For p ∈ R, the generalized logarithmic mean Lp（a, b） and Seiffert＇s mean T（a, b） of two positive real numbers a and b are defined in （1.1） and （1.2） below respectively. In this paper, we find the greatest p ...For p ∈ R, the generalized logarithmic mean Lp（a, b） and Seiffert＇s mean T（a, b） of two positive real numbers a and b are defined in （1.1） and （1.2） below respectively. In this paper, we find the greatest p and least q such that the double-inequality Lp（a, b） 〈 T（a,b） 〈 Lq（a,b） holds for all a,b 〉 0 and a ≠ b.展开更多
In this article, we show that the generalized logarithmic mean is strictly Schurconvex function for p 〉 2 and strictly Schur-concave function for p 〈 2 on R_＋^2. And then we give a refinement of an inequality for t...In this article, we show that the generalized logarithmic mean is strictly Schurconvex function for p 〉 2 and strictly Schur-concave function for p 〈 2 on R_＋^2. And then we give a refinement of an inequality for the generalized logarithmic mean inequality using a simple majoricotion relation of the vector.展开更多
The （Noerlund） logarithmic means of the Fourier series is：tnf=1/ln ^n-1∑k=1 Skf/n-k, where ln=^n-1∑k=1 1/k In general, the Fej6r （C, 1） means have better properties than the logarithmic ones. We compare them an...The （Noerlund） logarithmic means of the Fourier series is：tnf=1/ln ^n-1∑k=1 Skf/n-k, where ln=^n-1∑k=1 1/k In general, the Fej6r （C, 1） means have better properties than the logarithmic ones. We compare them and show that in the case of some unbounded Vilenkin systems the situation changes.展开更多
The （NSrlund） logarithmic means of the Fourier series of the integrable function f is：1/lnn-1∑k=1Sk（f）/n-k, where ln：=n-1∑k=11/k.In this paper we discuss some convergence and divergence properties of this loga...The （NSrlund） logarithmic means of the Fourier series of the integrable function f is：1/lnn-1∑k=1Sk（f）/n-k, where ln：=n-1∑k=11/k.In this paper we discuss some convergence and divergence properties of this logarithmic means of the Walsh-Fourier series of functions in the uniform, and in the L^1 Lebesgue norm. Among others, as an application of our divergence results we give a negative answer to a question of Móricz concerning the convergence of logarithmic means in norm.展开更多
It is well known in the literature that the logarithmic means 1/logn ^n-1∑k=1 Sk（f）/k of Walsh or trigonometric Fourier series converge a.e. to the function for each integrable function on the unit interval. This i...It is well known in the literature that the logarithmic means 1/logn ^n-1∑k=1 Sk（f）/k of Walsh or trigonometric Fourier series converge a.e. to the function for each integrable function on the unit interval. This is not the case if we take the partial sums. In this paper we prove that the behavior of the so-called NSrlund logarithmic means 1/logn ^n-1∑k=1 Sk（f）/n-k is closer to the properties of partial sums in this point of view.展开更多
Net primary productivity(NPP)is an important breakthrough point of current research on ecological footprint improvement.The energy eco-footprint(EEF)of the Four-City Area in Central China(FCACC)was measured by constru...Net primary productivity(NPP)is an important breakthrough point of current research on ecological footprint improvement.The energy eco-footprint(EEF)of the Four-City Area in Central China(FCACC)was measured by constructing an EEF-NPP model.This work has made the following efforts:(1)Gini coefficient was employed to analyze the degree of matching between the EEF and economic growth,population,and energy consumption.(2)LMDI decomposition method was used to explore the impacts of multiple factors on the EEF in the FCACC.(3)Tapio decoupling model was applied to verify the decoupling relationships between the above influencing factors and the EEF.(4)LMDI decomposition formula was embedded into the decoupling model to analyze the impacts of technical and non-technical factors on the decoupling elasticity of the above.The main findings show that from 2010 to 2020:(1)the degree of matching of EEF-GDP,EEF-population,and EEF-energy consumption increased.(2)energy intensity and per capita GDP were the main factors that affected the EEF.(3)the decoupling states between total energy consumption,energy consumption structure,energy intensity,per capita GDP,and population size with the EEF were expansive negative decoupling,expansive negative decoupling,strong negative decoupling,weak decoupling,and expansive negative decoupling,respectively.(4)the impact of non-technical factors was greater than that of technical factors,and their impacts were always in opposite directions.展开更多
Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considere...Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and also provided a scientific basis for the regional control of soil erosion.展开更多
The potential for mitigating climate change is growing worldwide,with an increasing emphasis on reducing CO_(2)emissions and minimising the impact on the environment.African continent is faced with the unique challeng...The potential for mitigating climate change is growing worldwide,with an increasing emphasis on reducing CO_(2)emissions and minimising the impact on the environment.African continent is faced with the unique challenge of climate change whilst coping with extreme poverty,explosive population growth and economic difficulties.CO_(2)emission patterns in Africa are analysed in this study to understand primary CO_(2)sources and underlying driving forces further.Data are examined using gravity model,logarithmic mean divisia index and Tapio's decoupling indicator of CO_(2)emissions from economic development in 20 selected African countries during 1984-2014.Results reveal that CO_(2)emissions increased by 2.11%(453.73 million ton)over the research period.Gravity centre for African CO_(2)emissions had shifted towards the northeast direction.Population and economic growth were primary driving forces of CO_(2)emissions.Industrial structure and emission efficiency effects partially offset the growth of CO_(2)emissions.The economic growth effect was an offset factor in central African countries and Zimbabwe due to political instability and economic mismanagement.Industrial structure and emission efficiency were insufficient to decouple economic development from CO_(2)emissions and relieve the pressure of population explosion on CO_(2)emissions in Africa.Thus,future efforts in reducing CO_(2)emissions should focus on scaleup energy-efficient technologies,renewable energy update,emission pricing and long-term green development towards sustainable development goals by 2030.展开更多
Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumpti...Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.展开更多
This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure change...This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure changed significantly.The changes of total primary energy consumption in Beijing are decomposed into production effects,structural effects and intensity effects using the additive version of the logarithmic mean Divisia index (LMDI) method.Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect fol- lowed by the intensity effect,and the structural effect was rela- tively insignificant.The total and production effects were all posi- tive.In contrast,the structural effect and intensity effect were all negative.Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption (TPEC) was to reduce the production of the energy-intensive industrial sectors and improving industrial en- ergy intensity.The results show that in this period,Beijing's economy has undergone a transformation from an industrial to a service economy.However,the structures of sectoral energy use have not been changed yet,and energy demand should be in- creasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak.As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered:agriculture, industry and service.However,further decomposition into secon- dary and tertiary sectors is definitely needed for detailed investi- gations.展开更多
Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carb...Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carbon emissions by Logarithmic Mean Divisia Index（LMDI） model. The main conclusions are as follows： 1） Total anthropogenic carbon emission of Nanjing increased from 1.22928 ×10^7 t in 2000 to 3.06939 × 10^7 t in 2009, in which the carbon emission of Inhabitation, mining ＆ manufacturing land accounted for 93% of the total. 2） The average land use carbon emission intensity of Nanjing in 2009 was 46.63 t/ha, in which carbon emission intensity of Inhabitation, mining ＆ manufacturing land was the highest（200.52 t/ha）, which was much higher than that of other land use types. 3） The average carbon source intensity in Nanjing was 16 times of the average carbon sink intensity（2.83 t/ha） in 2009, indicating that Nanjing was confronted with serious carbon deficit and huge carbon cycle pressure. 4） Land use area per unit GDP was an inhibitory factor for the increase of carbon emissions, while the other factors were all contributing factors. 5） Carbon emission effect evaluation should be introduced into land use activities to formulate low-carbon land use strategies in regional development.展开更多
Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also ...Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.展开更多
We investigate the strong law of large numbers(SLLN)for a large class of mean based on the Extended Negatively Dependent(END)sequences.The sufficient conditions are obtained for the mean of SLLN in this paper.As an im...We investigate the strong law of large numbers(SLLN)for a large class of mean based on the Extended Negatively Dependent(END)sequences.The sufficient conditions are obtained for the mean of SLLN in this paper.As an important application,the SLLN of Marcinkiewicz mean and logarithmic mean are presented immediately.In addition,we do some simulations for the mean of SLLN based on END random variables.展开更多
The paper brings an important integral inequality, which includes the famous Polya-Szego inequality and the logarithmical-arithmetic mean inequality as special cases.
【目的】核算东北三省种植业的碳氮足迹,建立完整的碳氮足迹数据库,并揭示其主要影响因素,以期为东北三省种植业的健康和可持续发展提供数据支撑。【方法】以东北三省种植业碳氮足迹为研究对象,采用全生命周期法和投入产出法构建碳氮足...【目的】核算东北三省种植业的碳氮足迹,建立完整的碳氮足迹数据库,并揭示其主要影响因素,以期为东北三省种植业的健康和可持续发展提供数据支撑。【方法】以东北三省种植业碳氮足迹为研究对象,采用全生命周期法和投入产出法构建碳氮足迹核算体系,建立碳氮足迹数据库,采用LMDI(Logarithmic Mean Divisia Index)模型分解种植业碳氮足迹驱动因素。【结果】辽宁省和吉林省碳氮足迹和碳氮排放量均呈现下降趋势,碳氮承载力均呈现增加趋势。黑龙江省碳氮足迹和碳氮排放量呈现上升趋势,碳承载力呈现增加趋势,氮承载力呈现下降趋势。截至2020年,东北三省均实现碳盈余,吉林省实现氮盈余,辽宁省和黑龙江省仍处于氮赤字状态。种植业碳排放量与氮排放量较高的地区主要分布在松嫩平原、三江平原和辽河平原地区,农业效率是降低碳氮足迹的关键驱动因素。【结论】辽宁省和吉林省种植业减污降碳效果显著,黑龙江省种植业碳氮排放仍持续增加,建议增加农业经济投入,提高农业生产效率,扩大种植业规模,降低投入产出比,转移农村富余劳动力,优化肥料施用,降低碳氮排放强度,推进东北三省种植业绿色高质量发展。展开更多
Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference ...Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference for developing policies to mitigate global climate change and promote sustainable agricultural development.Using the carbon emission calculation framework of the Intergovernmental Panel on Climate Change,this study examined the spatiotemporal characteristics of ACE,including total amount,intensity,structure and their influencing factors,in Fujian Province from 2002 to 2022.The logarithmic mean scale index model and Tapio decoupling model were used,with the GM(1,1)model to forecast carbon emissions from 2023 to 2040.The results indicate that both the total emissions and intensity of ACE had fluctuating downward trends and agricultural material inputs were the largest contributors to ACE.Additionally,total ACE was found to have a spatial pattern higher in the west and lower in the east and agricultural production efficiency was the primary factor in reducing ACE.ACE was clearly decoupled from economic development and is projected to continually decline after2023.展开更多
In the context of ＂two-wheel drive＂ development mode, China＇s construction land shows significant expansion characteristics. The carbon emission effect of construction land changes is an important factor for the in...In the context of ＂two-wheel drive＂ development mode, China＇s construction land shows significant expansion characteristics. The carbon emission effect of construction land changes is an important factor for the increase of carbon emissions in the atmosphere. In this study, the drivers of carbon emissions in Anhui Province from 1997 to 2011 were quantitatively measured using the improved Kaya identity and Logarithmic Mean Divisia Index. The results show that： economic growth, expansion of construction land and changes in population density have incremental effects on carbon emissions. The average contribution rate of economic growth as the first driver is 266.32 percent. The construction land expansion is an important driving factor with annual mean carbon effect of 6.4057 million tons and annual mean contribution rate of 187.30 percent. But the change in population density has little impact on carbon emission driving. Energy structure changes and energy intensity reduction have inhibitory effects on carbon emissions, of which the annual mean contribution rate is -212.06 percent and -158.115 percent respectively. The targeted policy approaches of carbon emission reduction were put forward based on the decomposition of carbon emission factors, laying a scientific basis to rationally use the land for the Government, which is conducive to build an ecological province for Anhui and achieve the purpose of emission reduction, providing a reference for the research on carbon emission effect of changes in provincial-scale construction land.展开更多
This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index ...This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index （LMDI） technique was applied with period-wise analysis and time-series analysis. Changes in energy- related CO2 emissions were decomposed into four factors： emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consump- tion is the largest contributor to the decrease in C02 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to C02 emissions, respectively. Policy implications are provided regarding the reduction of C02 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.展开更多
基金supported by the National Natural Science Foundation of China (11071069 and 11171307)Natural Science Foundation of Hunan Province(09JJ6003)Innovation Team Foundation of the Department of Education of Zhejiang Province (T200924)
文摘For p ∈ R, the generalized logarithmic mean Lp（a, b） and Seiffert＇s mean T（a, b） of two positive real numbers a and b are defined in （1.1） and （1.2） below respectively. In this paper, we find the greatest p and least q such that the double-inequality Lp（a, b） 〈 T（a,b） 〈 Lq（a,b） holds for all a,b 〉 0 and a ≠ b.
基金Foundation item： Supported by the Scientific Research Common Program of Beijing Municipal Commission of Education of China（Km200611417009） Suppoted by the Natural Science Foundation of Fujian Province Education Department of China（JA05324）
文摘In this article, we show that the generalized logarithmic mean is strictly Schurconvex function for p 〉 2 and strictly Schur-concave function for p 〈 2 on R_＋^2. And then we give a refinement of an inequality for the generalized logarithmic mean inequality using a simple majoricotion relation of the vector.
基金The first author is supported by the Békésy Postdoctoral fellowship of the Hungarian Ministry of Education B91/2003the second author is supported by the Hungarian National Foundation for Scientific Research (OTKA),grant no. M 36511/2001, T 048780the Széchenyi fellowship of the Hungarian Ministry of Education Sz184/2003.
文摘The （Noerlund） logarithmic means of the Fourier series is：tnf=1/ln ^n-1∑k=1 Skf/n-k, where ln=^n-1∑k=1 1/k In general, the Fej6r （C, 1） means have better properties than the logarithmic ones. We compare them and show that in the case of some unbounded Vilenkin systems the situation changes.
基金supported by the Hungarian National Foundation for Scientific Research (OTKA), Grant No. M 36511/2001, T 048780by the Szechenyi fellowship of the Hungarian Ministry of Education Sz 184/2003
文摘The （NSrlund） logarithmic means of the Fourier series of the integrable function f is：1/lnn-1∑k=1Sk（f）/n-k, where ln：=n-1∑k=11/k.In this paper we discuss some convergence and divergence properties of this logarithmic means of the Walsh-Fourier series of functions in the uniform, and in the L^1 Lebesgue norm. Among others, as an application of our divergence results we give a negative answer to a question of Móricz concerning the convergence of logarithmic means in norm.
基金the Hungarian National Foundation for Scientific Research(OTKA)(Grant No.T048780)the Georgian National Foundation for Scientific Research(Grant No.GNSF/ST07/3-171)
文摘It is well known in the literature that the logarithmic means 1/logn ^n-1∑k=1 Sk（f）/k of Walsh or trigonometric Fourier series converge a.e. to the function for each integrable function on the unit interval. This is not the case if we take the partial sums. In this paper we prove that the behavior of the so-called NSrlund logarithmic means 1/logn ^n-1∑k=1 Sk（f）/n-k is closer to the properties of partial sums in this point of view.
基金supported by the Science and Technology Projects of the Jiangxi Provincial Education Department(Grant No.GJJ2200518)the Ministry of Education in China Layout Project of Humanities and Social Sciences(Grant No.20YJAZH037).
文摘Net primary productivity(NPP)is an important breakthrough point of current research on ecological footprint improvement.The energy eco-footprint(EEF)of the Four-City Area in Central China(FCACC)was measured by constructing an EEF-NPP model.This work has made the following efforts:(1)Gini coefficient was employed to analyze the degree of matching between the EEF and economic growth,population,and energy consumption.(2)LMDI decomposition method was used to explore the impacts of multiple factors on the EEF in the FCACC.(3)Tapio decoupling model was applied to verify the decoupling relationships between the above influencing factors and the EEF.(4)LMDI decomposition formula was embedded into the decoupling model to analyze the impacts of technical and non-technical factors on the decoupling elasticity of the above.The main findings show that from 2010 to 2020:(1)the degree of matching of EEF-GDP,EEF-population,and EEF-energy consumption increased.(2)energy intensity and per capita GDP were the main factors that affected the EEF.(3)the decoupling states between total energy consumption,energy consumption structure,energy intensity,per capita GDP,and population size with the EEF were expansive negative decoupling,expansive negative decoupling,strong negative decoupling,weak decoupling,and expansive negative decoupling,respectively.(4)the impact of non-technical factors was greater than that of technical factors,and their impacts were always in opposite directions.
文摘Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and also provided a scientific basis for the regional control of soil erosion.
基金supported by the National Water Pollution Control and Treatment Science and Technology Major Project(No.2017ZX07101001)the National Natural Science Foundation of China(Nos.41690142 and 41371535)the Fundamental Research Funds for the Central Universities(No.SWU019047)。
文摘The potential for mitigating climate change is growing worldwide,with an increasing emphasis on reducing CO_(2)emissions and minimising the impact on the environment.African continent is faced with the unique challenge of climate change whilst coping with extreme poverty,explosive population growth and economic difficulties.CO_(2)emission patterns in Africa are analysed in this study to understand primary CO_(2)sources and underlying driving forces further.Data are examined using gravity model,logarithmic mean divisia index and Tapio's decoupling indicator of CO_(2)emissions from economic development in 20 selected African countries during 1984-2014.Results reveal that CO_(2)emissions increased by 2.11%(453.73 million ton)over the research period.Gravity centre for African CO_(2)emissions had shifted towards the northeast direction.Population and economic growth were primary driving forces of CO_(2)emissions.Industrial structure and emission efficiency effects partially offset the growth of CO_(2)emissions.The economic growth effect was an offset factor in central African countries and Zimbabwe due to political instability and economic mismanagement.Industrial structure and emission efficiency were insufficient to decouple economic development from CO_(2)emissions and relieve the pressure of population explosion on CO_(2)emissions in Africa.Thus,future efforts in reducing CO_(2)emissions should focus on scaleup energy-efficient technologies,renewable energy update,emission pricing and long-term green development towards sustainable development goals by 2030.
基金National Key Research and Development Program(2019YFB2103101)Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(GML2019ZD0301)+2 种基金GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002)GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003)National Natural Science Foundation of China(41501144)。
文摘Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.
文摘This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure changed significantly.The changes of total primary energy consumption in Beijing are decomposed into production effects,structural effects and intensity effects using the additive version of the logarithmic mean Divisia index (LMDI) method.Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect fol- lowed by the intensity effect,and the structural effect was rela- tively insignificant.The total and production effects were all posi- tive.In contrast,the structural effect and intensity effect were all negative.Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption (TPEC) was to reduce the production of the energy-intensive industrial sectors and improving industrial en- ergy intensity.The results show that in this period,Beijing's economy has undergone a transformation from an industrial to a service economy.However,the structures of sectoral energy use have not been changed yet,and energy demand should be in- creasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak.As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered:agriculture, industry and service.However,further decomposition into secon- dary and tertiary sectors is definitely needed for detailed investi- gations.
基金Under the auspices of National Natural Science Foundation of China(No.41301633)National Social Science Foundation of China(No.10ZD&030)+1 种基金Postdoctoral Science Foundation of China(No.2012M511243,2013T60518)Clean Development Mechanism Foundation of China(No.1214073,2012065)
文摘Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carbon emissions by Logarithmic Mean Divisia Index（LMDI） model. The main conclusions are as follows： 1） Total anthropogenic carbon emission of Nanjing increased from 1.22928 ×10^7 t in 2000 to 3.06939 × 10^7 t in 2009, in which the carbon emission of Inhabitation, mining ＆ manufacturing land accounted for 93% of the total. 2） The average land use carbon emission intensity of Nanjing in 2009 was 46.63 t/ha, in which carbon emission intensity of Inhabitation, mining ＆ manufacturing land was the highest（200.52 t/ha）, which was much higher than that of other land use types. 3） The average carbon source intensity in Nanjing was 16 times of the average carbon sink intensity（2.83 t/ha） in 2009, indicating that Nanjing was confronted with serious carbon deficit and huge carbon cycle pressure. 4） Land use area per unit GDP was an inhibitory factor for the increase of carbon emissions, while the other factors were all contributing factors. 5） Carbon emission effect evaluation should be introduced into land use activities to formulate low-carbon land use strategies in regional development.
基金financially supported by the National Natural Science Foundation of China(41771535)the National Social Science Foundation Major Project(20&ZD092)。
文摘Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.
基金Supported by the National Natural Science Foundation of China(Grant No.11701004)the Natural Science Foundation of Anhui Province(Grant Nos.1808085QA03,1808085QF212,1808085QA17)+2 种基金Provincial Natural Science Research Project of Anhui Colleges(Grant Nos.KJ2016A027,KJ2017A027,KJ2019A0006)Excellent Young Talents Research Project of Anhui Colleges(Grant No.gxyq2018102)Key Research Project of Suzhou University(Grant No.2017yzd16)。
文摘We investigate the strong law of large numbers(SLLN)for a large class of mean based on the Extended Negatively Dependent(END)sequences.The sufficient conditions are obtained for the mean of SLLN in this paper.As an important application,the SLLN of Marcinkiewicz mean and logarithmic mean are presented immediately.In addition,we do some simulations for the mean of SLLN based on END random variables.
基金the Scientific Research fund of Pingyuan University(2005006)
文摘The paper brings an important integral inequality, which includes the famous Polya-Szego inequality and the logarithmical-arithmetic mean inequality as special cases.
文摘【目的】核算东北三省种植业的碳氮足迹,建立完整的碳氮足迹数据库,并揭示其主要影响因素,以期为东北三省种植业的健康和可持续发展提供数据支撑。【方法】以东北三省种植业碳氮足迹为研究对象,采用全生命周期法和投入产出法构建碳氮足迹核算体系,建立碳氮足迹数据库,采用LMDI(Logarithmic Mean Divisia Index)模型分解种植业碳氮足迹驱动因素。【结果】辽宁省和吉林省碳氮足迹和碳氮排放量均呈现下降趋势,碳氮承载力均呈现增加趋势。黑龙江省碳氮足迹和碳氮排放量呈现上升趋势,碳承载力呈现增加趋势,氮承载力呈现下降趋势。截至2020年,东北三省均实现碳盈余,吉林省实现氮盈余,辽宁省和黑龙江省仍处于氮赤字状态。种植业碳排放量与氮排放量较高的地区主要分布在松嫩平原、三江平原和辽河平原地区,农业效率是降低碳氮足迹的关键驱动因素。【结论】辽宁省和吉林省种植业减污降碳效果显著,黑龙江省种植业碳氮排放仍持续增加,建议增加农业经济投入,提高农业生产效率,扩大种植业规模,降低投入产出比,转移农村富余劳动力,优化肥料施用,降低碳氮排放强度,推进东北三省种植业绿色高质量发展。
基金supported by the Humanities and Social Sciences Program of the Ministry of Education(21YJCZH006)the Water Conservancy Science and Technology Program of Fujian Province(MSK202435)the horizontal commissioned project of the Soil and Water Conservation Experimental Station of Fujian Province(Construction of SWAT Model for Soil Erosion Control in Typical Eco-clean Sub-watersheds of the Red Loam Erosion Area and Assessment of the Effectiveness)。
文摘Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference for developing policies to mitigate global climate change and promote sustainable agricultural development.Using the carbon emission calculation framework of the Intergovernmental Panel on Climate Change,this study examined the spatiotemporal characteristics of ACE,including total amount,intensity,structure and their influencing factors,in Fujian Province from 2002 to 2022.The logarithmic mean scale index model and Tapio decoupling model were used,with the GM(1,1)model to forecast carbon emissions from 2023 to 2040.The results indicate that both the total emissions and intensity of ACE had fluctuating downward trends and agricultural material inputs were the largest contributors to ACE.Additionally,total ACE was found to have a spatial pattern higher in the west and lower in the east and agricultural production efficiency was the primary factor in reducing ACE.ACE was clearly decoupled from economic development and is projected to continually decline after2023.
基金the Key Research Fund of Anhui Provincial Education Department (No.2010sk502zd)the National Natural Science Foundation of China (No.41071337)
文摘In the context of ＂two-wheel drive＂ development mode, China＇s construction land shows significant expansion characteristics. The carbon emission effect of construction land changes is an important factor for the increase of carbon emissions in the atmosphere. In this study, the drivers of carbon emissions in Anhui Province from 1997 to 2011 were quantitatively measured using the improved Kaya identity and Logarithmic Mean Divisia Index. The results show that： economic growth, expansion of construction land and changes in population density have incremental effects on carbon emissions. The average contribution rate of economic growth as the first driver is 266.32 percent. The construction land expansion is an important driving factor with annual mean carbon effect of 6.4057 million tons and annual mean contribution rate of 187.30 percent. But the change in population density has little impact on carbon emission driving. Energy structure changes and energy intensity reduction have inhibitory effects on carbon emissions, of which the annual mean contribution rate is -212.06 percent and -158.115 percent respectively. The targeted policy approaches of carbon emission reduction were put forward based on the decomposition of carbon emission factors, laying a scientific basis to rationally use the land for the Government, which is conducive to build an ecological province for Anhui and achieve the purpose of emission reduction, providing a reference for the research on carbon emission effect of changes in provincial-scale construction land.
文摘This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index （LMDI） technique was applied with period-wise analysis and time-series analysis. Changes in energy- related CO2 emissions were decomposed into four factors： emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consump- tion is the largest contributor to the decrease in C02 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to C02 emissions, respectively. Policy implications are provided regarding the reduction of C02 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.
