Climate change technology transfer to developing countries: evidence analysis and policy recommendations

Developing countries are experiencing unprecedented levels of economic growth. As a result, they will be responsible for most of the future growth in energy demand and greenhouse gas (GHG) emissions. Curbing GHG emissions in developing countries has become one of the cornerstones of a future international agreement under the United Nations Framework Convention for Climate Change (UNFCCC). However, setting caps for developing countries’ GHG emissions has encountered strong resistance in the current round of negotiations. Continued economic growth that allows poverty eradication is still the main priority for most developing countries, and caps are perceived as a constraint to future growth prospects. The development, transfer and use of low-carbon technologies have more positive connotations, and are seen as the potential path towards low-carbon development. So far, the success of the UNFCCC process in improving the levels of technology transfer (TT) to developing countries has been limited. This thesis analyses the causes for such limited success and seeks to improve on the understanding about what constitutes TT in the field of climate change, establish the factors that enable them in developing countries and determine which policies could be implemented to reinforce these factors. Despite the wide recognition of the importance of technology and knowledge transfer to developing countries in the climate change mitigation policy agenda, this issue has not received sufficient attention in academic research. Current definitions of climate change TT barely take into account the perspective of actors involved in actual climate change TT activities, while respective measurements do not bear in mind the diversity of channels through which these happen and the outputs and effects that they convey. Furthermore, the enabling factors for TT in non-BRIC (Brazil, Russia, India, China) developing countries have been seldom investigated, and policy recommendations to improve the level and quality of TTs to developing countries have not been adapted to the specific needs of highly heterogeneous countries, commonly denominated as “developing countries”. This thesis contributes to enriching the climate change TT debate from the perspective of a smaller emerging economy (Chile) and by undertaking a quantitative analysis of enabling factors for TT in a large sample of developing countries. Two methodological approaches are used to study climate change TT: comparative case study analysis and quantitative analysis. Comparative case studies analyse TT processes in ten cases based in Chile, all of which share the same economic, technological and policy frameworks, thus enabling us to draw conclusions on the enabling factors and obstacles operating in TT processes. The quantitative analysis uses three methodologies – principal component analysis, multiple regression analysis and cluster analysis – to assess the performance of developing countries in a number of enabling factors and the relationship between these factors and indicators of TT, as well as to create groups of developing countries with similar performances. The findings of this thesis are structured to provide responses to four main research questions: What constitutes technology transfer and how does it happen? Is it possible to measure technology transfer, and what are the main challenges in doing so? Which factors enable climate change technology transfer to developing countries? And how do different developing countries perform in these enabling factors, and how can differentiated policy priorities be defined accordingly? vi Resumen Los paises en desarrollo estan experimentando niveles de crecimiento economico sin precedentes. Como consecuencia, se espera que sean responsables de la mayor parte del futuro crecimiento global en demanda energetica y emisiones de Gases de Efecto de Invernadero (GEI). Reducir las emisiones de GEI en los paises en desarrollo es por tanto uno de los pilares de un futuro acuerdo internacional en el marco de la Convencion Marco de las Naciones Unidas para el Cambio Climatico (UNFCCC). La posibilidad de compromisos vinculantes de reduccion de emisiones de GEI ha sido rechazada por los paises en desarrollo, que perciben estos limites como frenos a su desarrollo economico y a su prioridad principal de erradicacion de la pobreza. El desarrollo, transferencia y uso de tecnologias bajas en carbono tiene connotaciones mas positivas y se percibe como la via hacia un crecimiento bajo en carbono. Hasta el momento, la UNFCCC ha tenido un exito limitado en la promocion de transferencias de tecnologia (TT) a paises en desarrollo. Esta tesis analiza las causas de este resultado y busca mejorar la comprension sobre que constituye transferencia de tecnologia en el area de cambio climatico, cuales son los factores que la facilitan en paises en desarrollo y que politicas podrian implementarse para reforzar dichos factores. A pesar del extendido reconocimiento sobre la importancia de la transferencia de tecnologia a paises en desarrollo en la agenda politica de cambio climatico, esta cuestion no ha sido suficientemente atendida por la investigacion existente. Las definiciones actuales de transferencia de tecnologia relacionada con la mitigacion del cambio climatico no tienen en cuenta la diversidad de canales por las que se manifiestan o los efectos que consiguen. Los factores facilitadores de TT en paises en desarrollo no BRIC (Brasil, Rusia, India y China) apenas han sido investigados, y las recomendaciones politicas para aumentar el nivel y la calidad de la TT no se han adaptado a las necesidades especificas de paises muy heterogeneos aglutinados bajo el denominado grupo de "paises en desarrollo". Esta tesis contribuye a enriquecer el debate sobre la TT de cambio climatico con la perspectiva de una economia emergente de pequeno tamano (Chile) y el analisis cuantitativo de factores que facilitan la TT en una amplia muestra de paises en desarrollo. Se utilizan dos metodologias para el estudio de la TT a paises en desarrollo: analisis comparativo de casos de estudio y analisis cuantitativo basado en metodos multivariantes. Los casos de estudio analizan procesos de TT en diez casos basados en Chile, para derivar conclusiones sobre los factores que facilitan u obstaculizan el proceso de transferencia. El analisis cuantitativo multivariante utiliza tres metodologias: regresion multiple, analisis de componentes principales y analisis cluster. Con dichas metodologias se busca analizar el posicionamiento de diversos paises en cuanto a factores que facilitan la TT; las relaciones entre dichos factores e indicadores de transferencia tecnologica; y crear grupos de paises con caracteristicas similares que podrian beneficiarse de politicas similares para la promocion de la transferencia de tecnologia. Los resultados de la tesis se estructuran en torno a cuatro preguntas de investigacion: .Que es la transferencia de tecnologia y como ocurre?; .Es posible medir la transferencia de tecnologias de bajo carbono?; .Que factores facilitan la transferencia de tecnologias de bajo carbono a paises en desarrollo? y .Como se puede agrupar a los paises en desarrollo en funcion de sus necesidades politicas para la promocion de la transferencia de tecnologias de bajo carbono?

Independent component analysis for the identification of sources of variation on an industrial nirs application

A Near Infrared Spectroscopy (NIRS) industrial application was developed by the LPF-Tagralia team, and transferred to a Spanish dehydrator company (Agrotécnica Extremeña S.L.) for the classification of dehydrator onion bulbs for breeding purposes. The automated operation of the system has allowed the classification of more than one million onion bulbs during seasons 2004 to 2008 (Table 1). The performance achieved by the original model (R2=0,65; SEC=2,28ºBrix) was enough for qualitative classification thanks to the broad range of variation of the initial population (18ºBrix). Nevertheless, a reduction of the classification performance of the model has been observed with the passing of seasons. One of the reasons put forward is the reduction of the range of variation that naturally occurs during a breeding process, the other is the variations in other parameters than the variable of interest but whose effects would probably be affecting the measurements [1]. This study points to the application of Independent Component Analysis (ICA) on this highly variable dataset coming from a NIRS industrial application for the identification of the different sources of variation present through seasons.

Relationship between solar radiation on watercore on apple fruit assessed with MRI

This work is a preliminary studio of the possibility of assess a relationship between solar radiation and watercore development on apple fruit, during maturation, using a non destructive method such as Magnetic Resonance Imaging (MRI). For such purpose, several low cost solar radiation sensors were designed for the trial and placed at 2 different heights (1.5 and 2.5 m) on 6 adult ?Esperiega? apple trees, in a commercial orchard in Ademuz (Valencia). Sensors were connected along 27 days, during the end of the growth period and start of the fruit maturation process, and radiation measurements of the a-Si sensors were recorded every 1 minute. At the end of this period, fruits from the upper and the lower part of the canopy of each tree were harvested. In all, 152 apples were collected and images with MRI. A Principal Component Analysis, perfomed over the histograms of the images, as well as segmentation methods were performed on the MR images in order to find a pattern involving solar radiation and watercore incidence.

Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.

In the last years significant efforts have been devoted to the development of advanced data analysis tools to both predict the occurrence of disruptions and to investigate the operational spaces of devices, with the long term goal of advancing the understanding of the physics of these events and to prepare for ITER. On JET the latest generation of the disruption predictor called APODIS has been deployed in the real time network during the last campaigns with the new metallic wall. Even if it was trained only with discharges with the carbon wall, it has reached very good performance, with both missed alarms and false alarms in the order of a few percent (and strategies to improve the performance have already been identified). Since for the optimisation of the mitigation measures, predicting also the type of disruption is considered to be also very important, a new clustering method, based on the geodesic distance on a probabilistic manifold, has been developed. This technique allows automatic classification of an incoming disruption with a success rate of better than 85%. Various other manifold learning tools, particularly Principal Component Analysis and Self Organised Maps, are also producing very interesting results in the comparative analysis of JET and ASDEX Upgrade (AUG) operational spaces, on the route to developing predictors capable of extrapolating from one device to another.

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

Application of remote sensing and GIS in mineral resource mapping - An overview

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Using multivariate analysis to predict the behaviour of soils under effluent irrigation

Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).

Independent component analysis for domain independent watermarking

A new principled domain independent watermarking framework is presented. The new approach is based on embedding the message in statistically independent sources of the covertext to mimimise covertext distortion, maximise the information embedding rate and improve the method's robustness against various attacks. Experiments comparing the performance of the new approach, on several standard attacks show the current proposed approach to be competitive with other state of the art domain-specific methods.

Watermarking of audio signals using independent component analysis

A novel approach to watermarking of audio signals using Independent Component Analysis (ICA) is proposed. It exploits the statistical independence of components obtained by practical ICA algorithms to provide a robust watermarking scheme with high information rate and low distortion. Numerical simulations have been performed on audio signals, showing good robustness of the watermark against common attacks with unnoticeable distortion, even for high information rates. An important aspect of the method is its domain independence: it can be used to hide information in other types of data, with minor technical adaptations.

Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Neuropathological heterogeneity in Alzheimer's disease:A study of 80 cases using principal components analysis

Three hypotheses have been proposed to explain neuropathological heterogeneity in Alzheimer's disease (AD): the presence of distinct subtypes ('subtype hypothesis'), variation in the stage of the disease ('phase hypothesis') and variation in the origin and progression of the disease ('compensation hypothesis'). To test these hypotheses, variation in the distribution and severity of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in 80 cases of AD using principal components analysis (PCA). Principal components analysis using the cases as variables (Q-type analysis) suggested that individual differences between patients were continuously distributed rather than the cases being clustered into distinct subtypes. In addition, PCA using the abundances of SP and NFT as variables (R-type analysis) suggested that variations in the presence and abundance of lesions in the frontal and occipital lobes, the cingulate gyrus and the posterior parahippocampal gyrus were the most important sources of heterogeneity consistent with the presence of different stages of the disease. In addition, in a subgroup of patients, individual differences were related to apolipoprotein E (ApoE) genotype, the presence and severity of SP in the frontal and occipital cortex being significantly increased in patients expressing apolipoprotein (Apo)E allele ε4. It was concluded that some of the neuropathological heterogeneity in our AD cases may be consistent with the 'phase hypothesis'. A major factor determining this variation in late-onset cases was ApoE genotype with accelerated rates of spread of the pathology in patients expressing allele ε4.