Data for PV market development and environmental footprint of PV production

For the study conducted here we needed several different sources of data: 1) historical data on the development of cumulative installed PV capacity (CIPC) including PV technology market shares, 2) PV cost data for the period under investigation, 3) forecast of the development of CIPC and PV technology market shares, 4) life-cycle assessment (LCA) results for PV for the period studied.

Historical data for CIPC was obtained mainly from the IEA PVPS reports4,31,32, reports from SolarPowerEurope (formerly EPIA)1,33 and other literature data16,25,26. Cost data was taken from16,25,26,34,35; all cost data in this paper is corrected for inflation by means of the Consumer Price Index36 and expressed in 2015 USD. Data for PV technology market shares was taken from16. Environmental impact data was obtained from LCA studies conducted between 1976 and 2014, shown in the Supplementary Information in Supplementary Tables 1–5. Data was filtered only to exclude ‘worst-case’ or ‘best-case’ scenarios, prospective studies, and studies that did not include results for complete PV systems (see also Supplementary Methods).

For the studies on the energy payback time and greenhouse gas footprint of PV module production, it is sometimes difficult to ascertain in retrospect whether the studies were performed using a consistent method, especially for the older studies selected here. Other meta-reviews of PV LCA’s employ a stringent screening process eliminating most of the studies available30,37. As we are interested in development of environmental footprint over time, a similar procedure would exclude most of the studies conducted before 2000. Therefore, we have adopted a simpler screening process: the LCA studies should report CED and/or GHG emissions for a complete PV system with enough meta-information to convert the reported units to our harmonised units (see section), and should analyse existing production processes (not prospective, worst or best case processes).

Harmonization of environmental footprint data

In the timeframe we are analysing, the environmental footprint of PV has been studied many times. The earliest study in our analysis dates from 1976, although most studies are from after 2000. The approach with which the environmental footprint of PV was determined has been steadily improving over the years. Only in 2009, standardised methodology guidelines specifically for PV systems were published38 as a result of an IEA PVPS project specifically focusing on the environmental impact of PV (Task 12). These guidelines were updated in 201121, although the practice of performing a Life Cycle Assessment (LCA) was standardised first in 1997.

For CED, we investigate megajoules of primary energy per watt-peak of PV system capacity (MJP ). For GHG emissions, we analyse the GHG emission associated with the production of a watt-peak of PV system capacity (gCO 2 -eq ) but often report emissions per kWh of generated electricity as well (gCO 2 -eq kWh−1), as this is the unit most commonly used to express the GHG impact of PV. Where needed, conversion to the desired units was performed using harmonisation criteria based on LCA guidelines from21: a conversion factor from primary energy to electricity of 0.311; an insolation of 1,700 kWh·m−2·year; a performance ratio of 0.75; a module degradation rate of 0.7% per year; and the reported system capacity (Wp). The performance ratio PR is defined as39:

where Y f is the final energy yield of a PV system per unit of capacity, and Y r is the reference yield per the same unit of capacity. Y r is calculated as H POA /G STC , where H POA is the plane of array irradiance, and G STC the irradiance at which PV system capacity is determined (STC conditions). Thus, the annual energy yield is given by:

where C PV is the system capacity. The ratio H POA /G STC gives us a figure that represents equivalent annual full load hours, and thus this calculation is not dependent on the efficiency of the PV systems investigated, but only the considered system capacity. The lifetime energy yield, corrected for the assumed (linear) degradation of performance is calculated as:

Experience curve

The production costs of PV decrease with increased cumulative production, based on the theory of technological learning. The relation between cost and cumulative units of production is described by the experience curve40:

where C n is the cost of the n-th unit of production, C 1 is the cost of the first unit of production, n is the cumulative production volume and a is the ‘experience parameter’17. The ‘experience parameter’ describes the decrease in cost as a function of increased cumulative production. In the context of experience curves, there is often mention of the ‘learning rate’, which is the cost decrease for a doubling of cumulative production. This ‘learning rate’ can be obtained by rewriting equation 4:

where l is the ‘learning rate’. The logarithm (base 2) in the exponent shows us that for each doubling of production volume, the cost of produced units decrease with a factor l. In this paper, we use this relation to establish the learning rate for PV price, CED, EPBT and LCGHG emissions, by performing orthogonal distance regression analysis of the environmental impact data to this non-linear model, using the open source Python library ‘SciPy’ (http://www.scipy.org). Production volume for photovoltaics is most accurately reported in terms of cumulative installed PV capacity in watt-peak (W p ), so we will use this metric instead of cumulative number of produced units (cells, modules, systems). We have used the ‘Delta method’ to calculate confidence intervals for the fitted models41.

As discussed in17, the relationship between price and cumulative production is indirect (while that between production cost and cumulative production is direct), as market dynamics can influence the margin between cost and price. Only in a stable market phase does the price-experience curve have the same slope as a cost-experience curve17. However, as only price data is available for the period under study, we focus on the price-experience curve.

Production location

Both the production and installation location of PV influence its environmental impact. The production location mainly because the environmental impact of the electricity used in production is very locationally dependent, and as a result, production of PV in China has, for example, almost twice the GHG footprint compared to production in Europe7,42,43. For CED the difference is smaller but still significant. For installation, the environmental benefit of PV is larger where the environmental footprint of local electricity is greater, as it is assumed that the production of electricity from PV replaces electricity from fossil sources.

In our analysis, we have investigated the effect of production location in three scenarios: first, production in Europe; secondly, globally dispersed production, based on actual production location data from16; and thirdly, production in China.

To account for production location, we have combined data on the development of main PV production regions with the development of environmental impact of electricity production in those locations. For CED and GHG, we calculated a correction factor which is the average of the relative CED or GHG-footprint of electricity in each location, weighted by the share of production in each location. As the production has shifted from the US, to Europe and more recently to Asia (mainly China), this factor was calculated for each year. We also accounted for the development of the GHG-footprint of electricity over time, based on data from the UN28 and the World Resources Institute (http://cait.wri.org). No data was found suitable to include the development of CED of electricity over time, so the relative CED for each location was assumed constant over time and was based on44. For production in China, the factor is based on only the relative CED and GHG-footprint of Chinese electricity (over time). For production in Europe, the factors are set to 1, as the results from the environmental impact studies are mostly based on production in Europe.

Projections and net contribution

From the data we have analysed we have established fitted models of development of CED, life cycle GHG emissions as a function of CIPC and their development over time. These models combined were used to calculate the total CED of PV production by integrating the learning curve. For instance, the environmental impact of production of a unit of PV in a certain year is given by:

where EI 1;t is the environmental impact of the first unit of production of technology t (see also C 1 in equation 4), C t is the cumulative installed PV capacity of technology t in year y, and l t is the learning rate of that technology. For each year, the environmental impact is calculated for mono- and polycrystalline silicon-based PV systems. To calculate the total annual environmental impact from PV we extrapolate the results for mono- and polycrystalline PV to total installed capacity, and for production location:

where pC t,i (y) is the PV system capacity of technology t produced in country i in year y, and f EI;t,i (y) is a factor relating the environmental impact of production of PV in location i to the baseline results obtained from the learning curve, and is calculated as:

where EI elec;t;i and EI elec;t;base are the environmental impact of electricity production in country i and the baseline scenario, respectively, and f elec;t;PV is the fraction of environmental impact related to electricity use in production, taken from45. For GHG emissions EI elec is calculated from databases from the UN28 and the World Resources Institute (http://cait.wri.org), for CED historic data was not available, and we assumed a constant factor between countries based on data from the ecoinvent database44. Thus, we account for the effect of production location on environmental impact by varying the impact of electricity production. We assume here that direct electricity use in the lifecycle of PV production, from silicon to PV system, is changed from the baseline to the country average.

The cumulative net environmental impact of PV is then calculated, for CED and GHG separately as:

where EI avoided (y) is calculated based on installed capacity shares from2,46,47 and given by:

where PR is the Performance Ratio, H i is the population weighted plane-of-array insolation in country i, G STC is the standard testing condition irradiance (1,000 W m−2), and C active;i is the active installed PV capacity in country i. See also equations (1 and 2). The active capacity was calculated by correcting the cumulative installed capacity figures with an assumed degradation rate of 0.7% per year and a lifetime of 25 years.

The PR is an important metric relating the actual yield of a PV system to the theoretical yield calculated with just the annual insolation and the systems’ rated (peak) power. It takes into account loss factors like higher operating temperatures, inverter and cabling losses, and other losses such as due to soiling, periods of outages, and suboptimal orientation. There likely is a trend of PR versus time, as increasing knowledge about and monitoring of PV performance, as well as improved system design and inverter efficiencies have led to a increase in system yields, as shown in39,48. Accurate data on the actual performance of CIPC is however practically non-existent. There are in general two approaches in determining the actual PR of CIPC figure: a top-down analysis combining installed capacity figures with electricity production figures for PV installations, and a bottom-up, detailed analysis of PV performance using dedicated test facilities or a limited number of PV systems. Data for the former approach is readily available, but lacks in geographical and temporal scope. For instance, statistics from both the U.S. Energy Information Administration (EIA, http://www.eia.gov/beta/international/browser/) and the UN statistics database28 are insufficient to result in timeseries, from 1975 to now, of country-specific PR values, or other metrics that allow us to calculate historical energy generation from CIPC. The data goes back only to 1990, and for most countries, data is only available from 2010 onwards. Furthermore, especially for the older data, accuracy seems to be very low as unrealistic yield figures are obtained especially from the UN database (see also the Discussion section). Bottom-up studies result in very reasonable PR figures (around 75–85%) but their scope is even more limited (in terms of period and geography). More recently, due to improved output monitoring of PV systems (e.g., by embedded monitoring solutions in PV inverters) the amount of available data has increased and studies are published on the performance of large numbers of privately owned PV systems49,50. Unfortunately, the results from these studies are also (still) very limited in geographical but especially temporal scope, due to unavailability of older data. As PR is a very significant factor in determining annual yields, we have analysed two separate scenarios for the development of PR over time, which we consider represent a worst-case and more-likely scenario:

· A constant low-PR scenario with a PR of 0.5 based on the lowest estimate mentioned in literature referring to general PV performance.

· A increasing PR scenario, with PR increasing linearly with respect to time from 0.5 in 1975 to a maximum of 0.8 for the years 2015 and later, for all countries.

Aside from a trend in time for PR, there is also a variation of PR per location, as ambient temperature, but also spectral variations have effect on the performance of PV systems. Statistics on the actual performance of systems in all countries analysed, for the whole period studied, are however much too limited or inaccurate. Therefore we have assumed an equal PR for all locations.

Insolation data (H i ) was taken from51. We opted to use data that gives population-density weighted country insolation for surfaces with a fixed tilt and optimal orientation.We thus assume most systems to be installed in or near population centra, as is common at least in large parts of Europe. For some locations this might not be accurate. For instance, in the USA, large PV installations are built at locations in the South-West of the country, while population centres can be more confined to lower irradiance area’s like the ‘Boston-Washington Corridor’, which has a population of almost 50 million, or 15.4% of the total US population.

Monte Carlo analysis

The parameters of the nonlinear models fitted to the data (EI t and l t in equation (6)) have a certain standard error as a result of deviation of the data from the fitted model, which are established by the fitting tool by determining the covariance of the fit parameters. We use Monte Carlo simulation to analyse the effect of these fit errors on our results. For each calculation, we generated 10,000 random samples of the parameters from a normal distribution for which the mean is the fitted parameter value and the standard deviation is the calculated error in the parameter. We then recalculated the results using these samples, and present the intervals that cover the range from 2.5th percentile to the 97.5th percentile of the results, e.g., a 95% confidence interval around the main result.

Data availability

The authors declare that all the data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding authors upon reasonable request.