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.

Animal health and welfare in organic livestock production in Europe: Current state and future challenges

Existing data on animal health and welfare in organic livestock production systems in the European Community countries are reviewed in the light of the demands and challenges of the recently implemented EU regulation on organic livestock production. The main conclusions and recommendations of a three-year networking project on organic livestock production are summarised and the future challenges to organic livestock production in terms of welfare and health management are discussed. The authors conclude that, whilst the available data are limited and the implementation of the EC regulation is relatively recent, there is little evidence to suggest that organic livestock management causes major threats to animal health and welfare in comparison with conventional systems. There are, however, some well-identified areas, like parasite control and balanced ration formulation, where efforts are needed to find solutions that meet with organic standard requirements and guarantee high levels of health and welfare. It is suggested that, whilst organic standards offer an implicit framework for animal health and welfare management, there is a need to solve apparent conflicts between the organic farming objectives in regard to environment, public health, farmer income and animal health and welfare. The key challenges for the future of organic livestock production in Europe are related to the feasibility of implementing improved husbandry inputs and the development of evidence-based decision support systems for health and feeding management.

State and society in the English countryside: the rural community movement 1918-1939

This paper assesses the relationship between state and society in interwar rural England, focusing on the hitherto neglected role of the Rural Community Councils (RCCs). The rise of statutory social provision in the early twentieth century created new challenges and opportunities for voluntaryism, and the rural community movement was in part a response. The paper examines the early development of the movement, arguing that a crucial role was played by a close-knit group of academics and local government officials. While largely eschewing party politics, they shared a commitment to citizenship, democracy and the promotion of rural culture; many of them had been close associates of Sir Horace Plunkett. The RCCs engaged in a wide range of activities, including advisory work, adult education, local history, village hall provision, support for rural industries and an ambivalent engagement with parish councils. The paper concludes with an assessment of the achievements of the rural community movement, arguing that it was constrained by its financial dependence on voluntary contributions.

The British state and the Irish rebellion of 1916: an intelligence failure or an failure of response

The teleological narrative that has dominated the handling of intelligence by the British state in the events that led up to the 1916 Irish Rebellion in Dublin has been characterised as a cocktail of incompetence and mendacity. Using new and existing archive material this article argues that both the cabinet in London and key members of the Irish Executive in Dublin were supplied with accurate and timely intelligence by the Admiralty's signals intelligence unit, the Royal Irish Constabulary and the Dublin Metropolitan Police with respect to this event. Far from being a failure of intelligence here is evidence to show that there occurred a failure of response on behalf of key decision-makers. The warnings that were given by intelligence organisations were filtered through the existing policy preferences and assumptions. As a result of these factors accurate evaluations and sound judgement were not exercised by key officials, such as Sir Matthew Nathan, in Dublin Castle.

Data assimilation for state and parameter estimation: application to morphodynamic modelling

Data assimilation is predominantly used for state estimation; combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts we need good estimates of both the current system state and the model parameters. This paper presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three dimensional variational(3D-Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from SAR satellite imagery and a ship-borne survey is shown to significantly improve the predictive capability of the model over a 2 year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this paper considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D-Var data assimilation algorithm for joint state-parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation-based state-parameter estimation in coastal morphodynamic modelling.

Steady state and dynamic modelling of nitrogen in the River Kennet: impacts of land use change since the 1930s

Steady state and dynamic models have been developed and applied to the River Kennet system. Annual nitrogen exports from the land surface to the river have been estimated based on land use from the 1930s and the 1990s. Long term modelled trends indicate that there has been a large increase in nitrogen transport into the river system driven by increased fertiliser application associated with increased cereal production, increased population and increased livestock levels. The dynamic model INCA Integrated Nitrogen in Catchments. has been applied to simulate the day-to-day transport of N from the terrestrial ecosystem to the riverine environment. This process-based model generates spatial and temporal data and reproduces the observed instream concentrations. Applying the model to current land use and 1930s land use indicates that there has been a major shift in the short term dynamics since the 1930s, with increased river and groundwater concentrations caused by both non-point source pollution from agriculture and point source discharges. �

Importance of initial state and atmospheric conditions for urban land surface models' performance

Urban land surface models (LSM) are commonly evaluated for short periods (a few weeks to months) because of limited observational data. This makes it difficult to distinguish the impact of initial conditions on model performance or to consider the response of a model to a range of possible atmospheric conditions. Drawing on results from the first urban LSM comparison, these two issues are considered. Assessment shows that the initial soil moisture has a substantial impact on the performance. Models initialised with soils that are too dry are not able to adjust their surface sensible and latent heat fluxes to realistic values until there is sufficient rainfall. Models initialised with too wet soils are not able to restrict their evaporation appropriately for periods in excess of a year. This has implications for short term evaluation studies and implies the need for soil moisture measurements to improve data assimilation and model initialisation. In contrast, initial conditions influencing the thermal storage have a much shorter adjustment timescale compared to soil moisture. Most models partition too much of the radiative energy at the surface into the sensible heat flux at the probable expense of the net storage heat flux.

Language learning in the public eye: an analysis of newspapers and official documents in England

This article considers the issue of low levels of motivation for foreign language learning in England by exploring how language learning is conceptualised by different key voices in that country through the examination of written data: policy documents and reports on the UK's language needs, curriculum documents, and press articles. The extent to which this conceptualisation has changed over time is explored, through the consideration of documents from two time points, before and after a change in government in the UK. The study uses corpus analysis methods in this exploration. The picture that emerges is a complex one regarding how the 'problems' and 'solutions' surrounding language learning in that context are presented in public discourse. This, we conclude, has implications for the likely success of measures adopted to increase language learning uptake in that context.

A joint state and parameter estimation scheme for nonlinear dynamical systems

We present a novel algorithm for concurrent model state and parameter estimation in nonlinear dynamical systems. The new scheme uses ideas from three dimensional variational data assimilation (3D-Var) and the extended Kalman filter (EKF) together with the technique of state augmentation to estimate uncertain model parameters alongside the model state variables in a sequential filtering system. The method is relatively simple to implement and computationally inexpensive to run for large systems with relatively few parameters. We demonstrate the efficacy of the method via a series of identical twin experiments with three simple dynamical system models. The scheme is able to recover the parameter values to a good level of accuracy, even when observational data are noisy. We expect this new technique to be easily transferable to much larger models.