CO2 Emission Rates Research Articles

Oil palm (Elaeis guineensis) plantation on tropical peatland in South East Asia: Photosynthetic response to soil drainage level for mitigation of soil carbon emissions

While existing moratoria in Indonesia and Malaysia should preclude continued large-scale expansion of palm oil production into new areas of South-East Asian tropical peatland, existing plantations in the region remain a globally significant source of atmospheric carbon due to drainage driven decomposition of peatland soils. Previous studies have made clear the direct link between drainage depth and peat carbon decomposition and significant reductions in the emission rate of CO2 can be made by raising water tables nearer to the soil surface. However, the impact of such changes on palm fruit yield is not well understood and will be a critical consideration for plantation managers. Here we take advantage of very high frequency, long-term monitoring of canopy-scale carbon exchange at a mature oil palm plantation in Malaysian Borneo to investigate the relationship between drainage level and photosynthetic uptake and consider the confounding effects of light quality and atmospheric vapour pressure deficit. Canopy modelling from our dataset demonstrated that palms were exerting significantly greater stomatal control at deeper water table depths (WTD) and the optimum WTD for photosynthesis was found to be between 0.3 and 0.4 m below the soil surface. Raising WTD to this level, from the industry typical drainage level of 0.6 m, could increase photosynthetic uptake by 3.6 % and reduce soil surface emission of CO2 by 11 %. Our study site further showed that despite being poorly drained compared to other planting blocks at the same plantation, monthly fruit bunch yield was, on average, 14 % greater. While these results are encouraging, and at least suggest that raising WTD closer to the soil surface to reduce emissions is unlikely to produce significant yield penalties, our results are limited to a single study site and more work is urgently needed to confirm these results at other plantations.

Gas Emissions and Environmental Benefits of Wheat Cultivated under Different Fertilization Managements in Mollisols

The NH3, N2O and CO2 emissions from farmland soil pose a great threat to the environment, and the application of organic fertilizer and other reasonable fertilization measures can reduce soil gas emissions. However, research into greenhouse gas emissions and environmental benefits under the combined measures of partial substitution of organic fertilizer and phased application of chemical fertilizer is limited. Herein, a field experiment involving soil gas emission monitoring was conducted to study the effects of chemical fertilizer application in stages on Mollisols’ gas emissions and environmental benefits based on the partial replacement of chemical fertilizer with organic fertilizer. Five treatments were set up, including conventional nitrogen application (CF); no nitrogen application (N0); and one-stage (N1), two-stage (N2) and three-stage (N3) application of chemical nitrogen based on 25% of chemical nitrogen being replaced with organic fertilizer. The results showed that N1 had the best emission reduction. Compared with CF, N1 reduced NH3 volatilization and N2O and CO2 emission accumulation by 27.64%, 12.09% and 15.48%, respectively. Compared with N2 and N3, N1 could better reduce the soil urease, nitrate reductase, catalase and β-glucosidase activities, reduce the rate of the conversion of urea and organic carbon, increase the content of NH4+-N in the soil and reduce the NH3 volatilization rate and N2O and CO2 emission rates. A comprehensive analysis showed that N1 showed the best effects in reducing the soil gas emission rate, and environmental cost.

Functional Diversity and CO2 Emission Characteristics of Soil Bacteria during the Succession of Halophyte Vegetation in the Yellow River Delta.

Carbon dioxide (CO2) is the most important greenhouse gas in the atmosphere, which is mainly derived from microbial respiration in soil. Soil bacteria are an important part of the soil ecosystem and play an important role in the process of plant growth, mineralization, and decomposition of organic matter. In this paper, we discuss a laboratory incubation experiment that we conducted to investigate the CO2 emissions and the underlying bacterial communities under the natural succession of halophyte vegetation in the Yellow River Delta by using high-throughput sequencing technology and PICRUSt functional prediction. The results showed that the bacterial abundance and diversity increased significantly along with the succession of halophyte vegetation. Metabolic function is the dominant function of soil bacteria in the study area. With the succession of halophyte vegetation, the rate of CO2 emissions gradually increased, and were significantly higher in soil covered with vegetation than that of the bare land without vegetation coverage. These results helped to better understand the relationships of soil bacterial communities under the background of halophyte vegetation succession, which can help to make efficient strategies to mitigate CO2 emissions and enhance carbon sequestration.

Effects of addition of leaf litter with different chemical properties on soil organic carbon mineralization and priming effect

Litter inputs can affect the mineralization of soil organic carbon (SOC). However, it is yet unknown how the input of leaf litter with different chemical properties drives SOC mineralization and priming effect. In this study, 13C-labeled leaf litter of six tree species were added to soil cores (10 cm depth) collected from a natural secondary forest in subtropical region. We examined the effects of different leaf litters on total soil CO2, litter-derived and soil-derived CO2 emission rates and accumulation and priming effect. We further examined the relationships between litter chemical properties and CO2 accumulation and priming effect. Our results showed that leaf litter addition increased total soil CO2 and soil-derived CO2 emission rates and accumulations, and that there were positive priming effects ranging from 68% to 128%. Soil organic carbon mineralization and priming effects varied among tree species. The Pearson correlation and stepwise multiple linear regression analysis showed that the litter-derived CO2 accumulation had negative correlation with leaf litter C, P and cellulose concentrations, whereas the soil-derived CO2 accumulation were positively correlated to litter C:N and lignin:N. The results implied that tree species could influence SOC mineralization and litter-induced priming effect. Thus it could mitigate soil C loss when we afforested plantation with high quality leaf litter in subtropical region.

Response of Carbon Emissions and the Bacterial Community to Freeze-Thaw Cycles in a Permafrost-Affected Forest-Wetland Ecotone in Northeast China.

Climate warming can affect freeze–thaw cycle (FTCs) patterns in northern high-latitude regions and may affect permafrost carbon emissions. The response of carbon release and microbial communities to FTCs has not been well characterized. Here, we conducted laboratory incubation experiments to investigate the relationships among carbon emissions, bacterial community, and soil variables in a permafrost-affected forest–wetland ecotone in Northeast China. The emission rates of CO2 and CH4 increased during the FTCs. FTC amplitude, FTC frequency, and patch type had significant effects on carbon emissions. FTCs increased the contents of soil DOC, NH4+-N, and NO3−-N but reduced bacterial alpha diversity. CO2 emissions were mainly affected by bacterial alpha diversity and composition, and the inorganic nitrogen content was the important factor affecting CH4 emissions. Our findings indicated that FTCs could significantly regulate CO2 and CH4 emissions by reducing bacterial community diversity and increasing the concentration of available soil substrates. Our findings shed new light on the microorganism-substrate mechanisms regulating the response patterns of the soil carbon cycle to FTCs in permafrost regions.

Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm

COVID-19 has affected energy consumption and production pattern in various sectors in both rural and urban areas. Consequently, energy demand has increased. Therefore, most health care centers report a shortage of energy, particularly during the summer seasons. Therefore, integrating renewable energies into hospitals is a promising method that can generate electricity demand reliably and emits less CO2. In this research paper, a hybrid renewable energy system (HRES) with hydrogen energy storage is simulated to cover the energy demand of sections and wards of a hospital that dealt with COVID-19 patients. Produced Oxygen from the hydrogen storage system is captured and stored in medical capsules to generate the oxygen demand for the patients. Results indicate that 29.64% of the annual consumed energy is utilized in COVID-19 sections. Afterward, modeled system has been optimized with a neural network-genetic algorithm to compute the optimum amount of the demand power from the grid, CO2 emission, oxygen capsules, and cost rate. Results determine that by having 976 PV panels, 179 kW fuel cell, and 171.2 kW electrolyzer, annual CO2 emission is 315.8 tons and 67,833 filled medical oxygen capsules can be achieved. The cost rate and demand electricity from the grid for the described system configuration are 469.07 MWh/year and 18.930 EUR/hr, respectively.

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO2 emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO2 emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO2 emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO2 emission rates. The CO2 emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R2) and the root mean square error (RMSE) between the monitored CO2 emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO2 emissions from LDDTs.

Progressive freeze-thaw redistributes water, solute and CO2 emissions across soil layers – The role of soil particle size

Progressive freeze-thaw redistributes water and solute among soil layers, and thus perturbs soil carbon mineralization. This potentially introduces variations when differently-sized soil aggregates experience progressive thawing and detachment. In this study, a Mollisol was dry-sieved into three particle sizes (<0.125, 0.125–0.5 and 0.5–1 mm). Each was refilled into a soil column, rewetted to saturation, frozen at −15 °C for 12 h, and then thawed at ambient temperature. During thawing, the soil columns were peeled by layers from the outermost to the inner core into six layers (T1 ∼ T6), to simulate soil detachment while progressive thawing. Our results show that: 1) The outermost layer T1, thawed earlier and thus peeled off first, had the highest water content (102.1–151.2 %) but the lowest electrical conductivity (EC, 63.2–68.3 μS·cm−1). On the contrary, the inner core T6, thawed the last, was driest with merely 24.1–28.4 % water content yet higher EC of 61.0–87.0 μS·cm−1. Such polarized distributions were most pronounced among the soil layers composed of fine aggregates. 2) The CO2 emission rates from the T1 were in general lower than that from the T6 (on average 56.1–65.6 vs. 70.0–84.8 μg CO2-C·g soil −1 7d −1). The soil columns composed of fine aggregates released significantly less CO2 than the coarser aggregates. Overall, the convection of free water during freezing and the diffusion of ice-insoluble materials from freezing front to unfrozen zones, collectively resulted in the polarized patterns of soil water, solute and CO2 emissions. This challenges the conventional investigations based on complete freeze-thaw, highlighting the necessity to distinguish inter-layer heterogeneity during progressive thawing. The varying responses of differently-sized aggregates call for systematic investigations on how progressively thawing and detachment affect slope-scale carbon biogeochemical processes.

Evaluation of real-world emissions of China V heavy-duty vehicles fueled by diesel, CNG and LNG on various road types.

Natural gas has been widely considered as an alternative fuel to diesel on heavy-duty vehicles (HDVs). To evaluate the real-world emissions, 19 diesel HDVs, 3 CNG HDVs and 9 LNG HDVs were tested using portable emission measurement system on urban, suburban and freeway roads . The results show that average emission rates of CO, HC and NOx from diesel, CNG and LNG HDVs tend to rise with the increasing of VSP. Due to different emission control strategies and complicated driving cycle, NOx reduction rates of real-world emission factors are lower than those of the corresponding limits from China II to China V diesel HDVs. CO and NOx emission factors from all tested HDVs on urban road are generally higher than those on suburban road in the same velocity intervals, which means driving behaviors on different road types have great influence on emission factors in the same velocity intervals. NOx emission factors from LNG HDVs are higher than those from diesel HDVs, indicating that using China V LNG HDVs instead of China V diesel HDVs could not be an ideal alternative for freeway transportation. As CO and NOx comprehensive emission factors from China V CNG HDVs are higher than those from China V diesel, even higher than China IV diesel HDVs, China V CNG HDVs are unable to be considered as cleaner vehicles when compared to China V diesel HDVs in the study. It is noticeable that HC emission factors from CNG and LNG HDVs are extremely higher compare with diesel HDVS. NO2 emission factors from LNG HDVs are 2.14-9.19 and 4.74-8.53 times than those from diesel and CNG HDVs with various road types. Our study can provide development road map for different fuel types of HDVs and the reference for new emission legislation of HDVs.

Nexus between selected macroeconomic variables and carbon emission in Kenya

Agriculture is not only influenced by climate change, but it is also one of the significant contributors of CO2 emission. Understanding CO2 emission and macroeconomic variables is critical to solving the challenges and threats faced by Kenya's agriculture and environment. This study used the Environmental Kuznets Curve (EKC) hypothesis and the autoregressive distribution lag model (ADLM) to analyze the relationships of CO2 emission with agricultural output, government direct investment, trade openness, and inflation rate in Kenya from 1983 to 2019. The study found that there exists a positive (direct) relationship between CO2 emission and foreign direct investment in the long run in Kenya. Additionally, CO2 emission and trade openness have a negative (indirect) and statistically significant relationship after the error correction term adjustment in the long run. Moreover, the relationship between CO2 emission and agricultural output is positive (direct) and statistically significant in the long run. There is a positive (direct) and statistically insignificant relationship between CO2 emission and inflation rate in the short run. Notably, the EKC hypothesis indicated that the Kenya's economy is still on the environmental degradation trade-off through the gradual increase of both CO2 emission and agricultural output. Our results are important to Kenya's economy because the derived insights will assist in relevant departments to formulate sustainable strategies to minimize environmental degradation.

Summer soil CO2 emission from bog surface: controlling factors

The peatlands in the northern hemisphere cover just 3% of the global landmass but their impact to the carbon emission is huge. The current climate changes exert influence on these ecosystems by changing water supply, temperature regime, plant growing activity and others. Nowadays studies predicted the important role of the northern bogs and peatlands as an additional source of atmospheric CO2. In our study we estimated how microrelief and microclimatic conditions can control the CO2 emission from the bog area. We compared also the waterlogged bog conditions and forest ecosystem to find out the main drivers of soil emission dynamics during the summer season. The rate of CO2 emission varies widely within bog area depending on the microrelief of the area: hollow – 0.74±0.03, ridge – 1.69±0.08 kg CO2 m–2. The comparative analysis versus the forest area showed that the upland parts of the bog area are not inferior to the forest area in terms of the emission rate. Moisture conditions determined the CO2 efflux for the hollow site (r=0.49, p<0.05) and forested area (r=0.39, p<0.05). The temperature impact is observed for all sites and it is significant throughout the season.

Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions

Currently, most of the performance analysis and optimization design of the gas turbine combined cycle (GTCC) units ignore the economic loss to the environment caused by the pollutant emissions. In this paper, based on the structure theory of thermoeconomics, an environmental thermoeconomic cost model is established. And then an annual total cost model is established, which can reflect the real economic performance. Based on the comprehensive analysis of the energy, economy and environment, with the objectives of the higher exergy efficiency, less annual total cost and lower specific CO2 emission rate, a multi-objective optimization study of the GTCC unit is conducted using the genetic algorithm (GA) in this paper. Pareto frontiers for the multi-objective optimization has been obtained with GA, and in turn a design plan can be selected from the Pareto frontier according to the preference. The multi-objective optimization results show that the optimized system can greatly increase the system environmental thermoeconomic performance. Moreover, the effects of main variables on the economic performance index of the optimized system are investigated. The results show that the natural gas price has the greater impact on the economic performance index compared with the pollutants environmental damage cost and the non-energy cost.

Sequential air pollution emission estimation using a hybrid deep learning model and health-related ventilation control in a pig building

Ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) are predominant gases that are responsible for indoor air quality and air pollution emitted from pig buildings. They are critical for the health of pigs, farm workers, and people living nearby. To achieve an accurate estimation of gas emissions, firstly, hybrid deep learning driven sequential Concentration Transport Emission Model (DL-CTEM) was proposed to estimate the emissions of NH3, CO2, and H2S from a pig building. Then, optimal ventilation control strategies were put forward to improve health-related gas concentrations and air pollution from the pig building. Fifty-three days of hourly measurements data were divided into training data and test data for the DL-CTEM. It was shown that the mean errors between the measurements and the predictions of the proposed model for NH3, CO2, and H2S concentrations were 0.1 ppm, 79.2 ppm, and 106.3 ppb, respectively. The proposed model outperformed when it was built with an optimal structure in the long short-term memory (LSTM) layer. The mean emission rates of NH3, CO2, and H2S based on DL-CTEM were 4.2 mg min−1, 2887.5 mg min−1, and 2.1 μg min−1. It could provide a feasible way for air pollution emission estimation and health-related ventilation control in a pig building.

Fuel use and emission rates reduction potential for light-duty gasoline vehicle eco-driving

Eco-driving offers potential to reduce fuel use and emission rates for light-duty gasoline vehicles (LDGVs). The objective is to quantify real-world route-level and segment-level fuel use and emission rates reduction potential for LDGV eco-driving. Three million seconds of real-world speed trajectory data were analyzed based on predominantly naturalistic driving of 160 drivers on eight mesoscale routes. The routes were further divided into 199 segments. A Vehicle Specific Power modal model was used to estimate trajectory-average fuel use and emission rates of CO2, CO, hydrocarbons, NOx, and particulate matter and to identify eco-driving trajectories. For route-level eco-driving, fuel use and emission rates reduction potential ranges from 6% to 40%, compared to average fuel use and emission rates estimated based on all trajectories. Eco-driving focused on fuel savings typically reduced air emissions and vice versa. Route-level eco-driving typically but not always concurrently reduces segment-level fuel use and emission rates. These co-benefits and tradeoffs can be used to guide LDGV eco-driving decisions.

Prediction of exhaled carbon dioxide concentration using a computer-simulated person that included alveolar gas exchange.

Accurate prediction of inhaled CO2 concentration and alveolar gas exchange efficiency would improve the prediction of CO2 concentrations around the human body, which is essential for advanced ventilation design in buildings. We therefore, developed a computer-simulated person (CSP) that included a computational fluid dynamics approach. The CSP simulates metabolic heat production at the skin surface and carbon dioxide (CO2 ) gas exchange at the alveoli during the transient breathing cycle. This makes it possible to predict the CO2 distribution around the human body. The numerical model of the CO2 gas exchange mechanism includes both the upper and lower airways and makes it possible to calculate the alveolar CO2 partial pressure; this improves the prediction accuracy. We used the CSP to predict emission rates of metabolically generated CO2 exhaled by a person and assumed that the tidal volume will be unconsciously reduced as a result of exposure to poor indoor air quality. A reduction in tidal volume resulted in a decrease in CO2 emission rates of the same magnitude as was observed in our published experimental data. We also observed that the predicted inhaled CO2 concentration depended on the flow pattern around the human body, as would be expected.

Modification of a 240 kWth grate incineration system for oxyfuel combustion of wood chips

The objective of this work is the application of the oxyfuel combustion technology for grate incineration systems to increase the CO2 concentration in the flue gas for higher efficiency when processing the gas for storage or utilization. Therefore, an experimental 240 kWth moving grate combustion facility has been modified to allow the investigation of the combustion behavior under various oxyfuel conditions. Subsequent to the modification, combustion experiments with dried wood chips under air and several oxyfuel atmospheres have been performed. During oxyfuel operation, the oxygen concentration in the gas supply streams towards the furnace has been set to 30 vol.-% dry with recycled flue gas being the rest of the gas composition. The oxygen distribution between primary and secondary zone has been varied (30%/70%, 40%/60%). Additionally, one case with increased oxygen concentration in the primary zone (40 vol.-% dry) has been investigated. The results show that oxyfuel grate incineration of the given fuel in this setup is feasible. However, the combustion behavior differs significantly between air and oxyfuel conditions in the investigated cases. The CO2 concentration in the dry flue gas could be increased to around 73 vol.-% with 5.7 vol.-% excess O2. Compared to the reference air cases the emission rates of CO were also increased during oxyfuel operation. However, it seems manageable given enough excess oxygen (∼7 vol.-% dry). Although the temperature at the end of the furnace is comparable among the cases, oxyfuel combustion leads to lower maximum temperatures along the combustion chamber. Differences between 150 °C and 200 °C have been measured among corresponding air and oxyfuel cases. Also, the temperatures of the grate and inside the fuel bed are reduced during oxyfuel operation even in the case with increased O2 concentration leading to the impression that the thermal strain on the grate is lower compared to air operation with the same fuel throughput.

Volume and rate of volcanic CO2 emissions governed the severity of past environmental crises

The emplacement of large igneous provinces (LIPs) has been linked to catastrophic mass extinctions in Earth's history, but some LIPs are only associated with less severe oceanic anoxic events, and others have negligible environmental effects. Although it is widely accepted that massive magma outpouring can affect the environment through volatile degassing, it remains debated what controls the severity of environmental crises. Here, we demonstrate that the second-most-voluminous Phanerozoic LIP, the Kerguelen LIP, may have contributed to the early Aptian oceanic anoxic event 1a, a global event previously believed to have been caused by the Ontong Java LIP. Geochronological data show that the earliest eruptions of the Kerguelen LIP preceded the onset of oceanic anoxic event 1a by at least ∼5 million years. Analyses of CO2 abundances in melt inclusions combined with Monte Carlo simulations reveal that the volume and degassing rate of CO2 emissions from the Kerguelen LIP are an order of magnitude lower compared to LIPs that caused severe mass extinctions. We propose that the severity of volcanism-related environmental and biotic perturbations is positively correlated with the volume and rate of CO2 emissions. Our results highlight the significant importance of reducing and slowing down CO2 emission in preventing future disastrous environmental consequences.

Forecasting of Emission Co-reduction of Greenhouse Gases and Pollutants for the Road Transport Sector in Lanzhou Based on the LEAP Model

With the continuous increase in transportation activities, the transportation sector has become an important source of global greenhouse gases. In 2019, road vehicles accounted for nearly three-quarters of the CO2 emissions of the entire transportation sector and will be the key to achieving carbon peaks in the transportation sector. At the same time, air pollutants emitted by road vehicles are also one of the threats to the environment and human health. Based on the long-range energy alternatives planning system (LEAP) model, we constructed the baseline (BAU) scenario, low-carbon (LC) scenario, and enhanced low-carbon (ELC) scenario for the development of the road transport sector in Lanzhou from 2015 to 2040 and simulated energy consumption and emission co-reduction of greenhouse gases and pollutants under policies and measures. The results showed that the energy consumption and CO2 emissions of the LC scenario will peak in 2026, whereas those in the ELC scenario will peak in 2020. In these two scenarios, pollutant emissions such as NOx, CO, HC, PM2.5, and PM10 began to decline sharply between 2015 and 2017, and the downward trend will slow down gradually around 2023. Based on the feasibility of measures and the cost of abatement, the LC scenario can be used as a road vehicle carbon peak scenario in Lanzhou. In this scenario, the reduction rates of energy consumption, CO2, NOx, CO, HC, PM2.5, and PM10 emissions will reach -24.17%, -26.57%, -55.38%, -65.91%, -72.87%, -76.66%, and -77.18% compared with those under the BAU scenario by 2040. At present, the road vehicles in Lanzhou City should focus on structural optimization measures such as clean-energy use of public transportation, electrification of small passenger cars, and phasing out old cars, as well as vigorously promoting low-carbon travel and improving energy efficiency accompanying the development of automotive technology. These efforts will effectively control CO2 and pollutant emissions by road vehicles, and carbon peaks will be achieved as soon as possible. In addition, it is necessary to pay attention to the changes in vehicle types during the implementation of these measures, which most contribute CO2 and various pollutants, in order to make the measures more targeted by changing the number or the market share of new energy of focused vehicle types.

Perennial alley cropping contributes to decrease soil CO2 and N2O emissions and increase soil carbon sequestration in a Mediterranean almond orchard

The implementation of alley cropping in orchards can be a sustainable strategy to increase farm productivity by crop diversification and contribute to climate change mitigation. In this research, we evaluated the short-term effect of alley cropping with reduced tillage on soil CO2 and N2O emissions and soil total organic carbon (TOC) in an almond orchard under Mediterranean rainfed conditions. We compared an almond monoculture with tillage in all plot surface (MC) with almond crop with reduced tillage and growth of Capparis spinosa (D1) and almond crop with reduced tillage and growth of Thymus hyemalis (D2). For two years, soil CO2 and N2O were measured, with soil sampling at the start and end of the experimental period. Results showed that CO2 emission rates followed the soil temperature pattern, while N2O emissions were not correlated with temperature nor moisture. Soil CO2 emissions were significantly higher in MC (87 mg m−2 h−1), with no significant differences between D1 and D2 (69 mg m−2 h−1). Some peaks in CO2 effluxes were observed after tillage operations during warm days. Soil N2O emission rates were not significantly different among treatments. Cumulative CO2 and CO2 equivalent (CO2e) emissions were significantly highest in MC. When CO2e emissions were expressed on a crop production basis, D2 showed the significantly lowest values (5080 g kg−1) compared to D1 (50,419 g kg−1) and MC (87,836 g kg−1), owing to the high thyme yield, additional to the almond yield. No production was obtained for C. spinosa, since at least two more years are required. TOC did not change with time in MC neither D1, but it significantly increased in D2 from 3.85 g kg−1 in 2019 to 4.62 g kg−1 in 2021. Thus, alley cropping can contribute to increase the agroecosystem productivity and reduce CO2 emissions. However, it is necessary to grow evergreen alley crops such as thyme to obtain short-term increases in soil organic matter. Thus, to estimate increases in TOC with alley cropping, the plantation density and the period required by the crop to cover most of the surface are essential factors at planning the cropping strategy.

The Digital Carbon Footprint: Threat to an Environmentally Sustainable Future

With digitalization at its peak, every online action we take has an environmental impact. There is a growing concern about the world's ever-increasing carbon emission due to technological advancement. The vast majority of human actions have been proved harmful to the environment. This effect has been mostly tied to available carbon emissions. On the other hand, recent findings have raised awareness of digital carbon emissions. These harmful emissions represent the available CO2 emissions rate resulting from generic digitization concepts. The advancement of technology has considerably contributed to CO2 emissions. This study paper discusses the total effects of carbon emissions. It also shows the rates of carbon emissions caused by the tech industry worldwide. The article describes how digital services have boosted carbon emissions and the number of regions affected by the higher rates. The study focuses on the relationship between carbon emissions and digitization, remedies to the problem, and an overall analysis of the global digital carbon footprint.