Looking for orders of magnitude : five essays in trade and the environment and economic geography

General Summary Although the chapters of this thesis address a variety of issues, the principal aim is common: test economic ideas in an international economic context. The intention has been to supply empirical findings using the largest suitable data sets and making use of the most appropriate empirical techniques. This thesis can roughly be divided into two parts: the first one, corresponding to the first two chapters, investigates the link between trade and the environment, the second one, the last three chapters, is related to economic geography issues. Environmental problems are omnipresent in the daily press nowadays and one of the arguments put forward is that globalisation causes severe environmental problems through the reallocation of investments and production to countries with less stringent environmental regulations. A measure of the amplitude of this undesirable effect is provided in the first part. The third and the fourth chapters explore the productivity effects of agglomeration. The computed spillover effects between different sectors indicate how cluster-formation might be productivity enhancing. The last chapter is not about how to better understand the world but how to measure it and it was just a great pleasure to work on it. "The Economist" writes every week about the impressive population and economic growth observed in China and India, and everybody agrees that the world's center of gravity has shifted. But by how much and how fast did it shift? An answer is given in the last part, which proposes a global measure for the location of world production and allows to visualize our results in Google Earth. A short summary of each of the five chapters is provided below. The first chapter, entitled "Unraveling the World-Wide Pollution-Haven Effect" investigates the relative strength of the pollution haven effect (PH, comparative advantage in dirty products due to differences in environmental regulation) and the factor endowment effect (FE, comparative advantage in dirty, capital intensive products due to differences in endowments). We compute the pollution content of imports using the IPPS coefficients (for three pollutants, namely biological oxygen demand, sulphur dioxide and toxic pollution intensity for all manufacturing sectors) provided by the World Bank and use a gravity-type framework to isolate the two above mentioned effects. Our study covers 48 countries that can be classified into 29 Southern and 19 Northern countries and uses the lead content of gasoline as proxy for environmental stringency. For North-South trade we find significant PH and FE effects going in the expected, opposite directions and being of similar magnitude. However, when looking at world trade, the effects become very small because of the high North-North trade share, where we have no a priori expectations about the signs of these effects. Therefore popular fears about the trade effects of differences in environmental regulations might by exaggerated. The second chapter is entitled "Is trade bad for the Environment? Decomposing worldwide SO2 emissions, 1990-2000". First we construct a novel and large database containing reasonable estimates of SO2 emission intensities per unit labor that vary across countries, periods and manufacturing sectors. Then we use these original data (covering 31 developed and 31 developing countries) to decompose the worldwide SO2 emissions into the three well known dynamic effects (scale, technique and composition effect). We find that the positive scale (+9,5%) and the negative technique (-12.5%) effect are the main driving forces of emission changes. Composition effects between countries and sectors are smaller, both negative and of similar magnitude (-3.5% each). Given that trade matters via the composition effects this means that trade reduces total emissions. We next construct, in a first experiment, a hypothetical world where no trade happens, i.e. each country produces its imports at home and does no longer produce its exports. The difference between the actual and this no-trade world allows us (under the omission of price effects) to compute a static first-order trade effect. The latter now increases total world emissions because it allows, on average, dirty countries to specialize in dirty products. However, this effect is smaller (3.5%) in 2000 than in 1990 (10%), in line with the negative dynamic composition effect identified in the previous exercise. We then propose a second experiment, comparing effective emissions with the maximum or minimum possible level of SO2 emissions. These hypothetical levels of emissions are obtained by reallocating labour accordingly across sectors within each country (under the country-employment and the world industry-production constraints). Using linear programming techniques, we show that emissions are reduced by 90% with respect to the worst case, but that they could still be reduced further by another 80% if emissions were to be minimized. The findings from this chapter go together with those from chapter one in the sense that trade-induced composition effect do not seem to be the main source of pollution, at least in the recent past. Going now to the economic geography part of this thesis, the third chapter, entitled "A Dynamic Model with Sectoral Agglomeration Effects" consists of a short note that derives the theoretical model estimated in the fourth chapter. The derivation is directly based on the multi-regional framework by Ciccone (2002) but extends it in order to include sectoral disaggregation and a temporal dimension. This allows us formally to write present productivity as a function of past productivity and other contemporaneous and past control variables. The fourth chapter entitled "Sectoral Agglomeration Effects in a Panel of European Regions" takes the final equation derived in chapter three to the data. We investigate the empirical link between density and labour productivity based on regional data (245 NUTS-2 regions over the period 1980-2003). Using dynamic panel techniques allows us to control for the possible endogeneity of density and for region specific effects. We find a positive long run elasticity of density with respect to labour productivity of about 13%. When using data at the sectoral level it seems that positive cross-sector and negative own-sector externalities are present in manufacturing while financial services display strong positive own-sector effects. The fifth and last chapter entitled "Is the World's Economic Center of Gravity Already in Asia?" computes the world economic, demographic and geographic center of gravity for 1975-2004 and compares them. Based on data for the largest cities in the world and using the physical concept of center of mass, we find that the world's economic center of gravity is still located in Europe, even though there is a clear shift towards Asia. To sum up, this thesis makes three main contributions. First, it provides new estimates of orders of magnitudes for the role of trade in the globalisation and environment debate. Second, it computes reliable and disaggregated elasticities for the effect of density on labour productivity in European regions. Third, it allows us, in a geometrically rigorous way, to track the path of the world's economic center of gravity.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Sierpinski curve Julia sets for quadratic rational maps

We investigate under which dynamical conditions the Julia set of a quadratic rational map is a Sierpiński curve.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

The conditions for the reproduction of the SAGUAPAC water cooperative in the city of Santa Cruz de la Sierra, Bolivia : discourse analysis

The SAGUAPAC cooperative in the city of Santa Cruz de la Sierra (Eastern Bolivia) is regularly presented as an example of cooperative successes regarding water supply and sanitation. Its efficiency, both economic and technical, is widely considered as the main reason for its attractiveness. However, without denying its importance, we show, through a discourse analysis from and about SAGUAPAC in local media, that moral and non-instrumental factors are crucial in the reproduction of the cooperative. These factors create attachment and affection toward the cooperative, through a storytelling using a four-dimensional rhetoric (mythification, identification, emotionalisation and personification). This storytelling technique, internalized in the local media discourse and materializing the so-called new spirit of capitalism, exploits the affects and instrumentalisation of local myths and legends, as well as the 'camba' ethnic identity. In that, it tends to retain SAGUAPAC members and to canvass new ones, by providing them with recognition in their quality of local community members. However, the mobilisation of social norms and power hierarchies might end up reinforcing the social exclusion of Andean non-camba immigrants, inspite of an a priori inclusive and democratic organisation.

Sierpinski curve Julia sets for quadratic rational maps

We investigate under which dynamical conditions the Julia set of a quadratic rational map is a Sierpiński curve.

Stochasticity in economic losses increases the value of reputation in indirect reciprocity.

Recent theory predicts harsh and stochastic conditions to generally promote the evolution of cooperation. Here, we test experimentally whether stochasticity in economic losses also affects the value of reputation in indirect reciprocity, a type of cooperation that is very typical for humans. We used a repeated helping game with observers. One subject (the "Unlucky") lost some money, another one (the "Passer-by") could reduce this loss by accepting a cost to herself, thereby building up a reputation that could be used by others in later interactions. The losses were either stable or stochastic, but the average loss over time and the average efficiency gains of helping were kept constant in both treatments. We found that players with a reputation of being generous were generally more likely to receive help by others, such that investing into a good reputation generated long-term benefits that compensated for the immediate costs of helping. Helping frequencies were similar in both treatments, but players with a reputation to be selfish lost more resources under stochastic conditions. Hence, returns on investment were steeper when losses varied than when they did not. We conclude that this type of stochasticity increases the value of reputation in indirect reciprocity.

High-density linkage maps fail to detect any genetic component to sex determination in a Rana temporaria family.

Sex chromosome differentiation in Rana temporaria varies strikingly among populations or families: whereas some males display well-differentiated Y haplotypes at microsatellite markers on linkage group 2 (LG2 ), others are genetically undistinguishable from females. We analysed with RADseq markers one family from a Swiss lowland population with no differentiated sex chromosomes, and where sibship analyses had failed to detect any association between the phenotypic sex of progeny and parental haplotypes. Offspring were reared in a common tank in outdoor conditions and sexed at the froglet stage. We could map a total of 2177 SNPs (1123 in the mother, 1054 in the father), recovering in both adults 13 linkage groups (= chromosome pairs) that were strongly syntenic to Xenopus tropicalis despite > 200 My divergence. Sexes differed strikingly in the localization of crossovers, which were uniformly distributed in the female but limited to chromosome ends in the male. None of the 2177 markers showed significant association with offspring sex. Considering the very high power of our analysis, we conclude that sex determination was not genetic in this family; which factors determined sex remain to be investigated.

A feasability study of the analysis of the economic outcomes of specialized nutrition in obesity and weight management in Switzerland

The increasing prevalence of obesity and its associated complications requires specialized care to improve outcomes and control health care costs. Obesity is associated with numerous serious and costly medical problems requiring specialized care in managing health. The economic burden of obesity includes increased inpatient and outpatient medical expenditures as well as employer-related issues of absenteeism and associate costs. The objectives of this study are: - To describe the health consequences and the economic burden of obesity, - To review the existing treatment - To argue in favor of a specialized nutritional intervention that has shown to improve health and reduce obesity related health care costs. Therefore, expose the possibility of introducing the specialized nutrition in Switzerland and the feasibility of this project considering the medical trends and reimbursement system in Switzerland The benefits and outcomes for the patients will be the significant weight loss which reduces the severity and risk factors for complications and the improved health and quality of life. Weight loss will be a combination of a diet, exercise and behavioral interventions which are the basic recommendations for obesity treatment in addition to the specialized nutritional support. By nutritional support, we mean products that are intended to provide nutritional support in the dietary management of people with specific diseases and conditions when adequate intake of regular foods is compromised. These products are called, Food for special medical purposes FSMP. They are not intended to treat, cure, prevent, mitigate or have a direct impact on disease in a manner similar to drugs or other medical treatments and should be used under medical supervision. They also provide a low cost alternative to surgery. From a health care system perspective, the specialized nutrition will drive its advantage by reducing the utilization of medical services for obesity associated complications like medication, physician's consultations and surgical interventions arriving to a cost effective care for the hospitals, the health care organizations and the third party payers which are the health insurances. [Author, p. 4]

Human Capital Composition and Economic Growth at a Regional Level

With this paper we build a two-region model where both innovation and imitation are performed. In particular imitation takes the form of technological spillovers that lagging regions may exploit given certain human capital conditions. We show how the high skill content of each region’s workforce (rather than the average human capital stock) is crucial to determine convergence towards the income level of the leader region and to exploit the technological spillovers coming from the frontier. The same applies to bureaucratic/institutional quality which are conductive to higher growth in the long run. We test successfully our theoretical result over Spanish regions for the period between 1960 and 1997. We exploit system GMM estimators which allow us to correctly deal with endogeneity problems and small sample bias.

Managerial Careers in the IT Industry: Women in China and in Finland

This research focuses on the career experiences of women managers in the IT industry in China and Finland, two countries with different cultures, policies, size of population, and social and economic structures regarding work-life support and equal opportunities. The object of this research is to present a cross-cultural comparison of women’s career experiences and how women themselves understand and account for their careers. The study explores how the macro and the micro levels of cultural and social processes become manifested in the lives of individual women. The main argument in this thesis is that culture plays a crucial role in making sense of women’s career experiences, although its role should be understood through its interrelationship with other social processes, e.g., institutional relations, social policies, industrial structures and organizations, as well as globalization. The interrelationship of a series of cultural and social processes affects individuals’ attitudes to, and arrangement and organization of, their work and family lives. This thesis consists of two parts. The first part introduces the research topic and discusses the overall results. The second part comprises five research papers. The main research question of the study is: How do cultural and social processes affect the experiences of women managers? Quantitative and qualitative research methods, which include in-depth interviews, Q-methodology, interpretive analysis, and questionnaires, are used in the study. The main theoretical background is culturally sensitive career theory and the theory of individual differences. The results of this study are viewed through a feminist lens. The research methodology applied allows new explorations on how demographic factors, work experiences, lifestyle issues, and organizational cultures can jointly affect women’s managerial careers. The sample group used in the research is 42 women managers working in IT companies in China (21) and Finland (21). The results of the study illustrate the impact of history, tradition, culture, institutional relations, social politics, industry and organizations, and globalization on the careers of women managers. It is claimed that the role of culture – cultural norms within nations and organizations – is of great importance in the relationship of gender and work. Women’s managerial careers are affected by multiple factors (personal, social and cultural) reflecting national and inter-individual differences. The results of the study contribute to research on careers, adding particularly to the literature on gender, work and culture, and offering a complex and holistic perspective for a richer understanding of pluralism and global diversity. The results of the study indicate how old and new career perspectives are evidenced in women managers in the IT industry. The research further contributes to an understanding of women’s managerial careers from a cross-culture perspective. In addition, the study contributes to the literature on culture and extends understanding of Hofstede’s work. Further, most traditional career theories do not perceive the importance of culture in determining an individual’s career experience and this study richens understanding of women managers’ careers and has considerable implications for international human resource management. The results of this study emphasize the need, when discussing women managers’ careers, to understand the ways by which gendering is produced rather than merely examining gender differences. It is argued that the meaning of self-knowledge is critical. Further, the environment where the careers under study develop differs greatly; China and Finland are very different – culturally, historically and socially. The findings of this study should, therefore, be understood as a holistic, specific, and contextually-bound.

PYROLIGNEOUS LIQUOR PRODUCED FROM ACACIA MEARNSII DE WILD WOOD UNDER CONTROLLED CONDITIONS AS A RENEWABLE SOURCE OF CHEMICALS

Acacia mearnsii de Wild (black wattle) is one of the most important trees planted in Southern Brazil for tannin extraction and charcoal production. The pyrolysis of the black wattle wood used for obtaining charcoal is performed in brick ovens, with the gas fraction being sent directly into the environment. The present study examines the condensable compounds present in the liquor produced from black wattle wood at different thermal degradation conditions, using gas chromatography coupled with mass spectrometry (GC/MS). Branches of black wattle were thermally degraded at controlled ambient and temperature conditions. Overall, a higher variety of compounds were obtained under atmospheric air pressure than under synthetic air pressure. Most of the tentatively identified compounds, such as carboxylic acids, phenols, aldehydes, and low molecular mass lignin fragments, such as guayacol, syringol, and eugenol, were products of lignin thermoconversion. Substituted aromatic compounds, such as vanillin, ethyl vanillin, and 2-methoxy-4-propeny-phenol, were also identified. At temperatures above 200 ºC, furan, 2-acetylfuran, methyl-2-furoate, and furfural, amongst others, were identified as polysaccharide derivatives from cellulose and hemicellulose depolymerization. This study evidences the need for adequate management of the condensable by-products of charcoal production, both for economic reasons and for controlling their potential environmental impact.

Traffic flow development at North European company level with respect of China and Russia

The aim of the thesis is to analyze traffic flows and its development from North European companies` point of view to China and Russia using data from logistics questionnaire. Selected North European companies are large Finnish and Swedish companies. The questionnaire was sent via email to the target group. The study is based on the answers got from respondent companies from years 2006, 2009 and 2010. In the thesis Finnish Talouselämä newspaper and Swedish Affärsdata are used as a database to find the target companies for the survey. Respondents were most often logistics managers in companies. In the beginning of the thesis concepts of transportation logistics is presented, including container types, trade terms, axel loads in roads and in railways. Also there is information about warehousing types and terminals. After that, general information of Chinese and Russian transportation logistics is presented. Chinese and Russian issues are discussed in two sections. In both of them it is analyzed economic development, freight transport and trade balance. Some practical examples of factory inaugurations in China and Russia are presented that Finnish and Swedish companies have completed. In freight transport section different transportation modes, logistics outsourcing and problems of transportation logistics is discussed. The results of the thesis show that transportation flows between Europe and China is changing. Freight traffic from China to European countries will strengthen even more from the current base. When it comes to Russia and Europe, traffic flows seem to be changing from eastbound traffic to westbound traffic. It means that in the future it is expected more freight traffic from Russia to Europe. Some probable reasons for that are recent factory establishments in Russia and company interviews support also this observation. Effects of the economic recession are mainly seen in the lower transportation amounts in 2009.

China's 'Assertive’ South China Sea Policy And Rhetoric: Proactive, Reactive Or Myth?

In March 2010, Chinese State Councillor, Dai Bingguo, in a private meeting with US Deputy Secretary of State, James Steinberg, allegedly referred to the South China Sea (SCS) as one of the country’s ‘core interests’, a term normally only used to refer to regions like Taiwan, Tibet and Xinjiang upon whose sovereignty Beijing will make no compromises. This alleged wording by Mr Dai caused a strong global reaction, with many countries around the world expressing a fear that China, on the back of its rise to the status of the world's second largest economic power, was now about to implement a more assertive foreign policy more in keeping with its new status of global superpower. As the use of the term ‘core interest’ took place in a private meeting and appears to have been subsequently leaked, it is impossible to prove what was said or meant, yet in 2011, with China and the US continuing to eye each other with suspicion, the adverse repercussions of people trying to deduce what was meant are undeniable. By analysing the views of experts and the evolution or otherwise of Chinese rhetoric and policy towards the SCS, this thesis will show how the alleged use of a term in a private meeting can have consequences that far exceed what was originally intended. It will also show that it is highly unlikely that China’s maritime policy is becoming more assertive as, at China's present stage of social and economic development, it simply cannot afford the ill will and adverse consequences that would result from an act of international aggression. It will show how easy it seems to be for a country like the US to project a misleading image of another country’s intentions, which can in turn serve partially to mask its own intentions. Finally, it will show that the China’s stance on the SCS is starting to be seen by the world as a litmus test for the assertiveness of overall Chinese foreign policy.