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
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
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
With the rapid development of aviation industry and its increasing impact on the global climate change,the contributions of carbon emissions frominternational flights are attracting more and more attention worldwide.T...With the rapid development of aviation industry and its increasing impact on the global climate change,the contributions of carbon emissions frominternational flights are attracting more and more attention worldwide.This study,taking Macao as the aviation hub,established the cross-border aviation carbon emission evaluation model to explore dynamic carbon emissions and net-zero path of international flights.The aviation hubmainly covers 58 routes and five types of civil aircraft from 12 countries or regions during 2000-2022.The results show that the aviation transportation in Macao emitted about 1.44 million tons CO_(2)eq in 2019,which is high 3.6 times that of 2000.The COVID-19 has led to a rapid decline in aviation carbon emissions in a short period of time,carbon emissions in 2020 decreased by 80%compared to 2019.In terms of cumulative carbon emissions from 2000 to 2019,the A321 and A320 Airbus contribute to 80%of carbon emissions.And the Chinese mainland(37%)and Taiwan(29%)are the main sources of emissions.In 2000-2019,the proportion of carbon emissions from China(including Taiwan and Hong Kong)decrease from 91%to 53%,while the contribution from Southeast Asia(from 5% to 26%),Japan and South Korea(from 2% to 19%)keep the growth trends.In the optimal scenario(B3C3),net zero emissions of cross-border aviation in Macao can be not achieved,and there is still only by removing 0.3 million tons CO_(2)eq.Emission reduction technology and new energy usage are priorities for the aviation emission reduction.展开更多
This study investigates the impact of carbon tax policies on carbon emission reductions in G20 countries to support the achievement of the Net Zero Emissions target by 2060.As the G20 collectively accounts for a signi...This study investigates the impact of carbon tax policies on carbon emission reductions in G20 countries to support the achievement of the Net Zero Emissions target by 2060.As the G20 collectively accounts for a significant share of global greenhouse gas emissions,effective policy interventions in these nations are pivotal to addressing the climate crisis.The research employs the Pearson correlation test to quantify the statistical relationship between carbon tax rates and emission levels,alongside a content analysis of sustainability reports from G20 countries to evaluate policy implementation and outcomes.The results reveal a moderate yet statistically significant negative correlation(r=-0.30,p<0.05),indicating that higher carbon taxes are associated with lower emission levels.Content analysis further demonstrates that countries with high and consistently enforced carbon taxes,such as Japan and South Korea,achieve more substantial emissions reductions compared to nations with lower tax rates or inconsistent policy implementation.The findings emphasize that while carbon taxes serve as an effective instrument to internalize the social costs of carbon pollution,their impact is maximized when integrated with broader strategies,including investments in renewable energy,advancements in energy efficiency,and technological innovation.This research contributes to the understanding of carbon tax effectiveness and offers policy recommendations to strengthen fiscal measures as part of comprehensive climate action strategies toward achieving global sustainability targets.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
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
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
In light of the increasing recognition of the necessity to evaluate and mitigate the environmental impact of human activities, the aim of this study is to assess the greenhouse gases emitted in 2022 by the Kossodo the...In light of the increasing recognition of the necessity to evaluate and mitigate the environmental impact of human activities, the aim of this study is to assess the greenhouse gases emitted in 2022 by the Kossodo thermal power plant as a consequence of its electricity production. The specific objective was to identify the emission sources and quantify the gases generated, with the purpose of proposing effective solutions for reducing the plant’s ecological footprint. In order to achieve the objectives set out in the study, the Bilan Carbone® method was employed. Following an analysis of the plant’s activities, seven emission items were identified as requiring further investigation. The data was gathered from the plant’s activity reports, along with measurements and questionnaires distributed to employees. The data collected was subjected to processing in order to produce the sought activity data. The Bilan Carbone® V7.1 spreadsheet was employed to convert the activity data into equivalent quantities of CO2. The full assessment indicates that the majority of the power plant’s emissions come from the combustion of HFO and DDO, accounting for 96.11% of the Kossodo power plant’s total GHG emissions in 2022. The plant produced 280,585,676 kilowatt-hours (kWh), resulting in emissions of 218,492.785 ± 10,924.639 tCO2e, which yielded an emission factor of 0.78 kgCO2e/kWh for the year 2022. In order to reduce this rate, recommendations for improved energy efficiency have been issued to management and all staff.展开更多
Logistics service providers significantly contribute to environmental degradation through improper waste disposal,hazardous packaging materials,excessive fuel consumption,and emissions.This study examines the impact o...Logistics service providers significantly contribute to environmental degradation through improper waste disposal,hazardous packaging materials,excessive fuel consumption,and emissions.This study examines the impact of green in-bound logistics and green outbound logistics on environmental,economic,and social performance of logistics companies using survey data from 221 Vietnamese logistics firms.Statistical analysis using Structural Equation Modeling revealed that green inbound logistics positively influences environmental and social performance while moderately affecting eco-nomic outcomes.In contrast,green outbound logistics demonstrates stronger effects on economic and environmental performance but exhibits limited impact on social dimensions.The measurement model showed strong reliability and validity(Cronbach's Alpha>0.70,robust Composite Reliability and Average Variance Extracted values),with excellent fit indices(Chi-Square/df=1.681,GFI=0.898,TLI=0.945,CFI=0.956,RMSEA=0.056).These findings highlight important distinctions between inbound and outbound green logistics impacts,offering valuable insights for an industry with currently low adoption rates of sustainable practices.The research demonstrates that implementing green logistics enhances both environmental preservation and business performance,providing compelling evidence for companies to accelerate their sustainability transition.By understanding these differential impacts,logistics firms can develop more tar-geted and effective sustainability strategies that optimize triple bottom line outcomes.展开更多
Sewage sludge in cities of Yangzi River Belt,China,generally exhibits a lower organic content and higher silt contentdue to leakage of drainage system,which caused low bioenergy recovery and carbon emission benefits i...Sewage sludge in cities of Yangzi River Belt,China,generally exhibits a lower organic content and higher silt contentdue to leakage of drainage system,which caused low bioenergy recovery and carbon emission benefits in conventional anaerobic digestion(CAD).Therefore,this paper is on a pilot scale,a bio-thermophilic pretreatment anaerobic digestion(BTPAD)for low organic sludge(volatile solids(VS)of 4%)was operated with a long-term continuous flow of 200 days.The VS degradation rate and CH_(4) yield of BTPAD increased by 19.93%and 53.33%,respectively,compared to those of CAD.The analysis of organic compositions in sludge revealed that BTPAD mainly improved the hydrolysis of proteins in sludge.Further analysis of microbial community proportions by high-throughput sequencing revealed that the short-term bio-thermophilic pretreatment was enriched in Clostridiales,Coprothermobacter and Gelria,was capable of hydrolyzing acidified proteins,and provided more volatile fatty acid(VFA)for the subsequent reaction.Biome combined with fluorescence quantitative polymerase chain reaction(PCR)analysis showed that the number of bacteria with high methanogenic capacity in BTPAD was much higher than that in CAD during the medium temperature digestion stage,indicating that short-term bio-thermophilic pretreatment could provide better methanogenic conditions for BTPAD.Furthermore,the greenhouse gas emission footprint analysis showed that short-term bio-thermophilic pretreatment could reduce the carbon emission of sludge anaerobic digestion system by 19.18%.展开更多
Agriculture is part of the food production that feeds the expanding population though it produces considerable greenhouse gas(GHG)emissions.It's crucial to balancing food security and emission reduction for a win-...Agriculture is part of the food production that feeds the expanding population though it produces considerable greenhouse gas(GHG)emissions.It's crucial to balancing food security and emission reduction for a win-win scenario.However,the lack of sufficient comprehensive district-level assessments makes it difficult to determine the specific mitigation potential for agriculture emissions.In this study,we deployed the IPCC Tier 1 approach and estimated GHG at district/division level in Bangladesh from the year 2010 to 2021.We computed three primary GHG(CO_(2),N_(2)O,and CH_(4))from five sources of agriculture,namely,rice-growing CH_(4),other crops-growing N_(2)O,enteric fermentation,urea fertilizer-induced N_(2)O,and energy-related CO_(2)emissions in the 64 districts,and aggregated them into eight divisions.We observed from this study that GHG emissions in Bangladesh gradually increased from 2010 to 2021 and reached the peak(34.3 MtCO_(2)e)in 2021.Rangpur division emitted the highest amount of GHG(6.03 MtCO_(2)e in 2021)during this period.We also observed significant variations in the sources and structure of emissions within each division.Moreover,regional differences were observed in overall emissions and per capita emissions after additional spatial analysis,with per capita GHG emissions declining from 2010(1.97 t CO_(2)e)to 2021(1.90 t CO_(2)e).Findings of this regional(district/division)estimation will help stakeholders of the country to develop suitable mitigation approaches which targets particular emission sources and geographic areas.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
Synergistic reduction of carbon emissions and air pollution is the core means to address the two major strategic tasks of fundamentally improving the ecological environment and the‘Dual-carbon target’.The issue of s...Synergistic reduction of carbon emissions and air pollution is the core means to address the two major strategic tasks of fundamentally improving the ecological environment and the‘Dual-carbon target’.The issue of synergistic reduction at the provincial level needs to be addressed as a matter of urgency.Taking Henan Province,the largest economy in central China,as an example,this study uses environmentally extended input-output analysis and structural path analysis to identify the key sectors that contribute to CO_(2),SO_(2),and total particulate matter(TPM)emissions,and to sort out key emission pathways(e.g.,Final Demand→Sector…).The results indicate that S2(Mining of Fossil Energy),S10(Nonmetal Mineral Products),S11(Metal Smelting),S13(Power and Heat)and S17(Transportation)are mainly responsible for CO_(2),SO_(2),and TPM direct emissions on the production side,while S16(Construction),S12(Equipment)and S18(Services)account for more than 45%of CO_(2),SO_(2),and TPM embodied emissions on the consumption side.32 shared emission pathways are extracted from the top 100 pathways for CO_(2),SO_(2),and TPM emissions,which account for 27%-51%of total emissions in Henan Province.P9(Export→Nonmetal Mineral Products),P10(Export→Metal Smelting)and P21(Gross Capital Formation→Construction→Nonmetal Mineral Products)are the leading paths responsible for embodied emissions.The research results provide the foundation and guidance for well-designed mitigation policies,as well as a reference for better synergistic control in provinces facing similar situations.展开更多
In the digital era,the development of the digital economy has gained significant practical importance for enhancing reductions in carbon dioxide(CO_(2))emissions.However,existing studies must clarify the key“hidden”...In the digital era,the development of the digital economy has gained significant practical importance for enhancing reductions in carbon dioxide(CO_(2))emissions.However,existing studies must clarify the key“hidden”mechanisms driving actual changes in CO_(2)emissions.Although both the digital economy and CO_(2)emissions are widely researched topics,previous literature has rarely provided an explicit examination of their underlying mechanisms.This study conducts a detailed literature review and finds that the digital economy affects CO_(2)emissions through four main channels:technical,structural,resource allocation,and spatial spillover.However,these channels should not be examined independently due to their interactive effects.Moreover,each of these four channels can be further subdivided,making it essential to explore the subpaths and interconnections among them.By offering a more nuanced understanding of how the digital economy contributes to CO_(2)emissions reduction,this study provides valuable insights that can inform strategic policy development.展开更多
Digital transformation,as a recent trend in socioeconomic development,is considered as a critical pathway for urban carbon reduction because of its potential to increase productivity and energy efficiency.However,few ...Digital transformation,as a recent trend in socioeconomic development,is considered as a critical pathway for urban carbon reduction because of its potential to increase productivity and energy efficiency.However,few studies have explored the relationship between urban digitalization and carbon emissions(CE).Therefore,this study systematically analyzed the spatiotemporal distribution and interaction mechanism between digitalization and CE in the Yangtze River Delta(YRD)urban agglomerations of China during 2006-2020 based on a multidimensional indicator system,including digitalization industry level,digitalization application level,and urban green digitalization willingness.The findings revealed that both digitalization and CE in the YRD exhibit a significant and synchronously evolving“core-periphery”spatial pattern.Core cities generated substantial positive spillover effect on periphery cities through technology diffusion and policy demonstration,advancing both regional digitalization and the collaborative governance of CE.However,digitalization had dual impact on CE.On the one hand,it promoted the reduction of CE by enhancing energy efficiency,optimizing industrial structures,and promoting the application of green technologies.On the other hand,the expansion of digital infrastructure introduced a potential risk of increased energy consumption.Therefore,targeted policy recommendations are proposed to facilitate the coordination of environmental sustainability and digitalization in the YRD.This study provides empirical support and policy insights for advancing the coordinated development of regional digital transformation and green low-carbon initiatives.展开更多
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.
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 Science and Technology Development Fund,Macao SAR,China(Nos.0033/2022/AFJ and 0011/2023/AMJ)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515012017).
文摘With the rapid development of aviation industry and its increasing impact on the global climate change,the contributions of carbon emissions frominternational flights are attracting more and more attention worldwide.This study,taking Macao as the aviation hub,established the cross-border aviation carbon emission evaluation model to explore dynamic carbon emissions and net-zero path of international flights.The aviation hubmainly covers 58 routes and five types of civil aircraft from 12 countries or regions during 2000-2022.The results show that the aviation transportation in Macao emitted about 1.44 million tons CO_(2)eq in 2019,which is high 3.6 times that of 2000.The COVID-19 has led to a rapid decline in aviation carbon emissions in a short period of time,carbon emissions in 2020 decreased by 80%compared to 2019.In terms of cumulative carbon emissions from 2000 to 2019,the A321 and A320 Airbus contribute to 80%of carbon emissions.And the Chinese mainland(37%)and Taiwan(29%)are the main sources of emissions.In 2000-2019,the proportion of carbon emissions from China(including Taiwan and Hong Kong)decrease from 91%to 53%,while the contribution from Southeast Asia(from 5% to 26%),Japan and South Korea(from 2% to 19%)keep the growth trends.In the optimal scenario(B3C3),net zero emissions of cross-border aviation in Macao can be not achieved,and there is still only by removing 0.3 million tons CO_(2)eq.Emission reduction technology and new energy usage are priorities for the aviation emission reduction.
文摘This study investigates the impact of carbon tax policies on carbon emission reductions in G20 countries to support the achievement of the Net Zero Emissions target by 2060.As the G20 collectively accounts for a significant share of global greenhouse gas emissions,effective policy interventions in these nations are pivotal to addressing the climate crisis.The research employs the Pearson correlation test to quantify the statistical relationship between carbon tax rates and emission levels,alongside a content analysis of sustainability reports from G20 countries to evaluate policy implementation and outcomes.The results reveal a moderate yet statistically significant negative correlation(r=-0.30,p<0.05),indicating that higher carbon taxes are associated with lower emission levels.Content analysis further demonstrates that countries with high and consistently enforced carbon taxes,such as Japan and South Korea,achieve more substantial emissions reductions compared to nations with lower tax rates or inconsistent policy implementation.The findings emphasize that while carbon taxes serve as an effective instrument to internalize the social costs of carbon pollution,their impact is maximized when integrated with broader strategies,including investments in renewable energy,advancements in energy efficiency,and technological innovation.This research contributes to the understanding of carbon tax effectiveness and offers policy recommendations to strengthen fiscal measures as part of comprehensive climate action strategies toward achieving global sustainability targets.
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 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.
文摘In light of the increasing recognition of the necessity to evaluate and mitigate the environmental impact of human activities, the aim of this study is to assess the greenhouse gases emitted in 2022 by the Kossodo thermal power plant as a consequence of its electricity production. The specific objective was to identify the emission sources and quantify the gases generated, with the purpose of proposing effective solutions for reducing the plant’s ecological footprint. In order to achieve the objectives set out in the study, the Bilan Carbone® method was employed. Following an analysis of the plant’s activities, seven emission items were identified as requiring further investigation. The data was gathered from the plant’s activity reports, along with measurements and questionnaires distributed to employees. The data collected was subjected to processing in order to produce the sought activity data. The Bilan Carbone® V7.1 spreadsheet was employed to convert the activity data into equivalent quantities of CO2. The full assessment indicates that the majority of the power plant’s emissions come from the combustion of HFO and DDO, accounting for 96.11% of the Kossodo power plant’s total GHG emissions in 2022. The plant produced 280,585,676 kilowatt-hours (kWh), resulting in emissions of 218,492.785 ± 10,924.639 tCO2e, which yielded an emission factor of 0.78 kgCO2e/kWh for the year 2022. In order to reduce this rate, recommendations for improved energy efficiency have been issued to management and all staff.
文摘Logistics service providers significantly contribute to environmental degradation through improper waste disposal,hazardous packaging materials,excessive fuel consumption,and emissions.This study examines the impact of green in-bound logistics and green outbound logistics on environmental,economic,and social performance of logistics companies using survey data from 221 Vietnamese logistics firms.Statistical analysis using Structural Equation Modeling revealed that green inbound logistics positively influences environmental and social performance while moderately affecting eco-nomic outcomes.In contrast,green outbound logistics demonstrates stronger effects on economic and environmental performance but exhibits limited impact on social dimensions.The measurement model showed strong reliability and validity(Cronbach's Alpha>0.70,robust Composite Reliability and Average Variance Extracted values),with excellent fit indices(Chi-Square/df=1.681,GFI=0.898,TLI=0.945,CFI=0.956,RMSEA=0.056).These findings highlight important distinctions between inbound and outbound green logistics impacts,offering valuable insights for an industry with currently low adoption rates of sustainable practices.The research demonstrates that implementing green logistics enhances both environmental preservation and business performance,providing compelling evidence for companies to accelerate their sustainability transition.By understanding these differential impacts,logistics firms can develop more tar-geted and effective sustainability strategies that optimize triple bottom line outcomes.
基金supported by the National Key Research and Development Project (Nos.2020YFC1908702 and 2021YFC3200700)the National Natural Science Foundation of China (Nos.52192684 and 52192680).
文摘Sewage sludge in cities of Yangzi River Belt,China,generally exhibits a lower organic content and higher silt contentdue to leakage of drainage system,which caused low bioenergy recovery and carbon emission benefits in conventional anaerobic digestion(CAD).Therefore,this paper is on a pilot scale,a bio-thermophilic pretreatment anaerobic digestion(BTPAD)for low organic sludge(volatile solids(VS)of 4%)was operated with a long-term continuous flow of 200 days.The VS degradation rate and CH_(4) yield of BTPAD increased by 19.93%and 53.33%,respectively,compared to those of CAD.The analysis of organic compositions in sludge revealed that BTPAD mainly improved the hydrolysis of proteins in sludge.Further analysis of microbial community proportions by high-throughput sequencing revealed that the short-term bio-thermophilic pretreatment was enriched in Clostridiales,Coprothermobacter and Gelria,was capable of hydrolyzing acidified proteins,and provided more volatile fatty acid(VFA)for the subsequent reaction.Biome combined with fluorescence quantitative polymerase chain reaction(PCR)analysis showed that the number of bacteria with high methanogenic capacity in BTPAD was much higher than that in CAD during the medium temperature digestion stage,indicating that short-term bio-thermophilic pretreatment could provide better methanogenic conditions for BTPAD.Furthermore,the greenhouse gas emission footprint analysis showed that short-term bio-thermophilic pretreatment could reduce the carbon emission of sludge anaerobic digestion system by 19.18%.
基金funded by the National Natural Science Foundation of China(Grant No.72221002)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI)(Grant No.2024VCA0001)。
文摘Agriculture is part of the food production that feeds the expanding population though it produces considerable greenhouse gas(GHG)emissions.It's crucial to balancing food security and emission reduction for a win-win scenario.However,the lack of sufficient comprehensive district-level assessments makes it difficult to determine the specific mitigation potential for agriculture emissions.In this study,we deployed the IPCC Tier 1 approach and estimated GHG at district/division level in Bangladesh from the year 2010 to 2021.We computed three primary GHG(CO_(2),N_(2)O,and CH_(4))from five sources of agriculture,namely,rice-growing CH_(4),other crops-growing N_(2)O,enteric fermentation,urea fertilizer-induced N_(2)O,and energy-related CO_(2)emissions in the 64 districts,and aggregated them into eight divisions.We observed from this study that GHG emissions in Bangladesh gradually increased from 2010 to 2021 and reached the peak(34.3 MtCO_(2)e)in 2021.Rangpur division emitted the highest amount of GHG(6.03 MtCO_(2)e in 2021)during this period.We also observed significant variations in the sources and structure of emissions within each division.Moreover,regional differences were observed in overall emissions and per capita emissions after additional spatial analysis,with per capita GHG emissions declining from 2010(1.97 t CO_(2)e)to 2021(1.90 t CO_(2)e).Findings of this regional(district/division)estimation will help stakeholders of the country to develop suitable mitigation approaches which targets particular emission sources and geographic areas.
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.
基金supported by the National Natural Science Foundation of China(No.42001246)the Energy Foundation(No.G-2209-34120).
文摘Synergistic reduction of carbon emissions and air pollution is the core means to address the two major strategic tasks of fundamentally improving the ecological environment and the‘Dual-carbon target’.The issue of synergistic reduction at the provincial level needs to be addressed as a matter of urgency.Taking Henan Province,the largest economy in central China,as an example,this study uses environmentally extended input-output analysis and structural path analysis to identify the key sectors that contribute to CO_(2),SO_(2),and total particulate matter(TPM)emissions,and to sort out key emission pathways(e.g.,Final Demand→Sector…).The results indicate that S2(Mining of Fossil Energy),S10(Nonmetal Mineral Products),S11(Metal Smelting),S13(Power and Heat)and S17(Transportation)are mainly responsible for CO_(2),SO_(2),and TPM direct emissions on the production side,while S16(Construction),S12(Equipment)and S18(Services)account for more than 45%of CO_(2),SO_(2),and TPM embodied emissions on the consumption side.32 shared emission pathways are extracted from the top 100 pathways for CO_(2),SO_(2),and TPM emissions,which account for 27%-51%of total emissions in Henan Province.P9(Export→Nonmetal Mineral Products),P10(Export→Metal Smelting)and P21(Gross Capital Formation→Construction→Nonmetal Mineral Products)are the leading paths responsible for embodied emissions.The research results provide the foundation and guidance for well-designed mitigation policies,as well as a reference for better synergistic control in provinces facing similar situations.
基金financial support from the National Natural Science Foundation of China[Grant No.72274159].
文摘In the digital era,the development of the digital economy has gained significant practical importance for enhancing reductions in carbon dioxide(CO_(2))emissions.However,existing studies must clarify the key“hidden”mechanisms driving actual changes in CO_(2)emissions.Although both the digital economy and CO_(2)emissions are widely researched topics,previous literature has rarely provided an explicit examination of their underlying mechanisms.This study conducts a detailed literature review and finds that the digital economy affects CO_(2)emissions through four main channels:technical,structural,resource allocation,and spatial spillover.However,these channels should not be examined independently due to their interactive effects.Moreover,each of these four channels can be further subdivided,making it essential to explore the subpaths and interconnections among them.By offering a more nuanced understanding of how the digital economy contributes to CO_(2)emissions reduction,this study provides valuable insights that can inform strategic policy development.
文摘Digital transformation,as a recent trend in socioeconomic development,is considered as a critical pathway for urban carbon reduction because of its potential to increase productivity and energy efficiency.However,few studies have explored the relationship between urban digitalization and carbon emissions(CE).Therefore,this study systematically analyzed the spatiotemporal distribution and interaction mechanism between digitalization and CE in the Yangtze River Delta(YRD)urban agglomerations of China during 2006-2020 based on a multidimensional indicator system,including digitalization industry level,digitalization application level,and urban green digitalization willingness.The findings revealed that both digitalization and CE in the YRD exhibit a significant and synchronously evolving“core-periphery”spatial pattern.Core cities generated substantial positive spillover effect on periphery cities through technology diffusion and policy demonstration,advancing both regional digitalization and the collaborative governance of CE.However,digitalization had dual impact on CE.On the one hand,it promoted the reduction of CE by enhancing energy efficiency,optimizing industrial structures,and promoting the application of green technologies.On the other hand,the expansion of digital infrastructure introduced a potential risk of increased energy consumption.Therefore,targeted policy recommendations are proposed to facilitate the coordination of environmental sustainability and digitalization in the YRD.This study provides empirical support and policy insights for advancing the coordinated development of regional digital transformation and green low-carbon initiatives.
