How Large Are the Welfare Gains from Technological Innovation Induced by Environmental Policies?

This paper examines whether the welfare gains from technological innovation that reduces future abatement costs are larger or smaller than the “Pigouvian” welfare gains from optimal pollution control. The relative welfare gains from innovation depend on three key factors: the initially optimal level of abatement, the speed at which innovation reduces future abatement costs, and the discount rate. We calculate the welfare gains from innovation under a variety of different scenarios. Mostly they are less than the Pigouvian welfare gains. To be greater, innovation must reduce abatement costs substantially and quickly and the initially optimal abatement level must be fairly modest.innovation, welfare, regulation, endogenous, technological, change, R&D

How Large Are the Welfare Gains from Technological Innovation Induced by Environmental Policies?

This paper examines whether the welfare gains from technological innovation that reduces future abatement costs are larger or smaller than the “Pigouvian” welfare gains from optimal pollution control. The relative welfare gains from innovation depend on three key factors - the initially optimal level of abatement, the speed at which innovation reduces future abatement costs, and the discount rate. We calculate the welfare gains from innovation under a variety of different scenarios. Mostly they are less than the Pigouvian welfare gains. To be greater, innovation must reduce abatement costs substantially and quickly and the initially optimal abatement level must be fairly modest.innovation, welfare, regulation, endogenous, technological, change, R&D

Instrument Choice for Environmental Protection When Technological Innovation is Endogenous

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.

How Important is Technological Innovation in Protecting the Environment?

Economists have speculated that the welfare gains from technological innovation that reduces the future costs of environmental protection could be a lot more important than the "Pigouvian" welfare gains over time from correcting a pollution externality. If so, then a primary concern in the design of environmental policies should be the impact on induced innovation, and a potentially strong case could be made for additional instruments such as research subsidies. This paper examines the magnitude of the welfare gains from innovation relative to the discounted Pigouvian welfare gains, using a dynamic social planning model in which research and development (R&D) augments a knowledge stock that reduces future pollution abatement costs. We find that the discounted welfare gains from innovation are typically smaller....and perhaps much smaller....than the discounted Pigouvian welfare gains. This is because the long-run gain to innovation is bounded by the maximum reduction in abatement costs and, since R&D is costly, it takes time to accumulate enough knowledge to substantially reduce abatement costs. Only in cases when innovation substantially reduces abatement costs quickly (by roughly 50% within 10 years) and the Pigouvian amount of abatement is initially modest, can the welfare gains from innovation exceed the welfare gains from pollution control. These results apply for both flow and stock pollutants, and for linear and convex environmental damage functions. Our results suggest that spurring technological innovation should not be emphasized at the expense of achieving the optimal amount of pollution control. More generally, our results appear to have implications for a broad range of policy issues. They suggest that the welfare gains from innovation that reduces the costs of supplying any public good (defense, crime prevention, infrastructure, etc.) may be fairly small relative to those from providing the optimal amount of the public good over time.

The Conditional Entropy Bottleneck

Much of the field of Machine Learning exhibits a prominent set of failure modes, including vulnerability to adversarial examples, poor out-of-distribution (OoD) detection, miscalibration, and willingness to memorize random labelings of datasets. We characterize these as failures of robust generalization, which extends the traditional measure of generalization as accuracy or related metrics on a held-out set. We hypothesize that these failures to robustly generalize are due to the learning systems retaining too much information about the training data. To test this hypothesis, we propose the Minimum Necessary Information (MNI) criterion for evaluating the quality of a model. In order to train models that perform well with respect to the MNI criterion, we present a new objective function, the Conditional Entropy Bottleneck (CEB), which is closely related to the Information Bottleneck (IB). We experimentally test our hypothesis by comparing the performance of CEB models with deterministic models and Variational Information Bottleneck (VIB) models on a variety of different datasets and robustness challenges. We find strong empirical evidence supporting our hypothesis that MNI models improve on these problems of robust generalization