Pedosphere18(2):171–182,2008
ISSN1002-0160/CN32-1315/P
c 2008Soil Science Society of China
Published by Elsevier Limited and Science Press
Eco-Environmental Vulnerability Evaluation in the Yellow River Basin,China?1
WANG Si-Yuan1,2,LIU Jing-Shi1and YANG Cun-Jian3
1Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing100085(China).E-mail:w siyuan@https://www.doczj.com/doc/5f15545516.html, 2Institute of Electronics,Chinese Academy of Sciences,Beijing100080(China)
3Centre of RS and GIS Applications,Sichuan Normal University,Chengdu610066(China)
(Received January15,2007;revised November13,2007)
ABSTRACT
Using remote sensing(RS)data and geographical information system(GIS),eco-environmental vulnerability and its changes were analyzed for the Yellow River Basin,China.The objective of this study was to improve our understanding of eco-environmental changes so that a strategy of sustainable land use could be established.An environmental numerical model was developed using spatial principal component analysis(SPCA)model.The model contains twelve factors that include variables of land use,soil erosion,topography,climate,and https://www.doczj.com/doc/5f15545516.html,ing this model,synthetic eco-environmental vulnerability index(SEVI)was computed for1990and2000for the Yellow River Basin.The SEVI was classi?ed into six levels,potential,slight,light,medium,heavy,and very heavy,following the natural breaks classi?cation. The eco-environmental vulnerability distribution and its changes over the ten years from1990to2000were analyzed and the driving factors of eco-environmental changes were investigated.The results show that the eco-environmental vulnerability in the study area was at medium level,and the eco-environmental quality had been gradually improved on the whole.However,the eco-environmental quality had become worse over the ten years in some regions.In the study area,population growth,vegetation degradation,and governmental policies for eco-environmental protection were found to be the major factors that caused the eco-environmental changes over the ten years.
Key Words:eco-environmental vulnerability,geographic information system(GIS),remote sensing(RS),spatial prin-cipal component analysis,Yellow River Basin
Citation:Wang,S.Y.,Liu,J.S.and Yang,C.J.2008.Eco-environmental vulnerability evaluation in the Yellow River Basin,China.Pedosphere.18(2):171–182.
INTRODUCTION
The ecosystem of the Yellow River Basin has degraded owing to both the global change and popula-tion growth.Environmental problems,such as grassland degeneration,sandy deserti?cation,and water erosion,have seriously a?ected sustainable development of the region(Ding,2003).Many studies have been conducted on eco-environment within the Yellow River Basin with a focus on part of the basin or a tributary(e.g.,Xu and Cheng,2002;Lu et al.,2003;Li et al.,2006;Zheng,2006).Studies on eco-environmental vulnerability at larger and even the basin scale are essential to understanding the patterns of land use and soil erosion and to formulating measures for the management of the entire basin.However,studies on eco-environmental vulnerability taking the basin as a whole were very few.
The term vulnerability is used di?erently in many contexts,from medicine to poverty and deve-lopment literature.In eco-environmental studies,the concept of vulnerability is often derived from the social sciences,but there is no general agreement of how to de?ne vulnerability in an environmental impact assessment(Kvarner et al.,2006).The general de?nition from the IPCC(2001),i.e.,the degree to which a system is susceptible to,or unable to cope with,adverse e?ects of climate changes,including climate variability and extremes,is useful because this de?nition includes both the traditional elements ?1Project supported by the National Key Basic Research Support Foundation of China(973Program)(No.2005CB422003) and the National Natural Science Foundation of China(No.40571037).
172S.Y.WANG et al. of an impact assessment(i.e.,potential impacts of a system to exposures),and adaptive capacity to cope with the potential impacts of global changes(Turner et al.,2003).Assessment of vulnerability is important as it enables identi?cation of areas or resources at risk,and the threats posed by the diminution or loss of such resources that will threaten future sustainable development.
How to e?ectively analyze the eco-environmental vulnerability is critical to geographical and eco-environmental management over a basin.Though it is a relatively new research area,eco-environmental vulnerability trend evaluation has been developed rapidly in recent years and may provide useful in-formation about ecological and environmental background information for environmental restoration. Investigators have developed many methods for such studies,such as comprehensive evaluation method (Li et al.,2005),indices weight method(IWM)(Li et al.,2001),and analytical hierarchy process(AHP) (Klungboonkrong and Taylor,1998;Li et al.,2005).However,these methods depend on experts’evalu-ation to weigh the importance of factors,and the level of experts in?uences the?nal evaluation results directly.For example,the AHP method needs to establish a weight system so that the comprehensive function for eco-environment can be e?ectively assessed.Determining the weight of each index is very important for the assessment.
Recently,remote sensing(RS)and geographic information system(GIS)have been used as powerful tools for geo-environmental evaluation in support of land use planning.Satellite images are especially valuable because they provide frequently updated maps in inaccessible areas or areas that contain rapidly changing landforms(Chuvieco,1999).Satellite remote sensing provides multi-spectral and multi-temporal data that can be used to quantify the type,amount,and location of land use changes (Liu and Buheaosier,2000;Honnay et al.,2003).Most recently,satellite remote sensing has often been integrated into a GIS.The integration of RS with GIS provides an excellent framework for data capture, storage,synthesis,measurement,and analysis,all of which are essential to eco-environmental analysis (Anthony and Li,1998).In order to provide an objective result for eco-environmental vulnerability evaluation,a new environmental numerical evaluation model was developed and applied using RS and GIS in this study.Under the support of RS and GIS,the study took the whole basin as an integrated system.The objectives of this study were to:1)develop an environmental numerical evaluation model supported by GIS,2)to establish a synthetic eco-environmental vulnerability index(SEVI)using spatial principal component analysis(SPCA),and3)to analyze spatial distribution and temporal changes of eco-environmental vulnerability.
MATERIALS AND METHODS
Study area
The Yellow River is the second largest river in China and originates in the northern part of the Bayankala Mountains in Qinghai Province.It is5464km long,and?ows through seven provinces, Qinghai,Sichuan,Gansu,Shaanxi,Shanxi,Henan,and Shandong,and two autonomous regions,Ningxia and Inner Mongolia(Yang et al.,1999).Its annual runo?is about688×108m3,59%of which comes from the upper reaches and33%from the middle reaches,the Loess Plateau.About90%of the sediment in the Yellow River originated from the soil erosion on the Loess Plateau.The Yellow River Basin, located in the northwestern part of China,extends about10degrees of latitude(32–42?N)and23 degrees of longitude(96–119?E),with a total area of more than794000km2and a population of107 million.Climate of the basin is characterized by continental monsoon with annual average temperature of1–8?C in northwest and12–14?C in southeast.Annual precipitation ranges from300mm in northwest to700mm in southwest.Most of the basin is located in temperate semi-arid zones.Due to its unique geological and geomorphologic characteristics,major social and environmental challenges of this basin include easily erodable sediment,severe rainstorm events,rapid population increase,irrational land use, and the highest soil erosion rate in the world.
ECO-ENVIRONMENTAL VULNERABILITY EVALUATION173
Data acquisition and standardization
There are many factors that a?ect eco-environmental quality,including natural factors and human factors.It is important and essential to select appropriate factors for eco-environmental evaluation. Investigators have used many factors in eco-environmental evaluation studies(Giaoutzi and Nijkamp, 1993;Molden and Billharz,1997),with land use/land cover and soil erosion being most frequently used(IGBP,1993;Selman,1996).It is true that land is a key resource for human activities and en-vironmental changes are deeply embedded in how land is used,especially in agricultural regions.In order to synthetically analyze eco-environmental vulnerability in the Yellow River Basin,twelve factors were initially selected after the observation of a?eld work by considering the climate condition,land use/land cover,soil and water losses,terrain and physiognomy situation in the basin,including land use/land cover,soil erosion,climate factors,and topography factors.Based on the analysis,the eco-environmental factors of the Yellow River Basin were divided into three groups:land resource conditions, water-heat meteorological conditions,and topographical conditions(Table I).It should be noted that this selection was not exhaustive,and that only those salient factors for which information is of great signi?cance were considered.Although interrelated,each factor had a di?erent role in eco-environmental development.For example,land use,soil erosion,and vegetation cover may be more important in pro-TABLE I
Factors used in assessing eco-environmental vulnerability in the Yellow River Basin
Group Factor Value De?nition and major impact on eco-environment Data acquisition
Land Land use Land use/land cover over land surfaces Interpreted from Landsat resource thematic mapper(TM)images conditions Soil erosion Soil erosion by rainfall over a period of time,Interpreted from Landsat
which in?uences plant growth and eco-envi-TM images
ronmental changes
Normalized0–255Vegetation cover over land surfaces Calculated from national
di?erence oceanographic and atmospheric
vegetation administration-advanced
index very high resolution
(NDVI)radiometer)(NOAA/AVHRR)
images
Water-heat Average?10–25?C Annual average temperature,which in?uences Observed and calculated by meteo-temperature plant spatial distribution weather stations
rological>10?C0–5300Annual accumulated temperature for days with Observed and calculated by conditions accumulated average temperatures≥10?C,which strongly weather stations temperature in?uences plant growth
>0?C0–9000Annual accumulated temperature for days with Observed and calculated by
accumulated average temperatures≥0?C,which strongly weather stations
temperature in?uences plant growth
Average0–1600mm Annual average precipitation,which strongly Observed and calculated by
precipitation in?uences plant growth weather stations
Average0–100Annual average evaporation,which strongly Observed and calculated by
evaporation in?uences soil moisture and plant growth weather stations
Index of?60–120Moisture status,which strongly in?uences Observed and calculated by
moisture soil moisture weather stations
Topogra-Elevation50–5000m Height above average sea level of the Yellow Calculated from digital
phical Sea,which is very important in deriving other elevation model(DEM) conditions factors listed in the table because of its in?u-
ence on temperature,precipitation,and soil
Slope0?–90?Slope gradient in?uences the overall movement Calculated from DEM
gradient of?ow down slope and the amount of solar
radiation received at a site
Slope0–360?Situation from convex(positive)to concave Calculated from DEM
aspect(negative),which in?uences soil moisture
174S.Y.WANG et al. moting environmental sustainability,while climate factors and topography factors play a more important role in in?uencing eco-environmental quality.Values of all factors used in the study were derived from a digital elevation model(DEM),Landsat thematic mapper(TM)images,national oceanographic and atmospheric administration-advanced very high resolution radiometer(NOAA/AVHRR)images, climate stations or?eld survey(Table I).Finally,all data were projected using the Albers projection system,which includes parameters of the1st standard parallel of25.0000,the2nd standard of47.0000, the central meridian of105.0000,and the Krasovsky https://www.doczj.com/doc/5f15545516.html,ing ArcGIS software,all data were transferred to100m×100m raster data.
Due to great di?erences and di?erent units among values of assessment factors,it was di?cult to evaluate the eco-environmental status using these factors indirectly.Their values must be standardized to re?ect a uniform measurement system across all factors for eco-environmental evaluation.The original values of each factor were standardized by the following equation:
Y ij=
x ij?x min,j
x max,j?x min,j
×10(1)
where Y ij represents the standardized value of factor j of grid i,varying from0to10,x ij represents
the measured value of factor j of grid i,and x min,j and x max,j represent minimum and maximum value of factor j of grid i,respectively.
Spatial principal component analysis and vulnerability classi?cation
How to convert data of climate condition,land use/land cover,soil and water losses,and landform into an integrated evaluation index is crucial for environmental evaluation and often remains di?cult to solve(Munda et al.,1994).The principal component analysis(PCA)is designed to transform the original variables into new,uncorrelated variables(axis),namely,the principal components,which are linear combinations of the original variables.The new axes lie along the directions of maximum variance. PCA provides an objective way of?nding indices of this type so that the variation in the data set can be accounted for as concisely as possible(Shaw and Wheeler,1985).The principal components(PCs) provide information on the most meaningful parameters,which describe the whole data set a?ording data reduction with minimum loss of original information.PCA can be expressed as:
Y i=a i1x1+a i2x2+a i3x3+···+a im x m(2) where Y is the component score,a is the component loading,x is the measured value of a variable,i is
the component number,and m is the total number of variables.
Spatial principal component analysis(SPCA)is constructed to transform the data attributes in a multiband spatial data into a new multivariate attribute space whose axes are rotated with respect to the original space.The axes in the new space are uncorrelated.The result of SPCA is a multiband new spatial data set with the same number of bands as the original data.The?rst principal component will have the greatest variance,the second will show the second most variance not described by the?rst, and so forth.Many times the?rst three to four layers(PCs)of the resulting data represent over95 percent of the total variance so that the remaining layers of the principal components may be dropped. SPCA has certain advantages over the conventional orthogonal functions,since they are not of any predetermined form,but are developed as unique functions from the data matrix.This is particularly useful if nothing is known in advance about the existence or nature of the component patterns(Li et al., 2006).So in this study the SPCA was used to evaluate eco-environmental vulnerability.The formulae of SPCA evaluation are:
E=r1Y1+r2Y2+r3Y3+···+r n Y n(3) where E is the eco-environmental synthetic evaluation index,r is the contribution ratio of the principal
ECO-ENVIRONMENTAL VULNERABILITY EVALUATION175 component,Y is the principal component,and n is the number of principal components retained,and
r i=
λi
m
i=1
λi
(4)
where r i is the contribution ratio of the i th principal component,andλi is the eigenvalue of the i th principal component.
Results computed from the SPCA model were continuous values and classi?ed into several classes standing for di?erent eco-environmental vulnerability levels.The classi?cation is crucial to the evalua-tion,so it should be objective and logical.The natural breaks classi?cation(NBC)is a graphical tool to explore statistical distribution of the classes and clusters in the attribute space.Because the classes are based on natural grouping inherent in the data,NBC can identify break points by picking the class breaks that group similar values and maximize the di?erences between classes,and the features are divided into classes whose boundaries are set where there are relatively big jumps in the data values. This study applied the NBC to discrete computed values through analyzing natural properties of the computed values to line out dividing points between clusters.With this standard,eco-environmental vulnerability was graded into six levels de?ned as potential,slight,light,medium,heavy,and very heavy levels,and each level was characterized by typical features,as shown in Table II.
TABLE II
Eco-environmental vulnerability classi?cation in the Yellow River Basin
Evaluation level Number SEVI a)Feature description
Potential I<2.8Stable ecosystem,super high anti-interference ability,rich soil,rich water and heat,
and good vegetation cover
Slight II 2.8–4.1Relatively stable ecosystem,high anti-interference ability,rich soil,rich water and
heat,and relatively good vegetation cover
Light III 4.1–5.8Relatively stable ecosystem,relatively high anti-interference ability,infertile soil,
and relatively poor vegetation cover
Medium IV 5.8–7.2Relatively unstable ecosystem,low anti-interference ability,bad-quality soil,and
poor vegetation cover
Heavy V7.2–9.0Unstable ecosystem,low anti-interference ability,deteriorated soil,and poor
vegetation cover
Very heavy VI>9.0Extremely unstable ecosystem,low anti-interference ability,and severe water and
soil losses
a)SEVI represents the synthetic eco-environmental vulnerability index calculated using spatial principal component analysis model.
RESULTS AND DISCUSSION
Eco-environmental vulnerability evaluation in2000
Based on the correlations between the twelve selected factors for eco-environmental vulnerability evaluation,they were classi?ed into three groups(Table I):Group1,the factors which all re?ected climate-environmental changes and were calculated using observed data of the weather stations;Group 2,the factors which re?ected topography-environmental changes and were calculated using the DEM; and Group3,the factors which synthetically re?ected the status of land cover in the basin and were obtained from Landsat TM images and NOAA/AVHRR images.
First,the factors in Group1were related to the water-heat meteorological conditions.In order to further evaluate the climatic in?uences on eco-environment,synthetic climatic environmental index (SCEI)was calculated using the SPCA model.The principal components(PCs)with eigenvalues greater than0.7were extracted with PC loading rotated for the maximum variance.A total of two PCs were extracted,which accounted for93.63%of the total variance(Table III).Second,the factors in Group
176S.Y.WANG et al.
2were related to topographical conditions.To further evaluate the topographical in?uences on eco-environment,synthetic topographical environmental index(STEI)was calculated by means of the SPCA method.A total of two PCs were extracted,which accounted for83.17%of the total variance.Finally, the factors in Group3together with SCEI and STEI were selected for evaluating the eco-environmental vulnerability.Synthetic eco-environmental vulnerability index(SEVI)was calculated using the SPCA model.The principal components(PCs)with eigenvalues greater than1were extracted with the PC loading rotated for the maximum variance.A total of two PCs were extracted,which accounted for 89.21%of the total variance.Linear formulae for computing SCEI,STEI,and SEVI were shown as follows:
SCEI=Climatic Princ6c1×0.6468+Climatic Princ6c2×0.2895(5)
where Cimatic Princ6c1is the?rst PC,Climatic Princ6c2is the second PC,and0.6468and0.2895are the contribution ratios of the?rst and second PCs;
STEI=Dem princ3c1×0.4754+Dem princ3c2×0.3563(6)
where Dem Princ3c1is the?rst PC,Dem princ3c2is the second PC,and0.4754and0.3563are the contribution ratios of the?rst and second PCs;
SEVI=Env Princ5c1×0.6350+Env Princ5c2×0.2571(7)
where Env Princ5c1is the?rst PC,Env Princ5c2is the second PC,and0.6350and0.2571are the contribution ratios of the?rst and second PCs.
TABLE III
Eigenvalues and contribution ratios in the spatial principal component(PC)analysis for eco-environmental vulnerability evaluation in2000in the Yellow River Basin
Index of PC1PC2PC3
evaluation a)
Eigen-Contri-Accumulated Eigen-Contri-Accumulated Eigen-Contri-Accumulated
value bution contribution value bution contribution value bution contribution ratio ratio ratio ratio ratio ratio
%%%
SCEI 1.6541564.6864.680.7402628.9593.630.16269 6.37100
STEI0.6337947.5447.540.4749535.6383.170.2243716.83100
SEVI 3.4027763.5063.50 1.3779425.7189.210.5781910.79100
a)SCEI:synthetic climatic environmental index;STEI:synthetic topographical environmental index;SEVI:synthetic eco-environmental vulnerability index.
Using these formulae,the synthetic eco-environmental vulnerability index of the Yellow River Basin was calculated.According to the standard mentioned above,SEVI was classi?ed to generate corre-sponding results shown in Fig.1.Table IV shows the statistics of synthetic eco-environmental vulner-ability evaluation in the Yellow River Basin.From Fig.1and Table IV,it could be seen that the eco-environmental vulnerability was at medium level for the study area as a whole.In2000,the two largest vulnerable zones were the lightly and potentially vulnerable zones,which accounted for24.09% and21.16%,and were mainly distributed in the west and south of the Yellow River Basin,including Qinghai Province and part of Shanxi and Henan provinces.In these regions there was a good vegetation cover and the intensity of soil erosion was weak and slight.In the meantime,the slightly vulnerable zone accounted for18.94%,and the moderately vulnerable zone accounted for18.15%.The very heavily vulnerable zone only accounted for a very small proportion of1.79%,and was mainly distributed in the east-north of the Yellow River Basin,especially concentrated in the municipality of Inner Mongolia and Shanxi Province.
ECO-ENVIRONMENTAL VULNERABILITY EVALUATION177
Fig.1Spatial distribution of eco-environmental vulnerability in2000in the Yellow River Basin.
TABLE IV
Statistics of synthetic eco-environmental vulnerability evaluation in2000in the Yellow River Basin
Eco-environment vulnerability level Number of grids Area Percentage
km2
Potential16736816736821.16 Slight14980914980918.94
Light19057719057724.09 Medium14357014357018.15 Heavy12547612547615.86
Very heavy1415814158 1.79
Sum790958790958100.00 Changes in eco-environmental vulnerability in ten years
Using the same SPCA model as for the year2000,the eco-environmental vulnerability in1990was computed.The eco-environmental vulnerability changes were evaluated for the ten years from1990to 2000(Fig.2;Table V).Statistical analysis indicated that areas of the potentially vulnerable zone,slightly vulnerable zone,and moderately vulnerable zone all increased in2000compared with1990(Table V). The potentially vulnerable zone increased from9.20%to21.16%,which was the fastest among all zones. The moderately vulnerable zone increased by3.04×104km2,and the slightly vulnerable zone increased by5.90×103km2.However,the lightly,heavily,and very heavily vulnerable zones decreased by1.21×105,3.69×103and7.51×103km2,respectively.Overall,it should be noted that the eco-environmental quality had been gradually improved as a whole,but in some regions the eco-environmental quality became worse in the ten years(Fig.2b).In the middle of the1990s,increasing attention was paid to eco-environmental restoration and agricultural sustainable development in the Yellow River Basin by the Chinese central government.On the national policy level,there were a number of campaigns on soil erosion control and actions on comprehensive eco-environmental management.The central government also continuously and?nancially supported a number of programs to research,demonstrate,and extend the e?cient and e?ective example models of soil and water conservation.These campaigns and programs had led to widespread re-vegetation,implementation of conservation tillage,dryland farming(water saving agriculture),and building terraces and dams,resulting in a great progress in soil erosion control and eco-environmental restoration.All of these factors improved the eco-environment on the whole.But it is also noted that with human activity aggrandized,forestland and grassland decreased and built-up land expanded in some regions,all of which made the eco-environmental quality worse.
Eco-environmental vulnerability and its changes for di?erent administrative zones The eco-environmental vulnerability was also computed for each county after the SEVI map was
178S.Y.WANG et al.
Fig.2Spatial distribution of eco-environmental vulnerability in1990(a)and its changes in the ten years from1990to 2000(b)in the Yellow River Basin.
TABLE V
Changes of eco-environmental vulnerability in the ten years from1990to2000in the Yellow River Basin
Eco-environment Area Percentage of area vulnerability level
20001990Change20001990
km2
Potential167368729129445621.169.20 Slight149809143909590018.9418.17 Light190577311397?12082024.0939.31 Medium1435701131663040418.1514.28 Heavy125476129164?368815.8616.30 Very heavy1415821670?7512 1.79 2.73
overlaid on the county boundary map.Fig.3shows the eco-environmental vulnerability levels and changes for each county in the Yellow River Basin.The vulnerability was heavy in the east-north counties while it was relatively light in the west and south counties(Fig.3b).For example,in Fugu, Huangjinqi,Wushenqi,Dingbian,Shenmu,Lingxian,and Etuokeqi counties the vulnerability levels were heavy,which re?ected that the eco-environment was worse in these counties than the other counties.At the same time,it could be seen that the vulnerability levels changed for di?erent counties.In Tongxin, Zizhou,Alashanzuoqi,and Jiangxian counties,the eco-environmental vulnerability levels decreased by one grade,showing that in these regions human activity was aggrandized,forestland and grassland decreased,and soil and water losses were more severe.But in Huining,Dingxi,Zhongning,Huachi,and Qingyang counties,the eco-environmental vulnerability levels increased by one grade,suggesting that in these regions the eco-environment had been ameliorated to a certain extent.
ECO-ENVIRONMENTAL VULNERABILITY EVALUATION179
Fig.3Spatial distribution of eco-environmental vulnerability in2000(a)and its changes in the ten years from1990to 2000(b)for di?erent counties in the Yellow River Basin.
Driving forces of eco-environmental vulnerability changes
The results of the eco-environmental vulnerability and its changes in the Yellow River Basin showed that eco-environmental quality had been gradually improved for the whole basin,but in some regions the eco-environmental quality became worse in the ten years from1990to2000.There were many factors a?ecting the eco-environmental vulnerability of the Yellow River Basin,such as structural geology, meteorological conditions,sediment discharge,human activity,and forest degradation,among others. But for the ten years from1990to2000,the driving forces of the eco-environmental vulnerability changes were the rapid population growth,governmental policy,and vegetation degradation.
In the Yellow River Basin grassland degeneration,sandy deserti?cation,and soil erosion had seriously a?ected the sustainable development of eco-environment.Soil erosion is particularly one of the key factors that a?ect the eco-environmental changes(Ding,2003).Natural processes of erosion may level and move substances controlled by physical factors.Human activities may have enhanced the erosion processes.Soil erosion on the Yellow River Basin was a combination of natural erosion and accelerated erosion(Shi and Shao,2000).The accelerated erosion arose from cultivation,uncontrolled development, overgrazing,mining,road construction,and other human activities.In the middle of the1990s,the total population of the Yellow River Basin was97.8million,but in the end of the1990s,the total population was107million,and the ratio of peasant population to the total population was76.58%. The average annual growth rate was1.88%in?ve years.To meet the demand of food supplies for the increasing population,more and more forestland and grassland had been reclaimed into farmland.This put high pressure on the environment and resulted in imprudent land use practices including careless water management,which obviously were the culprit of deforestation,high levels of soil erosion,and depletion of water resources.So the rate of vegetation coverage decreased from61.77%to60.93%.Zheng et al.(1993)studied the characteristics of natural erosion in woodlands and accelerated erosion after
180S.Y.WANG et al. development of the Ziwuling forest area.They found that in woodlands,soil erosion was1.0–14.4 t km?2year?1but after development associated with human activities,it increased to9700–21700 t km?2year?1.Obviously,the population growth in the basin had a signi?cant e?ect on soil erosion, remarkably through unreasonable land-use practices such as slope cultivation and vegetation devastation. Also,accelerated erosion rates caused by human activity damaged the ecology of the basin,leading to the unsustainable use of the land resources.These were the reasons why in some regions the eco-environment quality became worse over the ten years.
In recent several years,increased attention was paid by the Chinese central government to soil erosion control and agricultural sustainable development in the Yellow River Basin(Yu,2002).To reduce soil erosion,improve the ecological and environmental status,and promote the development of the rural areas in the western part of China,some sloping croplands have been converted to woodlands and grasslands in the past decade as part of the government e?ort(Zhou,2000;Li et al.,2001;George and Samuel,2003).The central government initiated a nationwide cropland set-aside program known as Grain-for-Green Program in1999.The program required those sloping arable lands with a slope of greater than25?be converted to woodland and pasture.The program is believed to be one of the world’s largest conservation projects.The project not only plays a vital role in maintaining the ecological security,but also promotes regional sustainable development.The cultivation of slope land is a major factor associated with serious soil and water erosion.For the Loess Plateau in the Yellow River Basin, 50%of the total arable land is on slopes and up to70%of arable land is on slopes in the loess hilly and gully areas.Studies indicated that erosion rises greatly when the slope is steeper than25?(Tang et al., 1998).Therefore,in the end of the1990s some slopes greater than25?had been returned to forest and grassland,which led to the eco-environment amelioration to a certain extent.
Vegetation was an important factor a?ecting eco-environment.Areas with serious soil and water losses were often also areas where natural vegetation had been destroyed and the environment had been degraded.For areas with copious natural vegetation,there was normally little or no erosion even if the geomorphology was hilly with precipitous slopes and gullies.For several decades,most of natural vegetation on the Yellow River Basin had been severely destroyed,especially on the Loess Plateau. Presently,remote sensing monitoring showed that forest cover was only13.03%,and even as low as3% in some areas.Grass cover in2000was only47.9%,decreased by0.59%in the ten years from1990. The destruction of forest and grassland made the soil more susceptible to erosion.Yang et al.(1999) reported that8%–12%of soil erosion is caused by deforestation.So,in some areas the destruction of vegetation resources had caused serious soil and water losses.
CONCLUSIONS
Comparing the eco-environmental vulnerability in2000with that in1990,the areas of the potentially vulnerable zone,slightly vulnerable zone,and moderately vulnerable zone were all increased.However, the lightly vulnerable zone,heavily vulnerable zone,and very heavily vulnerable zone were decreased. Based on the results from the process studies,it was emphasized that the eco-environmental quality had been gradually improved for the whole basin,but in some regions the eco-environmental quality became worse in the ten years from1990to2000.There were many factors a?ecting the spatial pattern changes of eco-environmental vulnerability,but for a period of ten years,the driving forces of the spatial changes of eco-environment were the rapid population growth,farming,governmental policy, and vegetation degradation.
The methods developed in this study(e.g.,SPCA model)were useful,as the evaluation results seem to closely re?ect the real situation of the Yellow River Basin.Of course,these methods were not perfect and need further improvement in some aspects.This study also indicated that using RS and GIS,the study on eco-environmental vulnerability in the Yellow River Basin was improved,especially on geospatial resolution.In contrast to normative approaches,the outcomes as presented here were more likely to re?ect the local situation,and thus more likely to be accepted by the local governments
ECO-ENVIRONMENTAL VULNERABILITY EVALUATION181
and local communities who would implement the recommended policies.
As a crucial factor that in?uencing ecological processes,social indicators are essential for judgement of the vulnerability and the sustainability of the eco-environment.Due to limited data and technical problems,the social indicators were not integrated into this study.Thereby,more attention should be paid to this aspect in future studies in order to better understand the eco-environmental processes in the Yellow River Basin.
ACKNOWLEDGEMENTS
The authors are grateful to Prof.Liu Ji-Yuan and the anonymous reviewers for providing valuable suggestions during reviews of the earlier versions of this manuscript.Thanks are also due to Dr.Fengjing Liu at the University of California for editing English of this manuscript.
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“杜邦分析法”实例应用 ——分析中国工商银行2007年和2013年年报分析企业资本利润利润率的主要模型是杜邦模型，其核心是通过分解资本利润率ＲＯＥ来分析影响企业盈利水平的各种因素。对于中国工商银行年报的分析也应用此方法： （一）资本利润率的第一步分解 ＲＯＥ＝资产利润率ＲＯＡ×股权乘数ＥＭ 其中：ＲＯＥ＝净利润／总资本 ＲＯＡ＝净利润／总资产 ＥＭ＝总资产／总资本＝１／（１－资本负债率） 中国工商银行２０１３年的ＲＯＥ与２００７年的相比有所上升，上升幅度为５.７１％。而其中，ＲＯＡ上涨０.４３％，ＥＭ下降了０.７９％。ＲＯＡ表示银行单位资产的净利润，其数值越大，说明银行资产盈利水平越高。而ＥＭ下降，代表着资本负债率下降。在两者作用下，ＲＯＥ的增长说明银行资本的盈利水平上升。 （二）资本利润率的第二步分解 ＲＯＡ＝ＰＭ×ＡＵ 其中：ＰＭ＝净利润／总收入 ＡＵ＝总收入／总资产 由于净利润等于总收入减去成本和税收的余额，因此，收入利润率的上升能够反映银行控制成本与降低税负的能力增强。虽然资产利润率略有下降，意味着银行的资产产生收入的能力略有下降，但是根据ROA的上升可以得知，收入的下降幅度不及成本降低的幅度。 （三）资本利润率的第三部分解 利息支出系数=利息支出/总收入 非利息支出系数=非利息支出/总收入 资产减值损失系数=资产减值损失/总收入
税收系数=所得税/总收入 利息收入系数=利息收入/总资产 非利息收入系数=非利息收入/总资产 成本方面： 影响利息支出的因素主要有三个：利率、规模、结构。利息支出系数上升，原因是：在其他因素不变的情况下，利率越高、总规模越大、高利率负债占比越高，银行的利息支出就越多。 影响非利息因素略微上升的因素主要包括职工工资、管理费用等，这体现了银行的经营效率略微下降。 资产减值损失系数的下降说明了该银行贷款回收率提升，这极大地降低了银行的成本，是导致收入利润率PM上升的最主要因素。 税收负担主要取决于一般不受银行自身控制的税率。 收入方面： 从利息收入系数的略微下降，可以得知银行贷款的利率可能略有下降或者贷款规模缩水。而非利息收入上升代表着银行收费项目即表外业务在银行的收入构成中所占有的比重略微上升。 （四）衡量商业银行经营绩效的其他财务指标 净利息收益率是净利息收入与生息资产之间的比例，它的下降反映了盈利资产带来净利息收入的能力下降。 净利息差是生息资产的平均收益率与计息负债的平均成本率之间的差额，它的下降反映了银行资产的收益能力略有下降。 成本收入比是非利息支出与扣除利息之后的营业收入之间的比率，它的下降反映了银行的成本控制能力增强。
EVA计算方法 说明: 经济增加值(EVA)＝税后净营业利润（NOPAT）－资本成本（cost of capital） 资本成本＝资本×资本成本率 由上知，计算EVA可以分做四个大步骤：（1）税后净营业利润（NOPAT）的计算; (2)资本的计算；(3)资本成本率的计算；（4）EVA的计算。下面列出EVA的计算步骤，并以深万科（0002）为例说明EVA（2000年）的计算。 深万科（0002）简介： 公司名称：万科企业股份有限公司公司简称：深万科Ａ上市日期：1991-01-29 上市地点：上海证券交易所行业：房地产业股本结构：A 股398711877股，B股121755136 股，国有股、境内法人股共110504928股，股权合计数：630971941股。 一、税后净营业利润（NOPAT）的计算 1．以表格列出的计算步骤 下表中，最左边一列（以IS开头）代表损益表中的利润计算步骤，最右边一列（以NOPAT开头）代表计算EVA所用的税后净营业利润（NOPAT）的计算步骤。空格代表在计算相应指标（如NOPAT）的步骤中不包含该行所对应的项。
损益表中的利润计算步骤 税后净营业 利润 （NOPAT） 的计算步骤主营业务收入 - 销售折扣和折让- - 主营业务税金及附加- - 主营业务成本- 主营业务利润 + 其它业务利润+ 当年计提或冲销的坏帐准备+ - 当年计提的存货跌价准备 - 管理费用- - 销售费用- = 营业利润/调整后的营业利润 + 投资收益+
= 总利润/税前营业利润 - EVA税收调整* - = 净利润/税后净营业利润 2. 计算公式：(蓝色斜体代表有原始数据，紫色下划线代表此数据需由原始数据推算出) （1）税后净营业利润=主营业务利润＋其他业务利润＋当年计提或冲销的坏帐准备—管理费用—销售费用＋长期应付款，其他长期负债和住房公积金所隐含的利息＋投资收益—EVA税收调整 注：之所以要加上长期应付款，其他长期负债和住房公积金所隐含的利息是因为sternstewart公司在计算长期负债的利息支出时，所用的长期负债中包含了其实不用付利息的长期应付款，其他长期负债和住房公积金。即，高估了长期负债的利息支出，所以需加回。 （2）主营业务利润＝主营业务收入—销售折扣和折让—营业税金及附加—主营业务成本 注: 主营业务利润已在sternstewart公司所提供的原始财务数据中直接给出 (3)EVA税收调整=利润表上的所得税＋税率×（财务费用＋长期应
政 策 研 究POLICY RESEARCH 经济增加值（Economic Value Added，EV A）的概念在10多年前进入中国。随着对其研究的深入，EVA的应用也在扩展。国务院国有资产监督管理委员会在2009年12月31日颁布了第22号令，宣布自2010年1月1日起，对中央企业的业绩考核引入经济增加值这一考核指标。 一、EVA与公司价值的含义和计算 1. EVA的定义及计算 EVA指企业的税后净营业利润减去包括股权和债务的全部投入资本的机会成本后的所得，是为出资人创造的“真正的利润”。该概念明确强调，企业经营使用的所有资本都是有成本的，不仅包括具体的债务成本，还包括股东权益资本的机会成本。EVA的目标是提高资本使用效率。 计算EVA的基础是企业的财务损益表、资产负债表和资本结构。 EVA＝税后净营业利润（NOPAT）－资本成本 ＝税后净营业利润－资本占用×加权平均资本成本率（WACC） 式（1）NOPAT＝息税前利润（EBIT）×（1－所得税率）。 目前，对石油石化企业进行EVA业绩考核所用的计算公式是： NOPAT＝净利润＋少数股东损益＋财务费用×（１－所得税率） 式（2）调整后资本占用＝平均所有者权益＋平均少数股东权益＋平均负债合计－平均无息流动负债－平均在建工程 式（3） 经济增加值（EVA）考核与 提升公司价值的关系及影响因素 周 颖1 诸 鸣2 张军贤3 杜国敏1 （1 中国石油规划总院 2 中国石油勘探开发研究院 3 中国石油天然气集团公司规划计划部） 摘 要 国资委对中央企业引入的经济增加值（EVA）考核指标，强调所有资本都是有成 本的，只有在投资资本回报率（ROIC）高于资本成本时，企业才能创造价值。在使用收益法 评估企业价值时，ROIC是驱动企业价值的核心，而EVA的计算与ROIC的计算在本质上没有 区别，追求EVA最大化就是追求公司价值最大化。投资回报率对成本的敏感性最大，对价格次 之，对投资第三，相应地直接影响企业EVA业绩。资源税政策的变化对企业EVA的影响巨大。 人民币汇率的变化，从不同的时期来看，对于上下游企业的EVA有着不同的影响。为此，建议 石油石化企业加强成本控制，实行精细化管理，转变发展方式；研究人民币汇率变化的具体影 响和应对措施；研究会计计算科目调整和重大事项调整的具体内容，最大限度地争取有利于企 业EVA业绩考核的剔除政策。同时，建议国家统筹考虑资源税和特别收益金的征收办法，逐步 理顺天然气价格，对引进天然气实行增值税减免政策。 关键词 经济增加值（EVA） 投资资本回报率（ROIC） 公司价值 收益法 敏感性 分析 特别收益金 资源税 人民币汇率 2010.7 国际石油经济·5·
基于可持续增长的杜邦财务分析体系的构 建 文献综述 作者姓名*** 专业财务管理 指导教师姓名*** 专业技术职务副教授
一、引言 杜邦财务分析法是在20世纪20年代由美国杜邦公司首创的一种综合财务分析方法，它以净资产收益率作为主线，利用各个主要财务指标之间的联系，层层分解，逐步深入，形成一个完整的财务分析体系。通过杜邦分析能够反映出企业的经营成果和财务状况，发现企业存在的问题和不足，以促进企业改善经营状况，加强财务管理，是一种评价企业绩效的经典方法。然而，随着我国经济的不断发展，企业会计信息含量的多样化、企业资本运作和经营者盈余管理技能的不断深化，杜邦分析体系越来越多的暴露出自身的缺陷和不足，为了更好的适应现代企业的发展要求，国内外的很多学者针对杜邦分析题的局限性作了许多有益的补充和改进，为人们更好的了解企业的经营成果和财务状况提供了有效地方法和途径。 二、国内外研究现状 2.1国内研究状况 国内学者对杜邦分析体系的研究成果并不是很多，他们的研究主要集中在以下几个方面：一是研究怎样对杜邦分析体系进行改进。一般都是先从分析杜邦财务体系的局限性或不足之处入手，然后根据这些局限和不足提出改进的建议和方法。例如将现金流量分析引入原有的杜邦分析体系，如许秀梅和王秀华在《论杜邦分析体系及其改进》一文中指出，杜邦分析体系的主要缺陷是只利用了资产负债表及损益表中的数据，没有利用现金流量表中的数据。她们认为通过引入现金流量的分析，可以得到企业现金流量来源、结构、数量等信息，从而可以对企业经营资产的真实效率，企业的收益品质即净利润与现金流量的相关程度以及创造现金利润的真正能力作出评价，同时预测企业未来的现金流量，准确判断企业的偿债能力。罗芳在她的《改进现行财务分析指标体系的相关思考》中也认为以现金流量为基础的财务分析指标体系能够更加全面系统和完整的分析企业的财务状况。杨化峰在《改进财务分析体系，提高财务分析质量》一文中除了引进现金流量因素，还将成本管理的目标和所得税负担分析引入到杜邦分析体系中来。李金龙、彭皎和路明明在《传统杜邦分析体系与改进杜邦分析体系的比较》中认为改进的杜邦分析体系要求对资产和负债进行重新分类，区分经营损益和金融损益，改进后的杜邦指标体系为：权益净酬率= 净利润/ 股东权益= (税后经营净利润- 税后利息费用) / 股东权益= (税后经营净利润/ 净经营资产) ×(净经营资产/ 股东权益)- (税后利息费用/ 净负债) ×(净负债/ 股东权益) = 净经营资产利润率×(1 + 净负债/ 股东权益)- 税后利息率×净财务杠杆= 净经营资产利润率+ (净经营资产利润率-税后利息率) ×净财务杠杆。但是这种杜邦体系的改进只适合股东回报能力的分析，并且还要注意指标的波动性以及净财务杠杆大小和企业的偿债能力。肖莹在其《杜邦体系与财务报表新探》中加入了可持续发展率、现金股利、风险、成本习性、盈余构成与盈余质量、自由现金流量以及公司生命周期等因素，从而更能准确地反映和评价企业的综合财务状况，直接揭示各种影响企业经营问题的因素。黄爱华等在《财务分析中杜邦系统与SWOT的结合使用》一文中，将SWOT分析引入到财务管理的财务分析中，并与杜邦分析结合起来，不但能够解决杜邦分析的不足之处，还可以了解企业外部环境，并且更好地从理论和实践上将财务管理融合在企业管理中。二是研究杜邦分析体系在企业中的具
附件3： 经济增加值考核实施方案 为适应以管资本为主加强国有资产监管需要，推进监管企业加快调整结构，加快转型升级，提升价值管理水平，提高价值创造能力，制定本方案。 一、经济增加值的定义及计算公式 经济增加值是指企业税后净营业利润减去资本成本后的余额。计算公式为： 经济增加值=税后净营业利润－资本成本=税后净营业利润－调整后资本×平均资本成本率 税后净营业利润=净利润＋（利息支出＋研究开发费用＋视同利润调整项－国有资本经营预算费用化支出－资产处置收益－省国资委审核认定的其他非经常性收益）×（1－25%）调整后资本=平均所有者权益＋平均带息负债－平均在建工程 平均资本成本率=债权资本成本率×平均带息负债/（平均带息负债+平均所有者权益）×（1-25%）+股权资本成本率×平均所有者权益/（平均带息负债+平均所有者权益）。其中，债权资本成本率=利息支出总额/平均带息负债。 二、规范和完善经济增加值调整项目 按照“重要性、战略性、导向性和可操作性”原则，充分结合新形势对经济增加值考核优化调整的新需求，规范和完善经济增加值调整项目，突出可持续发展，引导企业更加重视自主创新和品牌建设，更加重视战略投资和长远回报。
（一）利息支出。利息支出是指企业财务报表中“财务费用”项下的“利息支出”。 （二）研究开发费用。为鼓励科技创新和为获取战略资源进行的风险投入，将企业研究开发支出视同净营业利润予以加回。研究开发费用，是指企业准确归集的、计入当期“管理费用”项下“研究与开发费”。对于勘探投入费用较大的资源类企业，经省国资委认定后，其成本费用情况表中的“勘探费用”可视同研究开发费用调整项按照一定比例（原则上不超过50%）予以加回。 （三）视同利润调整项。为鼓励企业积极处理历史遗留问题，加快推进改革改制工作，对企业考核年度内发生的且金额较大的改革改制、处理遗留问题等有关费用，经企业申请、省国资委认定，可部分或全部视同净营业利润予以加回。 （四）资产处置收益。即企业（不含投资类企业）非因产业结构调整、转型升级需要而发生的处置流动资产、长期投资、固定资产、在建工程、无形资产、其他长期资产等资产产生的收益。 （五）平均在建工程。为鼓励可持续发展投入，对符合主业的在建工程，从资本成本中予以扣除。在建工程是指企业财务报表中的符合主业规定的“在建工程”。企业在考核年度已完工达到预定可使用状态的、但未转入“固定资产”科目核算的“在建工程”，应按新会计准则有关规定调整计算。 三、差异化资本成本率的确定： 充分考虑监管企业资本结构、融资成本差异，坚持功能分类考核原则，合理确定企业的资本回报要求。突出资本成本属性，强化资本纪律，提升资本回报水平；突出提高发展质量，引导企业做强主业、控制风险、优化结构。 （一）股权资本成本率结合企业功能分类和国债利率水平差
600074南京中达财务报告分析 1.上游：供应商 BOPET的原材料来自于PTA(精对苯二甲酸)及MEG(一乙二醇) PTA的原料是PX，PX的原料是石油。PTA的下游产品主要为涤纶长丝、短纤、切片（包括纤维切片、瓶用切片、薄膜切片). MEG(一乙二醇) 乙二醇通过水解后得到，乙二醇是一种重要的石油化工基础有机原料. BOPP主料成分是PP（聚丙烯） CPP薄膜是由流延方法制得的未拉伸聚丙烯PP薄膜 BOPA双向拉伸尼龙薄膜主要原材料是PA6(聚酰胺6或尼龙6),聚酰胺是由二元醇和二元胺合成的。工业上这些原料也是从石油中得到的. 主要原料如PX、PTA、MEG、PP、AN等均来自石油衍生物 600074南京中达财务报告分析 1.上游：供应商 1999年2月1日价格从3400元/吨开始启动，至同年 10月18日价格升到了6450元/吨，涨幅高达89.7%。 2001年11月19日价格又下跌到了4100元/吨原因为 原油价格大涨1999年2月8日原油价格为11.65美元/ 桶，之后价格大幅上涨，2000年3月7日原油价格涨 至33.9美元/桶，但是2001年则由于全球聚酯纤维 需求低迷，PTA市场供过于求，PTA价格在5000元/ 吨左右持续徘徊了两年。 由于911的事件，2002年1月下旬 价格从4100元/吨开始启动，至同 年5月底价格升至7200元/吨，之 后价格又快速回落5400元/吨。 而对MEG的需求减弱使价格下跌2003年开始石油价格开始走高，在2008年达到96美元/桶，而在2009年下跌至76美元/桶。从图中可以看到在2009年原料价格都有所下降。 由于美伊战争及非典影响，使原油上升
宝钢集团经济增加值分析 一、宝钢集团介绍 中国宝武钢铁集团有限公司，是国务院国有资产监督管理委员会监管的国有重要骨干中央企业，总部位于上海和武汉。子公司宝山钢铁股份有限公司，简称宝钢股份，是中国宝武钢铁集团有限公司在上海证券交易所的上市公司。中国宝武钢铁集团有限公司是中国最大、最现代化的钢铁联合企业。 二、经济增加值 2.1概念 经济增加值（Economic Value Added，EVA），是指从企业税后净营业利润中扣除股权和债务在内的所有资本成本之后的剩余收益。EVA的思想源于经济利润基础之上的剩余收益，其中经济利润是指公司从成本补偿角度获得的利润，它不仅要求企业将所有的运营费用计入成本，而且还要将全部资本成本计入成本。这种资本成本不但包括向债权人支付的利息这类显而易见的成本，还包括股东投入资本的机会成本。EVA指标的核心理念反映了现代企业制度所追求的企业目标——股东价值最大化。所以，EVA指标可以成为资本市场评价企业是否为股东创造价值、资本是否保值增值的标准。投资收益率高低不是企业经营状况好坏和价值创造能力的评估标准，关键在于是否超过资本成本。 2.2计算方法 按照国资委的标准，经济增加值的计算公式为： 经济增加值= 税后净营业利润- 资本成本 = 税后净营业利润- 调整后资本×平均资本成本率 税后净营业利润= 净利润+（利息支出+ 研究开发费用调整项- 非经常性收益调整项×50%）×（1- 25%） 调整后资本= 平均所有者权益+ 平均负债合计- 平均无息流动负债- 平均在建工程 其中部分指标计算如下： （1）利息支出是指企业财务报表中“财务费用”项下的“利息支出”。 （2）研究开发费用调整项是指企业财务报表中“管理费用”项下的“研究与开发费”和当期确认为 无形资产的研究开发支出。 （3）非经常性收益调整项包括以下内容：变卖主业优质资产收益；主业优质资产以外的非流动资产转让收益；与主业发展无关的资产置换收益、与经常活动无关的补贴收入等其他非经常性收益。 （4）无息流动负债是指企业财务报表中“应付票据”、“应付账款”、“预收款项”、“应交税费”、“应付利息”、“其他应付款”和“其他流动负债”；对于因承担国家任务等原因造成“专项应付款”、“特种储备基金”余额较大的，可视同无息流动负债扣除。 （5）在建工程是指企业财务报表中的符合主业规定的“在建工程”。
杜邦分析法 杜邦分析法（DuPont Analysis）是利用几种主要的财务比率之间的关系来综合地分析企业的财务状况。具体来说，它是一种用来评价公司赢利能力和股东权益回报水平，从财务角度评价企业绩效的一种经典方法。其基本思想是将企业净资产收益率逐级分解为多项财务比率乘积，这样有助于深入分析比较企业经营业绩。由于这种分析方法最早由美国杜邦公司使用，故名杜邦分析法。 1特点 杜邦模型最显著的特点是将若干个用以评价企业经营效率和财务状况的比率按其内在联系有机地结合起来，形成一个完整的指标体系，并最终通过权益收益率来综合反映。 2应用 采用这一方法，可使财务比率分析的层次更清晰、条理更突出，为报 杜邦分析法表分析者全面仔细地了解企业的经营和盈利状况提供方便。 杜邦分析法有助于企业管理层更加清晰地看到权益基本收益率的决定因素，以及销售净利润与总资产周转率、债务比率之间的相互关联关系，给管理层提供了一张明晰的考察公司资产管理效率和是否最大化股东投资回报的路线图。 3基本思路 杜邦分析法的的基本思路 1、权益净利率，也称权益报酬率，是一个综合性最强的财务分析指标，是杜邦分析系统的核心。 2、资产净利率是影响权益净利率的最重要的指标，具有很强的综合性，而资产净利率又取决于销售净利率和总资产周转率的高低。总资产周转率是反映总资产的周转速度。对资产周转率的分析，需要对影响资产周转的各因素进行分析，以判明影响公司资产周转的主要问题在哪里。销售净利率反映销售收入的收益水平。扩大销售收入，降低成本费用是提高企业销售利润率的根本途径，而扩大销售，同时也是提高资产周转率的必要条件和途径。 3、权益乘数表示企业的负债程度，反映了公司利用财务杠杆进行经营活动的程度。资产负债率高，权益乘数就大，这说明公司负债程度高，公司会有较多的杠杆利益，但风险也高；反之，资产负债率低，权益乘数就小，这说明公司负债程度低，公司会有较少的杠杆利益，但相应所承担的风险也低。 4财务关系编辑 杜邦分析法的财务指标关系 杜邦分析法中的几种主要的财务指标关系为： 净资产收益率=资产净利率(净利润/总资产)×权益乘数(总资产/总权益资本) 而：资产净利率(净利润/总资产)=销售净利率(净利润/总收入)×资产周转率（总收入/总资产) 即：净资产收益率=销售净利率(NPM)×资产周转率(AU,资产利用率)×权益乘数(EM) 在杜邦体系中，包括以下几种主要的指标关系： （1）净资产收益率是整个分析系统的起点和核心。该指标的高低反映了投资者的净资产获利能力的大小。净资产收益率是由销售报酬率，总资产周转率和权益乘数决定的。 （2）权益系数表明了企业的负债程度。该指标越大，企业的负债程度越高，它是资产权益率的倒数。 （3）总资产收益率是销售利润率和总资产周转率的乘积，是企业销售成果和资产运营的综合反映，要提高总资产收益率，必须增加销售收入，降低资金占用额。
杜邦分析法优缺点 令狐采学 杜邦分析法（DuPont Analysis） 杜邦分析法利用几种主要的财务比率之间的关系来综合地分析企业的财务状况，这种分析方法最早由美国杜邦公司使用，故名杜邦分析法。杜邦分析法是一种用来评价公司赢利能力和股东权益回报水平，从财务角度评价企业绩效的一种经典方法。其基本思想是将企业净资产收益率逐级分解为多项财务比率乘积，这样有助于深入分析比较企业经营业绩。 杜邦分析法的概念 杜邦分析法利用几种主要的财务比率之间的关系来综合地分析企业的财务状况，这种分析方法最早由美国杜邦公司使用，故名杜邦分析法。杜邦分析法是一种用来评价公司赢利能力和股东权益回报水平，从财务角度评价企业绩效的一种经典方
法。其基本思想是将企业净资产收益率逐级分解为多项财务比率乘积，这样有助于深入分析比较企业经营业绩。 杜邦分析法的特点 杜邦模型最显著的特点是将若干个用以评价企业经营效率和财务状况的比率按其内在联系有机地结合起来，形成一个完整的指标体系，并最终通过权益收益率来综合反映。采用这一方法，可使财务比率分析的层次更清晰、条理更突出，为报表分析者全面仔细地了解企业的经营和盈利状况提供方便。 杜邦分析法有助于企业管理层更加清晰地看到权益资本收益率的决定因素，以及销售净利润率与总资产周转率、债务比率之间的相互关联关系，给管理层提供了一张明晰的考察公司资产管理效率和是否最大化股东投资回报的路线图。 杜邦分析法的的基本思路 1、权益净利率是一个综合性最强的财务分析指标，是杜邦分析系统的核心。
2、资产净利率是影响权益净利率的最重要的指标，具有很强的综合性，而资产净利率又取决于销售净利率和总资产周转率的高低。总资产周转率是反映总资产的周转速度。对资产周转率的分析，需要对影响资产周转的各因素进行分析，以判明影响公司资产周转的主要问题在哪里。销售净利率反映销售收入的收益水平。扩大销售收入，降低成本费用是提高企业销售利润率的根本途径，而扩大销售，同时也是提高资产周转率的必要条件和途径。 3、权益乘数表示企业的负债程度，反映了公司利用财务杠杆进行经营活动的程度。资产负债率高，权益乘数就大，这说明公司负债程度高，公司会有较多的杠杆利益，但风险也高；反之，资产负债率低，权益乘数就小，这说明公司负债程度低，公司会有较少的杠杆利益，但相应所承担的风险也低。 杜邦分析法的财务指标关系 杜邦分析法中的几种主要的财务指标关系为： 净资产收益率＝资产净利率×权益乘数
经济增加值（EVA）管理学习笔记 目前，我国许多企业都在推行经济增加值（EVA）管理方式，国资委也准备对下属的中央企业采用EVA评价体系，一些大型的民营企业在咨询过程中也提出了这方面的需求。什么是EVA，以及如何在企业中实施EVA成了大家比较感兴趣的话题，下面我想通过论坛这个园地，共同学习、交流EVA知识和实施经验，达到共同提高的目的。欢迎大家参与讨论。 一，好企业的判断标准 什么样的企业是好企业？不同的时期，人们对企业的判断标准也不尽相同，这主要是在社会发展的各个阶段，人们对企业的性质、发展目标、经营方式都有不同的理解。在论坛里，我和rgang兄曾专门就此讨论过（有兴趣的可以看看），fxzfxz9999兄也提出了很有见地的观点，这些讨论都涉及到现代企业理论研究的一些前沿的问题，理论的东西较多。经济增加值是上世纪80 年代在企业管理实践中逐渐发展起来的企业价值管理工具，因此在对企业的认识上，仍然以股东价值最大化为企业目标，但既然是学习经济增加值，我们就接受和承认这个目标。 从19世纪，真正现代意义上的企业出现以来，企业目标经历了利润最大化、效益最大化、股东价值最大化三个阶段。在利润最大化阶段，投资者普遍追求利润的绝对值，这时候的企业规模比较小，投资者和管理者并没有分离，因此也不存在“委托代理”的机制问题。到了20世纪初，由于金融资本市场的发展，出现了许多巨型规模的企业，企业的所有者已经不能有效的管理和控制企业，企业的所有权和经营权分离逐步成为趋势。特别是经济学中资源禀赋概念的扩展，投资者越来越关注企业资源的使用效率，显然，简单的利润判断已不能准确全面的反映企业的经营状况，为此，人们发展了总资产收益率、股本收益率、每股赢利等指标作为企业经营状况的判断标准。 20世纪80 年代后，越来越多的企业认识到，效益最大化判断工具也有不足，因为其中的主要指标信息采集、分析都基于企业财务报表（基于已发生的权责），反映的是企业的发生成本，没有考虑到股东投资的机会成本，因此很可能激励公司管理者的短期行为，忽视企业的长期价值的创造。具体表现在片面追求企业规模和企业利润会导致企业过度投资和过度的扩大生产，国内的长虹就是这样的一个典型例子。
国资委经济增加值考核细则 （详细资料 https://www.doczj.com/doc/5f15545516.html,/flfg/2010-01/22/content_1517096.htm） 一、经济增加值的定义及计算公式 经济增加值是指企业税后净营业利润减去资本成本后的余额。 计算公式： 经济增加值=税后净营业利润-资本成本=税后净营业利润-调整后资本×平均资本成本率 税后净营业利润=净利润+（利息支出+研究开发费用调整项-非经常性收益调整项×50%）×（1-25%） 调整后资本=平均所有者权益+平均负债合计-平均无息流动负债-平均在建工程 二、会计调整项目说明 （一）利息支出是指企业财务报表中“财务费用”项下的“利息支出”。 （二）研究开发费用调整项是指企业财务报表中“管理费用”项下的“研究与开发费”和当期确认为无形资产的研究开发支出。对于为获取国家战略资源，勘探投入费用较大的企业，经国资委认定后，将其成本费用情况表中的“勘探费用”视同研究开发费用调整项按照一定比例（原则上不超过50%）予以加回。 （三）非经常性收益调整项包括 1.变卖主业优质资产收益：减持具有实质控制权的所属上市公司股权取得的收益（不包括在二级市场增持后又减持取得的收益）；企业集团（不含投资类企业集团）转让所属主业范围内且资产、收入或者利润占集团总体10%以上的非上市公司资产取得的收益。
2.主业优质资产以外的非流动资产转让收益：企业集团（不含投资类企业集团）转让股权（产权）收益，资产（含土地）转让收益。 3.其他非经常性收益：与主业发展无关的资产置换收益、与经常活动无关的补贴收入等。 （四）无息流动负债是指企业财务报表中“应付票据”、“应付账款”、“预收款项”、“应交税费”、“应付利息”、“其他应付款”和“其他流动负债”；对于因承担国家任务等原因造成“专项应付款”、“特种储备基金”余额较大的，可视同无息流动负债扣除。 （五）在建工程是指企业财务报表中的符合主业规定的“在建工程”。 三、资本成本率的确定 （一）中央企业资本成本率原则上定为5.5%。 （二）承担国家政策性任务较重且资产通用性较差的企业，资本成本率定为4.1%。 （三）资产负债率在75%以上的工业企业和80%以上的非工业企业，资本成本率上浮0.5个百分点。 （四）资本成本率确定后，三年保持不变。 四、其他重大调整事项 发生下列情形之一，对企业经济增加值考核产生重大影响的，国资委酌情予以调整。 （一）重大政策变化； （二）严重自然灾害等不可抗力因素； （三）企业重组、上市及会计准则调整等不可比因素
杜邦财务分析法及案例分析 获利能力是企业的一项重要的财务指标，对所有者、债权人、投资者及政府来说，分析评价企业的获利能力对其决策都是至关重要的，获利能力分析也是财务管理人员所进行的企业财务分析的重要组成部分。 传统的评价企业获利能力的比率主要有：资产报酬率，边际利润率（或净利润率），所有者权益报酬率等；对股份制企业还有每股利润，市盈率，股利发放率，股利报酬率等。这些单个指标分别用来衡量影响和决定企业获利能力的不同因素，包括销售业绩，资产管理水平，成本控制水平等。 这些指标从某一特定的角度对企业的财务状况以及经营成果进行分析，它们都不足以全面地评价企业的总体财务状况以及经营成果。为了弥补这一不足，就必须有一种方法，它能够进行相互关联的分析，将有关的指标和报表结合起来，采用适当的标准进行综合性的分析评价，既全面体现企业整体财务状况，又指出指标与指标之间和指标与报表之间的内在联系，杜邦分析法就是其中的一种。 杜邦财务分析体系（The Du Pont System）是一种比较实用的财务比率分析体系。这种分析方法首先由美国杜邦公司的经理创造出来，故称之为杜邦财务分析体系。这种财务分析方法从评价企业绩效最具综合性和代表性的指标-权益净利率出发，层层分解至企业最基本生产要素的使用，成本与费用的构成和企业风险，从而满足通过财务分析进行绩效评价的需要，在经营目标发生异动时经营者能及时查明原因并加以修正，同时为投资者、债权人及政府评价企业提供依据。 一．杜邦分析法 杜邦模型最显著的特点是将若干个用以评价企业经营效率和财务状况的比率按其内在联系有机地结合起来，形成一个完整的指标体系，并最终通过权益收益率来综合反映。采用这一方法，可使财务比率分析的层次更清晰、条理更突出，为报表分析者全面仔细地了解企业的经营和盈利状况提供方便。 杜邦分析法有助于企业管理层更加清晰地看到权益资本收益率的决定因素，以及销售净利润率与总资产周转率、债务比率之间的相互关联关系，给管理层提供了一张明晰的考察公司资产管理效率和是否最大化股东投资回报的路线图。 杜邦分析法利用各个主要财务比率之间的内在联系，建立财务比率分析的综合模型，来综合地分析和评价企业财务状况和经营业绩的方法。采用杜邦分析图将有关分析指标按内在联系加以排列，从而直观地反映出企业的财务状况和经营成果的总体面貌。 二、对杜邦模型的分析 1.模型中各财务指标之间的关系： 可以看出杜邦分析法实际上从两个角度来分析财务，一是进行了内部管理因素分析，二是进行了资本结构和风险分析。 权益净利率＝资产净利率×权益乘数 权益乘数＝1÷（1－资产负债率） 资产净利率＝销售净利率×总资产周转率 销售净利率＝净利润÷销售收入 总资产周转率＝销售收入÷总资产 资产负债率＝负债总额÷总资产 2.杜邦分析模型提供了下列主要的财务指标关系的信息：
EVA 计算方法 说明: 经济增加值(EV A)＝税后净营业利润（NOPA T ）－资本成本（cost of capital ） 资本成本＝资本×资本成本率 由上知，计算EV A 可以分做四个大步骤： （1）税后净营业利润（NOPA T ）的计算; (2)资本的 计算； (3)资本成本率的计算； （4）EV A 的计算。下面列出EV A 的计算步骤，并以深万科（0002）为例说明EV A （2000年）的计算。 深万科（0002）简介： 公司名称：万科企业股份有限公司 公司简称：深万科Ａ 上市日期：1991-01-29 上市地点：上海证券交易所 行业：房地产业 股本结构：A 股398711877股，B 股121755136 股，国有股、境内法人股共110504928股，股权合计数：630971941股。 一、税后净营业利润（NOPA T ）的计算 1． 以表格列出的计算步骤 下表中，最左边一列（以IS 开头）代表损益表中的利润计算步骤，最右边一列（以NOPA T 开头）代表计算EV A 所用的税后净营业利润（NOPA T ）的计算步骤。空格代表在计算相应指标（如NOPA T ）的步骤中不包含该行所对应的项。 损益表中的利润计算步骤 税后净营业 利润 （NOPA T ）的计算步骤 主营业务收入 - 销售折扣和折让 - - 主营业务税金及附加 - - 主营业务成本 - 主营业务利润 - 管理费用 - - 销售费用 - = 营业利润/调整后的营业利润 + 投资收益 + = 总利润/税前营业利润 - EVA 税收调整* - = 净利润/税后净营业利润
2.计算公式：(蓝色斜体代表有原始数据，紫色下划线代表此数据需由原始数据推算出) （1）税后净营业利润=主营业务利润＋其他业务利润＋当年计提或冲销的坏帐准备—管理费用—销售费用＋长期应付款，其他长期负债和住房公积金所隐含的利息＋投资收益—EV A 税收调整 注：之所以要加上长期应付款，其他长期负债和住房公积金所隐含的利息是因为sternstewart公司在计算长期负债的利息支出时，所用的长期负债中包含了其实不用付利息的长期应付款，其他长期负债和住房公积金。即，高估了长期负债的利息支出，所以需加回。 （2）主营业务利润＝主营业务收入—销售折扣和折让—营业税金及附加—主营业务成本注: 主营业务利润已在sternstewart公司所提供的原始财务数据中直接给出 (3)EV A税收调整=利润表上的所得税＋税率×（财务费用＋长期应付款，其他长期负债 和住房公积金所隐含的利息＋营业外支出－营业外收入－补贴收入） (4)长期应付款，其他长期负债和住房公积金所隐含的利息＝长期应付款，其他长期负债 和住房公积金×3～5 年中长期银行贷款基准利率 长期应付款，其他长期负债和住房公积金=长期负债合计—长期借款—长期债券 税率=0.33（从1998年，1999年和2000年） 说明：上面计算公式所用数据大多直接可以在sternstewart公司所提供的原始财务数据中找到（主营业务利润已直接给出）。而长期应付款，其他长期负债和住房公积金所隐含的利息需由原始财务数据推算得出。 3. 计算深万科的税后净营业利润（NOPAT 2000年） 首先计算出需由其他原始财务数据推算的间接数据项－长期应付款，其他长期负债和住房公积金所隐含的利息和EV A税收调整，然后利用计算结果及其他数据计算出NOPA T. （1）长期应付款，其他长期负债和住房公积金所隐含的利息的计算； 单位：元 长期负债合计123895991.54 减：长期借款80000000.00 减：长期债券 ――――――――――――――――――――――――――――― 长期应付款，其他长期负债和住房公积金43895991.54 乘：3～5 年中长期银行贷款基准利率 6.03％ 长期应付款，其他长期负债2646928.29 和住房公积金所隐含的利息 （2）EV A税收调整的计算； 财务费用1403648.37 加：长期应付款，其他长期负债2646928.29 和住房公积金所隐含的利息 加：营业外支出6595016.31 减：营业外收入23850214.53
杜邦分析法及案例
杜邦分析法 杜邦分析法（DuPont Analysis）是利用几种主要的财务比率之间的关系来综合地分析企业的财务状况。具体来说，它是一种用来评价公司赢利能力和股东权益回报水平，从财务角度评价企业绩效的一种经典方法。其基本思想是将企业净资产收益率逐级分解为多项财务比率乘积，这样有助于深入分析比较企业经营业绩。由于这种分析方法最早由美国杜邦公司使用，故名杜邦分析法。 1特点 杜邦模型最显著的特点是将若干个用以评价企业经营效率和财务状况的比率按其内在联系有机地结合起来，形成一个完整的指标体系，并最终通过权益收益率来综合反映。 2应用 采用这一方法，可使财务比率分析的层次更清晰、条理更突出，为报 杜邦分析法表分析者全面仔细地了解企业的经营和盈利状况提供方便。 杜邦分析法有助于企业管理层更加清晰地看到权益基本收益率的决定因素，以及销售净利润与总资产周转率、债务比率之间的相互关联关系，给管理层提供了一张明晰的考察公司资产管理效率和是否最大化股东投资回报的路线图。 3基本思路 杜邦分析法的的基本思路 1、权益净利率，也称权益报酬率，是一个综合性最强的财务分析指标，是杜邦分析系统的核心。
2、资产净利率是影响权益净利率的最重要的指标，具有很强的综合性，而资产净利率又取决于销售净利率和总资产周转率的高低。总资产周转率是反映总资产的周转速度。对资产周转率的分析，需要对影响资产周转的各因素进行分析，以判明影响公司资产周转的主要问题在哪里。销售净利率反映销售收入的收益水平。扩大销售收入，降低成本费用是提高企业销售利润率的根本途径，而扩大销售，同时也是提高资产周转率的必要条件和途径。 3、权益乘数表示企业的负债程度，反映了公司利用财务杠杆进行经营活动的程度。资产负债率高，权益乘数就大，这说明公司负债程度高，公司会有较多的杠杆利益，但风险也高；反之，资产负债率低，权益乘数就小，这说明公司负债程度低，公司会有较少的杠杆利益，但相应所承担的风险也低。 4财务关系编辑 杜邦分析法的财务指标关系 杜邦分析法中的几种主要的财务指标关系为： 净资产收益率=资产净利率(净利润/总资产)×权益乘数 (总资产/总权益资本) 而：资产净利率(净利润/总资产)=销售净利率(净利润/总收入)×资产周转率（总收入/总资产) 即：净资产收益率=销售净利率(NPM)×资产周转率(AU,资产利用率)×权益乘数(EM) 在杜邦体系中，包括以下几种主要的指标关系： （1）净资产收益率是整个分析系统的起点和核心。该指标的高低反映了投资者的净资产获利能力的大小。净资产收益率是由销售报酬率，总资产周转率和权益乘数决定的。
EV A计算方法 说明: 经济增加值(EV A)＝税后净营业利润（NOPAT）－资本成本（cost of capital） 资本成本＝资本×资本成本率 由上知，计算EV A可以分做四个大步骤： （1）税后净营业利润（NOPAT）的计算; （2）资本的计算； （3）资本成本率的计算； （4）EV A的计算。 下面列出EV A的计算步骤，并以深万科（0002）为例说明EV A（2000年）的计算。 深万科（0002）简介： 公司名称：万科企业股份有限公司公司简称：深万科Ａ上市日期：1991-01-29 上市地点：上海证券交易所行业：房地产业股本结构：A股398711877 股，B股121755136 股，国有股、境内法人股共110504928股，股权合计数：630971941股。一、税后净营业利润（NOPAT）的计算 1．以表格列出的计算步骤 下表中，最左边一列（以IS开头）代表损益表中的利润计算步骤，最右边一列（以NOPA T 开头）代表计算EV A所用的税后净营业利润（NOPA T）的计算步骤。空格代表在计算相 应指标（如NOPA T）的步骤中不包含该行所对应的项。 损益表中的利润计算步骤 税后净营业 利润 （NOPAT） 的计算步骤主营业务收入 - 销售折扣和折让- - 主营业务税金及附加- - 主营业务成本- 主营业务利润 - 管理费用- = 营业利润/调整后的营业利润 + 投资收益+
= 总利润/税前营业利润 = 净利润/税后净营业利润 2.计算公式：(蓝色斜体代表有原始数据，紫色下划线代表此数据需由原始数据推算出) （1）税后净营业利润=主营业务利润＋其他业务利润＋当年计提或冲销的坏帐准备—管理费用—销售费用＋长期应付款，其他长期负债和住房公积金所隐含的利息＋投资收益—EV A 税收调整 注：之所以要加上长期应付款，其他长期负债和住房公积金所隐含的利息是因为sternstewart公司在计算长期负债的利息支出时，所用的长期负债中包含了其实不用付利息的长期应付款，其他长期负债和住房公积金。即，高估了长期负债的利息支出，所以需加回。 （2）主营业务利润＝主营业务收入—销售折扣和折让—营业税金及附加—主营业务成本注: 主营业务利润已在sternstewart公司所提供的原始财务数据中直接给出 (3)EVA税收调整=利润表上的所得税＋税率×（财务费用＋长期应付款，其他长期负债 和住房公积金所隐含的利息＋营业外支出－营业外收入－补贴收入） (4)长期应付款，其他长期负债和住房公积金所隐含的利息＝长期应付款，其他长期负债 和住房公积金×3～5 年中长期银行贷款基准利率 长期应付款，其他长期负债和住房公积金=长期负债合计—长期借款—长期债券 税率=0.33（从1998年，1999年和2000年） 说明：上面计算公式所用数据大多直接可以在sternstewart公司所提供的原始财务数据中找到（主营业务利润已直接给出）。而长期应付款，其他长期负债和住房公积金所隐含的利息需由原始财务数据推算得出。 3. 计算深万科的税后净营业利润（NOPAT 2000年） 首先计算出需由其他原始财务数据推算的间接数据项－长期应付款，其他长期负债和住房公积金所隐含的利息和EV A税收调整，然后利用计算结果及其他数据计算出NOPA T. （1）长期应付款，其他长期负债和住房公积金所隐含的利息的计算； 单位：元 长期负债合计123895991.54 减：长期借款80000000.00 减：长期债券 ――――――――――――――――――――――――――――― 长期应付款，其他长期负债和住房公积金43895991.54 乘：3～5 年中长期银行贷款基准利率 6.03％ 长期应付款，其他长期负债2646928.29 和住房公积金所隐含的利息 （2）EV A税收调整的计算； 财务费用1403648.37
国资委EV A考核环境下非经常性收益调整探析 摘要：国资委采用经济增加值指标替代了传统的净资产收益率指标，在实际计算经济增加值时需要对利润和资本等项目进行调整，主要的调整内容包括非经常性收益。对现行非经常性收益调整方式进行了分析，阐明了当前非经常性收益调整方式的优缺点，并提出了合理化建议。 关键词： 经济增加值（EV A）；非经常性收益；资本化 中图分类号：F2 文献标识码：A 文章编号：1672-3198（2011）10-0024-02 1 国资委非经常性收益指标计算细则分析 经济增加值的调整项可以达到160多项，这在一定程度上限制了经济增加值的普及和应用。国资委此次出台的经济增加值计算办法，本着简单可操作性原则，将经济增加值计算中复杂的会计调整项大大缩减，只调整影响决策判断和鼓励长期发展的重要因素，增强了经济增加值计算的可操作性。国资委经济增加值指标的计算公式及调整内容如下：经济增加值=税后净营业利润?C资本成本=税后净营业利润?C调整后资本×平均资本成本率
税后净营业利润=净利润+（利息支出+研究开发费用调整项?C非经常性收益调整项×50%）×（1?C25%）调整后资本=平均所有者权益+平均负债合计?C平均无息流动负债?C平均在建工程 根据证监会颁布的《公开发行证券的公司信息披露解释性公告第1号一非经常性损益（2008）》，非经常性损益是指与公司正常经营无直接关系，以及虽与正常经营业务相关但由于其性质特殊性和偶发性，影响报表使用人对公司经营业绩和盈利能力做出正常判断的各项交易和事项产生的损益。非经常性损益，尤其是非经常性收益常被企业用做盈余管理的主要手段。国资委规定的非经常性收益调整项包括以下几个方面： （1）变卖主业优质资产收益：减持具有实质控制权的所属上市公司股权取得的收益（不包括在二级市场增持后又减持取得的收益）；企业集团（不含投资类企业集团）转让所属主业范围内且资产、收入或者利润占集团总体10%以上的非上市公司资产取得的收益。 （2）主业优质资产以外的非流动资产转让收益：企业集团（不含投资类企业集团）转让股权（产权）收益，资产（含土地）转让收益。 （3）其他非经常性收益：与主业发展无关的资产置换收益、与经常活动无关的补贴收入等。