From the perspective of bio fuel production algae they have enormous advantages over plants which can be summarized as (Exxon Mobil, 2010):
[16] The use of otherwise unsuitable land: Algae can be grown using land and water unsuitable for plant or food production, unlike some other first – and second- generation biofuel feedstocks.
[17] Direct use of several naturally occurring polymers after subjecting them to thermal treatment, chemical derivatisation or blending.
• Thermochemical conversions, such as Fischer-Tropsch process of converting coal to oil, and methanol-to-olefins (MTO) via pyrolysis or gasification. There is tremendous potential for using low-cost coal and stranded gas feedstocks for MTO
[18] A private company has no incentive in pursuing RD&D which brings about society-wide benefits, since the benefits of RD&D do not uniquely accrue to it, but
[19] The oil price shock of 1970s led to a sharp increase in RD&D to find alternatives to oil. When the oil prices collapsed in 1980s and remained low in 1990s, there was no incentive to find alternative fuels.
• As a consequence of the operation of market forces, natural gas technologies for the generation of power and heat got into stream in a big way. No new RD&D investment was needed for this, as it was based on RD&D already made.
• Public concerns over reactor safety, nuclear wastes and nuclear proliferation coupled with cost overruns and production relays, led to reduction in RD&D in nuclear power.
(Source: Energy Technology Perspectives, 2008, p. 205) costs as they are based on the higher costs of more efficient components), but also the impact of “learning-by-doing’’ which tend to reduce the costs.
• Most technologies spill over national boundaries, and hence global learning rates would be more meaningful. Where learning occurs locally (for instance, photovoltaic installations in tropical countries), national learning costs would be more relevant.
• Learning curves may be affected by changes in technology regimes resulting from government regulations, and changes in the design of devices. The learning curve rate may be affected depending upon the starting year from which data has been collected.
[21] Learning curve rates are also affected by supply-chain effects, such as, shortage of silicon in PV industry, steel for making wind turbines, and reactor vessels in the nuclear industry. This led to innovations, such as Cd-Te/thin-film technologies in PV industry, and 10 MW wind power generators using blades of light-weight materials, and avoiding gear boxes, in the case of wind power installations.
In sum, it is important to remember that the learning curves are not set in stone, but
are subject to change as the processes underlying them, change.