Temperature trends derived from Stratospheric Sounding Unit radiances: the effect of increasing CO2 on the weighting function

Stratospheric Sounding Units (SSU) on the NOAA operational satellites have been the main source of near global temperature trend data above the lower stratosphere. They have been used extensively for comparison with model-derived trends. The SSU senses in the 15 micron band of CO2 and hence the weighting function is sensitive to changes in CO2 concentrations. The impact of this change in weighting function has been ignored in all recent trend analyses. We show that the apparent trends in global mean brightness temperature due to the change in weighting function vary from about -0.4 K/decade to 0.4 K/decade depending on the altitude sensed by the different SSU channels. For some channels, this apparent trend is of a similar size to the trend deduced from SSU data but ignoring the change in weighting function. In the mid-stratosphere, the revised trends are now significantly more negative and in better agreement with model-calculated trends.

Making 'dirty' nations look clean? The nation state and the problem of selecting and weighting indices as tools for measuring progress towards sustainability

Pressing global environmental problems highlight the need to develop tools to measure progress towards "sustainability." However, some argue that any such attempt inevitably reflects the views of those creating such tools and only produce highly contested notions of "reality." To explore this tension, we critically assesses the Environmental Sustainability Index (ESI), a well-publicized product of the World Economic Forum that is designed to measure 'sustainability' by ranking nations on league tables based on extensive databases of environmental indicators. By recreating this index, and then using statistical tools (principal components analysis) to test relations between various components of the index, we challenge ways in which countries are ranked in the ESI. Based on this analysis, we suggest (1) that the approach taken to aggregate, interpret and present the ESI creates a misleading impression that Western countries are more sustainable than the developing world; (2) that unaccounted methodological biases allowed the authors of the ESI to over-generalize the relative 'sustainability' of different countries; and, (3) that this has resulted in simplistic conclusions on the relation between economic growth and environmental sustainability. This criticism should not be interpreted as a call for the abandonment of efforts to create standardized comparable data. Instead, this paper proposes that indicator selection and data collection should draw on a range of voices, including local stakeholders as well as international experts. We also propose that aggregating data into final league ranking tables is too prone to error and creates the illusion of absolute and categorical interpretations. (c) 2004 Elsevier Ltd. All rights reserved.

Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings

The main objectives of this paper are to: firstly, identify key issues related to sustainable intelligent buildings (environmental, social, economic and technological factors); develop a conceptual model for the selection of the appropriate KPIs; secondly, test critically stakeholder's perceptions and values of selected KPIs intelligent buildings; and thirdly develop a new model for measuring the level of sustainability for sustainable intelligent buildings. This paper uses a consensus-based model (Sustainable Built Environment Tool- SuBETool), which is analysed using the analytical hierarchical process (AHP) for multi-criteria decision-making. The use of the multi-attribute model for priority setting in the sustainability assessment of intelligent buildings is introduced. The paper commences by reviewing the literature on sustainable intelligent buildings research and presents a pilot-study investigating the problems of complexity and subjectivity. This study is based upon a survey perceptions held by selected stakeholders and the value they attribute to selected KPIs. It is argued that the benefit of the new proposed model (SuBETool) is a ‘tool’ for ‘comparative’ rather than an absolute measurement. It has the potential to provide useful lessons from current sustainability assessment methods for strategic future of sustainable intelligent buildings in order to improve a building's performance and to deliver objective outcomes. Findings of this survey enrich the field of intelligent buildings in two ways. Firstly, it gives a detailed insight into the selection of sustainable building indicators, as well as their degree of importance. Secondly, it tesst critically stakeholder's perceptions and values of selected KPIs intelligent buildings. It is concluded that the priority levels for selected criteria is largely dependent on the integrated design team, which includes the client, architects, engineers and facilities managers.

A method of weighting indicators in building energy efficiency assessment

This paper identifies the indicators of energy efficiency assessment in residential building in China through a wide literature review. Indicators are derived from three main sources: 1) The existing building assessment methods; 2)The existing Chinese standards and technology codes in building energy efficiency; 3)Academia research. As a result, we proposed an indicator list by refining the indicators in the above sources. Identified indicators are weighted by the group analytic hierarchy process (AHP) method. Group AHP method is implemented following key steps: Step 1: Experienced experts are selected to form a group; Step 2: A survey is implemented to collect the individual judgments on the importance of indicators in the group; Step 3: Members’ judgments are synthesized to the group judgments; Step 4: Indicators are weighted by AHP on the group judgments; Step 5: Investigation of consistency estimation shows that the consistency of the judgment matrix is accepted. We believe that the weighted indicators in this paper will provide important references to building energy efficiency assessment.

A TVD finite difference scheme with non-uniform meshes and without upstream weighting

We present a finite difference scheme, with the TVD (total variation diminishing) property, for scalar conservation laws. The scheme applies to non-uniform meshes, allowing for variable mesh spacing, and is without upstream weighting. When applied to systems of conservation laws, no scalar decomposition is required, nor are any artificial tuning parameters, and this leads to an efficient, robust algorithm.

Temporal-envelope constancy of speech in rooms and the perceptual weighting of frequency bands

Three experiments measured constancy in speech perception, using natural-speech messages or noise-band vocoder versions of them. The eight vocoder-bands had equally log-spaced center-frequencies and the shapes of corresponding “auditory” filters. Consequently, the bands had the temporal envelopes that arise in these auditory filters when the speech is played. The “sir” or “stir” test-words were distinguished by degrees of amplitude modulation, and played in the context; “next you’ll get _ to click on.” Listeners identified test-words appropriately, even in the vocoder conditions where the speech had a “noise-like” quality. Constancy was assessed by comparing the identification of test-words with low or high levels of room reflections across conditions where the context had either a low or a high level of reflections. Constancy was obtained with both the natural and the vocoded speech, indicating that the effect arises through temporal-envelope processing. Two further experiments assessed perceptual weighting of the different bands, both in the test word and in the context. The resulting weighting functions both increase monotonically with frequency, following the spectral characteristics of the test-word’s [s]. It is suggested that these two weighting functions are similar because they both come about through the perceptual grouping of the test-word’s bands.

Stated attribute non-attendance in successive choice experiments

Attribute non-attendance in choice experiments affects WTP estimates and therefore the validity of the method. A recent strand of literature uses attenuated estimates of marginal utilities of ignored attributes. Following this approach, we propose a generalisation of the mixed logit model whereby the distribution of marginal utility coefficients of a stated non-attender has a potentially lower mean and lower variance than those of a stated attender. Model comparison shows that our shrinkage approach fits the data better and produces more reliable WTP estimates. We further find that while reliability of stated attribute non-attendance increases in successive choice experiments, it does not increase when respondents report having ignored the same attribute twice.

Simple uncertainty frameworks for selecting weighting schemes and interpreting multimodel ensemble climate change experiments

Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response. The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases. Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response. The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.

Visual attention and attribute attendance in multi-attribute choice experiments

Decision strategies in multi-attribute Choice Experiments are investigated using eye-tracking. The visual attention towards, and attendance of, attributes is examined. Stated attendance is found to diverge substantively from visual attendance of attributes. However, stated and visual attendance are shown to be informative, non-overlapping sources of information about respondent utility functions when incorporated into model estimation. Eye-tracking also reveals systematic nonattendance of attributes only by a minority of respondents. Most respondents visually attend most attributes most of the time. We find no compelling evidence that the level of attention is related to respondent certainty, or that higher or lower value attributes receive more or less attention

Using attribute importance rankings within discrete choice experiments: an application to valuing bread attributes

We present a new Bayesian econometric specification for a hypothetical Discrete Choice Experiment (DCE) incorporating respondent ranking information about attribute importance. Our results indicate that a DCE debriefing question that asks respondents to rank the importance of attributes helps to explain the resulting choices. We also examine how mode of survey delivery (online and mail) impacts model performance, finding that results are not substantively a§ected by the mode of survey delivery. We conclude that the ranking data is a complementary source of information about respondent utility functions within hypothetical DCEs

Finite sample weighting of recursive forecast errors

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.