BLUE Map 2050
BLUE High Ren 2050
Other Solar Wind
Biomass + CCS Biomass and waste Hydro Nuclear
Natural gas + CCS Natural gas Oil
Coal + CCS Coal
These cost reductions are expected to come, in large part, from an early deployment of these technologies. This is the crux of the longer-term perspective: even if the early deployment of some renewables now has higher costs of immediate emissions reductions than other options, this deployment must be undertaken if the resulting cost reductions are key to future large-scale deployment. The early deployment of RE technologies can be a cost-effective measure for long-term climate-change mitigation, even if it looks too costly when only short-term reductions are considered.
38y Technical change can be seen as a cyclical process, based on two-way feedback between market experiences and technical developments. Not only are market prospects the most vital stimulant of industry R&D efforts, but more importantly the deployment of technologies in a competitive marketplace is a key source of information on their strengths and weaknesses, critical to orient applied R & D efforts. Market development and technology development go hand in hand.
This perspective is borne out by lessons from past technological developments, which reveal that the costs of technologies decrease as total unit volume rises. The metric of such change is the progress ratio, defined as the reduction of cost for every doubling of cumulative installed capacity. This ratio has proven roughly constant for most technologies - although it differs significantly from one technology to another. New techniques, although more costly at the outset, may become cost-effective over time if they benefit from sufficient deployment. So-called learning curves illustrate this phenomenon with straight lines on log-log graphs (Figure 2).
Still, it remains difficult to clearly distinguish between the effects of R&D efforts and those arising from market deployment (see Fischer and Newell, 2008; Philibert, 2011). Moreover, the coexistence of increased market shares and decreased costs does not necessarily demonstrate that the former caused the latter. The causality relationship works both ways: when costs decrease, market shares increase.
Some recent studies attempt to shed light on the determinants of cost reduction associated with the learning-curve theory. For example, Nemet (2006) studied the cost reductions of photovoltaics (PV) - whose support policies are perhaps the most controversial. The cost of PV has declined by almost a factor of 100 since 1950, more than any other energy technology in that period. His study focussed on crystalline silicon PV modules and explores the drivers behind technical change in PV by disaggregating historic cost reductions into observable technical factors. He identified three major factors of cost reductions from 1980 to 2001: manufacturing plant size, module efficiency and silicon cost.
PV learning curve
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