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Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Alkylated PAHs (APAHs) have been shown to be more toxic and persistent than their non-alkylated parent compounds. However, little is known about the extent of soil contamination by these pollutants. To help understand agricultural soil pollution by these compounds at a regional scale, a total of 18 methylated PAHs (MPAHs, a major class of APAHs) in 243 soil samples were analyzed. These soil samples were collected from 11 sites in the Yangtze River Delta (YRD) region, a representative fast developing area in China. The total concentration of MPAHs (∑18MPAHs) ranged from 5.5 to 696.2 ng/g dry soil, with methylnaphthalenes (M-NAPs) and methylphenanthrenes (M-PHEs) accounting for more than 70% of the compositional profile. Relatively high concentrations of ∑18MPAHs were found in Jiaxing and Huzhou areas of Zhejiang province, as well as on the border between the cities of Wuxi and Suzhou. Different MPAH groups showed dissimilar spatial distribution patterns. The spatial distribution of lower molecular weight MPAHs was related to agricultural straw burning and emissions/depositions from industrial activities, whereas that of higher molecular weight MPAHs was much more a function of the total organic carbon (TOC) content of soil. Although coal, biomass (crop straw and wood), and petroleum combustion were identified to be the major emission sources for most of the sampling sites, the areas with relatively severe pollution with ∑18MPAHs resulted from the localized hotspots of petroleum leakage. Isomeric MPAHs with methyl group substituted at 2- (β) position exhibited significantly higher concentrations than those substituted at 1- (α) position. Results of this work help to understand soil pollution by MPAHs, and are useful for designing effective strategies for pollution control so as to ensure food safety in areas with fast economic growth.

Alkylated PAHs (APAHs) have been shown to be more toxic and persistent than their non-alkylated parent compounds. However, little is known about the extent of soil contamination by these pollutants. To help understand agricultural soil pollution by these compounds at a regional scale, a total of 18 methylated PAHs (MPAHs, a major class of APAHs) in 243 soil samples were analyzed. These soil samples were collected from 11 sites in the Yangtze River Delta (YRD) region, a representative fast developing area in China. The total concentration of MPAHs (∑18MPAHs) ranged from 5.5 to 696.2 ng/g dry soil, with methylnaphthalenes (M-NAPs) and methylphenanthrenes (M-PHEs) accounting for more than 70% of the compositional profile. Relatively high concentrations of ∑18MPAHs were found in Jiaxing and Huzhou areas of Zhejiang province, as well as on the border between the cities of Wuxi and Suzhou. Different MPAH groups showed dissimilar spatial distribution patterns. The spatial distribution of lower molecular weight MPAHs was related to agricultural straw burning and emissions/depositions from industrial activities, whereas that of higher molecular weight MPAHs was much more a function of the total organic carbon (TOC) content of soil. Although coal, biomass (crop straw and wood), and petroleum combustion were identified to be the major emission sources for most of the sampling sites, the areas with relatively severe pollution with ∑18MPAHs resulted from the localized hotspots of petroleum leakage. Isomeric MPAHs with methyl group substituted at 2- (β) position exhibited significantly higher concentrations than those substituted at 1- (α) position. Results of this work help to understand soil pollution by MPAHs, and are useful for designing effective strategies for pollution control so as to ensure food safety in areas with fast economic growth.

Were the large investments in energy and transportation infrastructure effective in fostering economic growth? Or did economic growth trigger these infrastructure developments? To answer these questions, we develop a simple model of production capacity constraints and use China’s Western Development Strategy (WDS) as an example to investigate how the relationships among energy investment, transportation infrastructure expansion and economic growth differ in the pre- and post-WDS periods. Our Granger causality analysis uses a panel data sample for China’s 30 provinces in the Western and non-Western regions for the period of 1991-2012. We find Granger causality only in the post-WDS period from transportation infrastructure expansion to economic growth and from economic growth to energy investment. These results suggest energy and transportation capacity constraints in the post-WDS period but not the pre-WDS period. Their policy implication is that China should continue its energy and transportation infrastructure investments with improved coordination.

Were the large investments in energy and transportation infrastructure effective in fostering economic growth? Or did economic growth trigger these infrastructure developments? To answer these questions, we develop a simple model of production capacity constraints and use China’s Western Development Strategy (WDS) as an example to investigate how the relationships among energy investment, transportation infrastructure expansion and economic growth differ in the pre- and post-WDS periods. Our Granger causality analysis uses a panel data sample for China’s 30 provinces in the Western and non-Western regions for the period of 1991-2012. We find Granger causality only in the post-WDS period from transportation infrastructure expansion to economic growth and from economic growth to energy investment. These results suggest energy and transportation capacity constraints in the post-WDS period but not the pre-WDS period. Their policy implication is that China should continue its energy and transportation infrastructure investments with improved coordination.

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.