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Mappings play an important role in environmental science applications by allowing practitioners to monitor changes at national and global scales. Over the last decade, it has become increasingly popular to use satellite imagery data and machine learning techniques (MLTs) to construct such maps. Given the black-box nature of many of these MLTs though, quantifying uncertainty in these maps often relies on sampling reference data under stricter conditions. However, practical constraints can sampling such data expensive, which forces stakeholders to make a trade-off between the degree of uncertainty in predictions and the costs of collecting appropriately sampled reference data. Furthermore, quantifying any trade-off is often difficult, as it will depend on many interdependent factors that cannot be fully understood until more data is collected. This paper investigates how a combination of Bayesian inference and an adaptive approach to sampling reference data can offer a generalizable way of managing such trade-offs. The approach is illustrated and evaluated using a woodland mapping of England as a case study in which reference data is collected under constraints motivated by COVID-19 travel restrictions. The key findings of this paper are as follows: (a) an adaptive approach to sampling reference data allows an informed approach when quantifying this trade-off; and (b) Bayesian inference is naturally suited to adaptive sampling and can make use of Monte Carlo methods when dealing with more advanced problems and analytical techniques.
An increasing number of children, adolescents and adults with intellectual disabilities and/or autism are being admitted to general psychiatric wards and cared for by general psychiatrists.
Aims
The aim of this systematic review was to consider the likely effectiveness of in-patient treatment for this population, and compare and contrast differing models of in-patient care.
Method
A systematic search was completed to identify papers where authors had reported data about the effectiveness of in-patient admissions with reference to one of three domains: treatment effect (e.g. length of stay, clinical outcome, readmission), patient safety (e.g. restrictive practices) and patient experience (e.g. patient or family satisfaction). Where possible, outcomes associated with admission were considered further within the context of differing models of in-patient care (e.g. specialist in-patient services versus general mental health in-patient services).
Results
A total of 106 studies were included and there was evidence that improvements in mental health, social functioning, behaviour and forensic risk were associated with in-patient admission. There were two main models of in-patient psychiatric care described within the literature: admission to a specialist intellectual disability or general mental health in-patient service. Patients admitted to specialist intellectual disability in-patient services had greater complexity, but there were additional benefits, including fewer out-of-area discharges and lower seclusion rates.
Conclusions
There was evidence that admission to in-patient services was associated with improvements in mental health for this population. There was some evidence indicating better outcomes for those admitted to specialist services.
Chapter 10 surveys a range of partial survey and non-survey estimation approaches for creating input–output tables at the regional level. Variants of the commonly used class of estimating procedures using location quotients are reviewed; these presume a regional estimate of input–output data can be derived using some information about a target region. Cross-hauling is discussed and approaches to address it are presented. The RAS technique developed in Chapter 9 is applied using a base national table or a table for another region and some available data for the target region. Techniques for partial survey estimation of commodity flows between regions are also presented along with discussions of several real-world multinational applications, including the China–Japan Transnational Interregional Model and Leontief’s World Model.
Chapter 11 expands the input–output framework to a broader class of economic analysis tools known as social accounting matrices (SAM) and other so-called extended input–output models to capture activities of income distribution in the economy in a more comprehensive and integrated way, including especially employment and social welfare features of an economy. The basic concepts of SAMs are explored and derived from the SNA introduced in Chapters 4 and 5, and the relationships between SAMs and input–output accounts are presented. The concept of SAM multipliers as well as the decomposition of SAM multipliers into components with specific economic interpretations are introduced and illustrated. Finally, techniques for balancing SAM accounts for internal accounting consistency are discussed and several illustrative applications of the use of SAMs are presented.
The introductory chapter recaps the genesis of the field of input–output or interindustry analysis as a widely utilized framework to analyze the interdependence of industries in an economy. The introduction chronicles how the input–output framework, conceived originally by Wassily Leontief in the 1930s, has matured over the last seven decades to become a key component of many types of economic analysis and one of the most widely applied methods in economics. This book presents the framework set forth by Leontief and explores the many extensions that have been developed, and the introduction concludes by summarizing the key features of the succeeding chapters, appendices, and related online resources chronicling those developments.
The Preface recaps the history of the development of the 1985, 2009, and current editions of the text and how the content has evolved with the discipline of input–output analysis. It also includes a summary of the principal updated and added material in this new edition.
Chapter 15 briefly describes some additional extensions to input–output analysis for which space does not permit a detailed treatment in this text, including measuring total factor productivity, modeling economic impacts of disasters, the inoperatbility input–output model, accounting for alternative technologies, and linkages to econometric or computable general equilibrium models.
Chapter 9 introduces approaches designed to deal with the major challenge in input–output analysis that the kinds of information-gathering surveys needed to collect input–output data for an economy can be expensive and very time consuming, resulting in tables of input–output coefficients that are outdated before they are produced. These techniques, known as partial survey and nonsurvey approaches to input–output table construction, are central to modern applications of input–output analysis. The chapter begins by reviewing the basic factors contributing to the stability of input–output data over time, such as changing technology, prices, and the scale and scope of business enterprises. Several techniques for updating input–output data are developed and the economic implications of each described. The bulk of the chapter is concerned with the widely utilized biproportional scaling (or RAS) technique and some related “hybrid model” variants.
Chapter 6 examines a number of key summary analytical measures known as multipliers that can be derived from input–output models to estimate the effects of exogenous changes on (1) new outputs of economic sectors, (2) income earned by households resulting from new outputs, and (3) employment generated from new outputs, (4) value-added generated by production, or (5) energy and environmental effects. The general structure of multiplier analysis and special considerations associated with regional, IRIO, and MRIO models are developed. Extensions to capture the effects of income generation for various household groups are explored, as well as additional multiplier variants. Chapter appendices expand on mathematical formulations of household and income multipliers.
Chapter 5 explores variations to the commodity-by-industry input–output framework introduced in Chapter 4, expanding the basic input–output framework to include distinguishing between commodities and industries, i.e., the supply of specific commodities in the economy and the use of those commodities by collections of businesses defined as industries. The chapter introduces the fundamental commodity-by-industry accounting relationships and how they relate to the basic input–output framework. Alternative assumptions are defined for handling the common accounting issue of secondary production, and economic interpretations of those alternative assumptions are presented. The formulations of commodity-driven and industry-driven models are also presented along with illustrations of variants on combining alternative assumptions for secondary production. Finally, the chapter illustrates the problem encountered with commodity-by-industry models, such as non-square commodity–industry systems, mixed technology options, or the interpretation of negative elements.
Chapter 2 introduces Leontief’s conceptual input–output framework and explains how to develop the fundamental mathematical relationships from the interindustry transactions table. The key assumptions associated with the basic Leontief model and implications of those assumptions are recounted and the economic interpretation of the basic framework is explored. The basic framework is illustrated with a highly aggregated model of the US economy. In addition, the “price model” formulation of the input–output framework is introduced to explore the role of prices in input–output models. Appendices to this chapter include a fundamental set of mathematical conditions for input–output models, known as the Hawkins–Simon conditions.
Chapter 13 reviews the extensions of the input–output framework to incorporate activities of environmental pollution and elimination associated with economic activities as well as the linkages of input–output to models of ecosystems. The chapter begins with the augmented Leontief model for incorporating pollution generation and elimination, from which many subsequent approaches have been developed. The chapter then describes the now widespread application of input–output analysis to environmental lifecycle assessment and establishing a “pollution footprint” for industrial activity. Environmental input–output is also now widely used to evaluate global environmental issues. The special case of analyzing the relationship between global climate change and industrial activity with a carbon footprint is then explored along with using input–output to attribute pollution generation to the demands driving consumption compared with the more traditional attribution of pollution generation to the sectors of industrial production necessary to meet that demand.
Chapter 8 introduces and illustrates the basic concepts of structural decomposition analysis (SDA), in both additive and multiplicative forms, within an input–output framework. The concept of decomposition of the various types of multipliers is introduced and explored further in Chapter 11, as applied to Social Accounting Matrices (SAMs). The application of SDA to MRIO is developed to introduce a spatial context. Numerous applications are cited and summaries of their results are presented. Appendices to this chapter develop extended presentations of additional decomposition results as well as an overview of early applied studies and some further mathematical results.
Chapter 7 presents the so-called supply-side input–output model. It is discussed both as a quantity model (the early interpretation) and as a price model (the more modern interpretation). Relationships to the standard Leontief quantity and price models are also explored. In addition, the fast-growing literature on quantification of economic linkages and analysis of the overall structure of economies using input–output data is examined. Finally, approaches for identifying key or important coefficients in input–output models and alternative measures of coefficient importance are presented.
Chapter 14 describes so called mixed input–output models that are driven by a mix of output and final demand specifications rather than driven either solely by specification by final demand or total output. This chapter also introduces dynamic input–output models that more explicitly capture the role of capital investment and utilization in the production process.