT Macrophytes play a key role in coastal environments, acting to transform inorganic carbon into biologically available organic matter. This process supports the marine food web at large, however, the dynamics behind macrophyte carbon acquisition are not fully understood with factors influencing their ability to utilize different carbon forms (HCO3 − and/or CO2) and subsequent release mechanics of this carbon remaining rather poorly understood. This study aims to investigate the physiological responses of two important Baltic Sea macrophytes, Ulva intestinalis and Cladophora glomerata. By examining the effects of pH drift inhibitors, coupled with carbon-concentrating mechanisms (CCMs) and dissolved organic carbon (DOC) dynamics, we provide insights into the complex adaptations of these macroalgae to changing environmental conditions. The results demonstrate that both species exhibit distinct capabilities to adapt their carbon concentration mechanisms (CCMs) but suggest that C. glomerata may potentially gain a photosynthetic advantage in future high CO2. The observed differences between pH and water motion highlight species-specific nuances in the regulation of dissolved organic carbon (DOC) release, aligning with current theories on DOC dynamics. This research underscores the importance of understanding macroalgal adaptation and fitness in both present and future coastal ecosystems, particularly as environmental changes continue to evolve. By examining these factors, the study contributes valuable insights into how macroalgae may respond to future climate shifts.

Change Your Diet: How CO2, Plant Phenology and Genotype Alter Grapevine Quality and Affect Performance and Larval Transcriptome of an Insect Herbivore.

Herbivorous insects need to cope with changing host plant biochemistry caused by abiotic and biotic impacts, to meet their dietary requirements. Larvae of the multivoltine European grapevine moth Lobesia botrana, one of the main insect pests in viticulture, feed on both flowers and berries. The nutritional value and defence compounds of these organs are changing with plant phenology and are affected by climate change which may accordingly alter plant-insect interactions. Here, we assessed the impacts of future elevated atmospheric CO2 concentrations on the host plant quality of different grapevine organs and the larval performance and the transcriptome of L. botrana. Using the Geisenheim VineyardFACE facility, where 'Riesling' and 'Cabernet Sauvignon' were cultivated in the field under ambient or elevated (ca. + 20%) atmospheric CO2 concentrations, we found that nutrient (amino acids and sugars) and defence compound (phenolic compounds) concentrations of inflorescences and ripening berries differed strongly due to plant phenology and less due to cultivar and CO2 concentration. Assessing global gene expression after feeding on the respective organs, we found that larval transcriptomic plasticity largely mirrored the plant biochemical plasticity. Larval relative growth rate differed between treatments in a plant phenology-dependent manner. Grape berries contained higher amino acid concentrations and altered phenolics profiles after larval feeding. In the near future, the grapevine-L. botrana interaction will probably change less because of elevated CO2 concentrations than it does currently during one season. Changes associated with plant phenology, however, may be relevant for contemporary pest management.

Physical and Chemical Methods for Mitigating Carbon Dioxide

The greenhouse effect caused by greenhouse gases, especially carbon dioxide emissions, has become a central issue in global environmental governance. Traditional emission reduction strategies, although effective, are difficult to meet global climate goals. In response, the scientific community has begun to explore new technologies for capturing, separating, storing and utilizing carbon dioxide through physical and chemical means. These technologies offer potential avenues for greenhouse gas (GHG) management, but still face technical, economic, and environmental challenges before large-scale application. This study systematically evaluates the progress of the application of these physical and chemical means in carbon dioxide (CO2) treatment, and discusses in depth the technical bottlenecks and future improvement directions by analyzing the advantages and disadvantages of each technology. It is hoped that this study provides a valuable theoretical basis and practical reference for the development and application of future large-scale CO2 abatement technologies.

Scenario-based Assessment of Decarbonized Transport Sector in Thailand towards Carbon Neutrality 2050

Thailand's transport sector has been one of the highest CO2 emitters for several decades in the country due to the excessive increase in transport service demand and the high reliance on high-carbon-intensity petroleum fuels. This article discusses the potential of CO2 reduction in the transport sector in Thailand to achieve carbon neutrality by 2050. The Low Emissions Analysis Platform (LEAP) is adopted to estimate the future energy demand and CO2 emission between 2020 and 2050. This study designs two main scenarios, namely the Baseline (BAS) and the Decarbonization (DEC) scenarios. The BAS scenario is developed in a business-as-usual approach with frozen technologies, while the DEC scenario is constructed as a CO2 countermeasure by including multiple low carbon technologies such as i) improving fuel economy efficiency of engines, ii) promoting electric vehicles (EVs), iii) utilizing fuel cell electric vehicles (FCEVs), and iv) promoting mass transportation. The results indicate that by 2050, the transport sector's total energy demand will significantly increase to approximately 49,906 ktoe, with diesel, gasoline, and jet kerosene accounting for the majority of fuel consumption. In the BAS scenario, total CO2 emissions in the transport sector are estimated to be 119,737 ktCO2eq. By full implementation of the CO2 countermeasures and low carbon technologies in the decarbonization scenario, the total carbon emission in the transport sector is estimated to be 30,582 ktCO2 by 2050, which is in line with carbon neutrality pathways of Thailand. However, the nation-wide transport action plan should be developed in order to promote such sustainable transport technologies.

Forecasting CO₂ Emissions with Machine Learning Methods: Türkiye Example and Future Trends

Climate change is a critical problem that causes global environmental and social issues due to increased greenhouse gas emissions caused by human activities. Carbon dioxide (CO₂) emissions, in particular, are one of the main elements of global warming and have devastating effects on ecosystems. Keeping track of carbon dioxide emissions resulting from human activities like burning fossil fuels, clearing forests, and farming, as well as forecasting their future patterns, is essential for creating effective sustainable environmental strategies. The study utilized machine-learning models to evaluate CO₂ emissions per individual, using the dataset from the Global Carbon Atlas that has released in 2023. In the study, the traditional ARIMA model and deep learning-based LSTM networks were comparatively discussed. The models were trained with the aim of predicting Türkiye's future CO₂ emission levels by learning from past data, and their performances were evaluated with MAE, MSE, RMSE, and R² metrics. The LSTM model achieved an R² score of 90.4%, while the ARIMA model achieved an R² score of 94.3%. The findings show that machine learning techniques are a powerful tool in the fight against climate change and provide valuable insights for policymakers. The findings of the study guide more effective monitoring of CO₂ emissions and determination of strategies for sustainable development goals.

Decarbonization potential of fuel cell technologies in micro-cogeneration applications: spotlight on SOFCs in a Belgian case study

Abstract There is a plethora of fuel cell technologies, many of which hold great promise in terms of their decarbonization potential, which this paper aims to explore. In fact, this paper discusses the only two existing technologies on the market, Polymer Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs). Unfortunately, these commercial systems mainly use natural gas as primary fuel due to its cost and practicality (easy transport and storage, existing infrastructures, etc.). Using Belgium as a case study, this paper shows that their GHG mitigation potential remains rather insignificant compared to the average individual carbon footprint if their fuel is not decarbonised. Even so, their mitigation potential would still be far from sufficient, and other measures, including behavioural changes, would still need to be implemented. Nevertheless, some emerging fuel cell technologies, such as Direct Carbon Solid Oxide Fuel Cells (DC-SOFCs) or Direct Formic Acid Fuel Cells (DFAFCs), offer the possibility of facilitating pure CO2 capture at their anode outlet, thus allowing for potential negative emissions. Using a case study of the electricity demand of an average Belgian home (with two adults) supplied by an efficient biomass-fuelled DC-SOFC, this paper shows that these negative emissions could be up to about 4 tCO2eq/year. By comparison, the IPCC's Sixth Assessment Report estimated the emissions footprint that could never be mitigated, even with future net-zero CO2 emissions, to be 1 tCO2eq/year per capita, implying that climate neutrality will require similar levels of carbon sequestration. In populous Western countries, natural carbon sinks are unlikely to be sufficient, and the potential negative emissions of emerging fuel cell technologies will be welcome.

Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization

AbstractThe severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO2 emissions. For the SSP1‐1.9 decarbonization scenario there is >99% probability that mean global warming exceeds 1.5°C, approximately even odds that it reaches 2°C, and ∼90% probability that the hottest year globally exceeds 2023 by at least 0.5°C. Further, for the SSP2‐4.5 decarbonization scenario, there is >90% probability that the hottest annual global temperature anomaly is twice the 2023 anomaly. That our framework makes highly accurate out‐of‐sample predictions of the hottest historical year provides confidence in the predicted future probabilities, suggesting substantial risks from the extreme local conditions that are likely to result from globally hot years during rapid decarbonization.

Study on the Influence of Geological Parameters of CO2 Plume Geothermal Systems

At present, there are relatively few studies on the exploitation of geothermal resources in depleted high-temperature gas reservoirs with carbon dioxide (CO2) as the heat-carrying medium. Taking the high-temperature gas reservoir of the Huangliu Formation in the Yingqiong Basin as the research object, we construct an ideal thermal and storage coupling model of a CO2 plume geothermal system using COMSOL6.2 software to conduct a sensitivity analysis of geological parameters in the operation of a CO2 plume geothermal system. The simulation results show that, compared with medium- and low-temperature reservoirs, a high-temperature reservoir exhibits higher fluid temperature in the production well and a higher heat extraction rate owing to a higher initial reservoir temperature but has a shorter system operational lifetime; the influence of the thermal conductivity of a thermal reservoir on the CO2 plume geothermal system is relatively minor, being basically negligible; and the thinner the thermal reservoir is, the faster the fluid temperature in the production well decreases and the shorter the thermal breakthrough time. These findings form a basis for selecting heat extraction areas for CO2 plume geothermal systems and also provide a theoretical reference for future practical CO2 plume geothermal system projects.

ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO2 Emissions

Mitigating CO2 emissions is essential to reduce climate change and its adverse effects on ecosystems. Photovoltaic electricity is 30 times less carbon-intensive than coal-based electricity, making solar PV an attractive option in reducing electricity demand from fossil-fuel-based sources. This study looks into utilizing solar PV electricity production on a large university campus in an effort to reduce CO2 emissions. The study involved investigating 153 buildings on the campus, spanning nine years of data, from 2015 to 2023. The study comprised four key phases. In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in Phase IV to project future CO2 emissions post-solar PV installation. This comparison estimated a potential emissions reduction and assessed the university’s progress toward its net-zero emissions goals. The study’s findings suggest that solar PV implementation could reduce the campus’s CO2 footprint by approximately 18% for the studied cluster of buildings, supporting sustainability and cleaner energy use on the campus.

Assessing CO2 Reduction Effects Through Decarbonization Scenarios in the Residential and Transportation Sectors: Challenges and Solutions for Japan’s Hilly and Mountainous Areas

Depopulation, aging, and regional decline are becoming increasingly serious issues in Japan’s hilly and mountainous areas. Focusing on mitigating environmental damage and envisioning a sustainable future for these regions, this study examines the potential for reducing CO2 emissions in the residential and transportation sectors by 2050. Bottom-up simulations were used to estimate CO2 emissions. Subsequently, six decarbonization scenarios were formulated, considering various measures from the perspectives of population distribution and technological progress. Based on these scenarios, this study analyzes changes in future population, energy consumption, and CO2 emissions by 2050. The results of this study show the following. (1) Depopulation and aging problems in these regions are expected to become more severe in the future. It is necessary to take action to promote sustainable regional development. (2) Pursuing decarbonization has a positive impact on enhancing regional sustainability; however, maintaining the intensity of measures at the current level could lead to a reduction of only 40% in CO2 emissions per capita by 2050 compared with 2020. (3) Scenarios that strengthen decarbonization measures could achieve a reduction of over 95% by 2050, indicating that carbon neutrality is attainable. However, this will require implementing measures at a higher intensity, especially in the transportation sector.

Future response of ecosystem water use efficiency to CO2 effects in the Yellow River Basin, China

Abstract. Ecosystem water use efficiency (WUE) is pivotal for understanding carbon–water cycle interplay. Current research seldom addresses how WUE might change under future elevated CO2 concentrations, limiting our understanding of regional ecohydrological effects. We present a land–atmosphere attribution framework for WUE in the Yellow River basin (YRB), integrating the Budyko model with global climate models (GCMs) to quantify the impacts of climate and underlying surface changes induced by CO2. Additionally, we further quantitatively decoupled the direct and secondary impacts of CO2 radiative and biogeochemical effects. Attribution results indicate that WUE in the YRB is projected to increase by 0.36–0.84 gC kg−1H2O in the future, with climate change being the predominant factor (relative contribution rate of 77.9 %–101.4 %). However, as carbon emissions intensify, the relative importance of land surface changes becomes increasingly important (respective contribution rates of −1.4 %, 14.9 %, 16.9 %, and 22.1 % in SSP126, SSP245, SSP370, and SSP585). Typically, WUE is considered a reflection of an ecosystem's adaptability to water stress. Thus, we analyzed the response of WUE under different scenarios and periods and various drought conditions. The results show a distinct “two-stage” response pattern of WUE to drought in the YRB, where WUE increases under moderate–severe drought conditions but decreases as drought intensifies across most areas. Furthermore, GCM projections suggest that plant adaptability to water stress may improve under higher-carbon-emission scenarios. Our findings enhance the understanding of regional ecohydrological processes and provide insights for future predictions of drought impacts on terrestrial ecosystems.

Energy, exergy, environmental and economic analysis of solvent-based post-combustion carbon capture for ethylene production

Ethylene production is an energy-intensive process with significant CO2 emissions. As demand for ethylene rises, integrating carbon capture technology in ethylene plants is crucial for mitigating emissions. This study provides a thermodynamic, exergy, comparative life cycle assessment (LCA) and economic analysis of post-combustion carbon capture (PCC) for large-scale ethylene plants using chemical absorption with 30 wt% monoethanolamine (MEA) and 40 wt% piperazine (PZ) as solvent. In addition, a case study involving the integration of solar-assisted post-combustion carbon capture (SPCC) and photovoltaic (PV) power generation with ethylene production is built to evaluate future CO2 emission reduction potential. The results show that 0.98 tons of CO2 are captured per ton of ethylene, potentially reducing life cycle CO2 emissions by 30.3% to 68.9% for different PCC scenarios. Integrating SPCC and PV significantly reduces CO2 emissions, highlighting the potential of solar power in carbon reduction. Furthermore, feedstock acquisition of ethylene production emits large amount of greenhouse gas (GHG), indicating that low-carbon feedstock production can further reduce life cycle GHG emissions. This work is significant for the ethylene industry’s low-carbon transition and clean production, offering insights into enhancing sustainability and environmental performance through advanced carbon capture technologies and renewable energy integration.

Investigation of highly efficient CO2 hydrogenation at ambient conditions using dielectric barrier discharge plasma

The increasing utilization of CO2 for synthesizing high-value fuels or essential chemicals is a potentially effective approach to mitigating global warming and climate change. Compared to thermal catalytic CO2 conversion under hash operating conditions (400–500°C, 10 ​MPa), non-thermal plasma can overcome kinetic barriers and trigger reactions beyond thermal equilibrium at ambient temperature and pressure. In this study, the effects of operating conditions (discharge frequency, input power, and gas flow rate) and geometrical parameters (discharge length, discharge gap, and dielectric materials) have been extensively analyzed using typical cylindrical dielectric barrier discharge (DBD) plasma. The discharge characteristics changed by operating conditions (including waveforms of applied voltage and current) are compared, indicating higher applied voltage and lower gas flow rate can strengthen the filamentary discharges. The results demonstrate CO2 conversion rate increases with the increase of applied voltage and the decrease of CO2/H2 ratio, achieving its maximum value of 43.0% at 20 ​mL/min. The highest energy efficiency of 3771.9 ​μg/kJ for CO generation is obtained at the applied voltage of 5.5 ​kV and gas flow rate of 40 ​mL/min, respectively. Besides, the constructure of plasma reactor also impacts the performance of CO2 conversion. On the one hand, the discharge gap has a significant role in the variation of CO2 conversion and product selectivity, which is attributed to the electric field density and corresponding electron-induced reaction. On the other hand, the circulating water-cooling jacket was used to find out the influence of reaction temperature, which switched the product from CO to CH4. This work will pave the way for a sustainable alternative towards future CO2 conversion and utilization.

Acclimation of Photosynthesis to CO2 Increases Ecosystem Carbon Storage due to Leaf Nitrogen Savings.

Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose-1,5-bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon-nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO2. Plants acclimate to elevated CO2 by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO2 can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO2 conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO2.

Underestimation of global soil CO2 flux measurements caused by near-surface winds

Soil respiration (Rs) is the largest source of atmospheric CO2, and an accurate understanding of the relationship between near-surface winds, CO2 release from the soil surface, and measurement methods is critical for predicting future atmospheric CO2 concentrations. In this study, the relationship between wind speed and soil CO2 fluxes is elucidated on a global scale through meta-analysis, and the flux measurement methodology is further explored in conjunction with the results of a controlled trial to clarify the uncertainty of the measurement results. The results indicate that near-surface wind speed is positively correlated with soil CO2 release and that near-surface winds result in increased soil CO2 gas release. Wind disturbance affects both the concentration gradient and gas chamber measurements, and the lower calculated soil CO2 release conflicts with the notion that the wind pump effect and Bernoulli effect of negative pressure cause a greater surface gas exchange. The results of the log-response ratios indicate that near-surface winds lead to an underestimation of 12.19–19.75% in widely-used gas chamber method measurements. The results of this study imply that some of the current Rs measurements are biased and that the influence of near-surface winds on Rs measurements needs to be urgently addressed to assess the terrestrial carbon cycle more accurately and develop climate change response strategies.

A Modeling Framework of Atmospheric CO2 in the Mediterranean Marseille Coastal City Area, France

As atmospheric CO2 emissions and the trend of urbanization both increase, the ability to accurately assess the CO2 budget from urban environments becomes more important for effective CO2 mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO2 modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO2 analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO2 values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km−2 month−1 captured in Summer 2016 by the three measurement towers, providing evidence of the receptors’ ability to constrain the domain. Finally, a case study showcases the model’s ability to capture the rapid change in CO2 when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO2 investigations in the region, which can inform policymakers carrying out CO2 mitigation strategies.

Impacts of climate change on phenology, yield, and water productivity of wheat in a semi-arid region of India using the CERES-Wheat model

ABSTRACT In the present study, the Crop Environment Resource Synthesis (CERES)-Wheat model was used to study the impacts of climate change on phenology, yield, and water productivity of wheat. The model was run with the baseline period (1980–2010) and three future periods, namely, the 2030s, 2050s, and 2070s under two representative concentration pathway (RCP) scenarios, namely, RCP 4.5 and RCP 8.5. The results indicated a substantial decline in phenology, grain yield, biomass, and crop water productivity (CWP) under both scenarios. The grain yield of wheat showed a decline by 12.3, 20.5, and 19.8% during the 2030s, 2050s, and 2070s, respectively, under RCP 4.5 at baseline CO2 concentration, while at elevated concentration of CO2, the reduction was 10.2, 15.7, and 14.9%, respectively. Under RCP 8.5, the yield reduction was 18.8, 26.5, and 27.3% during the 2030s, 2050s, and 2070s, respectively, with baseline concentration of CO2, while with increased CO2 the yield reduction was 7, 12.6, and 8.9%, respectively. CWP decreased at baseline CO2 by 9, 17.6, and 18.3% for RCP 4.5 and 10.6, 20.6, and 22.6% for RCP 8.5 during the 2030s, 2050s, and 2070s, respectively. However, beneficial impact of CO2 fertilization on CWP was noticed in both RCP scenarios, resulting in relatively less reduction under future CO2 concentration.

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Marine carbon sink dominated by biological pump after temperature overshoot

In the event of insufficient mitigation efforts, net-negative CO2 emissions may be required to return climate warming to acceptable limits as defined by the Paris Agreement. The ocean acts as an important carbon sink under increasing atmospheric CO2 levels when the physico-chemical uptake of carbon dominates. However, the processes that govern the marine carbon sink under net-negative CO2 emission regimes are unclear. Here we assessed changes in marine CO2 uptake and storage mechanisms under a range of idealized temperature-overshoot scenarios using an Earth system model of intermediate complexity over centennial timescales. We show that while the fate of CO2 from physico-chemical uptake is very sensitive to future atmospheric boundary conditions and CO2 is partly lost from the ocean at times of net-negative CO2 emissions, storage associated with the biological carbon pump continues to increase and may even dominate marine excess CO2 storage on multi-centennial timescales. Our findings imply that excess carbon that is attributable to the biological carbon pump needs to be considered carefully when quantifying and projecting changes in the marine carbon sink.