Recent CO2 emission and projections in Chinese provinces: New drivers and ensemble forecasting.

Although extensive studies focused on the driver of changing CO2 emission, the roles of labor and capital were largely ignored in shaping spatiotemporal change in CO2 emission and forecasting differences on CO2 emission was few considered, hindering relevant policymaking towards sustainable development in both climate change mitigation and economic growth for developing countries in particular. To fill the gap above, the study explored the roles of capital and labor in contributing to recent CO2 emission in a case of China over 2010-2019 and projecting provincial CO2 emissions to 2030, by proposing two new spatiotemporal logarithmic mean Divisia index models with Cobb-Douglas production function and developing an ensemble forecasting model including machine learning. We found, first, the effects of capital and labor inputs and carbon factor were the positive drivers affecting aggregate CO2 emissions, while the effects of the total-factor productivity and energy intensity were negative drivers. Second, the effects of capital and labor inputs were the negative drivers for narrowing the emission gap. Third, the ensemble forecasting model can improve the generalization ability of CO2 emission predictions. Therefore, we recommend that policymakers focus on optimizing the carbon reduction effects of capital and labor inputs while promoting the development of a circular economy to achieve sustainable economic growth.

Fertilizer use and agricultural practices in the paradox of maize crop production and environmental sustainability.

The enduring existence of pollution presents a substantial danger to human health, natural systems, and social welfare. Human activities mostly generate greenhouse gas emissions, namely carbon dioxide, which negatively impacts the environment. This study used annual datasets to examine the association between maize crop production, maize yield, fertilizer consumption, agricultural land use, and environmental quality in China. In order to identify the positive and negative shocks with the assessment of short- and long-run dynamics, the study used an asymmetric Nonlinear Autoregressive Distributed Lag (NARDL) approach. A Robust Least Squares method was also used to locate the parameters nexus in order to assess the series' robustness. Results from the long-run interaction indicate that the maize crop production and agricultural land use has a positive impact on CO2 emissions with probability values of (0.000), (0.000), and (0.001), (0.780), respectively, via both positive and negative interruptions. Additionally, maize yield exposed a detrimental effect on environmental quality. Results of the robust least squares analysis showed that maize crop production, fertilizer consumption, and agricultural land use had a positive influence on environmental quality, with probability values of (0.000), (0.004), and (0.949), respectively. However, there is an unfavourable relationship between variable maize yields and CO2 emissions. China should play a significant role in seeking to reduce carbon dioxide emissions and adopt the beneficial policies necessary to ensure the environment's long-term sustainability, since these emissions are now a rising issue around the world.

Thermal and structural changes of a starch flexible film and cellulosic semi-rigid tray during the biodegradation process under controlled composting conditions.

Biopolymers used to mitigate the environmental impact needed establish biodegradation percentage. The thermal and structural changes of two plastic materials, a flexible film based on cassava starch - Poly(lactic acid) (PLA) and a semi-rigid cassava flour-stay cellulose fique fiber, were evaluated biodegradation under ISO 4855-1 standard. The tests were carried out for four weeks at constant temperature and flow of 58 °C ±â€¯2 °C and 250 mL/h, using a mature compost as inoculum. The percentages of CO2, thermal, morphological, and structural changes, variation of degradation temperatures, glass transition temperatures (Tg), Melting temperatures (Tm) and enthalpies of fusion (Hm), were properly evaluated as indicators of the materials biodegradation of two materials. Scanning electron microscopy (SEM), showed the microorganisms colonization on the materials surface, evidencing the appearance of cracks and microbial population. The flexible film showed a biodegradation percentage of 98.24 %, the semi-rigid tray 89.06 %, and the microcrystalline cellulose, 81.37 %.

Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO2 Benefits: A Case Study of the Yangtze River Delta, China.

Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.

Microplastic pollution promotes soil respiration: A global-scale meta-analysis.

Microplastic (MP) pollution likely affects global soil carbon (C) dynamics, yet it remains uncertain how and to what extent MP influences soil respiration. Here, we report on a global meta-analysis to determine the effects of MP pollution on the soil microbiome and CO2 emission. We found that MP pollution significantly increased the contents of soil organic C (SOC) (21%) and dissolved organic C (DOC) (12%), the activity of fluorescein diacetate hydrolase (FDAse) (10%), and microbial biomass (17%), but led to a decrease in microbial diversity (3%). In particular, increases in soil C components and microbial biomass further promote CO2 emission (25%) from soil, but with a much higher effect of MPs on these emissions than on soil C components and microbial biomass. The effect could be attributed to the opposite effects of MPs on microbial biomass vs. diversity, as soil MP accumulation recruited some functionally important bacteria and provided additional C substrates for specific heterotrophic microorganisms, while inhibiting the growth of autotrophic taxa (e.g., Chloroflexi, Cyanobacteria). This study reveals that MP pollution can increase soil CO2 emission by causing shifts in the soil microbiome. These results underscore the potential importance of plastic pollution for terrestrial C fluxes, and thus climate feedbacks.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry.

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

Undervalued dry riverbeds: A key factor in equating intermittent river CO2 emissions to perennial rivers.

Intermittent rivers in semiarid and arid regions, constituting over half of the world's rivers, alternate the carbon cycle interactions among the biosphere, hydrosphere, and atmosphere. Inadequate quantification of flow duration and river water surface area, along with overlooked CO2 emissions from dry riverbeds, result in notable inaccuracies in global carbon cycle assessments. High-resolution remote sensing images combined with intensive field measurements and hydrological modelling were used to estimate and extract the flow duration, river water surface area and dry riverbed area of Huangfuchuan, an intermittent river watershed that acts as a major tributary of the Yellow River in semiarid Northwest China. CO2 emission rates and partial pressures in water and air across the watershed were in-situ measured. In 2018, the flow duration of Huangfuchuan increased from less than 5 days in the first-order tributary to 150 days in the sixth-order mainstream. River water surface area estimated by remote sensing extraction plus the hydrodynamic model simulation varied from 3.9 to 88.6 km2 under 5 %-95 % discharge frequencies. CO2 emissions from the water-air interface and dry riverbed in 2018 were estimated at 582.3 × 103 and 355.2 × 103 ton, respectively. The estimated total annual emission (937.5 × 103 ton) aligns closely with the range of emissions (67.3 × 103-1377.2 × 103 ton) calculated for the water-air interface alone, derived using DEM river length and hydraulic geometry method. This similarity can be attributed to the overestimation of flow duration and flow velocity, as well as the over- or under-estimation of river water surface area and slope. The new method proposed in this study has large potential to be applied in estimating CO2 emissions from data-scarce intermittent rivers located in mountainous regions and provides a standardized solution in the estimation of CO2 emission. Results of this research reveal the spatiotemporal distribution of CO2 emissions along an intermittent river system and highlight the substantial role of dry riverbed in carbon cycle.

Investigating the effects of climate change and anthropogenic activities on SOC storage and cumulative CO2 emissions in forest soils across altitudinal gradients using the century model.

This study investigated the impact of climate change, grazing, manure application, and liming on soil organic carbon (SOC) stock and cumulative carbon dioxide (CO2) emissions in forest soils across different altitudes. Despite similar soil texture, acidity, and salinity across elevations, SOC stock significantly increased with altitude due to cooler temperatures and higher precipitation. The highest SOC stock (97.46 t ha-1) was observed at 2000-2500 m, compared to the lowest (44.23 t ha-1) at 500-1000 m. The Century C Model accurately predicted SOC stock, with correlation and determination coefficients exceeding 0.98. A climate change scenario projecting decreased precipitation (2.15 mm per decade) and increased temperature (0.4 °C) revealed potential SOC stock losses ranging from 28.36 to 36.35 %, particularly at higher altitudes. Grazing further decreased SOC stock, with a more pronounced effect at higher elevations. However, manure application (40 t ha-1 every four years) and liming (7-10 t ha-1 every three years) had positive effects on SOC stock, again amplified at higher altitudes and with an increase in lime application rate. In scenarios combining climate change with manure application and climate change with liming, manure application and liming mitigated some negative impacts of climate change, but could not fully offset them, resulting in 1.49-5.42 % and 0.39-4.07 % decreases respectively. Simulations of cumulative CO2 emissions mirrored the distribution of SOC stock, with higher emissions observed at higher altitudes and with management practices that increased SOC stock. This study emphasizes the critical role of conserving high-altitude forest soils and implementing optimal forest management strategies to combat climate change by minimizing SOC losses.

Spatiotemporal dynamics and influencing factors of CO2 emissions under regional collaboration: Evidence from the Beijing-Tianjin-Hebei region in China.

Against the backdrop of global climate change and the "dual carbon" target, cities have a significant responsibility to achieve carbon reduction targets. As a crucial urban agglomeration in northern China, effectively balancing economic growth with CO2 emission reduction to achieve high-quality economic development remains a significant challenge that the Beijing-Tianjin-Hebei region should address both presently and in the future. The objective of this study is to utilize nighttime lighting data and energy consumption information to quantify the CO2 emissions of diverse cities within the Beijing-Tianjin-Hebei region spanning from 2006 to 2020. The research aims to analyze the spatial progression patterns of CO2 emissions across these urban centers, identify key determinants and their interrelations, and delve into the underlying mechanisms pivotal for advancing carbon mitigation strategies within urban agglomerations. The results indicate that: with an exception in Beijing where CO2 emissions slightly decreased compared to 2006, CO2 emissions increased across cities in the Beijing-Tianjin-Hebei region by 2020. High-value CO2 emission areas are primarily concentrated in central of the study area, exhibiting negative spatial correlation characteristics. Based on its urban development positioning, it is imperative for the Beijing-Tianjin-Hebei urban agglomeration to formulate and implement carbon reduction strategies on innovative development, industrial upgrading, and ecological protection among other aspects towards coordinated low-carbon development.

Evolution and driving factors of inequality in CO2 emissions from agricultural energy consumption in China.

The inequality in CO2 emissions from agricultural energy consumption is a major challenge for coordinating low-carbon agricultural development across regions in China. However, the evolutionary characteristics and driving factors of inequality in China's agricultural energy-related CO2 emissions are poorly understood. In response, the Kaya-Theil model was adopted to examine the three potential factors influencing CO2 emission inequality in China's agricultural energy consumption. The results revealed that, from 1997 to 2021, agricultural energy-related CO2 emissions per capita showed a significant upward trend, with prominent polarization and right-tailing phenomena. Overall, the inequality was on a downward trend, with the Theil index falling from 0.4109 in 1997 to 0.1957 in 2021. Meanwhile, the decomposition of the national inequality revealed that the within-group inequality declined from 0.3991 to 0.1634, which was greater than between-group inequality, based on zoning the 28 provinces into three grain production functional areas. As for the three kaya factors, the energy intensity contributed the most to the overall inequality, followed by the agricultural economic development and CO2 emission intensity. Based on these results, this study provided some potential strategies to reduce agricultural-related CO2 emissions.

Application of extreme learning machine (ELM) forecasting model on CO2 emission dataset of a natural gas-fired power plant in Dhaka, Bangladesh.

Understanding and predicting CO2 emissions from individual power plants is crucial for developing effective mitigation strategies. This study analyzes and forecasts CO2 emissions from an engine-based natural gas-fired power plant in Dhaka Export Processing Zone (DEPZ), Bangladesh. This study also presents a rich dataset and ELM-based prediction model for a natural gas-fired plant in Bangladesh. Utilizing a rich dataset of Electricity generation and Gas Consumption, CO2 emissions in tons are estimated based on the measured energy use, and the ELM models were trained on CO2 emissions data from January 2015 to December 2022 and used to forecast CO2 emissions until December 2026. This study aims to improve the understanding and prediction of CO2 emissions from natural gas-fired power plants. While the specific operational strategy of the studied plant is not available, the provided data can serve as a valuable baseline or benchmark for comparison with similar facilities and the development of future research on optimizing operations and CO2 mitigation strategies. The Extreme Learning Machine (ELM) modeling method was employed due to its efficiency and accuracy in prediction. The ELM models achieved performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE), values respectively 3494.46 (<5000), 2013.42 (<2500), and 0.93 close to 1, which falls within the acceptable range. Although natural gas is a cleaner alternative, emission reduction remains essential. This data-driven approach using a Bangladeshi case study provides a replicable framework for optimizing plant operations and measuring and forecasting CO2 emissions from similar facilities, contributing to global climate change.

Catalyzing sustainable development: Exploring the interplay between access to clean water, sanitation, renewable energy and electricity services in shaping China's energy, economic growth, and environmental landscape.

The Sustainable Development Goals (SDGs) reflect the shift in global economic conversation toward inclusive growth. The growth can promote inclusivity and widespread sharing of its advancements by concentrating on four key dimensions. (a) Equality of opportunity, (b) sharing prosperity, (3) environmental sustainability/climate adaptation, and (4) macroeconomic stability. We used the Kao cointegration test to study how certain variables are connected over a long period. The relationship between CO2 and GDP per capita, renewable energy and tourism, improved water and sanitation, and access to power all have a positive feedback effect on each other. Based on FMOLS's findings, a 1 % increase in Inclusive growth leads to a 0.342 % (Model 1) and 0.258 % (Model 3) increase in CO2 emissions. An increase of 1 percent in energy consumption per person resulted in a rise of 1.343 % in CO2 emissions in Case 1, 0.524 % in Case 2, and 0.618 % in Case 3. Increasing the tourism sector's proportion of total exports by just one percent will reduce CO2 emissions by 0.221 % (case 1) and 0.234 % (case 3). Based on CCR findings, a 1 % improvement in inclusive growth leads to a 0.403.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Decomposition analysis of electricity generation on carbon dioxide emissions in Ghana.

This study analyses the factors driving CO2 emissions from electricity generation in Ghana from 1990 to 2020. Employing Logarithmic Mean Divisia Index (LMDI) and Autoregressive Distributed Lag (ARDL) techniques, the research decomposes electricity generation into different factors and assesses their impact on CO2 emissions, considering both short and long-run effects. The LMDI analysis reveals that the total CO2 emissions from electricity generation amount to 3.33%, with all factors contributing positively in each subperiod. Notably, fossil fuel intensity, production, and transformation factors exhibit substantial contributions of about 1.16%, 0.49%, and 0.48%, respectively. Contrastingly, the ARDL results highlight that only electricity intensity and production factors significantly increase CO2 emissions by about 0.20% and 0.09% (0.38% and 0.10%) in the short-run (long-run), while other factors contribute to a reduction in electricity generation emissions. Overall, we conclude that electricity intensity and production factors are the primary drivers of CO2 emissions from electricity generation in Ghana. Nevertheless, effective measures to address all decomposition factors is crucial for effective mitigation of electricity generation CO2 emissions.

Soil respiration and its response to climate change and anthropogenic factors in a karst plateau wetland, southwest China.

It is important to investigate the responses of greenhouse gases to climate change (temperature, precipitation) and anthropogenic factors in plateau wetland. Based on the DNDC model, we used meteorological, soil, and land cover data to simulate the soil CO2 emission pattern and its responses to climate change and anthropogenic factors in Guizhou, China. The results showed that the mean soil CO2 emission flux in the Caohai Karst Plateau Wetland was 5.89 ± 0.17 t·C·ha-1·yr-1 from 2000 to 2019, and the annual variation showed an increasing trend with the rate of 23.02 kg·C·ha-1·yr-1. The soil total annual mean CO2 emissions were 70.62 ± 2.04 Gg·C·yr-1 (annual growth rate was 0.28 Gg·C·yr-1). Caohai wetland has great spatial heterogeneity. The emissions around Caohai Lake were high (the areas with high, middle, and low values accounted for 3.07%, 70.96%, and 25.97%, respectively), and the emission pattern was characterized by a decrease in radiation from Caohai Lake to the periphery. In addition, the cropland and forest areas exhibited high intensities (7.21 ± 0.15 t·C·ha-1·yr-1 and 6.73 ± 0.58 t·C·ha-1·yr-1, respectively) and high total emissions (54.97 ± 1.16 Gg·C·yr-1 and 10.24 ± 0.88 Gg·C·yr-1, respectively). Croplands and forests were the major land cover types controlling soil CO2 emissions in the Caohai wetland, while anthropogenic factors (cultivation) significantly increased soil CO2 emissions. Results showed that the soil CO2 emissions were positively correlated with temperature and precipitation; and the temperature change had a greater impact on soil respiration than the change in precipitation. Our results indicated that future climate change (increased temperature and precipitation) may promote an increase in soil CO2 emissions in karst plateau wetlands, and reasonable control measures (e.g. returning cropland to lakes and reducing anthropogenic factors) are the keys to controlling CO2 emissions.

Aerobic and anaerobic decomposition rates in drained peatlands: Impact of botanical composition.

Drained peatlands in temperate climates are under threat from climate change and human activities. The resulting decomposition of organic matter plays a major role in regulating the associated land subsidence rates, yet the determinants of aerobic and anaerobic peat decomposition rates are not fully understood. In this study, we sought to gain insight into the drivers of decomposition rates in botanically diverse peatlands (sedge, reed, wood, and moss dominant) under oxic and anoxic conditions. Peat samples were collected from the anoxic zone and incubated for 24 h (short) and 15 weeks (long) under either oxic or anoxic conditions. CO2 emissions, hydrolytic and oxidative exoenzyme potential activities, phenolic compound concentrations, and several edaphic factors were measured at the end of each incubation period. We found that 15 weeks of oxygen exposure of anoxic peat samples accelerated the average CO2 emissions by 3.9-fold. Reed and sedge peat respired more than wood and moss peat under anoxic conditions. Interestingly, CO2 emissions from anoxic peat layers under permanently anoxic conditions were substantial and given the thickness of peat deposits in the field, such activities may play an important role in long-term land subsidence rates and total CO2 emissions from drained peatlands. The results from the long-term incubations showed that decomposition rates appear to be also controlled by factors other than oxygen intrusion such as substrate availability. In summary, the botanical composition of the peat matrix, incubation conditions and time of incubation are all important factors that need to be considered when predicting peat decomposition and subsequent land subsidence rates.

Integrated water management practice in tropical peatland agriculture has low carbon emissions and subsidence rates.

Hydrological management in the use of peatland for agriculture is the backbone of its sustainability and a critical factor in climate change mitigation. This study evaluates the application of an integrated water management practice known as the "Water Management Trinity" (WMT), implemented since 1986 on a coconut plantation on the eastern coast of Sumatra, in relation to CO2 emissions and subsidence rates. The WMT integrates canals, dikes, and dams with water gates to regulate water levels for both coconut agronomy and the preservation of the peat soil. The WMT has successfully regulated and maintained an average yearly water table depth of -45 to -51 cm below the surface. The methodology involved a closed chamber method for measuring soil CO2 flux using a portable Infrared Gas Analyzer, conducted weekly over a six-month period to cover dry and rainy season at bi-modal climate condition. Subsidence measurements have been ongoing from 1986 to 2022. The results show bare peat soil has heterotrophic respiration CO2 emissions of 7.77 t C-CO2 ha-1 yr-1, while in coconut plantations 7.99 t C-CO2 ha-1 yr-1, similar to emissions in mineral soils. Autotrophic respiration leads to the overestimation of CO2 emissions on peatland and accounts for 212-424% of the total emissions. The cumulative subsidence from 1986 to 2022 is -56.3 cm, with a soil rise of +0.8 cm in 2022, indicating a flattening rate of subsidence. This is characterized by an increase in bulk density at the surface from 0.072 to 0.144 gr/cm3, with approximately 81% of the subsidence being due to compaction. The statistical analysis found no relationship between water table depth and CO2 emissions, indicating that water table depth cannot be used as a predictor for CO2 emissions. In summary, peatland agriculture has a promising future when managed sustainably using an integrated hydrological management system.

Comprehensive life cycle assessment of NH2-functionalized magnetic graphene oxide for mercury removal: Carbon emissions and economic evaluation.

Heavy metals contamination critically affects human health and ecosystems, necessitating pioneering approaches to diminish their adverse impacts. Hence, this study synthesized aminated magnetic graphene oxide (mGO-NH2) for the removal of mercury (Hg) from aqueous solutions. Although functionalized GO is an emerging technology at the early stages of development, its synthesis and application require special attention to the eco-environmental assessment. Therefore, the life cycle assessment and life cycle cost of mGO-NH2 were investigated from the cradle-to-gate approach for the removal of 1 kg Hg. The adsorption process was optimized based on pH, Hg concentration, adsorbent dose, and contact time at 6.48, 40 mg/l, 150 mg/l, and 35 min, respectively, resulting in an adsorption capacity of 184.17 mg/g. Human carcinogenic toxicity with a 40.42% contribution was the main environmental impact, relating to electricity (35.76%) and ethylenediamine (31.07%) usage. The endpoint method also revealed the pivotal effect of the mGO-NH2 synthesis on human health (90.52%). The most energy demand was supplied by natural gas and crude oil accounting for 70.8% and 22.1%, respectively. A 99.02% CO2 emission originated from fossil fuels consumption based on the greenhouse gas protocol (GGP). The cost of mGO-NH2 was about $143.7/kg with a net present value of $21064.8 per kg Hg removal for a 20-year lifetime. Considering the significant role of material cost (>70%), the utilization of industrial-grade raw materials is recommended to achieve a low-cost adsorbent. This study demonstrated that besides the appropriate performance of mGO-NH2 for Hg removal, it is essential that further studies evaluate eco-friendly approaches to decrease the adverse impacts of this emerging product.

Mycorrhizosphere bacteria inhibit greenhouse gas emissions from microplastics contaminated soil by regulating soil enzyme activities and microbial community structure.

Microplastics (MPs) accumulation in terrestrial ecosystems can affect greenhouse gases (GHGs) production by altering microbial and soil structure. Presently, research on the MPs effect on plants is not consistent, and underlying molecular mechanisms associated with GHGs are yet unknown. For the first time, we conducted a microcosm study to explore the impact of MPs addition (Raw vs. aged) and Trichoderma longibrachiatum and Bacillus subtilis inoculation (Sole vs. combination) on GHGs emission, soil community structure, physiochemical properties, and enzyme activities. Our results indicated that the addition of aged MPs considerably enhanced the GHGs emissions (N2O (+16%) and CO2 (+21%), respectively), C and N cycling gene expression, microbial biomass carbon, and soil physiochemical properties than raw MPs. However, the soil microbial community structure and enzyme activities were enhanced in raw MPs added treatments, irrespective of the MPs type added to soil. However, microbial inoculation significantly reduced GHGs emission by altering the expression of C and N cycling genes in both types of MPs added treatments. The soil microbial community structure, enzymes activities, physiochemical properties and microbial biomass carbon were enhanced in the presence of microbial inoculation in both type of MPs. Among sole and combined inoculation of Trichoderma and Bacillus subtilis, the co-applied Trichoderma and Bacillus subtilis considerably reduced the GHGs emission (N2O (-64%) and CO2 (-61%), respectively) by altering the expression of C and N cycling genes regardless of MPs type used. The combined inoculation also enhanced soil enzyme activities, microbial community structure, physiochemical properties and microbial biomass carbon in both types of MPs treatment. Our findings provide evidence that polyethylene MPs likely pose a high risk of GHGs emission while combined application of Trichoderma and Bacillus subtilis significantly reduced GHGs emission by altering C and N cycling gene expression, soil microbial community structure, and enzyme activities under MPs pollution in a terrestrial ecosystem.

Are electric vehicles really the optimal option for the transportation sector in China to approach pollution reduction and carbon neutrality goals?

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation.