A medium and long-term carbon emission forecasting method for provincial power grid
The development of low-carbon electric power industry is a key way to deal with the greenhouse effect and lower the PM2.5. When integrated into power grid, renewable energy can efficiently cut down the output of the thermal power and reduce the carbon emission of the grid significantly. However, it lacks a more comprehensive and systematic approach to forecast a medium and long-term carbon emission for provincial power grid. Thus, in this paper, life cycle assessment (LCA) method and gray prediction model are used to forecast the medium and long-term carbon emission. In addition, the carbon intensity of provincial power grid is evaluated based on historical data such as electricity consumption, gross domestic product (GDP), carbon emission coefficient. Taking Jiangxi power grid as the example, the results indicate that the proposed method is of good accuracy and the integration of renewable energy can effectively reduce the carbon emission and the carbon intensity.
Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction
In this study, a Takagi-Sugeno-Kang based intuitionistic fuzzy logic system is proposed for the prediction of global carbon dioxide emission for the first time. The intuitionistic fuzzy logic system is an integration of artificial neural network learning and intuitionistic fuzzy logic reasoning. The gradient descent back propagation is applied in the optimization of the parameters of the proposed model. The model is evaluated based on some performance metrics. Results of evaluation revealed that the intuitionistic fuzzy logic system outperforms other existing models in the literature in terms of prediction accuracy
Application of System Dynamics model as decision making tool in urban planning process toward stabilizing carbon dioxide emissions from cities
In spite of the fact that cities are the main sources of CO2 emissions, presently there are still no specific measures directly addressing the global warming issue in the urban planning process in Malaysia. The present study thus aims to shed new light in the urban planning sector in Malaysia by adopting System Dynamics Model as one of the decision making tools in the urban planning process, with specific considerations on the future CO2 emission trends. This paper presented projections of future CO2 emission trends based on the case of Iskandar Development Region of Malaysia, under various options of urban policies, using the System Dynamics Model. The projections demonstrated the capability of the said model in serving as a decision making tool in the urban planning process, with specific reference to CO2 emissions from cities. Recommendations have been made on the possible approach of adopting the model in the process of Structure Plan study. If the current model was successfully adopted in the urban planning process in Malaysia, it will mark the first step for Malaysia in taking specific considerations on the issues of CO2 emissions and global warming in the urban planning process.