O presente trabalho tem como principal objetivo mostrar de forma empírica a importância relativa do setor agrícola na estrutura produtiva do estado do Mato Grosso, o qual tem apresentado um excepcional desempenho da atividade agrícola no contexto atual. Para tanto, utiliza-se de um modelo insumo-produto inter-regional construído para duas regiões, a saber, Mato Grosso e resto do Brasil, referente ao ano de 1999. A partir dos indicadores desta metodologia, procura-se identificar os setores mais importantes das economias em questão, seus encadeamentos e a propagação de impactos entre as regiões. Além disso, o trabalho faz ainda uma aplicação do modelo para verificar qual o impacto das exportações mato-grossenses, em especial da soja, sobre a produção total, valor adicionado e número de empregos gerados na economia, bem como calcula o PIB do agronegócio da soja no Mato Grosso, além de verificar qual o impacto causado na produção dos transportes rodoviário e ferroviário, nas duas regiões, ao atender a demanda final de cada atividade pertencente ao estado do Mato Grosso. Pelos resultados obtidos, pode-se confirmar a hipótese formulada a respeito da importância dos setores primários para a economia do estado. De acordo com os índices puros de ligações, verificou-se que os setores agrícolas como Soja, Bovinos e Outros da pecuária, foram considerados como chave ou pólos de desenvolvimento econômico na região em estudo, em função de suas fortes relações de compra e venda de insumos na economia. Além destes, quatro setores diretamente relacionados à agropecuária foram também identificados como chave, a saber: Fabricação de óleos vegetais, Abate de bovinos, Abate de outros animais e Álcool. Em especial merece destaque a cultura da soja no Mato Grosso, que embora tenha uma baixa geração direta de emprego e renda, apresenta um elevado efeito multiplicador destas variáveis na economia, uma característica intrínseca de setores altamente produtivos e intensivos em capital, além de destacar-se também como pólo de desenvolvimento econômico em função de suas fortes ligações, conforme mencionado no parágrafo anterior. As evidências empíricas confirmaram também a importância do setor em termos de suas exportações, as quais causam importante impacto na produção e geração de empregos em diversos outros setores, além de atrair ganhos cambiais ao país. Finalmente, o trabalho desenvolve um ferramental útil para a formulação de políticas públicas para o estado do Mato Grosso, embora sejam necessárias algumas atualizações e refinamentos da metodologia utilizada, para que se possa desenvolver um instrumental de avaliação de políticas de estímulo ao desenvolvimento da infra-estrutura de transportes da região, uma vez que esta última constitui-se peça fundamental ao crescimento econômico. / The main goal of this study is to show the empiric frame of the relative importance of agricultural sector into the productive framework of the Mato Grosso State, which has shown an exceptional agricultural activities growth in the actual context. For this purposes, it becomes useful to adopt an inter-regional input-output model built for two regions: Mato Grosso State and rest of Brazil, regarding to 1999. From the indicators of this methodology, it looks for finding the more important sectors of those economies, and its linkages and impacts between the regions as well. Moreover, this study even encourages to make some simulations to verify how the Mato Grosso’s exportations impacts on the total product, income and employment, specially those ones originated from soybean. It was possible to calculate the Mato Grosso’s soybean agribusiness GDP (Gross Domestic Product), besides verifying how much impacts were generated on highways and railways transports. According to results obtained, it is possible to confirm the created hypotheses regarding importance of primary sectors for the state economy. According to pure linkages indexes, it was checked that some sectors such soybean production, beef cattle production and other ones were considered like key sectors or economic developed pools for the region studied. In addition of those ones, four other sectors directly linked with agriculture were also found out like key sectors, so they are vegetable oils production, beef cattle and other slaughters, and alcohol production. The soybean crops is really a spot in Mato Grosso State, even though it has a low generation of directs income and employment. As a matter of fact, the soybean crops have a high multiplier effect on these economy variables, an own feature of highly productive and capital intensive sectors, and it is a prominence sector like a developing economic pool, as seen before. The empiric evidences confirmed the importance of sector in terms of its exportations too, which strongly impacts on product and employments generation in other ones sectors, besides attracting exchanges gains to the country. Finally, this input-output analysis creates an important tool to the public policy formulations of Mato Grosso State, although its important to make some updates and fits on the methodology applied to be able developing a whole evaluation instrumental kit of entire stimulant policies that might develop for example an efficient regional transport structure, due to this one consists of an essential device to the economic growth.

A rapidly growing share of global agricultural areas is devoted to the production of biomass for non-food purposes. The expanding non-food bioeconomy can have far-reaching social and ecological implications; yet, the non-food sector has attained little attention in land footprint studies. This paper provides the first assessment of the global cropland footprint of non-food products of the European Union (EU), a globally important region regarding its expanding bio-based economy. We apply a novel hybrid land flow accounting model, combining the biophysical trade model LANDFLOW with the multi-regional input-output model EXIOBASE. The developed hybrid approach improves the level of product and country detail, while comprehensively covering all global supply chains from agricultural production to final consumption, including highly-processed products, such as many non-food products. The results highlight the EU's role as a major processing and the biggest consuming region of cropland-based non-food products while at the same time relying heavily on imports. Two thirds of the cropland required to satisfy the EU's non-food biomass consumption are located in other world regions, particularly in China, the US and Indonesia, giving rise to potential impacts on distant ecosystems. With almost 39% in 2010, oilseeds used to produce for example biofuels, detergents and polymers represented the dominant share of the EU's non-food cropland demand. Traditional non-food biomass uses, such as fibre crops for textiles and animal hides and skins for leather products, also contributed notably (22%). Our findings suggest that if the EU Bioeconomy Strategy is to support global sustainable development, a detailed monitoring of land use displacement and spillover effects is decisive for targeted and effective EU policy making.

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling the market as a dynamic system and a reinforcement learning algorithm that learns profitable market-making strategies when run on this model. The sequence of buys and sells for a particular stock, the order flow, we model as an Input-Output Hidden Markov Model fit to historical data. When combined with the dynamics of the order book, this creates a highly non-linear and difficult dynamic system. Our reinforcement learning algorithm, based on likelihood ratios, is run on this partially-observable environment. We demonstrate learning results for two separate real stocks.

Bibliography: p. 76-77. / Title page reads: Final report ESL-FR-361. / NASA Research Grant NSG-496. M.I.T. Project DSR 76152.

IVL Svenska Miljöinstitutet utvecklade år 2001 – 2002 i projektet “Klimat.nu – Den stora miljöutmaningen”, ett webbaserat verktyg för att upplysa och vägleda individen i klimatfrågan. Verktygets syfte är att kvantifiera fossila koldioxidutsläpp som en konsekvens av individens energikonsumtion; hushållsel, drivmedel m.m. Syftet är även att individen ges råd om hur man minskar CO2-utsläpp genom att förändra sitt leverne. IVL Svenska Miljöinstitutet vill utöka beräkningsverktygets innehåll till att även omfatta konsumentvaror. Studien har sökt svar hur enskilda individers olika typer av varukonsumtion sättas i samband med CO2-utsläpp och vilka konsumentråd är rimliga att ge för att uppnå utsläppsminskningar, samt hur dessa minskningar kan kvantifieras. I studien har ett systemanalytiskt tillvägagångssätt tillämpats och empirin har bestått av miljöexpanderad input-output-data (MIOA). Data har samlats in från Statistiska Centralbyrån (SCB) på miljöräkenskapernas data- och analyssidor. Insamlad data beskriver utsläpp som sker i varors livscykel till och med distribution till affär (indirekta utsläpp). Det är dock viktigt att ha varuklassers hela livscykel i åtanke så att försök till att minska konsumentens indirekta CO2-utsläpp inte leder till ökade totala utsläpp. Dataosäkerheter har identifierats, vilka visar att insamlad data underskattar varors indirekta CO2-utsläpp. Data grundar sig på antagandet att Sverige skulle ha producerat alla varor som importeras. I genomsnitt är ca 69 % av varors indirekta CO2-utsläpp av utländsk härkomst, dessa länder har vanligtvis högre utsläppsintensitet än Sverige i sina produktionsstrukturer. I Sverige finns data endast tillgänglig med ca tre års fördröjning. I sin nuvarande form representerar data trots osäkerheter en lägsta nivå på olika varuklassers indirekta CO2-utsläpp. För att göra insamlad data funktionell i beräkningsverktyget prövades en metodik där utsläppsintensiteter beräknades. Utsläppsintensiteter tillgodoser kravet för att enskilda individers olika typer av varukonsumtion ska kunna kopplas till dess CO2-utsläpp. I beräkningsverktyget innebär det att utsläppsintensiteter integreras, som tillsammans med en viss summa pengar, utgör underlaget för att beräkna individens indirekta CO2-utsläpp. Ur ett individperspektiv är metodiken särskilt tilltalande eftersom pengar används som beräkningsenhet, enheten är något som individen oftast har lätt att relatera till. Användningen av utsläppsintensiteter möjliggör kvantifiering av en utsläppsminskning om individen spenderar en summa pengar på en varuklass med lägre utsläppsintensitet i stället en med högre. Med pengar som enhet kan även ”rebound-effekten” undvikas. På grund av osäkerheter i dataunderlaget kan studien inte påvisa att förändrad konsumtion av varor leder till en faktisk utsläppsminskning. Störst sannolikhet att uppnå en faktisk minskning är dock om individen råds att fördela en summa pengar från en varuklass till en annan, i vilka det finns stora kvantitativa skillnader mellan utsläppsintensiteterna.

<p>Statistical analysis of massive array data is becoming indispensable in answering important scientific and business questions. Most analysis tasks consist of multiple steps, each making one or multiple passes over the arrays to be analyzed and generating intermediate results. In the big data setting, storage and I/O efficiency is a key to efficient analytics. Because of the distinct characteristics of disk-resident arrays and the operations performed on them, we need a computing environment that is easy to use, scalable to big data, and different from traditional, CPU- and memory-centric solutions.</p><p>R is a popular computing environment for statistical/numerical data analysis. Like many such environments, R performs poorly for large datasets. This dissertation presents RIOT (R with I/O Transparency), a framework to make R programs I/O-efficient in a way transparent to users. RIOT-DB, an implementation of RIOT using a relational database system as its backend, significantly outperforms R in many big-data scenarios. RIOT users are insulated from the data management backend and I/O optimization specifics. Because of this transparency, RIOT is easy to adopt by the majority of the R users.</p><p>While RIOT-DB demonstrates the feasibility of transparent I/O efficiency and the potential of database-style inter-operator optimizations, it also reveals significant deficiencies of database systems in handling statistical computation. To improve the efficiency of array storage, RIOT uses a novel storage structure called Linearized-Array B-tree, or LAB-tree. LAB-tree supports flexible array layouts and automatically adapts to varying sparsity across parts of an array and over time. It also implements splitting strategies and update batching policies with good theoretical guarantees and/or practical performance.</p><p>While LAB-tree removes many I/O inefficiencies that arise in accessing individual arrays, programs consisting of multiple operators need further optimization. To this end, RIOT incorporates an I/O optimization framework, RIOTShare, which is able to jointly optimize I/O sharing and array layouts for a broad range of analysis tasks expressible in nested-loop forms. RIOTShare explores the middle ground between the high-level, database-style operator-based query optimization and low-level, compiler-style loop-based code optimization.</p><p>In sum, combining a transparent language binding mechanism, an efficient and flexible storage engine, and an accurate I/O sharing and array layout optimizer, RIOT provides a systematic solution for data-intensive array-based statistical computing.</p> / Dissertation

Interindustry transactions recorded at a macro level are simply summations of commodity shipment decisions taken at a micro level. The resulting statistical problem is to obtain minimally biased estimates of commodity flow distributions at the disaggregated level, given various forms of aggregated information. This study demonstrates the application of the entropy-maximizing paradigm in its traditional form, together with recent adaptations emerging from information theory, to the area of spatial and non-spatial input-output analysis. A clear distinction between the behavioural and statistical aspects of entropy modelling is suggested. The discussion of non-spatial input-output analysis emphasizes the rectangular and dynamic extensions of Leontief's original model, and also outlines a scheme for simple aggregation, based on a criterion of minimum loss of information. In the chapters on spatial analysis, three complementary approaches to the estimation of interregional flows are proposed. Since the static formulations cannot provide an accurate picture of the gross interregional flows between any two sectors, Leontief's dynamic framework is adapted to the problem. The study concludes by describing a hierarchical system of models to analyse feasible paths of economic development over space and time. / digitalisering@umu

This thesis is concerned with input-output stability theory. Within this framework, it is of interest how inputs map to outputs through an operator that represents a system to be controlled or the controller itself. The Small Gain, Passivity, and Conic Sector Stability Theorems can be used to assess the stability of a negative feedback interconnection involving two systems that each have specific input-output properties. Our first contribution concerns characterization of the input-output properties of linear time-varying (LTV) systems. We present various theorems that ensure that a LTV system has finite gain, is passive, or is conic. We also consider the stability of various negative feedback interconnections. Motivated by the robust nature of passivity-based control, we consider how to overcome passivity violations. This investigation leads to the hybrid conic systems framework whereby systems are described in terms of multiple conic bounds over different operating ranges. A special case relevant to systems that experience a passivity violation is the hybrid passive/finite gain framework. Sufficient conditions are derived that ensure the negative feedback interconnection of two hybrid conic systems is stable. The input-output properties of gain-scheduled systems are also investigated. We show that a gain-scheduled system composed of conic subsystems has conic bounds as well. Using the conic bounds of the subsystems along with the scheduling signal properties, the overall conic bounds of the gain-scheduled system can be calculated. We also show that when hybrid very strictly passive/finite gain (VSP/finite gain) subsystems are gain-scheduled, the overall map is also hybrid VSP/finite gain. Being concerned with the control of aerospace systems, we use the theory developed in this thesis to control two interesting plants. We consider passivity-based control of a spacecraft endowed with magnetic torque rods and reaction wheels. In particular, we synthesize a LTV input strictly passive controller. Using hybrid theory we control single- and two-link flexible manipulators. We present two controller synthesis schemes, each of which employs numerical optimization techniques and attempts to have the hybrid VSP/finite gain controllers mimic a H2 controller. One of our synthesis methods uses the Generalized Kalman-Yakubovich-Popov Lemma, thus realizing a convex optimization problem.

This thesis is concerned with input-output stability theory. Within this framework, it is of interest how inputs map to outputs through an operator that represents a system to be controlled or the controller itself. The Small Gain, Passivity, and Conic Sector Stability Theorems can be used to assess the stability of a negative feedback interconnection involving two systems that each have specific input-output properties. Our first contribution concerns characterization of the input-output properties of linear time-varying (LTV) systems. We present various theorems that ensure that a LTV system has finite gain, is passive, or is conic. We also consider the stability of various negative feedback interconnections. Motivated by the robust nature of passivity-based control, we consider how to overcome passivity violations. This investigation leads to the hybrid conic systems framework whereby systems are described in terms of multiple conic bounds over different operating ranges. A special case relevant to systems that experience a passivity violation is the hybrid passive/finite gain framework. Sufficient conditions are derived that ensure the negative feedback interconnection of two hybrid conic systems is stable. The input-output properties of gain-scheduled systems are also investigated. We show that a gain-scheduled system composed of conic subsystems has conic bounds as well. Using the conic bounds of the subsystems along with the scheduling signal properties, the overall conic bounds of the gain-scheduled system can be calculated. We also show that when hybrid very strictly passive/finite gain (VSP/finite gain) subsystems are gain-scheduled, the overall map is also hybrid VSP/finite gain. Being concerned with the control of aerospace systems, we use the theory developed in this thesis to control two interesting plants. We consider passivity-based control of a spacecraft endowed with magnetic torque rods and reaction wheels. In particular, we synthesize a LTV input strictly passive controller. Using hybrid theory we control single- and two-link flexible manipulators. We present two controller synthesis schemes, each of which employs numerical optimization techniques and attempts to have the hybrid VSP/finite gain controllers mimic a H2 controller. One of our synthesis methods uses the Generalized Kalman-Yakubovich-Popov Lemma, thus realizing a convex optimization problem.

The objective of this thesis is to study the combination of environmental Input-Output (IO) models with Life Cycle Inventory (LCI) into a common framework which allows for regional/national environmental analyses meanwhile conserving process details. Our results show that this is possible by "adapting" the two methods: compiling LCI in matrix form and disaggregating as much as possible the IO tables. Given the poor data availability for detailed analyses at regional/national level, the results of this work suggest starting with a "traditional" IO analysis together with a Structural Path Analysis to identify the most polluting sectors and the paths through which the pollution is propagated. For the identified most polluting sectors, LCA data should be collected and fed into a tiered-hybrid IO-LCA model which gives the regional picture meanwhile conserves process details.