Cuban Annexation, Slave Power Paranoia, And the Collapse of the Democratic Party In Maine, 1850-1854

This article reviews the impact of attempts to annex Cuba to the United States prior to the Civil and also the direct impact of this issue on the politics of the State of Maine

Development of an operational, risk-based approach to surface water flood forecasting

Surface water flooding occurs regularly across England and Wales, especially during the summer months. It is widely acknowledged that surface water flooding presents a particular challenge to forecasters because of the difficulties inherent in forecasting intense localised rainfall and the highly complex runoff and drainage processes which operate at the surface, particularly in urban areas. The Flood Forecasting Centre (FFC) has a responsibility to provide guidance on the risk of surface water flooding to Category 1 and 2 responders across England and Wales. Consequently, there is the requirement for improved methods for forecasting surface water flood risk and the FFC is currently involved in developing and trialling a novel surface water flood forecasting system, the Surface Water Flooding Hazard Impact Model (SWF HIM). The SWF HIM offers significant advances over existing surface water flood forecasting methods used by the FFC, including provision of a risk-based approach. The SWF HIM links probabilistic runoff forecasts from the Centre for Ecology & Hydrology’s Grid-to-Grid model with a library of pre-calculated surface water impact information compiled by the Health and Safety Laboratory. These probabilistic runoff forecasts are combined with impact information to provide a forecast of surface water flood risk at a 1km2 resolution across England and Wales. This presentation outlines the methodology together with some initial results from the trial. The work has been undertaken as part of the UK’s Natural Hazards Partnership (NHP) and also benefits from the close working relationship between the Environment Agency and the Met Office through the FFC

Defining the hundred year flood:A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates

This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed.</p

Accounting for Sustainability in Asia:Stock Market Regulation and Reporting in Hong Kong and Singapore

Sustainability reporting nudges firms into behaving more sustainably by forcing them to account publicly for their wider social and environmental performance. This libertarian paternalist approach to governance through disclosure rather than command-and-control regulation is well established in Anglo-Saxon jurisdictions but comparatively untested in the emerging markets of Asia, where different state traditions and forms of business organization raise questions about its transferability and effectiveness. This article contributes to research on corporate social responsibility, neoliberal environmental governance, and Asian varieties of capitalism by providing the first comparative analysis of the origins, design, and initial impact of new sustainability reporting requirements on the stock markets of Hong Kong and Singapore. In mandating sustainability reporting, both exchanges were similarly concerned with following international norms and competitors but differed in the style and granularity of their company disclosure requirements. These policy design choices reflected different developmental state traditions and the different audiences that market regulators in Hong Kong and Singapore sought to influence through these public accounts. Notwithstanding substantial differences between Hong Kong’s rules-based and Singapore’s principles-based approach to reporting, the response in both markets was remarkably similar. In both cases, sustainability reporting was largely ignored by local market players who dismissed it as a foreign practice of interest to only a small number of Western institutional investors and providing little incentive to go beyond tick box compliance. These findings raise questions about the effectiveness of disclosure requirements at nudging Asian businesses toward sustainability.</p

Climate change and mandatory carbon reporting:Impacts on business process and performance

UK-listed companies are now required to disclose their greenhouse gas emissions as part of their annual directors’ report to publicly account for their contributions to climate change. This paper uses this mandatory carbon reporting to explore wider debates about corporate social responsibility and the purpose, practice and impacts of such non-financial reporting. Empirically, it combines documentary analysis 176 firms’ carbon reporting practices with 60 interviews with stakeholders involved in carbon reporting. Firms disclose their emissions in response to financial incentives, social pressure and/or regulatory compulsion. In turn rationales shape whether and how carbon reporting influences internal business processes and performance. The importance of reporting to the bottom line vary by sector depending on two variables—energy intensity and economic regulator status—yet there is limited evidence that carbon reporting is driving substantial reductions in emissions. Findings suggest reasons for caution about hopes for ‘nudging’ firms to improve their environmental performance and social responsibility through disclosure requirements

Steering by their own lights:Why regulators across Europe use different indicators to measure healthcare quality

Despite widespread faith that quality indicators are key to healthcare improvement and regulation, surprisingly little is known about what is actually measured in different countries, nor how, nor why. To address that gap, this article compares the official indicator sets--comprising some 1100 quality measures-- used by statutory hospital regulators in England, Germany, France, and the Netherlands. The findings demonstrate that those countries’ regulators strike very different balances in: the dimensions of quality they assess (e.g. between safety, effectiveness, and patient-centredness); the hospital activities they target (e.g. between clinical and non-clinical activities and management); and the ‘Donabedian’ measurement style of their indicators (between structure, process and outcome indicators). We argue that these contrasts reflect: i) how the distinctive problems facing each country's healthcare system create different ‘demand-side’ pressures on what national indicator sets measure; and ii) how the configuration of national healthcare systems and governance traditions create ‘supply-side’ constraints on the kinds of data that regulators can use for indicator construction. Our analysis suggests fundamental differences in the meaning of quality and its measurement across countries that are likely to impede international efforts to benchmark quality and identify best practice.</p

Intelligent Monitoring?:Assessing the ability of the Care Quality Commission's statistical surveillance tool to predict quality and prioritise NHS hospital inspections

Background The Care Quality Commission (CQC) is responsible for ensuring the quality of the health and social care delivered by more than 30000 registered providers in England. With only limited resources for conducting on-site inspections, the CQC has used statistical surveillance tools to help it identify which providers it should prioritise for inspection. In the face of planned funding cuts, the CQC plans to put more reliance on statistical surveillance tools to assess risks to quality and prioritise inspections accordingly. Objective To evaluate the ability of the CQC's latest surveillance tool, Intelligent Monitoring (IM), to predict the quality of care provided by National Health Service (NHS) hospital trusts so that those at greatest risk of providing poor-quality care can be identified and targeted for inspection. Methods The predictive ability of the IM tool is evaluated through regression analyses and Ï ‡ 2 testing of the relationship between the quantitative risk score generated by the IM tool and the subsequent quality rating awarded following detailed on-site inspection by large expert teams of inspectors. Results First, the continuous risk scores generated by the CQC's IM statistical surveillance tool cannot predict inspection-based quality ratings of NHS hospital trusts (OR 0.38 (0.14 to 1.05) for Outstanding/Good, OR 0.94 (0.80 to -1.10) for Good/Requires improvement, and OR 0.90 (0.76 to 1.07) for Requires improvement/Inadequate). Second, the risk scores cannot be used more simply to distinguish the trusts performing poorly - those subsequently rated either 'Requires improvement' or 'Inadequate' - from the trusts performing well - those subsequently rated either 'Good' or 'Outstanding' (OR 1.07 (0.91 to 1.26)). Classifying CQC's risk bandings 1-3 as high risk and 4-6 as low risk, 11 of the high risk trusts were performing well and 43 of the low risk trusts were performing poorly, resulting in an overall accuracy rate of 47.6%. Third, the risk scores cannot be used even more simply to distinguish the worst performing trusts - those subsequently rated 'Inadequate' - from the remaining, better performing trusts (OR 1.11 (0.94 to 1.32)). Classifying CQC's risk banding 1 as high risk and 2-6 as low risk, the highest overall accuracy rate of 72.8% was achieved, but still only 6 of the 13 Inadequate trusts were correctly classified as being high risk. Conclusions Since the IM statistical surveillance tool cannot predict the outcome of NHS hospital trust inspections, it cannot be used for prioritisation. A new approach to inspection planning is therefore required.</p