This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and car...This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.展开更多
Mid-infrared(MIR)-polarized thermal emission has broad applications in areas such as molecular sensing,information encryption,target detection,and optical communication.However,it is difficult for objects in nature to...Mid-infrared(MIR)-polarized thermal emission has broad applications in areas such as molecular sensing,information encryption,target detection,and optical communication.However,it is difficult for objects in nature to produce polarized thermal emission.Moreover,simultaneously generating and modulating broadband MIR thermal emission with both circular and linear polarization is even more difficult.We present a chiral plasmonic metasurface emitter(CPME)composed of asymmetric L-shaped and I-shaped antennas.The CPME consists of In_(3)SbTe_(2)(IST)phase-change material(PCM)antennas,an Al_(2)O_(3) dielectric layer,and an Au substrate.It is demonstrated that the CPME can selectively emit polarized light with different polarization states.Numerical simulations show that the CPME can achieve full Stokes parameter control of MIR thermal emission.By changing the state of the PCM IST,the spectral emissivity of 0 deg,45 deg,90 deg,and 135 deg linearly polarized(LP)lights and left-handed/right-handed circularly polarized(LCP/RCP)lights can be adjusted.In the crystalline state,the CPME exhibits the total degree of polarization(DoP)greater than 0.5 in the wavelength range of 3.4 to 5.3μm,the degree of linear polarization(DoLP)greater than 0.4 in the range of 3.0 to 5.1μm,and the degree of circular polarization(DoCP)greater than 0.4 in the range of 4.5 to 5.6μm.The physical mechanism of polarized emission has been investigated fully based on the near-field intensity distribution and power loss distribution.Finally,the potential applications of the designed CPME in infrared polarization detection and antidetection are verified through numerical calculations.展开更多
The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction...The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction(SCR)has the potential to achieve ultra-low NO_(x) emissions,several challenges remain,including the accurate prediction of ammonia(NH_(3))storage mass and the co-control of the two-stage SCR.The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy.Secondly,an initial method for predicting the NH_(3) storage based on the mass conservation law was proposed,which was subsequently improved by filling and emptying experiments.The third step involved the development of a two-stage SCR co-control strategy,including obtaining the steady-state NH_(3) storage target value,dynamic correction for NH_(3) storage target value,regulation of NH_(3) storage,and control of the close-coupled SCR urea injector state.Finally,the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle(WHTC).The results demonstrate that the composite value of engine outlet NO_(x) emissions under cold and hot start WHTC cycles is 13 g/(kW·h).Meanwhile,the composite value of tailpipe NO_(x) emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h),representing only 14%of the EU VI limit value of 0.46 g/(kW·h).Thus,the findings demonstrate that integrating an accurate NH_(3) storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NO_(x) emissions.展开更多
A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–...A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–BOF)steelmaking route.The proposed BF–IF–BOF process gains benefits from preheating and smelting scraps utilizing green electricity and further remarkably cuts down the CO_(2) emission in BOF steelmaking.The CO_(2) emissions of conventional and new processes have been comparatively analyzed according to the actual data from a commercial steel plant in China,taking into account the upstream CO_(2) emission,direct CO_(2) emission,and credit CO_(2) emission.The analysis revealed that the CO_(2) emission factor of internal scraps from the steel plant was different from that of purchased scraps from the society but equalled to that of crude steel.The CO_(2) injected into the BOF as a coolant could be defined as the upstream CO_(2) emission source,and there is a net reduction of 1 t CO_(2) emission for each ton of CO_(2) utilized in the BOF.Compared to the BF–BOF process with a scrap ratio of 19.23%,the CO_(2) emission reduction per ton of steel in the new process is 0.278,0.517,0.753,0.987,1.219,1.448,and 1.683 t,respectively,as the scrap ratio increases to 30%,40%,50%,60%,70%,80%,and 90%,and increasing the scrap ratio has a more significant impact on the emission reduction than CO_(2) injecting.A minimum CO_(2) emission model for the BF–IF–BOF process was established,and the minimum CO_(2) emission per ton crude steel was calculated to be 0.677,0.581,0.487,0.393,0.300,0.209,and 0.110 t,for the BF–IF–BOF process with the scrap ratios of 30%,40%,50%,60%,70%,80%,and 90%,respectively.展开更多
Transitioning real estate development toward low-carbon operations is a critical strategy for China to achieve its carbon peaking and neutrality targets.Accurately calculating CO_(2) emissions from real estate develop...Transitioning real estate development toward low-carbon operations is a critical strategy for China to achieve its carbon peaking and neutrality targets.Accurately calculating CO_(2) emissions from real estate development is essential for effective implementation of low-carbon strategies.However,research that specifically addresses CO_(2) emissions from real estate development is lacking.To fill this knowledge gap,this study examined CO_(2) emissions from China's real estate development between 2000 and 2020,presenting a comprehensive analysis of the production and consumption aspects of emissions,and inter-provincial transfers of emissions driven by the sector.Our findings reveal a significant increase in embodied CO_(2) emissions fromChina's real estate development,escalating from 145.5Mt in 2000 to 477.3Mt in 2020.The proportion of emissions attributable to real estate development among China's total CO_(2) emissions ranged from5%to 6%between 2000 and 2020,underscoring the sector's non-negligible impact on the country's overall CO_(2) emissions.Our analysis demonstrated that building material production,especially steel and cement,contributed significantly to the sector's emissions,underscoring the need for decarbonization and the adoption of green building materials.Additionally,a marginal increase in CO_(2) emissions per constructed area requires enhanced sustainable construction practices.Furthermore,our study revealed that the ongoing rise in inter-provincial CO_(2) emissions transfer due to real estate development intensifies carbon inequality across provinces.These findings are instrumental for policymakers and stakeholders to develop targeted interventions to mitigate CO_(2) emissions and promote sustainable growth in China's real estate sector.展开更多
The emission of heavy-duty vehicles has raised great concerns worldwide.The complex working and loading conditions,which may differ a lot from PEMS tests,raised new challenges to the supervision and control of emissio...The emission of heavy-duty vehicles has raised great concerns worldwide.The complex working and loading conditions,which may differ a lot from PEMS tests,raised new challenges to the supervision and control of emissions,especially during real-world applications.On-board diagnostics(OBD)technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles.This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals.Real-world NO_(x)and CO_(2)emissions of China-6 heavy-duty vehicles have been examined.The results showed that city heavy-duty vehicles had higher NO_(x)emission levels,which was mostly due to longer time of low SCR temperatures below 180°C.The application of novel methods based on 3BMAWalso found that heavy-duty diesel vehicles tended to have high NO_(x)emissions at idle.Also,little difference had been found in work-based CO_(2)emissions,and this may be due to no major difference were found in occupancies of hot running.展开更多
The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels...The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.展开更多
Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five bi...Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five biochar incubated Malaysian Tropical soils(MT-Soil).The recalcitrance index of palm kernel shell biochar(PKS)was higher than that of wood chip biochar(WCB),bamboo biochar(BB),coconut shell biochar(CHB)and rice husk biochar(RHB),and was different from the observed CO_(2) emission characteristics(WCB>CHB>RHB>BB>PKS).Thus,the carbon sequestration potential of biochar could not be evaluated solely by the recalcitrance index.This CO_(2) emission is linked not only to the total organic carbon(TOC)and total carbon(TC)of the biochar but also associated with mobile matter(MM),water holding capacity(WHC),available phosphorus(AP),exchangeable potassium(AK),and nitrogen content.The multiple linear regression analysis(MLRA)shows that the weights of these factors on CO_(2) emissions are as follows:TC>pH>MM>WHC>AP>AK.The results show that in addition to biochar stability,pore structure and available phosphorus release also affect carbon dynamics through indirect effects on microbial activity.This means that to minimize CO_(2) emissions during application of biochar,it is necessary to use soil that is rich in phosphorus and biochar that has undeveloped pore structure and high stable carbon.Finally,this study provides valuable theoretical underpinnings biochar application in MT-Soil.展开更多
CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotempora...CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.展开更多
The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducte...The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducted field studies to examine the response of N_(2)O emissions to N and P addition over two years in three typical alpine grasslands,alpine meadow(AM),alpine steppe(AS),and alpine cultivated grassland(CG)on the Qinghai-Tibet Plateau(QTP).Our results showed consistent increases in N_(2)O emissions under N addition alone or with P addition,and insignificant change in N_(2)O emissions under P addition alone in all three grasslands.N addition increased N_(2)O emissions directly in AM,by lowering soil pH in AS,and by lowering abundance of denitrification genes in CG.N and P co-addition increased N_(2)O emissions in AM and AS but only showed an interactive effect in AM.P addition enhanced the increase in N_(2)O emissions caused by N addition mainly by promoting plant growth in AM.Overall,our results illustrate that short-term P addition cannot alleviate the stimulation of N_(2)O emissions by N deposition in alpine grassland ecosystems,and may even further stimulate N_(2)O emissions.展开更多
The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influ...The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influence of policy constraints when evaluating MAC.To address this gap,this paper proposes a nonparametric directional distance function approach under policy regulations to estimate the CO_(2)shadow price in the construction industry across 30 Chinese provinces from 2010 to 2017.Based on this enhanced method,four key findings emerge:①the annual CO_(2)shadow price exhibits an overall upward trend during the observation period;②regional shadow prices display marked variation across provinces;③regional heterogeneity in shadow prices has increased steadily over time;and④both urbanization rate and energy consumption per unit of gross domestic product are significantly negatively correlated with the CO_(2)shadow price.Finally,the paper offers several policy recommendations for CO_(2)emissions reduction in the Chinese construction industry at the national,industry,and enterprise levels.展开更多
The energy sector is pivotal in Vietnam’s commitment to achieving net-zero emissions by 2050.This study employs a combination of Structural Decomposition Analysis(SDA)and decoupling approaches based on data from Viet...The energy sector is pivotal in Vietnam’s commitment to achieving net-zero emissions by 2050.This study employs a combination of Structural Decomposition Analysis(SDA)and decoupling approaches based on data from Vietnam’s energy statistics and the Vietnam Living Standards Survey(VHLSS)for 2016,2018,and 2020.The primary aim is to elucidate the effects of direct energy consumption by household groups on CO_(2)emissions,examine factors affecting emissions,and clarify the relationship between CO_(2)emissions from household energy consumption and economic growth in Vietnam.Research results underscore that household groups make considerable use of electricity and Liquefied Petroleum Gas(LPG),simultaneously reducing the proportion of firewood,rice husk,sawdust,agricultural by-products and other fuels.The decrease in energy intensity emerges as the primary factor in lowering household emissions,while population growth and economic efficiency exert the opposite effect.Additionally,the research reveals disparities in emissions between urban and rural areas,similarly among household groups within the given location.Despite maintaining a robust decoupling status between emissions from household consumption and economic growth,unsustainable risks persist,particularly with the increase in electricity demand.The study also highlights the uneven impact of the COVID-19 epidemic on CO_(2)emissions across household groups.Drawing upon these findings,several recommendations are proposed to control CO_(2)emissions from direct energy household consumption to facilitate the most effective household decarbonisation process while ensuring sustainable economic growth in Vietnam.展开更多
Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The obj...Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The objective of this study was to compare the impact of South Africa’s population growth,economic growth,and fertilizer consumption on CO_(2)emissions,with those of the US,China,and other BRICS countries.The study used panel data sourced from the World Bank’s World Development Indicators ranging from 1960 to 2023.Results of the fixed effects panel regression show that the coefficient of change for China’s population size(β=9.156,p<0.01)is the highest among the six countries.It is followed by the USA(β=9.156,p<0.05)and South Africa(β=1.474,p<0.01).The effects of GDP for China(β=1.128,p<0.01)on CO_(2)emissions are the largest,followed by South Africa(β=1.098,p<0.01)and the USA in third place(β=0.614,p<0.05).These results show that South Africa is highly reliant on coal-based energy resources.As a policy recommendation,South Africa needs to diversify its energy mix and invest more in renewable energy resources.展开更多
To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting perfo...To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.展开更多
Although atmospheric CO_(2) observations are becoming increasingly widespread in China,the identification of CO_(2) emission sources is still scarce,especially in undeveloped Central China.To effectively address this ...Although atmospheric CO_(2) observations are becoming increasingly widespread in China,the identification of CO_(2) emission sources is still scarce,especially in undeveloped Central China.To effectively address this issue,in a typical site in Central China,the simultaneous measurements of atmospheric CO_(2),CO,andδ^(13)C were conducted,and the characteristics of CO_(2) emission sources were systematically investigated based on the relationships among CO_(2),CO,andδ^(13)C.The average CO_(2)/CO ratio of winter increased from 52.4 ppm/ppm during 2018–2020 to 65.1 ppm/ppm during 2021–2022,which confirmed the improvement of energy consumption efficiency in China.Air-mass transportation from central China and Yangtze River Delta regions contributed largely to higher CO_(2)/CO ratios in 2021–2022.A highermean CO_(2)/CO ratio appeared during the morning rush hours(60.3 ppm/ppm)than in the afternoon rush hours(51.4 ppm/ppm)in winter.In addition,the meanδ^(13)C value of CO_(2) sources(δ^(13)Cs)also displayed more negative values during the morning rush hours(-28.3‰)than the afternoon rush hours(-22.2‰),suggesting the significant influence of vehicle and natural gas usage at themorning rush hours and the impact of straw burning in the afternoon rush hours.The meanδ^(13)Cs was-24.7‰for winter and-21.9‰for vegetation season,implying the main contribution of coal in winter and the impact of C4 plants during the vegetation season.The contribution of biogenic respiration CO_(2) was inferred to exceed 50%during the nighttime of summer according to the obtained meanδ^(13)C value of biogenic respiration CO_(2),which was calculated to be-21.4‰.展开更多
The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understandi...The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.展开更多
Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_...Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_(2) emissions embodied in manufacturing exports(CIE),and conducts an empirical test based on panel data from 37 countries from 2000 to 2019.This study found that first,IRA can significantly reduce CIE,but there is a U-shaped nexus between the two,which shows a rebound effect.Second,the heterogeneity test demonstrates that in com-parison to both the low-tech and high-tech sectors,IRA in the medium-tech industry can significantly reduce CIE;compared with the low-IRA sectors,the high-IRA sectors exhibit a more obvious reduction.In addition,IRA has a stronger effect on high-carbon-intensity areas.Third,the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects.Moreover,environmental regulations and the manufacturing in-telligence process positively moderate the nexus between IRA and CIE.Finally,these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.展开更多
Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism....Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.展开更多
China is the most important steel producer in the world,and its steel industry is one of themost carbon-intensive industries in China.Consequently,research on carbon emissions from the steel industry is crucial for Ch...China is the most important steel producer in the world,and its steel industry is one of themost carbon-intensive industries in China.Consequently,research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals.We constructed a carbon dioxide(CO_(2))emission model for China’s iron and steel industry froma life cycle perspective,conducted an empirical analysis based on data from2019,and calculated the CO_(2)emissions of the industry throughout its life cycle.Key emission reduction factors were identified using sensitivity analysis.The results demonstrated that the CO_(2)emission intensity of the steel industry was 2.33 ton CO_(2)/ton,and the production and manufacturing stages were the main sources of CO_(2)emissions,accounting for 89.84%of the total steel life-cycle emissions.Notably,fossil fuel combustion had the highest sensitivity to steel CO_(2)emissions,with a sensitivity coefficient of 0.68,reducing the amount of fossil fuel combustion by 20%and carbon emissions by 13.60%.The sensitivities of power structure optimization and scrap consumption were similar,while that of the transportation structure adjustment was the lowest,with a sensitivity coefficient of less than 0.1.Given the current strategic goals of peak carbon and carbon neutrality,it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies,increase the ratio of scrap steel to steelmaking,and build a new power system.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
基金supported by the top-level design of the National Natural Science Foundation of China(NSFC)Major Project“Realization of optimal carbon neutral pathway and coupling of multi-scale interaction patterns of natural-social systems in China”(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.
基金supported by the National Natural Science Foundation of China(Grant No.61775050).
文摘Mid-infrared(MIR)-polarized thermal emission has broad applications in areas such as molecular sensing,information encryption,target detection,and optical communication.However,it is difficult for objects in nature to produce polarized thermal emission.Moreover,simultaneously generating and modulating broadband MIR thermal emission with both circular and linear polarization is even more difficult.We present a chiral plasmonic metasurface emitter(CPME)composed of asymmetric L-shaped and I-shaped antennas.The CPME consists of In_(3)SbTe_(2)(IST)phase-change material(PCM)antennas,an Al_(2)O_(3) dielectric layer,and an Au substrate.It is demonstrated that the CPME can selectively emit polarized light with different polarization states.Numerical simulations show that the CPME can achieve full Stokes parameter control of MIR thermal emission.By changing the state of the PCM IST,the spectral emissivity of 0 deg,45 deg,90 deg,and 135 deg linearly polarized(LP)lights and left-handed/right-handed circularly polarized(LCP/RCP)lights can be adjusted.In the crystalline state,the CPME exhibits the total degree of polarization(DoP)greater than 0.5 in the wavelength range of 3.4 to 5.3μm,the degree of linear polarization(DoLP)greater than 0.4 in the range of 3.0 to 5.1μm,and the degree of circular polarization(DoCP)greater than 0.4 in the range of 4.5 to 5.6μm.The physical mechanism of polarized emission has been investigated fully based on the near-field intensity distribution and power loss distribution.Finally,the potential applications of the designed CPME in infrared polarization detection and antidetection are verified through numerical calculations.
基金supported by the National Natural Science Foundation of China(No.51921004).
文摘The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction(SCR)has the potential to achieve ultra-low NO_(x) emissions,several challenges remain,including the accurate prediction of ammonia(NH_(3))storage mass and the co-control of the two-stage SCR.The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy.Secondly,an initial method for predicting the NH_(3) storage based on the mass conservation law was proposed,which was subsequently improved by filling and emptying experiments.The third step involved the development of a two-stage SCR co-control strategy,including obtaining the steady-state NH_(3) storage target value,dynamic correction for NH_(3) storage target value,regulation of NH_(3) storage,and control of the close-coupled SCR urea injector state.Finally,the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle(WHTC).The results demonstrate that the composite value of engine outlet NO_(x) emissions under cold and hot start WHTC cycles is 13 g/(kW·h).Meanwhile,the composite value of tailpipe NO_(x) emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h),representing only 14%of the EU VI limit value of 0.46 g/(kW·h).Thus,the findings demonstrate that integrating an accurate NH_(3) storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NO_(x) emissions.
基金supported by the National High-end Foreign Expert Introduction Program Project(No.G2023105012L).
文摘A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–BOF)steelmaking route.The proposed BF–IF–BOF process gains benefits from preheating and smelting scraps utilizing green electricity and further remarkably cuts down the CO_(2) emission in BOF steelmaking.The CO_(2) emissions of conventional and new processes have been comparatively analyzed according to the actual data from a commercial steel plant in China,taking into account the upstream CO_(2) emission,direct CO_(2) emission,and credit CO_(2) emission.The analysis revealed that the CO_(2) emission factor of internal scraps from the steel plant was different from that of purchased scraps from the society but equalled to that of crude steel.The CO_(2) injected into the BOF as a coolant could be defined as the upstream CO_(2) emission source,and there is a net reduction of 1 t CO_(2) emission for each ton of CO_(2) utilized in the BOF.Compared to the BF–BOF process with a scrap ratio of 19.23%,the CO_(2) emission reduction per ton of steel in the new process is 0.278,0.517,0.753,0.987,1.219,1.448,and 1.683 t,respectively,as the scrap ratio increases to 30%,40%,50%,60%,70%,80%,and 90%,and increasing the scrap ratio has a more significant impact on the emission reduction than CO_(2) injecting.A minimum CO_(2) emission model for the BF–IF–BOF process was established,and the minimum CO_(2) emission per ton crude steel was calculated to be 0.677,0.581,0.487,0.393,0.300,0.209,and 0.110 t,for the BF–IF–BOF process with the scrap ratios of 30%,40%,50%,60%,70%,80%,and 90%,respectively.
文摘Transitioning real estate development toward low-carbon operations is a critical strategy for China to achieve its carbon peaking and neutrality targets.Accurately calculating CO_(2) emissions from real estate development is essential for effective implementation of low-carbon strategies.However,research that specifically addresses CO_(2) emissions from real estate development is lacking.To fill this knowledge gap,this study examined CO_(2) emissions from China's real estate development between 2000 and 2020,presenting a comprehensive analysis of the production and consumption aspects of emissions,and inter-provincial transfers of emissions driven by the sector.Our findings reveal a significant increase in embodied CO_(2) emissions fromChina's real estate development,escalating from 145.5Mt in 2000 to 477.3Mt in 2020.The proportion of emissions attributable to real estate development among China's total CO_(2) emissions ranged from5%to 6%between 2000 and 2020,underscoring the sector's non-negligible impact on the country's overall CO_(2) emissions.Our analysis demonstrated that building material production,especially steel and cement,contributed significantly to the sector's emissions,underscoring the need for decarbonization and the adoption of green building materials.Additionally,a marginal increase in CO_(2) emissions per constructed area requires enhanced sustainable construction practices.Furthermore,our study revealed that the ongoing rise in inter-provincial CO_(2) emissions transfer due to real estate development intensifies carbon inequality across provinces.These findings are instrumental for policymakers and stakeholders to develop targeted interventions to mitigate CO_(2) emissions and promote sustainable growth in China's real estate sector.
基金supported by the National Key Research and Development Project of China(No.2022YFC3701802)the National Natural Science Foundation of China(No.52272342)the Major Science and Technology Projects of Qinghai Province(No.2019-GX-A6).
文摘The emission of heavy-duty vehicles has raised great concerns worldwide.The complex working and loading conditions,which may differ a lot from PEMS tests,raised new challenges to the supervision and control of emissions,especially during real-world applications.On-board diagnostics(OBD)technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles.This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals.Real-world NO_(x)and CO_(2)emissions of China-6 heavy-duty vehicles have been examined.The results showed that city heavy-duty vehicles had higher NO_(x)emission levels,which was mostly due to longer time of low SCR temperatures below 180°C.The application of novel methods based on 3BMAWalso found that heavy-duty diesel vehicles tended to have high NO_(x)emissions at idle.Also,little difference had been found in work-based CO_(2)emissions,and this may be due to no major difference were found in occupancies of hot running.
基金jointly supported by the National Key Research and Development Plan(Grant No.2023YFB3907405)the National Natural Science Foundation of China(Grant No.42175132)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-037)。
文摘The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.
基金the support of the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS)(No.FRGS/1/2022/TK01/UM/02/2)the Young Innovative Talent Project-Guangdong Scientific Research Platform and Projects for the Higher-educational Institution&Education Science Planning Scheme(No.KY2022036401)+3 种基金University-level scientific research institution project(No.KY2023000401)Characteristic innovation project of colleges and universities in Guangdong Province(No.2021KTSCX191)Science and Technology developing Project of Dongguan City(No.20211800904572)the Instrument of Dongguan city college and Universiti Malaya for technical support。
文摘Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five biochar incubated Malaysian Tropical soils(MT-Soil).The recalcitrance index of palm kernel shell biochar(PKS)was higher than that of wood chip biochar(WCB),bamboo biochar(BB),coconut shell biochar(CHB)and rice husk biochar(RHB),and was different from the observed CO_(2) emission characteristics(WCB>CHB>RHB>BB>PKS).Thus,the carbon sequestration potential of biochar could not be evaluated solely by the recalcitrance index.This CO_(2) emission is linked not only to the total organic carbon(TOC)and total carbon(TC)of the biochar but also associated with mobile matter(MM),water holding capacity(WHC),available phosphorus(AP),exchangeable potassium(AK),and nitrogen content.The multiple linear regression analysis(MLRA)shows that the weights of these factors on CO_(2) emissions are as follows:TC>pH>MM>WHC>AP>AK.The results show that in addition to biochar stability,pore structure and available phosphorus release also affect carbon dynamics through indirect effects on microbial activity.This means that to minimize CO_(2) emissions during application of biochar,it is necessary to use soil that is rich in phosphorus and biochar that has undeveloped pore structure and high stable carbon.Finally,this study provides valuable theoretical underpinnings biochar application in MT-Soil.
基金supported by the National Natural Science Foundation of China(No.52200120)the R&D Program of Beijing Municipal Education Commission(No.KM202310011003).
文摘CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.
基金funded by the National Key R&D Program of China(2021YFE0112400 and 2023YFF1304303)the National Natural Science Foundation of China(32361143870 and 32101315)。
文摘The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducted field studies to examine the response of N_(2)O emissions to N and P addition over two years in three typical alpine grasslands,alpine meadow(AM),alpine steppe(AS),and alpine cultivated grassland(CG)on the Qinghai-Tibet Plateau(QTP).Our results showed consistent increases in N_(2)O emissions under N addition alone or with P addition,and insignificant change in N_(2)O emissions under P addition alone in all three grasslands.N addition increased N_(2)O emissions directly in AM,by lowering soil pH in AS,and by lowering abundance of denitrification genes in CG.N and P co-addition increased N_(2)O emissions in AM and AS but only showed an interactive effect in AM.P addition enhanced the increase in N_(2)O emissions caused by N addition mainly by promoting plant growth in AM.Overall,our results illustrate that short-term P addition cannot alleviate the stimulation of N_(2)O emissions by N deposition in alpine grassland ecosystems,and may even further stimulate N_(2)O emissions.
基金support of Dean Fund Project of the China National Institute of Standardization[Grant No.572025Y-12477]National Natural Science Foundation of China[Grant No.72204247].
文摘The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influence of policy constraints when evaluating MAC.To address this gap,this paper proposes a nonparametric directional distance function approach under policy regulations to estimate the CO_(2)shadow price in the construction industry across 30 Chinese provinces from 2010 to 2017.Based on this enhanced method,four key findings emerge:①the annual CO_(2)shadow price exhibits an overall upward trend during the observation period;②regional shadow prices display marked variation across provinces;③regional heterogeneity in shadow prices has increased steadily over time;and④both urbanization rate and energy consumption per unit of gross domestic product are significantly negatively correlated with the CO_(2)shadow price.Finally,the paper offers several policy recommendations for CO_(2)emissions reduction in the Chinese construction industry at the national,industry,and enterprise levels.
基金the Funds for Science and Technology Development of the Ministry of Education and Training,Vietnam(grant number B2023-DNA-21).
文摘The energy sector is pivotal in Vietnam’s commitment to achieving net-zero emissions by 2050.This study employs a combination of Structural Decomposition Analysis(SDA)and decoupling approaches based on data from Vietnam’s energy statistics and the Vietnam Living Standards Survey(VHLSS)for 2016,2018,and 2020.The primary aim is to elucidate the effects of direct energy consumption by household groups on CO_(2)emissions,examine factors affecting emissions,and clarify the relationship between CO_(2)emissions from household energy consumption and economic growth in Vietnam.Research results underscore that household groups make considerable use of electricity and Liquefied Petroleum Gas(LPG),simultaneously reducing the proportion of firewood,rice husk,sawdust,agricultural by-products and other fuels.The decrease in energy intensity emerges as the primary factor in lowering household emissions,while population growth and economic efficiency exert the opposite effect.Additionally,the research reveals disparities in emissions between urban and rural areas,similarly among household groups within the given location.Despite maintaining a robust decoupling status between emissions from household consumption and economic growth,unsustainable risks persist,particularly with the increase in electricity demand.The study also highlights the uneven impact of the COVID-19 epidemic on CO_(2)emissions across household groups.Drawing upon these findings,several recommendations are proposed to control CO_(2)emissions from direct energy household consumption to facilitate the most effective household decarbonisation process while ensuring sustainable economic growth in Vietnam.
文摘Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The objective of this study was to compare the impact of South Africa’s population growth,economic growth,and fertilizer consumption on CO_(2)emissions,with those of the US,China,and other BRICS countries.The study used panel data sourced from the World Bank’s World Development Indicators ranging from 1960 to 2023.Results of the fixed effects panel regression show that the coefficient of change for China’s population size(β=9.156,p<0.01)is the highest among the six countries.It is followed by the USA(β=9.156,p<0.05)and South Africa(β=1.474,p<0.01).The effects of GDP for China(β=1.128,p<0.01)on CO_(2)emissions are the largest,followed by South Africa(β=1.098,p<0.01)and the USA in third place(β=0.614,p<0.05).These results show that South Africa is highly reliant on coal-based energy resources.As a policy recommendation,South Africa needs to diversify its energy mix and invest more in renewable energy resources.
文摘To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.
基金supported by the National Natural Science Foundation of China(No.42105159)the Key Technologies Research and Development Program(No.2022YFF0606400)+2 种基金China Meteorological Administration“Research on value realization of climate ecological products”Youth Innovation Team Project(No.CMA2024QN15)Jiangxi Meteorological Technology Project(Nos.JX2021Z06,JX2022Z03,and JX2023Z03)the Joint Open Fund of the Institute of Atmospheric Environment,China Meteorological Administration,Shenyang and Key Laboratory of Agro-Meteorological Disasters of Liaoning Province(No.2024SYIAEKFZD05)。
文摘Although atmospheric CO_(2) observations are becoming increasingly widespread in China,the identification of CO_(2) emission sources is still scarce,especially in undeveloped Central China.To effectively address this issue,in a typical site in Central China,the simultaneous measurements of atmospheric CO_(2),CO,andδ^(13)C were conducted,and the characteristics of CO_(2) emission sources were systematically investigated based on the relationships among CO_(2),CO,andδ^(13)C.The average CO_(2)/CO ratio of winter increased from 52.4 ppm/ppm during 2018–2020 to 65.1 ppm/ppm during 2021–2022,which confirmed the improvement of energy consumption efficiency in China.Air-mass transportation from central China and Yangtze River Delta regions contributed largely to higher CO_(2)/CO ratios in 2021–2022.A highermean CO_(2)/CO ratio appeared during the morning rush hours(60.3 ppm/ppm)than in the afternoon rush hours(51.4 ppm/ppm)in winter.In addition,the meanδ^(13)C value of CO_(2) sources(δ^(13)Cs)also displayed more negative values during the morning rush hours(-28.3‰)than the afternoon rush hours(-22.2‰),suggesting the significant influence of vehicle and natural gas usage at themorning rush hours and the impact of straw burning in the afternoon rush hours.The meanδ^(13)Cs was-24.7‰for winter and-21.9‰for vegetation season,implying the main contribution of coal in winter and the impact of C4 plants during the vegetation season.The contribution of biogenic respiration CO_(2) was inferred to exceed 50%during the nighttime of summer according to the obtained meanδ^(13)C value of biogenic respiration CO_(2),which was calculated to be-21.4‰.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,project number MoE-IF-UJ-R2-22-20772-1.
文摘The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
基金the National Social Science Foundation of China(Grant No.23FGLB024)Special Project on“Promoting High-Quality Development through the Integration of the Yangtze River Delta”of Shaoxing University(Grant No.2024CSJYB01)to provide fund for the study。
文摘Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_(2) emissions embodied in manufacturing exports(CIE),and conducts an empirical test based on panel data from 37 countries from 2000 to 2019.This study found that first,IRA can significantly reduce CIE,but there is a U-shaped nexus between the two,which shows a rebound effect.Second,the heterogeneity test demonstrates that in com-parison to both the low-tech and high-tech sectors,IRA in the medium-tech industry can significantly reduce CIE;compared with the low-IRA sectors,the high-IRA sectors exhibit a more obvious reduction.In addition,IRA has a stronger effect on high-carbon-intensity areas.Third,the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects.Moreover,environmental regulations and the manufacturing in-telligence process positively moderate the nexus between IRA and CIE.Finally,these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.
基金supported by the Research Project of the Shanghai Health Commission,No.2020YJZX0111(to CZ)the National Natural Science Foundation of China,Nos.82021002(to CZ),82272039(to CZ),82171252(to FL)+1 种基金a grant from the National Health Commission of People’s Republic of China(PRC),No.Pro20211231084249000238(to JW)Medical Innovation Research Project of Shanghai Science and Technology Commission,No.21Y11903300(to JG).
文摘Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.
基金supported by Ningbo’s major scientific and technological breakthrough project“Research and Demonstration on the Technology of Collaborative Disposal of Secondary Ash in Typical Industrial Furnaces” (No.20212ZDYF020047)the central balance fund project“Research on Carbon Emission Accounting and Emission Reduction Potential Assessment for the Whole Life Cycle of Iron and Steel Industry” (No.2021-JY-07).
文摘China is the most important steel producer in the world,and its steel industry is one of themost carbon-intensive industries in China.Consequently,research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals.We constructed a carbon dioxide(CO_(2))emission model for China’s iron and steel industry froma life cycle perspective,conducted an empirical analysis based on data from2019,and calculated the CO_(2)emissions of the industry throughout its life cycle.Key emission reduction factors were identified using sensitivity analysis.The results demonstrated that the CO_(2)emission intensity of the steel industry was 2.33 ton CO_(2)/ton,and the production and manufacturing stages were the main sources of CO_(2)emissions,accounting for 89.84%of the total steel life-cycle emissions.Notably,fossil fuel combustion had the highest sensitivity to steel CO_(2)emissions,with a sensitivity coefficient of 0.68,reducing the amount of fossil fuel combustion by 20%and carbon emissions by 13.60%.The sensitivities of power structure optimization and scrap consumption were similar,while that of the transportation structure adjustment was the lowest,with a sensitivity coefficient of less than 0.1.Given the current strategic goals of peak carbon and carbon neutrality,it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies,increase the ratio of scrap steel to steelmaking,and build a new power system.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
