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This paper examines the hypothesis that there is a single economic market for the international steam coal industry and investigates the degree of steam coal market integration over time. A regression test of convergence is employed to test for group convergence within a panel of steam coal exporting countries. The long-run relations between international steam coal prices are tested through cointegration analysis and the Kalman Filter analysis is employed to examine the convergence path of the price series. Monthly Free on Board (F.O.B.) prices for Australia, China, Colombia, Indonesia, Poland and South Africa between January 1995 and July 2007 are used. Considering the outcomes of the three econometric techniques as a whole, we conclude that the international steam coal market is generally integrated.

Low-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...

We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning-by-doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are introduction or expansion constraints for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry and thight carbon constraints.

Abstract This study demonstrates the possibility to use energy-cost optimal building designs based on life cycle approach. There is a lack of studies that cover the comprehensive assessment of both embodied energy (EE) and operational energy (OE) in a single optimization problem. The primary goal of the current study is to compare the optimized results of using OE+EE together and OE only. The optimization is performed on a case study building (townhouse) in Finland with three structural alternatives (i.e., reinforced concrete (RC); cross-laminated timber (CLT) and Steel). Different options for insulation thickness of external wall, roof, floor and window types were considered as decision variables as the scope of the present study is on building envelope. The objectives of the optimization are to minimize life cycle energy (LCE) and life cycle costs (LCC). The LCE difference between the most and least energy efficient solution on the pareto front is greater in case of the OE optimization, compared to OE+EE optimization. For all studied structures, the EE of the optimal solutions from OE+EE optimization ranges 16%–23% of LCE. Many of the non-dominated optimal solutions obtained from the OE+EE optimization shows a higher U-value for the building envelope components compared to the optimal solutions from the OE optimization. A relationship between both OE and EE with the overall thermal resistance of the building envelope is discussed to obtain a deeper understanding of such differences in U-value for the optimal solutions obtained from the OE+EE and OE optimization.

AbstractThe aim is to develop a catalysed water-based capture process, avoiding the energy intensive steam stripping steps necessary with amine solvents. The naturally occurring zinc metallo-enzyme carbonic anhydrase/hydratase (CAH) can concentrate CO2 using a reversible hydration/dehydration cycle at neutral pH and at ambient temperatures. Some tripodal complexes of zinc (II)–[zinc-itrilo-tris(2-benzimidazoyl-methyl-6-sulfonic acid, (zinc-L1S), and zinc-tris(2-benzimidazoylmethyl)-amine, (zinc-LI)], and also cadmium (II), cobalt (II) and other metals complexed to these tripodal ligands have been prepared, and show activity that mimics the CAH catalytic process–hydration of CO2 to bicarbonate followed by the reverse dehydration of the bicarbonate to regenerate CO2 in a pH dependent mechanism. The thermal stability of these complex catalysts has been demonstrated by Differential Thermo-Gravimetric (DTG) studies to above 200 °C. A process for fast absorption/desorption is proposed.

Developing causal loop diagrams (CLDs) involves identifying stakeholders and endogenous variables and formulating variable causal relationships. Traditionally, the CLDs are developed mainly using a qualitative approach such as literature review, observations and interviews with stakeholders. However, modellers may question which stakeholders should be approached, whether the relevant variables are selected, and what to do when stakeholders perceive different variable relationships in the CLDs differently. Applying in a case study, this research proposes a multi-method approach by combining both quantitative and qualitative methods to select stakeholders, identify endogenous/exogenous variables, and develop the CLDs. The proposed quantitative method is expected to provide modellers with a justifiable stakeholder and variable selection process. The method also highlights possible hidden variables and relationships, which were further explored with a traditional qualitative approach.

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study—the linear regression (LR), Angstrom–Prescott–Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72%, 13.29%, and 8.11% for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method.

Improvements in energy efficiency and production of renewable energy hold significant potential for reducing greenhouse gas emissions of housing, which accounts for 14% of global greenhouse gas emissions. In our research, we focused on the willingness of owners of detached houses to adopt renewable energy production systems of their own, and we examined perceived barriers to adopting these systems. The research was conducted using a survey and a life cycle assessment model. The survey covered three residential areas in Lahti, Finland, and the potential reductions in greenhouse gas emissions were estimated using a life cycle assessment model based on the survey results. The barriers to transformation were identified as a lack of knowledge in the following three areas: (1) the possible annual savings attained; (2) the costs of implementing energy efficiency and renewable energy production solutions; and (3) the technologies used in renewable energy production. The greenhouse gas emission reductions in the residential areas surveyed would amount to approximately 15% if the consumers implemented the solutions they considered.