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Comparing the feed-in tariff incentives for renewable electricity in Ontario and Germany

The development of feed-in tariff (FIT) programs to support green electricity in Ontario (the Green Energy and Green Economy Act of 2009) and Germany (the Erneuerbare Energien-Gesetz of 2000) is compared. The two policies are highly comparable, offering similar rates for most renewable electricity technologies. Major differences between the policies include the level of differentiation found in the German policy, as well as the use of a price degression strategy for FIT rates in Germany compared to an escalation strategy in Ontario. The German renewable electricity portfolio is relatively balanced, compared to Ontario where wind power dominates the portfolio. At the federal level, Canada does not yet have a policy similar to the European Directive on Renewable Energy, and this lack may impact decisions taken by manufacturers of renewable technologies who consider establishing operations in the province. Ontario's Green Energy and Green Economy Act could be benefit from lessons in the German system, especially with regard to degression of feed-in tariff rates over time, which could significantly reduce payments to producers over the course of a contract, and in turn encourage greater competitiveness among renewable power providers in the future.
- York University Canada
- Queen's University Canada
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).90 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
