The relationship between climate change concern and national wealth

© 2015 Springer Science+Business Media Dordrecht Based on a cross-national social survey, this paper ascertains how perception of climate change is related to national wealth and adaptive capacity across 33 countries. Results indicate that citizens of wealthier countries tend to see climate change as the most important problem, but are less likely to rank it as a highly dangerous threat. We find that Gross Domestic Product (GDP) per capita correlates positively with perceived importance of climate change, but negatively with perceived risk. Also, climate change is less likely to be seen as highly dangerous in those countries that are better prepared for climate change. These findings have important implications for climate adaptation. The relatively weaker sense of danger among the wealthiest societies may eventually lead to maladaptation to climate change. Adequate economic resources provide people collective security and protection from impending crises, but could elevate a self-assuring attitude that might prematurely reduce their caution toward the impending threat and capacity for dealing with climate uncertainties.postprin

Voltage controlled oscillators for 40Gbit/s cascaded bit-interleaving PON

Technologies such as the Internet-of-Things and cloud services demand dynamic bandwidth allocation flexibility, which is not offered by the currently deployed solutions. The Bit-Interleaving PON (BiPON) and its cascaded extension the Cascaded Bit-Interleaving PON (CBI-PON) offer a solution that allows to increase bandwidths, reduce power consumption and have a much more flexible dynamic bandwidth allocation scheme. CBI-PON consists of multiple levels of BiPON with different line rates. For each of these line rates, clock-and-data recovery must be performed, which requires a set of different Voltage Controlled Oscillators (VCOs). This paper presents the VCOs designed for the CABINET chip, an implementation of a CBI-PON network device, allowing clock-and-data recovery for 40Gbit/s, 10 Gbit/s and 2.5 Gbit/s line rates

Combinatorial Algorithms for Computing Column Space Bases That Have Sparse Inverses

Abstract. This paper presents a new combinatorial approach towards constructing a sparse, implicit basis for the null space of a sparse, under-determined matrix. Our approach is to compute a column space basis of that has a sparse inverse, which could be used to represent a null space basis in implicit form. We investigate three different algorithms for computing column space bases: two greedy algorithms implemented using graph matchings, and a third, which employs a divide and conquer strategy implemented with hypergraph partitioning followed by a matching. Our results show that for many matrices from linear programming, structural analysis, and circuit simulation, it is possible to compute column space bases having sparse inverses, contrary to conventional wisdom. The hypergraph partitioning method yields sparser basis inverses and has low computational time requirements, relative to the greedy approaches. We also discuss the complexity of selecting a column space basis when it is known that such a basis exists in block diagonal form with a given small block size. Key words. sparse column space basis, sparse null space basis, block angular matrix, block diagonal matrix, matching, hypergraph partitioning, inverse of a basis AMS subject classifications. 65F50, 68R10, 90C20 1. Introduction. Man