Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon...Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.展开更多
A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector...A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.展开更多
This paper takes the climate change and low carbon economy development as the study background, based on the analysis of energy demand and carbon emissions status, which is aimed to provide the low carbon development ...This paper takes the climate change and low carbon economy development as the study background, based on the analysis of energy demand and carbon emissions status, which is aimed to provide the low carbon development path in Chinese cities. The method of scenario analysis can be used to predict long-term strategy for the uncertainty future development, and it was introduced to the field of social forecasting and public policy research, such as the environmental strategic planning, policy analysis, and support of decision in resource management, which can be used to explore the possible development trend and target of the results from the macro perspective. Scenario analysis has been gradually applied to the study area on low carbon economy, energy forecasting and other fields in recent years, and there have been many research results in different aspects. This paper takes the scenario analysis as basic study theory, spreading out the present situation of its application in low carbon city and some issues that need further study. As a tool for predicting the future development in low carbon city, the method of scenario analysis has been providing a powerful reference for policies and their executants.展开更多
文摘Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.
基金This work is supported by National Natural Science Foundation of China(No.51007047,No.51077087)Shandong Provincial Natural Science Foundation of China(No.20100131120039)National High Technology Research and Development Program of China(863 Program)(No.2011AA05A101).
文摘A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.
文摘This paper takes the climate change and low carbon economy development as the study background, based on the analysis of energy demand and carbon emissions status, which is aimed to provide the low carbon development path in Chinese cities. The method of scenario analysis can be used to predict long-term strategy for the uncertainty future development, and it was introduced to the field of social forecasting and public policy research, such as the environmental strategic planning, policy analysis, and support of decision in resource management, which can be used to explore the possible development trend and target of the results from the macro perspective. Scenario analysis has been gradually applied to the study area on low carbon economy, energy forecasting and other fields in recent years, and there have been many research results in different aspects. This paper takes the scenario analysis as basic study theory, spreading out the present situation of its application in low carbon city and some issues that need further study. As a tool for predicting the future development in low carbon city, the method of scenario analysis has been providing a powerful reference for policies and their executants.
