Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances

Data for: Efficiency assessment of hydroelectric power plants in Canada: A multi criteria decision making approach

Abstract of associated article: Hydropower plays a major role in the Canadian electricity generation industry. Few attempts have been made, however, to assess the efficiency of hydropower generation in Canada. This paper analyzes the overall efficiency of hydropower generation in Canada from comprehensive viewpoints of electricity generating capability, its profitability, as well as environmental benefits and social responsibility using the TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution) method. The factors that influence the efficiency of the hydropower generation are also presented to help to the sustainable hydropower production in Canada. The most important results of this study concern (1) the pivotal roles of energy saving and of the social responsibility in the overall efficiency of hydropower corporates and (2) the lower hydropower generation efficiency of some of the most important economic regions in Canada. Other results reveal that the overall efficiency of hydropower generation in Canada experienced an improvement in 2012, following a downtrend from 2005 to 2011. Amidst these influencing factors, energy saving and social responsibility are key factors in the overall efficiency scores while management (defined herein by the number of employees and hydropower stations of a corporation) has only a slightly negative impact on the overall efficiency score

Global and regional scenario data.xlsx

Comparable global and regional data for climate mitigation pathways under 2 and 1.5 degrees extracted from IPCC AR6 database.</p

Is Fe-catalyzed <i>ortho</i> C–H Arylation of Benzamides Sensitive to Steric Hindrance and Directing Group?

The previously reported Fe-catalyzed ortho C–H arylation of benzamides relied on bi- or tridentate amide groups and specific iron ligands and was sensitive to steric hindrance. By using new mixed titanates, our present protocol accommodates various weakly coordinating benzamides and tolerates high steric hindrance and sensitive functional groups only under the catalysis of FeCl3 and TMEDA. A wide range of privileged condensed ring compounds can thus be facilely accessed

Synergism of Fe/Ti Enabled Regioselective Arene Difunctionalization

Regioselective difunctionalization of arenes remains a long-standing challenge in organic chemistry. We report a novel and general Fe/Ti synergistic methodology for regioselective synthesis of various polysubstituted arenes through either E/E′ or Nu/E ortho difunctionalizations of arenes. Preliminary results showed that an unprecedented 1,2-Fe/Ti heterobimetallic arylene intermediate bearing two distinct C–M bonds is essential to the regioselective difunctionalization

Fe-Catalyzed Difunctionalization of Aryl Titanates Enabled by Fe/Ti Synergism

Fe-catalyzed difunctionalization of aryl titanates via double C–H activation has been developed, where aryl titanates were arylated via ortho C–H activation, followed by ipso electrophilic trapping of the C–Ti bond. The ortho C–H arylation should be promoted by a 1,2-Fe/Ti synergistic heterobimetallic arylene intermediate and represents an ortho C–H ferration directed by a readily transformable C–Ti group. Common benzamides, esters, and nitriles function as arylating reagents, which involves another ortho C–H activation directed by these functionalities

Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances

A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price

A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price