Technology transfer in the Clean Development Mechanism

Technology transfer is often mentioned as an ancillary benefit of the Kyoto Protocol's Clean Development Mechanism (CDM), but this claim has hardly been researched or substantiated. The question of technology transfer is important, both for developing countries in need for new technology and knowledge and for industrialized countries, as it provides export potential for climate-friendly technologies. To determine what technology transfer means, whether it is occurring through the CDM, and what the value of the associated capital flows is, this article examines technology transfer in the 63 CDM projects that were registered up until 1 January 2006. Technology hardware originates from outside the host country in almost 50% of the evaluated projects, particularly in non-CO₂ greenhouse gas projects, wind energy projects, and a substantial share of the hydropower projects. Bioenergy and projects in the agricultural sector mainly use local technology. The investment value associated with the CDM projects that transferred technology is estimated to be around €470 million, with about €390 million coming from the EU. As the non-CO₂ greenhouse gas projects had very low capital costs, the investment value was highest in the more capital-intensive wind energy and hydropower projects. We also found substantial soft technology transfer, but uncertainties for this finding are greater.     

ESTIMATING INFILTRATION PARAMETERS FROM BASIC SOIL PROPERTIES

Infiltration data were collected on two rectangular grids with 25 sampling points each. Both experimental grids were located in tropical rain forest (Guyana), the first in an Arenosol area and the second in a Ferralsol field. Four different infiltration models were evaluated based on their performance in describing the infiltration data. The model parameters were estimated using non-linear optimization techniques. The infiltration behaviour in the Ferralsol was equally well described by the equations of Philip, Green–Ampt, Kostiakov and Horton. For the Arenosol, the equations of Philip, Green–Ampt and Horton were significantly better than the Kostiakov model. Basic soil properties such as textural composition (percentage sand, silt and clay), organic carbon content, dry bulk density, porosity, initial soil water content and root content were also determined for each sampling point of the two grids. The fitted infiltration parameters were then estimated based on other soil properties using multiple regression. Prior to the regression analysis, all predictor variables were transformed to normality. The regression analysis was performed using two information levels. The first information level contained only three texture fractions for the Ferralsol (sand, silt and clay) and four fractions for the Arenosol (coarse, medium and fine sand, and silt and clay). At the first information level the regression models explained up to 60% of the variability of some of the infiltration parameters for the Ferralsol field plot. At the second information level the complete textural analysis was used (nine fractions for the Ferralsol and six for the Arenosol). At the second information level a principal components analysis (PCA) was performed prior to the regression analysis to overcome the problem of multicollinearity among the predictor variables. Regression analysis was then carried out using the orthogonally transformed soil properties as the independent variables. Results for the Ferralsol data show that the parameters of the Green–Ampt and Kostiakov model were estimated relatively accurately (maximum R² = 0.76). For the Arenosol, use of the second information level together with PCA produced regression models with an R² value ranging from 0.38 to 0.68. For the Ferralsol, most of the variance was explained by the root content and organic matter content. In the Arenosol plot, the fractions medium and fine sand explained most of the observed variance.     

LISEM: A SINGLE-EVENT,PHYSICALLY BASED HYDROLOGICAL AND SOIL EROSION MODEL FOR DRAINAGE BASINS. II: SENSITIVITY ANALYSIS,VALIDATION AND APPLICATION

A new hydrological and soil erosion model has been developed and tested: LISEM, the Limburg soil erosion model. The model uses physically based equations to describe interception, infiltration and soil water transport, storage in surface depressions, splash and flow detachment, transport capacity and overland and channel flow. From the validation results it is clear that, although the model has several advantages over other models, the results of LISEM 1.0 are far from perfect. Based on the sensitivity analysis and field observations, the main reasons for these differences seems to be the spatial and temporal variability of the soil hydraulic conductivity and the initial pressure head at the basin scale. Another reason for the differences between measured and simulated results is our lack or understanding of the theory of hydrological and soil erosion processes.     

ON SOME NEOLITHIC (AND POSSIBLY PALAEOLITHIC) FINDS IN MONGOLIA,SINKIANG AND WEST CHINA

In the course of the past two years,when they were in the field with theThird American (1930) and the Haardt-Citroen (1931) Central Asiatic Expedi-tions,the authors of the present note had opportunities for making a series ofarchaeological observations ov     

A computer-aided framework for subsurface identification of white shark pigment patterns

Subsurface video footage can be used as a successful identification tool for various marine organisms; however, processing of such information has proven challenging. This study tests the use of automated software to assist with photo-identification of the great white shark Carcharodon carcharias in the region of Gansbaai, on the south coast of South Africa. A subsurface photo catalogue was created from underwater video footage. Single individuals were identified by using pigmentation patterns. From this catalogue, two images of the head for each individual were inserted into automated contour-recognition software (Interactive Individual Identification System Beta Contour 3.0). One image was used to search the database, the other served as a reference image. Identification was made by means of a contour, assigned using the software to the irregular border of grey and white on the shark's head. In total, 90 different contours were processed. The output provided ranks, where the first match would be a direct identification of the individual. The method proved to be accurate, in particular for high-quality images where 88.24% and 94.12%, respectively, were identified by two independent analysts as first match, and with all individuals identified within the top 10 matches. The inclusion of metadata improved accuracy and precision, allowing identification of even low-quality images.     

CEMPS: A prototype spatial decision support system to aid in planning emergency evacuations

CEMPS is a prototype spatial decision support system which links the topographical support and analysis provided by a geographic information system, ARC/INFO, with the ability to simulate the dynamics of an evacuation process. CEMPS has been designed to enable emergency planners to experiment with different emergency evacuation plans in order to devise a plan which meets their requirements. ARC/INFO is used to parameterize a dynamic simulation with topographical information and to display its results. The prototype runs on a Sun SPARCStation cluster but could be modified to run on other hardware and software.