wind energy is being promoted as a promising 
 source of renewable 
 energy , consequently the wind energy market is growing notably both 
 in europe and globally . from 2006 to 2007 , alone , the gross production 
 of wind energy in the eu-27 countries grew by 21% to 99  430 
 gwh .
the united states for instance , have 
 set a target for 2030 in which 20% of the electricity originates solely 
 from wind power . to reach these targets , 
 more wind parks as well as larger turbines are built . with an 
 increased cumulative production of wind turbines , manufacturers 
 gain experience with the technology , which is commonly reflected in 
 a reduction of the investment costs .
the factors responsible for the 
 cost reduction can be grouped in size and learning effects .
size effects are described in the form of a power 
 law and are commonly developed to estimate properties at size x when no measurements or data are available .
cost scaling laws estimate the costs of bigger or smaller 
 equipment based on the costs of a known equipment size,1where c2 is the 
 investment cost of unknown equipment ; c1 is the investment cost of known equipment ; x2 is the capacity of unknown equipment ; x1 is the capacity of known equipment and b is the scaling factor .
commonly cost scaling factors between 0.5 
 and 1 are applied , however a scaling factor of 0.6 is recommended 
 if no data is available , meaning that a 1% size increase , results 
 in a 0.6% cost increase .
scaling factors between 
 0.5 and 1 have also been found for the environmental impacts from 
 the production phase of energy conversion equipment . besides 
pure  size effects , another 
 effect causing 
 production costs to reduce was identified by wright , who observed 
 that labor costs in airplane manufacturing decreased at a constant 
 percentage with every doubling of the cumulative output .
an approach quantifying both these mechanisms 
 together , scaling 
 and learning , is the experience curve concept , which estimates the 
 investment costs at a certain cumulative installed capacity , without 
 having detailed product specifications or cost indications . combining scaling and learning
few studies have tried to 
 disentangle scaling from learning , relevant examples come from photovoltaic 
 technologies .
experience curves are commonly 
 derived from empirical studies and widely applied in different energy 
 sectors .
a study for wind energy showed that due to the global cumulative 
 experience the investment costs of a wind farm display a progress 
 rate of 81% , meaning that costs decrease by 19% each time the cumulative 
 production doubles . an experience curve 
 is a function of the cumulative production and if plotted on a log 
the formula used is2where ccum is 
 the cost per unit ; c0 the cost of the 
 first produced unit ; cum the cumulative production ; z is the experience index . the progress 
 rate ( pr ) describes the rate at which the costs reduce with every 
 doubling of the production,3 usually b is used 
 as a notation to describe the 
 experience factor , however to avoid confusion with the scaling factor b , we use z to describe the experience 
 factor in this paper . wind parks or turbines
must not only be 
 financially competitive , 
 but also environmentally beneficial compared to other energy sources . 

a method commonly used to quantify the environmental performance of 
 energy systems is life cycle assessment ( lca ) .
knowledge about environmental 
 experience curves can be advantageous for comparative lca studies , 
 specifically when technologies in different developmental stages are 
 compared .
an unedited comparison of these technologies does not take 
 into account that the younger technology will still improve and grow , 
 while the established technology will have reached its maximum learning 
 and size .
such direct comparisons however , without incorporating experience 
 effects have been made previously in the lca literature . in the past decade , the importance of incorporating experience curves 
 in lca has been emphasized .
the 
 aim of this study is to quantify the environmental size and 
 learning effects for wind energy .
we first give an overview of onshore 
 wind energy systems and perform an analysis on a  micro level  , 
 examining the theoretical size effect expected from engineering - based 
 modeling as well as the observed size effect for turbine 
 components and environmental impacts ( the latter called empirical 
 modeling in this paper ) .
to derive empirical scaling factors , 
 life cycle inventories of 12 different wind turbine systems were harmonized 
 using the same modeling principles . combining the engineering - based 
 modeling with the empirical modeling allows disentangling pure turbine 
 size effects from learning effects on lca results of the produced 
 electricity from land - based wind energy technologies .
the second part 
 of the study quantifies environmental experience effects over time 
 and progress rates of the wind power industry as a whole in europe , 
 defined as the macro level .
a wind energy turbine consists of several components , such as the 
 rotor , nacelle , tower , foundation and electrical cables ( see figure 1 ) .
the captured kinetic wind power depends on the 
 air density  , 
 swept area of the rotor a and the wind speed v at hub height h. average wind speed at hub height depends on height ( h ) , swept area a , and wind shear n. as a general rule a vertical wind shear gradient described by the 
 hellman exponent of 1/7 is applied , which is at the lower range of 
 wind shear gradients reported in the literature ( between 0.15 and 
 0.25 for onshore regions ) .
all calculations in 
 this paper were based on a low wind speed site with an annual wind 
 speed of 5 m / s ( v1 ) at 10 m height ( h1 ) and a wind shear gradient n of 1/7 .
a generator in the nacelle converts the captured energy 
 into electric energy with a reported average mechanical - electrical 
 efficiency ( generator ) of 94% .
losses due to rotor blade soiling ( 12% ) , wind hysteresis 
 ( 1% ) and losses for the grid connection ( 13% ) were assumed 
 to amount to 5% ( losses ) .
an average turbine load of 8,760 h per year was assumed . to calculate 
 the captured wind power as well as other parameters of a wind turbine 
 system , several equations and constants were applied throughout this 
 paper and
air = 1.2 kg / m , v1 : wind speed at ground ; generator=94% , losses = 95% .
substituting swept area a and average 
 wind speed 
 at hub height ( v2 ) in eq 6 with eqs 4 
 and 5 ( table 1 ) , we derive p  dh .
classical scaling implies that m  v  l , stating that mass m scales directly with volume v which scales cubic with length l. hence , 
 size scaling laws for the mass of the rotor , nacelle , tower and foundation 
 are cubic , either with m  d or m  dh for the tower .
the tower design ( and hence 
 mass ) is based on the base moment and thrust from the rotors .
all other factors , such material innovations 
 and fatigue loads are not considered in these scaling laws .
the cables 
 and electronics inside the tower were assumed to scale with tower 
 height h. the cables from the tower to the electricity 
 grid were assumed independent from the turbine .
the environmental 
 impact categories used in this study were mainly 
 driven by the mass of the used materials , except for land use , hence 
 a relation of eiproduction  mcomponents was assumed for modeling the environmental 
 impact ( ei ) since material production and processing as well as the 
 transport were directly related to the mass .
the use phase is primarily 
 dominated by lubricating oil consumption and the diesel for the transport 
 of the lubricating oil to the wind tower location .
the transport 
 is directly linked to the amount of oil ; therefore the overall scaling 
 for the use phase is expected to scale with eiuse  p  dh . the expected scaling factor for the disposal 
 of the whole wind system
is again linked to the mass m of the individual components ; hence the factor is expected to be 
 eidisposal  mcomponents .
d : rotor diameter 
 ( m ) ; h : hub height ( m ) ; m : mass 
 ( kg ) ; v : volume ( m ) ; ei : environmental 
 impact .
several lca studies on wind turbines 
 have been published , including scientific papers , reports , and databases .
only publications which include life cycle inventories ( lci ) of 
 the turbine ( nacelle , rotor and tower ) as well as the foundation were 
 included in the current analysis .
a total of 12 turbine systems from 
 eight different sources ranging in power from 30 kw to 3 mw were included 
 ( see table 3 ) .
the lcis of the 12 turbine systems 
 were not based on or extrapolated from each other .
other studies which 
 did not include detailed lci data or were only specifically for one 
 element of a wind turbine were not included .
this paper includes two- and three - bladed 
 onshore wind turbines , which feed electricity into the national grid . 

the production year was not mentioned in all 12 cases , two data points 
 were therefore left out of the estimation of the environmental progress 
 rate . to make the impact assessments results 
 comparable
the harmonized system boundaries included 
 the following the life cycle phases : resource extraction , material 
 manufacturing and processing , production of the elements , transport 
 to the erection site , turbine maintenance and disposal . due to lack 
 of data ,
the assembly of the turbine and the energy for decommissioning 
 of the turbine were not included .
all major elements of the turbine 
 system were included , and these were the nacelle , rotor , turbine , 
 foundation , cables inside the turbine , cables to the grid , and the 
 electronic control box .
not included were roads to the turbine , cables 
 trenches and specific infrastructure for the operation of entire wind 
 parks , such as a main transformer room .
an important factor in the 
 life cycle of a wind turbine is the amount of electricity produced . 

the energy production however depends on many factors , such as wind 
 conditions , exposure and location .
the power output mentioned in the 
 original publications was not used in this analysis since it could 
 not be guaranteed that the conditions were similar in all publications . 

the power output was therefore recalculated under identical conditions , 
 assuming a wind speed of 5 m / s at 10 m height and a wind shear gradient 
 of 1/7 .
the equations and constants listed in table 1 were used and the results are given in the last two columns 
 of table 3 .
the maximum calculated captured 
 power at the rotors , pcaptured , max , was 
 calculated using eqs 47 .
note that the calculated power is 
 lower than the rated power , since the rated power refers to the maximum 
 power output at which a turbine can operate .
the calculated power 
 however refers to the previously defined site conditions ( v = 5 m / s at 10 m height ) , which is a conservative standard 
 site .
the calculated power production per year , pcal was calculated using eqs 8 and 9 .
power output calculated 
 for standard 
 site with wind speed of 5 m / s at 10 m height and a wind shear gradient 
 of 1/7 .
the major adjustments wereall metal and plastic production 
 processes were complemented with the corresponding metal and plastic 
 processing steps .
aluminum , 
 production mix  was complemented with  sheet rolling , 
 aluminum  using the same material amount.transport distances of the raw 
 materials to the production plant were modeled as 100 km lorry ( > 32 
 tonnes and according to the european emission standard euro 4 ) and 
 200 km freight train .
distances from the production plant to the erection 
 site were modeled as 100 km lorry ( > 32 t , euro 4 ) and 800 km freight 
 train .
it was assumed that 
 the materials were provided by a local producer ; hence 50 km lorry 
 ( > 32 t , euro 4 ) for concrete , 100 km lorry ( > 32 t , euro 4 ) for 
 plastics , 
 steel and iron as well as 200 km freight train for plastics , steel 
 and iron was assumed.many publications did not specify 
 whether the material was virgin or recycled material , also iron and 
 steel were occasionally not further specified ; hence material assumptions 
 were made based on the inventories that did specify the material in 
 more detail .
for instance , aluminum was included as a mix of primary 
 and secondary aluminum according to their share on worldwide production 
 and the steel used in the rotors was included as chromium steel 18/8.in two publications 
 the category 
  others  appeared . in the study by schleisner ,
this 
 involved 700 kg , which corresponds to 1.2% of the total turbine mass . in the study by martinez et al ,
0.2% of the 
 total turbine mass was declared as others . due to the low relative share ,
these amounts were left out in the 
 harmonized inventory.the electronic control units 
 as well as the electric cables were not included in all studies .
the 
 electronics box was considered independent of turbine size and modeled 
 according to martinez et al .
the electronic 
 cables were divided into cables running from the hub to the tower 
 base and from the tower base to the grid .
the first set of cables 
 depended directly on hub height , and the inventories were parametrized 
 according to hub height .
the second set was considered size independent 
 as the distance to the grid was assumed 1000 m for all cases.the published studies 
 used different 
 sources for unit process data .
the harmonized inventories all revert 
 to unit process data available in the ecoinvent database version 2.01 .
all metal and plastic production 
 processes were complemented with the corresponding metal and plastic 
 processing steps .
for instance , the production process  aluminum , 
 production mix  was complemented with 
transport distances of the raw 
 materials to the production plant were modeled as 100 km lorry ( > 32 
 tonnes and according to the european emission standard euro 4 ) and 
 200 km freight train .
distances from the production plant to the erection 
 site were modeled as 100 km lorry ( > 32 t , euro 4 ) and 800 km freight 
 train .
it was assumed that 
 the materials were provided by a local producer ; hence 50 km lorry 
 ( > 32 t , euro 4 ) for concrete , 100 km lorry ( > 32 t , euro 4 ) for 
 plastics , 
 steel and iron as well as 200 km freight train for plastics , steel 
 and iron was assumed .
many publications did not specify 
 whether the material was virgin or recycled material , also iron and 
 steel were occasionally not further specified ; hence material assumptions 
 were made based on the inventories that did specify the material in 
 more detail .
for instance , aluminum was included as a mix of primary 
 and secondary aluminum according to their share on worldwide production 
 and the steel used in the rotors was included as chromium steel 18/8 . in two publications 
 the category 
  others  appeared . in the study by schleisner ,
this 
 involved 700 kg , which corresponds to 1.2% of the total turbine mass . in the study by martinez et al ,
0.2% of the 
 total turbine mass was declared as others . due to the low relative share ,
the electronic control units 
 as well as the electric cables were not included in all studies .
the 
 electronics box was considered independent of turbine size and modeled 
 according to martinez et al .
the electronic 
 cables were divided into cables running from the hub to the tower 
 base and from the tower base to the grid .
the first set of cables 
 depended directly on hub height , and the inventories were parametrized 
 according to hub height .
the second set was considered size independent 
 as the distance to the grid was assumed 1000 m for all cases .
the harmonized inventories all revert 
 to unit process data available in the ecoinvent database version 2.01 .
a scaling law relating 
 size to environmental impact ( ei ) was derived from classical size 
 scaling .
equation 1 was adapted by replacing 
 costs by environmental impacts and is described as10where ei2 is the environmental 
 impact of equipment 2 ; ei1 is the 
 environmental impact of equipment 1 ; x2 is the capacity factor of equipment 2 ; x1 is the capacity 
 factor of equipment 1 and be is the 
 environmental scaling factor .
the 
 difference between the empirical and engineering - based model was interpreted 
 as the learning effect .
technological learning takes place by the 
 use of other materials like fiber - reinforced blades or , for instance , 
 by optimizing the blade design to capture more kinetic wind energy . 
 to model the environmental experience curve and the environmental 
 progress rate ( epr ) , eqs 2 and 3 were modified
the cost factors were substituted with environmental 
 indicators ( ei ) , resulting in1112where eicum is an environmental 
 indicator , such as global warming potential per unit after cumulative 
 units have been produced ; ei0 environmental indicator of 
 the first produced unit and ze is the 
 environmental experience index .
the reported production year of the 
 turbines mentioned in the lca studies was linked with the cumulative 
 wind power production in europe within that year .
this step enabled plotting the environmental impact from 
 each turbine to the cumulative production in europe and hence the 
 environmental progress rate was calculated according to eqs 11 and 12 .
the prevented environmental 
 impact ( gwp / kwh ) due to learning was calculated as the difference 
 between the environmental impact from the engineering - based modeling 
 and the empirical modeling . to enable and perform ordinary 
 least - squares ( ols ) linear regression ,
regression analysis was performed with the 
 statistical package for the social sciences ( spss ) statistical software 
 for windows , version 16.0 ( spss , il ) .
the applied power law was13 scaling factors were presented as b and the intercept as log a , with a 95% 
 confidence interval ( 95% ci ) .
we also reported the residual standard 
 error ( se ) and the pearson correlation coefficient ( r ) of the regression .
the mass of wind turbine components scaled with rotor diameter d with scaling factors ( b ) between 1.58 
 and 2.22 ( table 4 ) and showed high correlations 
 ( r = 0.84  0.97 ) .
the mass of 
 the cables and electronics however , scaled with rotor diameter d with a scaling factor of 0.22 ( figure 2a and table 4 ) .
the overall mass of 
 the turbine system scaled with rotor diameter d with b = 1.75 ( ci : 1.53  2.01 , r = 0.96 ) and with dh with b = 0.76 ( ci : 0.67  
 0.87 , r = 0.97 ) , see figure 2b
. a. mass m ( kg ) of turbine components and total 
 mass versus rotor diameter d ( m ) .
b. total mass m ( kg ) versus dh c. global warming potential per produced kwh ( kg co2-eq./kwh ) versus dh .
d. global warming potential per rotor ( kg co2-eq./rotor ) versus rotor diameter d ( m ) , 
 the dashed line presents the expected pure size scaling according 
 to the engineering - based model , the solid line presents the empirical 
 scaling line . note that the scaling factors for 
 the mass of the rotor , nacelle , tower and foundation were given as d , whereas in table 2 the engineering - based scaling laws were given as d. this representation was chosen to state more clearly 
 the difference between the engineering - based scaling factor of 3 and 
 the empirical scaling factor of below 3 .
the global warming potential of the produced electricity 
 varied 
 between 9.5 and 29.7 g co2-eq / kwh .
the main contributors 
 were the tower ( 2739% ) and the nacelle production ( 1237% ) . 

the scaling factors b for all the impact categories 
 per kwh varied between 0.55 and 0.22 , with an exception 
 of 0.87 for urban land occupation ( table 5 and supporting information ) .
figure 2d shows the engineering - based size 
 modeling for the relationship environmental impact of the rotor versus 
 rotor diameter , according to table 2 as well 
 as the empirical model for that relationship . to display the difference 
 between the engineering - based model and the empirical results , a reference 
 turbine model was arbitrarily chosen with a rotor diameter of 12.5 
 m , which corresponds to the oldest wind turbine included in this study . 

the result was a lower environmental impact for the empirical model 
 with increasing rotor size than for the engineering - based model . according 
 to the engineering - based modeling ( table 2 ) , 
 the environmental impact scales according to a cubic law with rotor 
 diameter , d. the empirical scaling factor 
 found was 1.79 , hence learning on a micro level took place with a 
 value of ( d  d ) .
95% ci : 95% confidence interval ; r : coefficient of determination ; se : standard 
 error ; n : number of observations . both , the calculated power output p and the rotor diameter d increased strongly over 
 the years ( figure 3a . )
. the global warming 
 potential ( gwp ) per produced kwh electricity continuously decreased 
 over the analyzed time period ( figure 3b ) . 
 after linking the construction year with the total european wind turbine 
 production ,
the environmental experience curve was described 
 by gwp / kwh = 0.11 cum , which corresponds to 
 an environmental progress rate ( epr ) of 86% , indicating that with 
 every doubling of the cumulative production the gwp / kwh was reduced 
 by 14% .
the epr varied per impact category between 69% and 86% , with 
 the exception of 57% for land occupation ( see supporting information ) .
a. calculated power output p versus erection year 
 on the left axis ( black squares ) and rotor diameter d on the right axis ( gray diamonds ) .
b. global warming potential ( gwp ) 
 per produced kwh energy versus erection year . c. empirical environmental 
 experience curve for global warming potential gwp per kwh produced 
 electricity versus the european cumulative production ( mw ) .
d. prevented 
 environmental impact ( gwp / kwh ) versus the european cumulative production 
 ( mw ) compared to the engineering - based model .
the environmental experience curve and the corresponding 
 epr encompass 
 both pure size scaling effects as well as learning effects due to , 
 for instance , learning - by - doing and technological learning . as can 
 be seen from table 4 , the empirical factors 
 for the mass of the nacelle , rotor and tower are all clearly below 
 cubic with values around 2 . from this information , the saved gwp / kwh 
 produced electricity per cumulative production was calculated .
figure 3d shows that with increasing experience in the form 
 of cumulative production , the amount of prevented kg co2-eq./kwh could be continuously increased compared to the engineering - based 
 scaling model , indicating that the more was learned about the technology , 
 the more gwp per produced kwh electricity could be saved .
the results showed that the bigger the wind 
 turbine is , the greener 
 the produced electricity is .
two effects contributed to this result , 
 namely pure size scaling as well as learning about the technology 
 over time , allowing through experience and innovation that the turbines 
 can be built bigger in the first place .
the empirical scaling factors 
 found in this study were in agreement with values reported in literature . 

the empirical scaling factors for the relationship rotor mass versus 
 rotor diameter were reported between 1.9 and 2.9 by various authors , 
 where low values correspond to advanced rotor technology and the higher 
 values to older technologies .
empirical scaling factors of the relationship nacelle mass versus 
 rotor diameter were reported between 1.91 and 1.95 .
the mass of the turbine , without the foundation and grid 
 connection was reported as m  d. the foundation was reported 
 to scale empirically with mfoundation  hd while 
 our values scaled with mfoundation  
 ( hd ) .
the impact assessment 
 results obtained after harmonization were in accordance with other 
 published emission values , such as a review study by kubiszewski , 
 who reported co2 emissions within a wide range of 2134 
 g co2/kwh .
the parameters 
 hub height h and rotor diameter d are easy to obtain , hence the found scaling laws can be applied 
 directly to estimate the environmental impacts if these two parameters 
 are given . as explained in the introduction , scaling is commonly used to estimate parameters when only few data 
 is available .
this approach can therefore be very useful for screening 
 lca studies , where only limited data or time to perform a lca study 
 is available .
therefore hub height h and rotor diameter d could be defined as environmental key performance indicators 
 for onshore wind energy technologies .
the experience curve 
 showed the reduction of environmental impact per cumulative wind turbine 
 production in europe .
this curve can be extrapolated into the near 
 future under the assumptions that no major technological developments 
 or market changes take place which influence the experience curve 
 drastically , hence the use of the experience curve concept and epr 
 for long - term forecasting purposes is limited .
it can be applied for 
 short - term extrapolation of the same turbine technology , if the limitations 
 of the experience curve are clearly communicated . in the case of a 
 large technological innovation , the experience curve shifts down by 
 a step function to subsequently resume on a lower level .
in addition , future environmental impacts , for 
 instance caused by changes in the supply chain of scarce metals , are 
 not covered by the epr and might cause a change in impact in the future , 
 not foreseen by the empirical experience curve .
due to the harmonization 
 of the inventories , the scaling factors in this paper are only valid 
 for a generic location and wind regime .
however , based on the equations 
 in table 1 , this can be adapted for other locations 
 with different wind shear factors and wind speeds .
for instance , if 
 the wind shear factor is 1/4 instead of the used 1/7 , the relation 
 global warming potential per kwh ranges from 5.4 to 23 g co2-eq./kwh instead of 9.5 to 29.7 g co2-eq./kwh , since more 
 wind energy can be captured at higher wind shear factors and hub heights . the environmental progress rate for gwp / kwh 
 drops to 81% , hence the scaling effect is more pronounced since wind 
 speed scales according to a cubic relation with power p. increasing the wind speed from 5 m / s , as assumed in our study , 
 to 15 m / s ( v1 ) ,
the output power was increased 
 by a factor of 27 , according to eq 5 ( v1 ) , but there was no effect on the scaling factors , only 
 on the intercept ( see supporting information , table s12 and s13 ) . in the calculations ,
however , based on previous work , it can be assumed 
 that efficiency may improve with size according to a power law . to analyze the sensitivity of this assumption , 
 a rough efficiency scaling law was established and the deviation of 
 the calculated power output between the scaled and nonscaled efficiencies 
 was calculated , resulting in a maximum deviation of 2.9% ( see supporting information , table s14 ) .
besides 
 the generator efficiency , the overall turbine performance 
 was also assumed constant at an efficiency of 53% , which resembles 
 a best - case scenario .
turbine performances have been reported to be 
 3540% in the early 1980s increasing to 48% mid-1990s . to analyze the sensitivity ,
the efficiency of 
 all turbines produced before 2000 was set to 48% , the modern turbine 
 efficiency remained 53% .
the power output of the older turbines decreased 
 by 9% , resulting in an environmental progress rate ( epr ) of 84% , indicating 
 that with every doubling of the cumulative production the gwp / kwh 
 was reduced by 16% instead of 14% .
both the single 
 score results per kwh produced electricity from impact 2002 + as well 
 as the nonrenewable cumulative energy demand ( ced ) per kwh produced 
 electricity were within the expected range of 0.55 and 0.22 
 ( see supporting information , table s15 ) .
 , the original studies omitted processes and materials , which 
 were described as  others  in the used publications . 

hence , the omission of these materials might underestimate the impacts , 
 in particular concerning impact categories such as resource consumption 
 or toxicity .
the boundaries of the study were set by a single 
 wind turbine and 
 not a wind park .
as turbines get larger , they need to be spaced further 
 apart and hence occupy more land .
the land use impact results in this 
 study are therefore limited to stand - alone wind turbines only .
because of the recalculation of the power output to a generic turbine 
 location , it has to be mentioned that a simplification took place 
 and the wind turbines might not be designed optimally for this  new  
 location , hence an over- or underestimation of the masses and respective 
 impacts occur , resulting in larger spread in the data . if empirical 
 laws are not available from literature or measurements , 
 the sole use of engineering - based scaling laws quantifies an upper 
 boundary for the size scaling factors .
therefore , it might be possible 
 to derive scaling relationships and upper boundaries in a similar 
 way for other technologies as well .
this paper presented how 
 size scaling relationships , environmental 
 experience curves and epr can be established and used for lca purposes . 

further studies are necessary to investigate the robustness of the 
 established relationships . in this sense
, it is recommended that due 
 to the effects of modeling assumptions such as turbine location , wind 
 shear and wind speeds on the lca results , they should be expressed 
 in a transparent way in lca reports .
furthermore , it is recommended 
 to clearly state the year of wind turbine production or installation 
 for which the data is valid .
though recommended by the iso standard 
 on life cycle assessment , this information is often lacking in lca 
 studies .
only with such a clear statement can reliable environmental 
 experience curves be established in the future .
 wind energy is a fast - growing and promising renewable 
 energy source . 
 
 the investment costs of wind turbines have decreased over the years , 
 making wind energy economically competitive to conventionally produced 
 electricity . 
 size scaling in the form of a power law , experience curves 
 and progress rates are used to estimate the cost development of ever - larger 
 turbines . in life cycle 
 assessment , scaling and progress rates are 
 seldom applied to estimate the environmental impacts of wind energy . 
 
 this study quantifies whether the trend toward larger turbines affects 
 the environmental profile of the generated electricity . 
 previously 
 published life cycle inventories were combined with an engineering - based 
 scaling approach as well as european wind power statistics . 
 the results 
 showed that the larger the turbine is , the greener the electricity 
 becomes . 
 this effect was caused by pure size effects of the turbine 
 ( micro level ) as well as learning and experience with the technology 
 over time ( macro level ) . 
 the environmental progress rate was 86% , 
 indicating that for every cumulative production doubling , the global 
 warming potential per kwh was reduced by 14% . 
 the parameters , hub 
 height and rotor diameter were identified as environmental key performance 
 indicators that can be used to estimate the environmental impacts 
 for a generic turbine . 
Introduction
Materials and Methods
Results
Discussion