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 parameters hub heightand rotor diameterare 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 heightand rotor diametercould be defined as Environmental Key Performance Indicators for onshore wind energy technologies.

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. (24, 39-42) Empirical scaling factors of the relationship nacelle mass versus rotor diameter were reported between 1.91 and 1.95. (43) The mass of the turbine, without the foundation and grid connection was reported as (24) The foundation was reported to scale empirically withwhile our values scaled with∝ ( (40) The impact assessment results obtained after harmonization were in accordance with other published emission values, such as a review study by Kubiszewski, who reported COemissions within a wide range of 2–134 g CO/kWh. (44)

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. (45) 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.

Sensitivities and Limitations

2 -eq./kWh instead of 9.5 to 29.7 g CO 2 -eq./kWh, since more wind energy can be captured at higher wind shear factors and hub heights.P. Increasing the wind speed from 5 m/s, as assumed in our study, to 15 m/s (v 1 ), the output power was increased by a factor of 27, according to eq 5 (v 1 3), but there was no effect on the scaling factors, only on the intercept (see 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 CO-eq./kWh instead of 9.5 to 29.7 g CO-eq./kWh, since more wind energy can be captured at higher wind shear factors and hub heights. (23) 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. Increasing the wind speed from 5 m/s, as assumed in our study, to 15 m/s (), the output power was increased by a factor of 27, according to eq 5 (), but there was no effect on the scaling factors, only on the intercept (see Supporting Information , Table S12 and S13).

In the calculations, the generator efficiency was assumed constant. However, based on previous work, it can be assumed that efficiency may improve with size according to a power law. (46) 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 35–40% in the early 1980s increasing to 48% mid-1990s. (47) 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%.

b in Table The scaling factorsin Table 5 were evaluated against other impact methods. 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).

As mentioned in section “ LCI Harmonization ”, the original studies omitted processes and materials, which were described as “others” in the used publications. These omissions might include scarce metals or hazardous chemicals. 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 suggestion, however, remains to be explored in further studies.

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.