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on Resource Economics |
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Issue of 2020‒06‒29
four papers chosen by |
| By: | Raouf Boucekkine (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche); Giorgio Fabbri (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - INRA - Institut National de la Recherche Agronomique); Salvatore Federico (UNISI - Università degli Studi di Siena); Fausto Gozzi (LUISS - Libera Università Internazionale degli Studi Sociali Guido Carli [Roma]) |
| Abstract: | We solve a linear-quadratic model of a spatio-temporal economy using a polluting one-input technology. Space is continuous and heterogenous: locations differ in productivity, nature self-cleaning technology and environmental awareness. The unique link between locations is transboundary pollution which is modelled as a PDE diffusion equation. The spatio-temporal functional is quadratic in local consumption and linear in pollution. Using a dynamic programming method adapted to our infinite dimensional setting, we solve the associated optimal control problem in closed-form and identify the asymptotic (optimal) spatial distribution of pollution. We show that optimal emissions will decrease at given location if and only if local productivity is larger than a threshold which depends both on the local pollution absorption capacity and environmental awareness. Furthermore, we numerically explore the relationship between the spatial optimal distributions of production and (asymptotic) pollution in order to uncover possible (geographic) environmental Kuznets curve cases. |
| Keywords: | Growth,geography,transboundary pollution,infinite dimensional optimal control problems |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02194227&r=all |
| By: | Robert S. Pindyck |
| Abstract: | There is a lot we know about climate change, but there is also a lot we don't know. Even if we knew how much CO2 will be emitted over the coming decades, we wouldn't know how much temperatures will rise as a result. And even if we could predict the extent of warming that will occur, we can say very little about its impact. I explain that we face considerable uncertainty over climate change and its impact, why there is so much uncertainty, and why we will continue to face uncertainty in the near future. I also explain the policy implications of climate change uncertainty. First, the uncertainty (particularly over the possibility of a catastrophic climate outcome) creates insurance value, which pushes us to earlier and stronger actions to reduce CO2 emissions. Second, uncertainty interacts with two kinds of irreversibilities. First, CO2 remains in the atmosphere for centuries, making the environmental damage from CO2 emissions irreversible, pushing us to earlier and stronger actions. Second, reducing CO2 emissions requires sunk costs, i.e., irreversible expenditures, which pushes us away from earlier actions. Both irreversibilities are inherent in climate policy, but the net effect is ambiguous. |
| JEL: | D81 Q5 Q54 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27304&r=all |
| By: | Stephen P. Holland; Erin T. Mansur; Nicholas Z. Muller; Andrew J. Yates |
| Abstract: | We determine the environmental benefit of using electric buses rather than diesel or CNG for urban transit. For diesel and CNG we calculate air pollution damages by combining emission rates with damage valuations from the AP3 integrated assessment model and the social cost of carbon. For electric buses we calculate air pollution damages by combining the damage valuations with estimates of the marginal increase in emissions from electricity usage. The environmental benefit is positive on average across all counties in the contiguous U.S. when comparing electric to either diesel or CNG. The environmental benefit of operating an electric bus fleet (rather than diesel) is about $65 million per year in Los Angeles and above $10 million per year in six other MSAs. Including the environmental benefit, we calculate the net present value (NPV) of bus investment. Relative to diesel, the NPV benefit of an electric bus is positive in about two thirds of urban counties. Relative to CNG, the NPV benefit is negative in all counties. |
| JEL: | D62 H23 Q53 R40 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27285&r=all |
| By: | Henderson, J. Vernon; Turner, Matthew A. |
| Abstract: | We describe patterns of urbanization in the developing world and the extent to which they differ from the developed world. We consider the extent to which urbanization in the developing world can be explained by conventional models of spatial equilibrium. Despite their relative poverty, developing world cities are relatively highly productive, and often provide good access to safe water, improved sanitation, schooling and inoculations. In some parts of the world, they are home to a surprisingly small number of factory workers and a surprisingly large number of farmers. Developing world cities seem to do less well at protecting their residents from lifestyle diseases and crime, their female residents from domestic violence and their children from illness. In thinking about these facts, we note that one strand of the literature focused on structural transformation has suggested that urbanization in the developing is occurring `too early’, while another strand argues that urbanization is occurring `too slow’ to be consistent with conventional models of spatial equilibrium. Despite many differences between developing and developed world cities, our new results combined with those in the literature suggest that models of spatial equilibrium can be adapted to be a useful guide to understanding the process of urbanization in the developing world. |
| JEL: | N0 |
| Date: | 2020–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:105010&r=all |