Assessing the Economic Impacts of Climate Change. An Updated CGE Point of View

The present research describes a climate change integrated impact assessment exercise, whose economic evaluation is based on a CGE approach and modeling effort. Input to the CGE model comes from a wide although still partial set of up-to-date bottom-up impact studies. Estimates indicate that a temperature increase of 1.92°C compared to pre-industrial levels in 2050 could lead to global GDP losses of approximately 0.5% compared to a hypothetical scenario where no climate change is assumed to occur. Northern Europe is expected to benefit from the evaluated temperature increase (+0.18%), while Southern and Eastern Europe are expected to suffer from the climate change scenario under analysis (-0.15% and -0.21% respectively). Most vulnerable countries are the less developed regions, such as South Asia, South-East Asia, North Africa and Sub-Saharan Africa. In these regions the most exposed sector is agriculture, and the impact on crop productivity is by far the most important source of damages. It is worth noting that the general equilibrium estimates tend to be lower, in absolute terms, than the bottom-up, partial equilibrium estimates. The difference is to be attributed to the effect of market-driven adaptation. This partly reduces the direct impacts of temperature increases, leading to lower damage estimates. Nonetheless these remain positive and substantive in some regions. Accordingly, market-driven adaptation cannot be the solution to the climate change problem.Computable General Equilibrium Modeling, Impact Assessment, Climate Change

an updated CGE point of view.

The present research describes a climate change integrated impact assessment exercise, whose economic evaluation is based on a CGE approach and modeling effort. Input to the CGE model comes from a wide although still partial set of up-to-date bottom-up impact studies. Estimates indicate that a temperature increase of 1.92°C compared to pre-industrial levels in 2050 could lead to global GDP losses of approximately 0.5% compared to a hypothetical scenario where no climate change is assumed to occur. Northern Europe is expected to benefit from the evaluated temperature increase (+0.18%), while Southern and Eastern Europe are expected to suffer from the climate change scenario under analysis (-0.15% and -0.21% respectively). Most vulnerable countries are the less developed regions, such as South Asia, South-East Asia, North Africa and Sub-Saharan Africa. In these regions the most exposed sector is agriculture, and the impact on crop productivity is by far the most important source of damages. It is worth noting that the general equilibrium estimates tend to be lower, in absolute terms, than the bottom-up, partial equilibrium estimates. The difference is to be attributed to the effect of market-driven adaptation. This partly reduces the direct impacts of temperature increases, leading to lower damage estimates. Nonetheless these remain positive and substantive in some regions. Accordingly, market-driven adaptation cannot be the solution to the climate change problem

LSST: Comprehensive NEO Detection, Characterization, and Orbits

(Abridged) The Large Synoptic Survey Telescope (LSST) is currently by far the most ambitious proposed ground-based optical survey. Solar System mapping is one of the four key scientific design drivers, with emphasis on efficient Near-Earth Object (NEO) and Potentially Hazardous Asteroid (PHA) detection, orbit determination, and characterization. In a continuous observing campaign of pairs of 15 second exposures of its 3,200 megapixel camera, LSST will cover the entire available sky every three nights in two photometric bands to a depth of V=25 per visit (two exposures), with exquisitely accurate astrometry and photometry. Over the proposed survey lifetime of 10 years, each sky location would be visited about 1000 times. The baseline design satisfies strong constraints on the cadence of observations mandated by PHAs such as closely spaced pairs of observations to link different detections and short exposures to avoid trailing losses. Equally important, due to frequent repeat visits LSST will effectively provide its own follow-up to derive orbits for detected moving objects. Detailed modeling of LSST operations, incorporating real historical weather and seeing data from LSST site at Cerro Pachon, shows that LSST using its baseline design cadence could find 90% of the PHAs with diameters larger than 250 m, and 75% of those greater than 140 m within ten years. However, by optimizing sky coverage, the ongoing simulations suggest that the LSST system, with its first light in 2013, can reach the Congressional mandate of cataloging 90% of PHAs larger than 140m by 2020.Comment: 10 pages, color figures, presented at IAU Symposium 23

The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves > 99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a non-physical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains >99.5% efficient at detecting objects on a single night but drops to 80% efficiency at producing orbits for objects detected on multiple nights. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.Comment: 57 Pages, 26 Figures, 13 Table

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Assessing the Economic Impacts of Climate Change. An Updated CGE Point of View

The present research describes a climate change integrated impact assessment exercise, whose economic evaluation is based on a CGE approach and modeling effort. Input to the CGE model comes from a wide although still partial set of up-to-date bottom-up impact studies. Estimates indicate that a temperature increase of 1.92°C compared to pre-industrial levels in 2050 could lead to global GDP losses of approximately 0.5% compared to a hypothetical scenario where no climate change is assumed to occur. Northern Europe is expected to benefit from the evaluated temperature increase (+0.18%), while Southern and Eastern Europe are expected to suffer from the climate change scenario under analysis (-0.15% and -0.21% respectively). Most vulnerable countries are the less developed regions, such as South Asia, South-East Asia, North Africa and Sub-Saharan Africa. In these regions the most exposed sector is agriculture, and the impact on crop productivity is by far the most important source of damages. It is worth noting that the general equilibrium estimates tend to be lower, in absolute terms, than the bottom-up, partial equilibrium estimates. The difference is to be attributed to the effect of market-driven adaptation. This partly reduces the direct impacts of temperature increases, leading to lower damage estimates. Nonetheless these remain positive and substantive in some regions. Accordingly, market-driven adaptation cannot be the solution to the climate change problem