Search

The aim of this paper is to define low carbon society (LCS) based on the phase diagram with the variables from the IPAT [Human Impact (I) on the environment equals the product of population (P) affluence (A) and technology (T)], starting from the problem definitions: (1) Economy is not everything for LCS and (2) it is not clear if speed on the constraint is more important than liberation from it. To tackle these problems, two research questions are set: (1) What are the basic indicators, objects, and constraints to shape the argument of LCSs and (2) what are the historical paths of several countries and what can be said for their future paths toward carbon societies? Time series data from 1900 or before are used, while the phase diagram with the variables from the IPAT equation is used as the core methodology. In addition to the IPAT variables, the importance of land per capita shall be considered as another basic indicator for LCS related to the carrying capacity. The three different kinds of objects [i.e., total gross domestic product (GDP), GDP per capita, and social indicator] and the two constraints (i.e., total emission and emission per capita) are considered, and it is shown that the combination of these objects and constraints strongly affects the argument of the carrying capacity of climate and land, development of economic system and economic man, and other human developments. Among the four cases analyzed in the phase diagram, the case where GDP per capita is object and total emission is constraint is given the most attention and requires the further research in the future since it does not contradict with a definition of LCS. It is also suggested that if changing the direction of the path with low carbon technology development takes time and cost, institutional arrangement would be additionally necessary in addition to market mechanism where optimal solutions are found on constraint. [ABSTRACT FROM AUTHOR]

The rapid development of wind energy in Japan and the associated high uncertainties and fluctuations in power generation present a big challenge for both wind power generators and electric grids. Accurate and reliable wind power predictions are necessary to optimize the integration of wind power into existing electrical systems. In this study, a hybrid forecasting system of wind power generation was developed by integrating the Kalman filter (KF) with the high resolution Weather Research and Forecasting (WRF) model as well as an empirical formula of wind power output (power curve). The system has been validated with observations including wind speed and power output over a six-month period for 15 turbine sites at a wind farm in Awaji-island, Japan. The results show that the tuned WRF model is able to provide hub-height wind speed prediction for the target area with reliability to some extent. The predicted wind field can be substantially improved by the Kalman filter as a post-processing procedure. The 15-turbine averaged improvements of mean error, root mean square error, and correlation coefficient are 97%, 22%, and 10%, respectively. Meanwhile, the Kalman filter also demonstrates a promising capability of reducing the uncertainties in the power curve model. Systematic validations regarding both wind speed and power output were carried out against the observations for the target wind farm, which show that the hybrid power forecasting system presented in this paper can be an effective and practical tool for short-term predictions of wind speed and power output in Japan area. [ABSTRACT FROM AUTHOR]