Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysi...Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.展开更多
Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;...Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.展开更多
Biological aging is a complex physiological process characterized by a decline in tissue function and the loss of cellular capabilities,which increase an individual's risk of various diseases[1].While genetic fact...Biological aging is a complex physiological process characterized by a decline in tissue function and the loss of cellular capabilities,which increase an individual's risk of various diseases[1].While genetic factors and lifestyle are key influences on biological aging,environmental factors also play a significant role.Given the rapid aging of the global population,elucidating the factors that influence biological aging is crucial for promoting healthy aging.展开更多
Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the acc...Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.展开更多
Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great s...Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great significance for seizing new opportunities in foreign trade,and advancing firms’green and low-carbon transformation.This study treats the creation of CBEC pilot zones as a quasi-natural experiment,employing panel data from Chinese A-share listed companies matched with city-level information from 2006 to 2021.We construct a multi-period difference-in-differences model to identify the impact of CBEC pilot zone policy on corporate carbon emissions.Our findings indicate the construction of these pilot zones significantly reduces firms’carbon emissions intensity,and the results are robust across multiple tests.We show the pilot zone initiative contributes to emission reductions by enhancing the adoption of digital infrastructure,promoting green technological innovation,and increasing environmental awareness among enterprises.Quantile regressions reveal pilot zones exert a more pronounced carbon-reduction effect on firms characterized by high carbon emissions intensity and advanced levels of digital transformation.Moreover,the policy effect is especially significant in heavily polluting industries,and regions with weaker governmental environmental regulations or lower public environmental concerns.This study makes an innovative contribution to the literature by empirically verifying the environmental governance effect of establishing CBEC pilot zones,and offers practical guidance for governments in formulating cross-border e-commerce policies and for enterprises pursuing low carbon development.展开更多
The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology.Although significant advancements have been witnessed in recent years for single-photon ...The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology.Although significant advancements have been witnessed in recent years for single-photon sources in the near-infrared band(λ∼700–1000 nm),several challenges have yet to be addressed for ideal single-photon emission at the telecommunication band.In this study,we present a droplet-epitaxy strategy for O-band to C-band single-photon source-based semiconductor quantum dots(QDs)using metal-organic vaporphase epitaxy(MOVPE).By investigating the growth conditions of the epitaxial process,we have successfully synthesized InAs/InP QDs with narrow emission lines spanning a broad spectral range of λ∼1200–1600 nm.The morphological and optical properties of the samples were characterized using atomic force microscopy and microphotoluminescence spectroscopy.The recorded single-photon purity of a plain QD structure reaches g^((2))(0)=0.16,with a radiative recombination lifetime as short as 1.5 ns.This work provides a crucial platform for future research on integrated microcavity enhancement techniques and coupled QDs with other quantum photonics in the telecom bands,offering significant prospects for quantum network applications.展开更多
Lakes are carbon dioxide(CO_(2))and methane(CH_(4))emission hotspots,whose associated flux is spatially vari-able.Many studies have investigated the impact of microorganisms and environmental factors on CO_(2) and CH_...Lakes are carbon dioxide(CO_(2))and methane(CH_(4))emission hotspots,whose associated flux is spatially vari-able.Many studies have investigated the impact of microorganisms and environmental factors on CO_(2) and CH_(4) emissions between different lakes.However,the carbon emissions and their influencing factors of different areas within a single lake remain poorly understood.Accordingly,this study investigates CO_(2) and CH_(4) emission hetero-geneity in a large floodplain lake system and distribution characteristics of associated functional microorganisms.Findings show that mean CO_(2) and CH_(4) flux values in the sub lake area were 62.03±24.21 mg/(m2·day)and 5.97±3.2μg/(m2·day),which were greater by factors of 1.78 and 2.96 compared to the water channel and the main lake area,respectively.The alpha diversity of methanogens in the sub lake area was lower than that in the main lake and water channel areas.The abundance of methanogens in bottom water layer was higher compared with the middle and surface layers.Conversely,the abundance of methane(CH_(4))-oxidizing bacteria in the surface layer was higher than that in the bottom layer.Additionally,the composition of methanogen and CH_(4)-oxidizing bacterial community,chlorophyll a(Chl-a),pH,total phosphorus(TP)and dissolved organic carbon(DOC)con-tent constituted the dominate driving factors affecting lake C emissions.Results from this study can be used to improve our understanding of lake spatial heterogeneous of CO_(2) and CH_(4) emission and the driving mechanisms within floodplain lakes under the coupling effects of functional C microorganisms and environmental factors.展开更多
Changes in the soil environment induced by major global changes in climate are affecting carbon emissions in cold-temperate coniferous forests.A randomized block experiment simulating warming,rainfall increase and nit...Changes in the soil environment induced by major global changes in climate are affecting carbon emissions in cold-temperate coniferous forests.A randomized block experiment simulating warming,rainfall increase and nitrogen addition in a Larix gmelinii forest was carried out to study the effects on soil carbon,nitrogen,and CO_(2)flux during the thawing,growing,and freezing periods.Our study found that warming(0-2.0℃)increased soil organic carbon(SOC)and total nitrogen(STN),dissolved organic carbon(DOC)and dissolved organic nitrogen(DON),and microbial biomass carbon(MBC)and microbial biomass nitrogen(MBN).Warming played a direct role in regulating soil CO_(2)emissions,stimulated microbial and plant root respiration and soil CO_(2)flux rapidly increased.Rainfall increase initially increased soil carbon and nitrogen,but a 30%increase in mean annual rainfall caused losses of SOC,STN,DOC,and DON,while MBC and MBN accumulated.Soil CO_(2)emissions were regulated by MBC after an increase in rainfall,excess moisture inhibited microbial activity,and soil CO_(2)flux showed a trend of R2(20%rainfall increase)>R1(10%rainfall increase)>CK(control)>R3(30%rainfall increase).The addition of nitrogen increased SOC,STN,DOC,DON,MBC and MBN.Soil CO_(2)flux progressively decreased with nitrogen inputs(2.5,5.0 and 10.0 g m^(-2)a^(-1)),as more N intensified plant-microbe competition.Nitrogen addition indirectly regulated soil CO_(2)emissions by altering SOC and STN,with MBC and MBN acting as secondary regulators.The results highlight the role of cold-temperate coniferous forest soils in predicting carbon-climate feedback in high-latitude forest permafrost regions.展开更多
Lakes are emission hotpots of nitrous oxide(N_(2)O),however,this phenomenon remains poorly constrained.Eutrophication is widespread in lakes,yet its contribution to N_(2)O emissions is still not well understood.Here,w...Lakes are emission hotpots of nitrous oxide(N_(2)O),however,this phenomenon remains poorly constrained.Eutrophication is widespread in lakes,yet its contribution to N_(2)O emissions is still not well understood.Here,we investigate the spatiotemporal variations of N_(2)O concentrations,fluxes and indirect emission factors(EF5r)and their drivers in Ulansuhai lake,a shallow eutrophic lake located in a semi-arid region in northern China,during 2019–2020.The mean concentration of N_(2)O in water was 20.0±6.7 nmol/L,with a mean diffusive N_(2)O flux of 16.50±21.52μmol/(m2·day),indicating that this lake acted as a persistent source of atmospheric N_(2)O.Estimated indirect emission factors(EF5r)(mean value 0.0037±0.0060)were significantly higher than the default values(0.0026)used in Intergovernmental Panel on Climate Change(IPCC)emission inventories.The N_(2)O concentrations,fluxes and EF5r exhibited substantial seasonal variations and small spatial variations.N_(2)O concentrations and fluxes were positively correlated with the trophic status and EF5r increased with increasing nutrient concentrations in the water.These findings demonstrate the role of eutrophication in influencing the N_(2)O dynamics and confirm that eutrophication can exacerbate N_(2)O emissions.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
Long-term manure application has the potential to alleviate soil acidification, and increase carbon sequestration and nutrient availability, thus improving cropland fertility. However, the mechanisms behind greenhouse...Long-term manure application has the potential to alleviate soil acidification, and increase carbon sequestration and nutrient availability, thus improving cropland fertility. However, the mechanisms behind greenhouse gas N_(2)O emissions from acidic soil mediated by long-term manure application remain poorly understood. Herein, we investigated N_(2)O emission and its linkage with gross N mineralization and nitrification rates, as well as nitrifying and denitrifying microbes in an acidic upland soil subjected to 36-year fertilization treatments, including an unfertilized control(CK), inorganic fertilizer(F), 2× rate of inorganic fertilizer(2F), manure(M), and the combination of inorganic fertilizer and manure(FM) treatments. Compared to the CK treatment(1.34 μg N kg^(-1) d^(-1)), fertilization strongly increased N_(2)O emissions by 34-fold on average, with more pronounced increases in the manure-amendment(10.6-169 μg N kg^(-1) d^(-1)) than those in the inorganic fertilizer treatments(3.26-5.51 μg N kg^(-1) d^(-1)). The manure amendment-stimulated N_(2)O emissions were highly associated with increased soil pH, mean weight diameter of soil aggregates, substrate availability(e.g., particulate organic carbon, NO_(3)^(-)and available phosphorus), gross N mineralization rates, denitrifier abundances and the(nirK+nirS)/nosZ ratio. These findings suggest that the increased N_(2)O emissions primarily resulted from alleviated acidification, increased substrate availability and improved soil structure, thus enhancing microbial N mineralization and favoring N_(2)O^(-)producing denitrifiers over N_(2)O consumers. Moreover, ammonia-oxidizing bacteria(AOB) rather than ammonia-oxidizing archaea(AOA) positively correlated with soil NO_(3)^(-)concentration and N_(2)O emissions, indicating that nitrification indirectly contributed to N_(2)O production by supplying NO_(3)^(-)for denitrification. Collectively, manure amendment potentially stimulates N_(2)O emissions, primarily resulting from alleviated soil acidification and increased substrate availability, thus enhancing N mineralization and denitrifier-mediated N_(2)O production. Our findings suggest that consideration should be given to the greenhouse gas budgets of agricultural ecosystems when applying manure for managing the pH and fertility of acidic soils.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
Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Si...Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.展开更多
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research probl...Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.展开更多
Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emiss...Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emissions from flooding to drying after organic fertilizer replacing for chemical fertilizer remain unclear.Here,a long-term experiment was conducted with four treatments:chemical fertilization only(control),organic fertilizer substituting 25%of chemical N fertilizer(NM1),50%of chemical N fertilizer(NM2),and NM2combined with crop straw(NMS).GHG emissions were monitored,and soil samples were collected to determine labile SOC fractions and microorganisms.Results revealed the GHG emissions in the NM2 significantly increased by 196.88%from flooding to drying,mainly due to the higher CO_(2) emissions.The GHG emissions per kg of C input in NMS was the lowest with the value of 9.17.From flooding to drying,organic fertilizer application significantly increased the readily oxidizable organic carbon(ROC)contents and C lability;the NM2 and NMS dramatically increased the SOC and non-readily oxidizable organic carbon(NROC).The bacterial communities showed significant differences among different treatments in the flooding,while the significant difference was only found between the NMS and other treatments in the drying.From flooding to drying,changing soil moisture conditions causes C fractions and microbial communities to jointly affect carbon emissions,and the NMS promoted carbon sequestration and mitigated GHG emissions.Our findings highlight the importance of the labile SOC fractions and microorganisms linked to GHG emissions in paddy fields.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
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
Civil aviation is one of the industries facing the greatest challenge in reaching carbon neutrality by the middle of this century,and this sector also contributes to adverse impacts on the regional air quality and hum...Civil aviation is one of the industries facing the greatest challenge in reaching carbon neutrality by the middle of this century,and this sector also contributes to adverse impacts on the regional air quality and human health.China exhibits the second highest air passenger turnover worldwide.Our understanding of civil aviation emissionsmust be urgently enhanced,and themitigation potential should be explored.In this study,on the basis of real domestic flight information for 2019,we built a greenhouse gas and air pollution emission inventory for the civil aviation sector in China with the fuel flowmethod based on the cruise and other phases.We thoroughly analyzed emissions by region,aircraft and engine types,and aircraft age,based on which we designed four measures to evaluate the abatement potential.We found that the hydrocarbon(HC),CO,NO_(x),SO_(2),particulate matter(PM)and CO_(2)emissions in 2019 reached 79.9 kt(95%CI[51.6–114.5]),176.3 kt(95%CI[114.5–248.2]),304.2 kt(95%CI[203.4–420.7]),23.2 kt(95%CI[14.2–33.7]),1.0 kt(95%CI[0.61–1.44])and 87.0 Mt(95%CI[57.4–119.6]),respectively.The cruise phase was the major emission phase,accounting for 67%-87%of the total pollutant emissions.If four measures were jointly implemented,the HC,CO,NO_(x),SO_(2),PM and CO_(2)emissions could be reduced by 61%,54%,55%,45%,32%and 38%,respectively.Utilizing lower-emission aircraft and switching travel modes could substantially reduce civil aviation emissions in China.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
Cutting farming-related methane emissions from ruminants is critical in the battle against climate change.Since scientists initially investigated the potential of marine macroalgae to reduce methane emissions,using se...Cutting farming-related methane emissions from ruminants is critical in the battle against climate change.Since scientists initially investigated the potential of marine macroalgae to reduce methane emissions,using seaweeds as an anti-methanogenic feed additive has become prevailing in recent years.Asparagopsis taxiformis is the preferred species because it contains a relatively higher concentration of bromoform.As a type of halogenated methane analogue,bromoform contained in A.taxiformis can specifically inhibit the activity of coenzyme M methyltransferase,thereby blocking the ruminal methanogenesis.However,bromoform is a potential toxin and ozone-depleting substance.In response,current research focuses on the effects of bromoform-enriched seaweed supplementation on ruminant productivity and safety,as well as the impact of large-scale cultivation of seaweeds on the atmospheric environment.The current research on seaweed still needs to be improved,especially in developing more species with low bromoform content,such as Bonnemaisonia hamifera,Dictyota bartayresii,and Cystoseira trinodis.Otherwise,seaweed is rich in bioactive substances and exhibits antibacterial,anti-inflammatory,and other physiological properties,but research on the role of these bioactive compounds in methane emissions is lacking.It is worthy of deeper investigation to identify more potential bioactive compounds.As a new focus of attention,seaweed has attracted the interest of many scientists.Nevertheless,seaweed still faces some challenges as a feed additive to ruminants,such as the residues of heavy metals(iodine and bromine)and bromoform in milk or meat,as well as the establishment of a supply chain for seaweed cultivation,preservation,and processing.We have concluded that the methane-reducing efficacy of seaweed is indisputable.However,its application as a commercial feed additive is still influenced by factors such as safety,costs,policy incentives,and regulations.展开更多
基金funded by the General Program of the National Natural Science Foundation of China[Grant No.42171300]the Strategic Research Program of the National Natural Science Foundation of China[Grant No.42542001]+1 种基金Post-funded Project of National Social Science Fund of China[Grant No.25FJYB015]Special Project of Strategic Research and Decision Support System of the Chinese Academy of Sciences[Grant No.GHJ-ZLZX-2025-48].
文摘Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.
基金The National Natural Science Foundation of China(No.52470211)Special Foundation of Jiangsu Province Science and Technology Plan(No.BZ2024017)RECLAIM Network Plus Project(No.EP/W034034/1).
文摘Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.
基金support from the Shenzhen Science and Technology program(grant number 202208183000115)。
文摘Biological aging is a complex physiological process characterized by a decline in tissue function and the loss of cellular capabilities,which increase an individual's risk of various diseases[1].While genetic factors and lifestyle are key influences on biological aging,environmental factors also play a significant role.Given the rapid aging of the global population,elucidating the factors that influence biological aging is crucial for promoting healthy aging.
文摘Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.
基金support provided by the National Natural Science Foundation of China[Grant No.72204202]2025 Annual Research Program of the China Society for Commercial Statistics[Grant No.2025STY10]General Project of Philosophy Society in Jiangsu Province Universities[Grant Nos.2023SJYB0923 and 2025SJYB0708].
文摘Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great significance for seizing new opportunities in foreign trade,and advancing firms’green and low-carbon transformation.This study treats the creation of CBEC pilot zones as a quasi-natural experiment,employing panel data from Chinese A-share listed companies matched with city-level information from 2006 to 2021.We construct a multi-period difference-in-differences model to identify the impact of CBEC pilot zone policy on corporate carbon emissions.Our findings indicate the construction of these pilot zones significantly reduces firms’carbon emissions intensity,and the results are robust across multiple tests.We show the pilot zone initiative contributes to emission reductions by enhancing the adoption of digital infrastructure,promoting green technological innovation,and increasing environmental awareness among enterprises.Quantile regressions reveal pilot zones exert a more pronounced carbon-reduction effect on firms characterized by high carbon emissions intensity and advanced levels of digital transformation.Moreover,the policy effect is especially significant in heavily polluting industries,and regions with weaker governmental environmental regulations or lower public environmental concerns.This study makes an innovative contribution to the literature by empirically verifying the environmental governance effect of establishing CBEC pilot zones,and offers practical guidance for governments in formulating cross-border e-commerce policies and for enterprises pursuing low carbon development.
基金supported by the National Natural Science Foundation of China (Grant Nos.12494604,12393834,12393831,62274014,6223501662335015)the National Key R&D Program of China (Grant No.2024YFA1208900)。
文摘The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology.Although significant advancements have been witnessed in recent years for single-photon sources in the near-infrared band(λ∼700–1000 nm),several challenges have yet to be addressed for ideal single-photon emission at the telecommunication band.In this study,we present a droplet-epitaxy strategy for O-band to C-band single-photon source-based semiconductor quantum dots(QDs)using metal-organic vaporphase epitaxy(MOVPE).By investigating the growth conditions of the epitaxial process,we have successfully synthesized InAs/InP QDs with narrow emission lines spanning a broad spectral range of λ∼1200–1600 nm.The morphological and optical properties of the samples were characterized using atomic force microscopy and microphotoluminescence spectroscopy.The recorded single-photon purity of a plain QD structure reaches g^((2))(0)=0.16,with a radiative recombination lifetime as short as 1.5 ns.This work provides a crucial platform for future research on integrated microcavity enhancement techniques and coupled QDs with other quantum photonics in the telecom bands,offering significant prospects for quantum network applications.
基金supported by the National Natural Science Foundation of China(No.42225103).
文摘Lakes are carbon dioxide(CO_(2))and methane(CH_(4))emission hotspots,whose associated flux is spatially vari-able.Many studies have investigated the impact of microorganisms and environmental factors on CO_(2) and CH_(4) emissions between different lakes.However,the carbon emissions and their influencing factors of different areas within a single lake remain poorly understood.Accordingly,this study investigates CO_(2) and CH_(4) emission hetero-geneity in a large floodplain lake system and distribution characteristics of associated functional microorganisms.Findings show that mean CO_(2) and CH_(4) flux values in the sub lake area were 62.03±24.21 mg/(m2·day)and 5.97±3.2μg/(m2·day),which were greater by factors of 1.78 and 2.96 compared to the water channel and the main lake area,respectively.The alpha diversity of methanogens in the sub lake area was lower than that in the main lake and water channel areas.The abundance of methanogens in bottom water layer was higher compared with the middle and surface layers.Conversely,the abundance of methane(CH_(4))-oxidizing bacteria in the surface layer was higher than that in the bottom layer.Additionally,the composition of methanogen and CH_(4)-oxidizing bacterial community,chlorophyll a(Chl-a),pH,total phosphorus(TP)and dissolved organic carbon(DOC)con-tent constituted the dominate driving factors affecting lake C emissions.Results from this study can be used to improve our understanding of lake spatial heterogeneous of CO_(2) and CH_(4) emission and the driving mechanisms within floodplain lakes under the coupling effects of functional C microorganisms and environmental factors.
基金funded by the Science and Technology Programme of Inner Mongolia Autonomous Region(Grant No.:2023YFDZ0026 and 2024KYPT0003)the 2024 Postgraduate Research and Innovation Programme of Inner Mongolia Agricultural University。
文摘Changes in the soil environment induced by major global changes in climate are affecting carbon emissions in cold-temperate coniferous forests.A randomized block experiment simulating warming,rainfall increase and nitrogen addition in a Larix gmelinii forest was carried out to study the effects on soil carbon,nitrogen,and CO_(2)flux during the thawing,growing,and freezing periods.Our study found that warming(0-2.0℃)increased soil organic carbon(SOC)and total nitrogen(STN),dissolved organic carbon(DOC)and dissolved organic nitrogen(DON),and microbial biomass carbon(MBC)and microbial biomass nitrogen(MBN).Warming played a direct role in regulating soil CO_(2)emissions,stimulated microbial and plant root respiration and soil CO_(2)flux rapidly increased.Rainfall increase initially increased soil carbon and nitrogen,but a 30%increase in mean annual rainfall caused losses of SOC,STN,DOC,and DON,while MBC and MBN accumulated.Soil CO_(2)emissions were regulated by MBC after an increase in rainfall,excess moisture inhibited microbial activity,and soil CO_(2)flux showed a trend of R2(20%rainfall increase)>R1(10%rainfall increase)>CK(control)>R3(30%rainfall increase).The addition of nitrogen increased SOC,STN,DOC,DON,MBC and MBN.Soil CO_(2)flux progressively decreased with nitrogen inputs(2.5,5.0 and 10.0 g m^(-2)a^(-1)),as more N intensified plant-microbe competition.Nitrogen addition indirectly regulated soil CO_(2)emissions by altering SOC and STN,with MBC and MBN acting as secondary regulators.The results highlight the role of cold-temperate coniferous forest soils in predicting carbon-climate feedback in high-latitude forest permafrost regions.
基金supported by the National Natural Science Foundation of China(No.52279067)the National Key R&D Program of China(No.2021YFC3201203)+1 种基金the Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region(No.2022QZBZ0003)the Research Foundation for Young Scholars of Inner Mongolia University(No.10000-23112101/333).
文摘Lakes are emission hotpots of nitrous oxide(N_(2)O),however,this phenomenon remains poorly constrained.Eutrophication is widespread in lakes,yet its contribution to N_(2)O emissions is still not well understood.Here,we investigate the spatiotemporal variations of N_(2)O concentrations,fluxes and indirect emission factors(EF5r)and their drivers in Ulansuhai lake,a shallow eutrophic lake located in a semi-arid region in northern China,during 2019–2020.The mean concentration of N_(2)O in water was 20.0±6.7 nmol/L,with a mean diffusive N_(2)O flux of 16.50±21.52μmol/(m2·day),indicating that this lake acted as a persistent source of atmospheric N_(2)O.Estimated indirect emission factors(EF5r)(mean value 0.0037±0.0060)were significantly higher than the default values(0.0026)used in Intergovernmental Panel on Climate Change(IPCC)emission inventories.The N_(2)O concentrations,fluxes and EF5r exhibited substantial seasonal variations and small spatial variations.N_(2)O concentrations and fluxes were positively correlated with the trophic status and EF5r increased with increasing nutrient concentrations in the water.These findings demonstrate the role of eutrophication in influencing the N_(2)O dynamics and confirm that eutrophication can exacerbate N_(2)O emissions.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金financially supported by the National Science & Technology Fundamental Resources Investigation Project of China (2021FY100501)the Youth Innovation of Chinese Academy of Agricultural Sciences (Y2023QC16)。
文摘Long-term manure application has the potential to alleviate soil acidification, and increase carbon sequestration and nutrient availability, thus improving cropland fertility. However, the mechanisms behind greenhouse gas N_(2)O emissions from acidic soil mediated by long-term manure application remain poorly understood. Herein, we investigated N_(2)O emission and its linkage with gross N mineralization and nitrification rates, as well as nitrifying and denitrifying microbes in an acidic upland soil subjected to 36-year fertilization treatments, including an unfertilized control(CK), inorganic fertilizer(F), 2× rate of inorganic fertilizer(2F), manure(M), and the combination of inorganic fertilizer and manure(FM) treatments. Compared to the CK treatment(1.34 μg N kg^(-1) d^(-1)), fertilization strongly increased N_(2)O emissions by 34-fold on average, with more pronounced increases in the manure-amendment(10.6-169 μg N kg^(-1) d^(-1)) than those in the inorganic fertilizer treatments(3.26-5.51 μg N kg^(-1) d^(-1)). The manure amendment-stimulated N_(2)O emissions were highly associated with increased soil pH, mean weight diameter of soil aggregates, substrate availability(e.g., particulate organic carbon, NO_(3)^(-)and available phosphorus), gross N mineralization rates, denitrifier abundances and the(nirK+nirS)/nosZ ratio. These findings suggest that the increased N_(2)O emissions primarily resulted from alleviated acidification, increased substrate availability and improved soil structure, thus enhancing microbial N mineralization and favoring N_(2)O^(-)producing denitrifiers over N_(2)O consumers. Moreover, ammonia-oxidizing bacteria(AOB) rather than ammonia-oxidizing archaea(AOA) positively correlated with soil NO_(3)^(-)concentration and N_(2)O emissions, indicating that nitrification indirectly contributed to N_(2)O production by supplying NO_(3)^(-)for denitrification. Collectively, manure amendment potentially stimulates N_(2)O emissions, primarily resulting from alleviated soil acidification and increased substrate availability, thus enhancing N mineralization and denitrifier-mediated N_(2)O production. Our findings suggest that consideration should be given to the greenhouse gas budgets of agricultural ecosystems when applying manure for managing the pH and fertility of acidic soils.
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 Humanities and Social Sciences Project of the Ministry of Education of the Peoples Republic(No.21YJCZH099)the National Natural Science Foundation of China(Nos.41401089 and 41741014)the Science and Technology Project of Sichuan Province(No.2023NSFSC1979).
文摘Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.
基金supported by the Key Research and Development Program in Shaanxi Province,China(No.2022ZDLSF07-05)the Fundamental Research Funds for the Central Universities,CHD(No.300102352901)。
文摘Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.
基金the support of the National Natural Science Foundation of China(No.42107247)the National Key Research and Development Project(No.2022YFD1901605)+1 种基金the Natural Science Foundation of Sichuan Province(Nos.2025YFHZ0142 and 2024NSFSC0800)the Tobacco Science Foundation of Sichuan Province(No.SCYC202407)。
文摘Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emissions from flooding to drying after organic fertilizer replacing for chemical fertilizer remain unclear.Here,a long-term experiment was conducted with four treatments:chemical fertilization only(control),organic fertilizer substituting 25%of chemical N fertilizer(NM1),50%of chemical N fertilizer(NM2),and NM2combined with crop straw(NMS).GHG emissions were monitored,and soil samples were collected to determine labile SOC fractions and microorganisms.Results revealed the GHG emissions in the NM2 significantly increased by 196.88%from flooding to drying,mainly due to the higher CO_(2) emissions.The GHG emissions per kg of C input in NMS was the lowest with the value of 9.17.From flooding to drying,organic fertilizer application significantly increased the readily oxidizable organic carbon(ROC)contents and C lability;the NM2 and NMS dramatically increased the SOC and non-readily oxidizable organic carbon(NROC).The bacterial communities showed significant differences among different treatments in the flooding,while the significant difference was only found between the NMS and other treatments in the drying.From flooding to drying,changing soil moisture conditions causes C fractions and microbial communities to jointly affect carbon emissions,and the NMS promoted carbon sequestration and mitigated GHG emissions.Our findings highlight the importance of the labile SOC fractions and microorganisms linked to GHG emissions in paddy fields.
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.
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 Natural Science Foundation of China(Nos.42375171 and 42105157)the Interdisciplinary Research Project for Young Teachers of USTB(No.06600083)+1 种基金Energy Foundation(Nos.G-2109-33379 and G-2306-34754)the Fundamental Research Funds for the Central Universities(No.06500166).
文摘Civil aviation is one of the industries facing the greatest challenge in reaching carbon neutrality by the middle of this century,and this sector also contributes to adverse impacts on the regional air quality and human health.China exhibits the second highest air passenger turnover worldwide.Our understanding of civil aviation emissionsmust be urgently enhanced,and themitigation potential should be explored.In this study,on the basis of real domestic flight information for 2019,we built a greenhouse gas and air pollution emission inventory for the civil aviation sector in China with the fuel flowmethod based on the cruise and other phases.We thoroughly analyzed emissions by region,aircraft and engine types,and aircraft age,based on which we designed four measures to evaluate the abatement potential.We found that the hydrocarbon(HC),CO,NO_(x),SO_(2),particulate matter(PM)and CO_(2)emissions in 2019 reached 79.9 kt(95%CI[51.6–114.5]),176.3 kt(95%CI[114.5–248.2]),304.2 kt(95%CI[203.4–420.7]),23.2 kt(95%CI[14.2–33.7]),1.0 kt(95%CI[0.61–1.44])and 87.0 Mt(95%CI[57.4–119.6]),respectively.The cruise phase was the major emission phase,accounting for 67%-87%of the total pollutant emissions.If four measures were jointly implemented,the HC,CO,NO_(x),SO_(2),PM and CO_(2)emissions could be reduced by 61%,54%,55%,45%,32%and 38%,respectively.Utilizing lower-emission aircraft and switching travel modes could substantially reduce civil aviation emissions in China.
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supported by the Youth Innovation Program of the Chinese Academy of Agricultural Sciences(Y2022QC10)the Agricultural Science and Technology Innovation Program,China(CAAS-ASTIP-2023-IFR-03,CAAS-IFR-ZDRW202302 and CAAS-IFR-ZDRW202404)the Basal Research Fund of the Institute of Feed Research of Chinese Academy of Agricultural Sciences(1610382024009)。
文摘Cutting farming-related methane emissions from ruminants is critical in the battle against climate change.Since scientists initially investigated the potential of marine macroalgae to reduce methane emissions,using seaweeds as an anti-methanogenic feed additive has become prevailing in recent years.Asparagopsis taxiformis is the preferred species because it contains a relatively higher concentration of bromoform.As a type of halogenated methane analogue,bromoform contained in A.taxiformis can specifically inhibit the activity of coenzyme M methyltransferase,thereby blocking the ruminal methanogenesis.However,bromoform is a potential toxin and ozone-depleting substance.In response,current research focuses on the effects of bromoform-enriched seaweed supplementation on ruminant productivity and safety,as well as the impact of large-scale cultivation of seaweeds on the atmospheric environment.The current research on seaweed still needs to be improved,especially in developing more species with low bromoform content,such as Bonnemaisonia hamifera,Dictyota bartayresii,and Cystoseira trinodis.Otherwise,seaweed is rich in bioactive substances and exhibits antibacterial,anti-inflammatory,and other physiological properties,but research on the role of these bioactive compounds in methane emissions is lacking.It is worthy of deeper investigation to identify more potential bioactive compounds.As a new focus of attention,seaweed has attracted the interest of many scientists.Nevertheless,seaweed still faces some challenges as a feed additive to ruminants,such as the residues of heavy metals(iodine and bromine)and bromoform in milk or meat,as well as the establishment of a supply chain for seaweed cultivation,preservation,and processing.We have concluded that the methane-reducing efficacy of seaweed is indisputable.However,its application as a commercial feed additive is still influenced by factors such as safety,costs,policy incentives,and regulations.
