Throughout the history, Energy transformations have had great impact on economies and societies in general. Today’s global developments towards a decarbonized economy is also transforming energy systems and socio-economic organizations of countries in many ways. Germany, with its Energiewende, presents itself or sometimes perceived as a model in this regard. The progress of Germany is much commended due to fast development of renewables in a relatively short time span. However as with all radical far-reaching socio-economic changes, it has not been spared from heavy criticism, especially regarding cost to society and unanticipated technological consequences regarding grid problems.
In the areas of affordability, sustainability, supply security therefore, the scorecard is mixed. Among the achievements of the Energiewende one surprising issue has not received much attention: the increased community ownership and decentralization of power generation and its potential impact on the socio-economic organization of society towards more democratization and effective community involvement at all levels of energy and economic policy-making. This paper thus, along with other aspects of Energiewende tries to focus on this issue.
The German energy transition far from reaching its overall ultimate targets and the challenges lying ahead are huge and needs much careful policy adjustments for the coming decades.
Aims and Place: The variable transparency, temperature, dissolved oxygen, conductivity, pH, total suspended solids (TSS), turbidity and ion balance (Na+, K+, Ca2+, Mg2+, Cl-, HCO3- + CO32- and SO42-) were measured in surface waters of the Amazon River and Mamiá Lake during the hydrological cycle 2008 – 2009 in order to understand the influence of fluvial-lacustrine connectivity in lentic systems of the Amazon floodplain.
Methodology: International standards methods of collection, transportation, preservation and analysis were used in this research and the results were statistically analyzed (Cluster and PCA) to verify the existence of seasonality, interference of the flood pulse and similarities between the sampling sites.
Results and Conclusion: A standard mixing and movement of water was identified for the Amazon River and its channel of connection, while the lake remained stratified throughout the study period. Statistical analysis confirmed seasonality in fluvial-lacustrine system, especially during periods of flooding (F) and ebb (E), when there was a significant change in the waters chemical composition of the lake and channel. Principal Components Analysis (PCA) identified the parameters conductivity, TSS and turbidity as indicator variables of the existence of flood pulse from the Amazon River to the Mamiá Lake.
A simplex-lattice mixture design and the surface response methodology (SRM) were used to modeling the methane production on the anaerobic co-digestion (AcoD) of three different substrates generated from slaughterhouses: manures (Ms), solid organic wastes (SOW) and wastewaters (SHWW). In the first stage of the study, a characterization of these residuals was carried out; meanwhile, the mixture design was used in the second step to determine the methane production obtained on the AcoD of the substrates considered. The results of the analysis of RSM show that the best adjusted model was the special cubic, with high values of R2 and R2adj of 95.13% and 90.96%, respectively. According to the statistical – mathematical model obtained in this study, wastes generated from slaughterhouses are appropriated material for acquire biogas; nonetheless, significant antagonistic effects was observed when increasing the amounts of SOW and SHWW, apparently by the increase in the levels of proteins and fat, oil and grease (FOGs). A good strategy to implement in order to achieve high methane productions for the effluent treatments from meat producing industries is a combination of substrates Ms and SOW; meanwhile, is preferable to separately treat the SHWW in high rate AD systems or anaerobic lagoons.
Aims: This paper aims to develop prediction models for forecasting rainfall occurrence over the Bagmati river basin of Nepal based upon climate related predictor variables.
Study Design: Time series design with statistical downscaling of large scale daily climate data and observed rainfall data.
Place and Duration of Study: Study was conducted at Central Department of Statistics, Tribhuvan University, Kirtipur, Nepal, between 2013 and 2015.
Methodology: A day is considered as a wet day if area weighted daily rainfall (AWDR) is more than 1 mm. Extreme rainfall is determined by the 98thpercentile of AWDR. Binary logistic regression models are built with 13 possible principal components (PCs) of 7 climate related predictor variables using daily data for 1981-2000 period. Thereafter, built models are validated for 2001-2008 period.
Results: Nine separate seasonal logistic models are fitted with Hosmer-Lemeshow tests having at least 0.207 p-values. The first PC of Air surface temperature has the greatest influence with odds ratio (OR) of 4.757 in predicting a wet day during post-monsoon across four models. It is followed by the first PC of Relative humidity with OR (4.112) in winter, first PC of Relative humidity with OR (3.443) in pre-monsoon and second PC of Relative humidity with OR (3.601) in monsoon. Similarly, second PC of Relative humidity has the highest contribution with OR (7.395) in predicting extreme rainfall in post-monsoon across all five models. It is followed by the first PC of Air surface temperature with OR (7.194) in monsoon, first PC of Relative humidity in winter with OR (6.820) and pre-monsoon with OR (5.076), and second PC of Relative humidity with OR (3.186) for the non-seasonal model.
Conclusion: The developed logistic regression models are applicable in forecasting rainfall occurrence seasonally in the Bagmati river basin of Nepal.
Environmental protection becomes a global challenge currently. Green roof is one of the innovative concepts to face this battle. An increase in its use is noticed in urban areas worldwide. But a question arises: what are the environmental consequences of the green roofs’ life cycle? In this paper, the environmental performance of two complete systems of lighter and heavier green roofs implemented in a global south low-income country are analyzed and compared in order to determine the potential impacts of both types of green roof systems. For proposing solutions aiming at reducing environmental loads of green roofs, Life-Cycle Assessment (LCA) approach was used in the present study. For this purpose, the approach consists of the following phases: definition of the objective, life cycle inventory, characterization of impacts, and interpretation of results. LCA calculations were done with the help of OpenLCA software. Results show that, non treated materials and / or imported ones are more environmentally impactful. Hence, it is profitable to reduce the use of cement, gravel, virgin plastics, and soil as well as imported materials whose transport is done by plane. In addition, use of natural fertilizer for amending the growth substrate and water from well for watering the green roof, is also recommended.