Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts of critical ecosystem services requires a process that engages scientists and decision-makers. Interdisciplinary linkages are necessary because of the climate and societal controls on ecosystems, the feedbacks involving social change, and the decision-making relevance of forecasts.
We demonstrate that when the future path of the discount rate is uncertain and highly correlated, the distant future should be discounted at significantly lower rates than suggested by the current rate. We then use two centuries of US interest rate data to quantify this effect. Using both random walk and meanreverting models, we compute the ''certainty-equivalent rate'' that summarizes the effect of uncertainty and measures the appropriate forward rate of discount in the future. Under the random walk model we find that the certainty-equivalent rate falls continuously from 4% to 2% after 100 years, 1% after 200 years, and 0.5% after 300 years. At horizons of 400 years, the discounted value increases by a factor of over 40,000 relative to conventional discounting. Applied to climate change mitigation, we find that incorporating discount rate uncertainty almost doubles the expected present value of mitigation benefits. r
This paper presents an analytical and numerical comparison of the welfare impacts of alternative instruments for environmental protection in the presence of endogenous technological innovation. We analyze emissions taxes and both auctioned and free (grandfathered) emissions permits. We find that under different sets of circumstances each of the three policies may induce a significantly higher welfare gain than the other two policies. In particular, the relative ranking of policy instruments can crucially depend on the ability of adopting firms to imitate the innovation, the costs of innovation, the slope and level of the marginal environmental benefit function, and the number of firms producing emissions. Moreover, although in theory the welfare impacts of policies differ in the presence of innovation, sometimes these differences are relatively small. In fact, when firms anticipate that policies will be adjusted over time in response to innovation, certain policies can become equivalent. Our analysis is simplified in a number of respects; for example, we assume homogeneous and competitive firms. Nonetheless, our preliminary results suggest there is no clear-cut case for preferring any one policy instrument on the grounds of dynamic efficiency.
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