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Abstract: With increasing attention on energy shortage and global warming, bioenergy is a promising alternative energy to mitigate CO 2 emissions. Despite the environmental friendly potential, a certain quantity of bioenergy-related CO 2 is still emitted in China. This paper investigates the temporal dynamics of bioenergy-related CO 2 emissions in 2000–2010 in China, and explores the underlying influence factors based on logarithmic mean Divisia index (LMDI). Then the core results are verified with a GIS-based approach from a spacial perspective. The results show that four impact factors: emission intensity, energy structure, energy efficiency and economic development have different impacts on bioenergy-related CO 2 emissions, of which economic development is the major factor contributing to its increase, while energy efficiency is the major determinant for the decline. Per capita bioenergy-related CO 2 emissions in different areas are verified in accordance with either economic development factor or energy efficiency factor to some extent. To achieve the dual purpose of sustainable development and CO 2 emissions reduction, this paper provides a practical policy framework for rural bioenergy and economic development. [ABSTRACT FROM AUTHOR]

An effective harvesting strategy with a low energy consumption as well as total cost is important for biofuel production. Microalgae harvesting by chitosan-based flocculants has received extensive attention, due to the outstanding structural features, biodegradability, applicability to various types of microalgae species and environmental friendliness. However, it is a significant strategy to select and prepare flocculants with high harvesting efficiencies for microalgae harvesting, based on different modifications strategies and external impact factors. This work reviews the latest research on chitosan-based flocculants for microalgae harvesting applications, including general aspects, harvesting mechanisms, several types of the dual harvesting system and surface modification methods commonly used to prepare chitosan-based flocculants. Suitable modification strategies for chitosan can achieve the high harvesting performance by improving synthetic effects and properties. The influences of external factors on microalgae harvesting are emphasized in detail. The effects of chitosan-based flocculants on downstream process are evaluated as well. This paper provides a potential prospect for future studies of chitosan-based flocculants on microalgae harvesting. Furthermore, uncertainties and research gaps of chitosan-based flocculants that exist in the usage of microalgae harvesting on a large scale have been put forward to foster the industry. • This paper reviews recent findings on microalgae harvesting using chitosan-based flocculants. • Chitosan-based flocculants can be modified by dual harvesting system and surface modification. • External factors affecting harvesting efficiency have been reviewed. • More studies of chitosan-based flocculants are needed before engineering application on a large scale. [ABSTRACT FROM AUTHOR]

Wind power forecasting (WPF) brings about decision risk to power system operation, because of its certain deviations. The uncertainty analysis of wind power forecasting (WPFUA) or wind power probabilistic forecasting (WPPF) provides probabilistic and confidence levels for decision makers to increase the forecasting certainty. In order to deeply and comprehensively understand WPFUA or WPPF model and then to optimize the forecasting model, the uncertain factors are firstly introduced in this paper. Secondly, the current WPFUA and WPPF methods are classified in terms of model input, modeling principle, express way and forecasting horizons. It summarizes the characteristics, merits and demerits, and application conditions of different methods. Thirdly, two advanced optimization strategies are discussed respectively from temporal and spatial perspectives. Finally, the development direction of uncertainty analysis technology in future is summarized. The objective of this review is, on one hand, to help WPPF users understand the model characteristics from different perspectives and select the most suitable one for various applications. On the other hand, it helps WPPF providers learn and optimize WPPF according to different characteristics. [ABSTRACT FROM AUTHOR]

In order to mitigate global climate change and air pollution, the Chinese government has assigned high priority to expanding low-carbon power generation in China. Recent studies have shown that wind power is one of the most promising renewable energy option in China. Although many studies have estimated the generation potential of onshore wind power, their results vary widely from 1783 TWh to 39,000 TWh. Therefore, we examine the different assumptions in these papers and identify three main factors influencing the results. The three influencing factors are: weather data set, land utilisation factor, and wind turbine configuration. For our model-based analysis, we define a reference scenario which is used to compare the results. Our analysis shows using a different weather data set increases the generation potential to roughly 35,000 TWh. This is 54% higher than the generation potential of the reference scenario. The land utilisation factor also has a large influence, ranging between −10% and −51%. The studies' assumptions and data should be subjected to careful scrutiny, as the calculated wind power potentials are widely used to develop decarbonisation strategies for the energy system. • Study results on wind power potentials in China vary from 1783 TWh to 39,000 TWh • Influencing factors: weather data set, land utilisation, and wind turbine configuration. • Model-based sensitivity analysis with nine scenario settings. • Chosen weather data set has the highest impact on the calculated results with a variation of 54%. [ABSTRACT FROM AUTHOR]