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
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
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
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
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
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
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
With the increasingly serious environmental problems,the use of sustainable materials is particularly important.This study focuses on the greenhouse gas emissions and economic costs of wood over its life cycle as a su...With the increasingly serious environmental problems,the use of sustainable materials is particularly important.This study focuses on the greenhouse gas emissions and economic costs of wood over its life cycle as a sustainable resource.We use a systematic life cycle assessment(LCA)approach to assess the entire process from raw material collection,processing,use to disposal.The study found that using wood can significantly reduce greenhouse gas emissions compared to traditional building materials such as steel and concrete.In addition,although the initial procurement costs of wood may be higher,its maintenance costs are lower in the long run,making the life cycle costs generally more economical.The results of this study highlight the environmental and economic advantages of wood in the selection of sustainable building materials,and provide a scientific basis for promoting the use of wood.展开更多
Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The obj...Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The objective of this study was to compare the impact of South Africa’s population growth,economic growth,and fertilizer consumption on CO_(2)emissions,with those of the US,China,and other BRICS countries.The study used panel data sourced from the World Bank’s World Development Indicators ranging from 1960 to 2023.Results of the fixed effects panel regression show that the coefficient of change for China’s population size(β=9.156,p<0.01)is the highest among the six countries.It is followed by the USA(β=9.156,p<0.05)and South Africa(β=1.474,p<0.01).The effects of GDP for China(β=1.128,p<0.01)on CO_(2)emissions are the largest,followed by South Africa(β=1.098,p<0.01)and the USA in third place(β=0.614,p<0.05).These results show that South Africa is highly reliant on coal-based energy resources.As a policy recommendation,South Africa needs to diversify its energy mix and invest more in renewable energy resources.展开更多
To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting perfo...To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.展开更多
Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbo...Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbon dioxide is one of the most important greenhouse gases contributing to climate change and is responsible for 60%of global warming.The facade of the building,as the main intermediary between the interior and exterior spaces,plays a significant role in adjusting the weather conditions and providing thermal comfort to the residents.In this research,715 different scenarios were defined with the combination of various types of construction materials,and the effect of each of these scenarios on the process of energy loss from the surface of the external walls of the building during the operation period was determined.In the end,these scenarios were compared during a one-year operation period,and the amount of energy consumption in each of these scenarios was calculated.Also,bymeasuring the amount of carbon emissions in buildings during the operation period and before that,let’s look at practical methods to reduce the effects of the construction industry on the environment.By comparing the research findings,it can be seen that the ranking of each scenario in terms of total energy consumption is not necessarily the same as the ranking of energy consumption for gas consumption or electricity consumption for the same scenario.That is,choosing the optimal scenario depends on the type of energy consumed in the building.Finally,we determined the scenarios with the lowest and highest amounts of embodied and operational carbon.In the end,we obtained the latent carbon compensation period for each scenario.This article can help designers and construction engineers optimize the energy consumption of buildings by deciding on the right materials.展开更多
Controlled-release/stable nitrogen(N)fertilizers can improve vegetable yields and achieve lower greenhouse gas emissions,resulting in cost-effective and environmentally friendly vegetable production.However,there has ...Controlled-release/stable nitrogen(N)fertilizers can improve vegetable yields and achieve lower greenhouse gas emissions,resulting in cost-effective and environmentally friendly vegetable production.However,there has been limited research on the controlled-release/stable N fertilization in long-term fixed-position vegetable rotation fields.In this study,a five-year field experiment was conducted to examine the effects of long-term controlled-release/stable N fertilization in reducing greenhouse gas emissions and increasing lettuce yield.Six distinct treatments were employed for N fertilization:the control without N fertilizer(CK),normal local farming practices with application of urea fertilizer at 400 kg N ha^(-1)(T1),optimized application of urea at 320 kg N ha^(-1)(T2),optimized application of urea at 320 kg N ha^(-1)with supplementation of 1.0 kg ha^(-1)3,4-dimethylpyrazole phosphate(DMPP)as N inhibitor(T3),application of polyurethane-coated urea at 320 kg N ha^(-1)(T4),and application of polyurethane-coated urea at 320 kg N ha^(-1)with supplementation of 1.0 kg ha^(-1)DMPP(T5).The results showed that the T3,T4,and T5 treatments using controlled-release/stable N fertilization emitted about 12.2%-56.7%less average annual cumulative nitrous oxide(N_(2)O)and 1.31%-10.0%less carbon dioxide(CO_(2))than the T2 treatment.Nitrous oxide and CO_(2)emissions from the T4 and T5 treatments were considerably lower than those from the T3 treatment.No significant seasonal or interannual variability was observed in N_(2)O and CO_(2)emissions.The observed phenomena were attributed to the fluctuations in soil ammonium-and nitrate-N contents.The findings in this study revealed that long-term controlled-release/stable N fertilization resulted in reduced field N loss,benefitting vegetable yields without increasing CO_(2)emissions and highlighting the application potential of this technique for sustainable agricultural production.展开更多
CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotempora...CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.展开更多
The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducte...The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducted field studies to examine the response of N_(2)O emissions to N and P addition over two years in three typical alpine grasslands,alpine meadow(AM),alpine steppe(AS),and alpine cultivated grassland(CG)on the Qinghai-Tibet Plateau(QTP).Our results showed consistent increases in N_(2)O emissions under N addition alone or with P addition,and insignificant change in N_(2)O emissions under P addition alone in all three grasslands.N addition increased N_(2)O emissions directly in AM,by lowering soil pH in AS,and by lowering abundance of denitrification genes in CG.N and P co-addition increased N_(2)O emissions in AM and AS but only showed an interactive effect in AM.P addition enhanced the increase in N_(2)O emissions caused by N addition mainly by promoting plant growth in AM.Overall,our results illustrate that short-term P addition cannot alleviate the stimulation of N_(2)O emissions by N deposition in alpine grassland ecosystems,and may even further stimulate N_(2)O emissions.展开更多
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.
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 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.
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With the increasingly serious environmental problems,the use of sustainable materials is particularly important.This study focuses on the greenhouse gas emissions and economic costs of wood over its life cycle as a sustainable resource.We use a systematic life cycle assessment(LCA)approach to assess the entire process from raw material collection,processing,use to disposal.The study found that using wood can significantly reduce greenhouse gas emissions compared to traditional building materials such as steel and concrete.In addition,although the initial procurement costs of wood may be higher,its maintenance costs are lower in the long run,making the life cycle costs generally more economical.The results of this study highlight the environmental and economic advantages of wood in the selection of sustainable building materials,and provide a scientific basis for promoting the use of wood.
文摘Despite countries having signed agreements and developed policy to reduce CO_(2)emissions,there is disproportionate compliance with the agreements,with developed countries continuing to be the largest emitters.The objective of this study was to compare the impact of South Africa’s population growth,economic growth,and fertilizer consumption on CO_(2)emissions,with those of the US,China,and other BRICS countries.The study used panel data sourced from the World Bank’s World Development Indicators ranging from 1960 to 2023.Results of the fixed effects panel regression show that the coefficient of change for China’s population size(β=9.156,p<0.01)is the highest among the six countries.It is followed by the USA(β=9.156,p<0.05)and South Africa(β=1.474,p<0.01).The effects of GDP for China(β=1.128,p<0.01)on CO_(2)emissions are the largest,followed by South Africa(β=1.098,p<0.01)and the USA in third place(β=0.614,p<0.05).These results show that South Africa is highly reliant on coal-based energy resources.As a policy recommendation,South Africa needs to diversify its energy mix and invest more in renewable energy resources.
文摘To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.
文摘Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbon dioxide is one of the most important greenhouse gases contributing to climate change and is responsible for 60%of global warming.The facade of the building,as the main intermediary between the interior and exterior spaces,plays a significant role in adjusting the weather conditions and providing thermal comfort to the residents.In this research,715 different scenarios were defined with the combination of various types of construction materials,and the effect of each of these scenarios on the process of energy loss from the surface of the external walls of the building during the operation period was determined.In the end,these scenarios were compared during a one-year operation period,and the amount of energy consumption in each of these scenarios was calculated.Also,bymeasuring the amount of carbon emissions in buildings during the operation period and before that,let’s look at practical methods to reduce the effects of the construction industry on the environment.By comparing the research findings,it can be seen that the ranking of each scenario in terms of total energy consumption is not necessarily the same as the ranking of energy consumption for gas consumption or electricity consumption for the same scenario.That is,choosing the optimal scenario depends on the type of energy consumed in the building.Finally,we determined the scenarios with the lowest and highest amounts of embodied and operational carbon.In the end,we obtained the latent carbon compensation period for each scenario.This article can help designers and construction engineers optimize the energy consumption of buildings by deciding on the right materials.
基金supported by the Natural Science Foundation of Shandong Province,China(Nos.ZR2022MD118 and ZR2022MD050)the Beijing Academy of Agricultural and Forestry Sciences Science and Technology Innovation Capacity Construction Project,China(No.20230207)+3 种基金the Humanities and Social Science Foundation Project of Ministry of Education,China(No.22YJCZH166)the Technical System of Ecological Agriculture of Modern Agricultural Technology System in Shandong Province,China(No.SDAIT-30-02)the National Key Research and Development Program of China(No.2023YFD1701901)the Fundamental Research Funds for Central Non-profit Scientific Institution of China(No.1610132023006).
文摘Controlled-release/stable nitrogen(N)fertilizers can improve vegetable yields and achieve lower greenhouse gas emissions,resulting in cost-effective and environmentally friendly vegetable production.However,there has been limited research on the controlled-release/stable N fertilization in long-term fixed-position vegetable rotation fields.In this study,a five-year field experiment was conducted to examine the effects of long-term controlled-release/stable N fertilization in reducing greenhouse gas emissions and increasing lettuce yield.Six distinct treatments were employed for N fertilization:the control without N fertilizer(CK),normal local farming practices with application of urea fertilizer at 400 kg N ha^(-1)(T1),optimized application of urea at 320 kg N ha^(-1)(T2),optimized application of urea at 320 kg N ha^(-1)with supplementation of 1.0 kg ha^(-1)3,4-dimethylpyrazole phosphate(DMPP)as N inhibitor(T3),application of polyurethane-coated urea at 320 kg N ha^(-1)(T4),and application of polyurethane-coated urea at 320 kg N ha^(-1)with supplementation of 1.0 kg ha^(-1)DMPP(T5).The results showed that the T3,T4,and T5 treatments using controlled-release/stable N fertilization emitted about 12.2%-56.7%less average annual cumulative nitrous oxide(N_(2)O)and 1.31%-10.0%less carbon dioxide(CO_(2))than the T2 treatment.Nitrous oxide and CO_(2)emissions from the T4 and T5 treatments were considerably lower than those from the T3 treatment.No significant seasonal or interannual variability was observed in N_(2)O and CO_(2)emissions.The observed phenomena were attributed to the fluctuations in soil ammonium-and nitrate-N contents.The findings in this study revealed that long-term controlled-release/stable N fertilization resulted in reduced field N loss,benefitting vegetable yields without increasing CO_(2)emissions and highlighting the application potential of this technique for sustainable agricultural production.
基金supported by the National Natural Science Foundation of China(No.52200120)the R&D Program of Beijing Municipal Education Commission(No.KM202310011003).
文摘CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.
基金funded by the National Key R&D Program of China(2021YFE0112400 and 2023YFF1304303)the National Natural Science Foundation of China(32361143870 and 32101315)。
文摘The response of N_(2)O emissions to nitrogen(N)addition is usually positive,but its response to phosphorus(P)addition varies,and the underlying mechanisms for the changes in N_(2)O emissions remain unclear.We conducted field studies to examine the response of N_(2)O emissions to N and P addition over two years in three typical alpine grasslands,alpine meadow(AM),alpine steppe(AS),and alpine cultivated grassland(CG)on the Qinghai-Tibet Plateau(QTP).Our results showed consistent increases in N_(2)O emissions under N addition alone or with P addition,and insignificant change in N_(2)O emissions under P addition alone in all three grasslands.N addition increased N_(2)O emissions directly in AM,by lowering soil pH in AS,and by lowering abundance of denitrification genes in CG.N and P co-addition increased N_(2)O emissions in AM and AS but only showed an interactive effect in AM.P addition enhanced the increase in N_(2)O emissions caused by N addition mainly by promoting plant growth in AM.Overall,our results illustrate that short-term P addition cannot alleviate the stimulation of N_(2)O emissions by N deposition in alpine grassland ecosystems,and may even further stimulate N_(2)O emissions.
