A comparison of the energy consumption and carbon emissions for different modes of transportation in open-cut coal mines

Transportation accounts for 80% of open-cut coal mine carbon emissions. With regard to the energy con- sumption and carbon emissions of transportation within an open-cut mine, this paper systematically compared the work and energy consumption of a truck and belt conveyor on a theoretical basis, and con- structed a model to calculate the energy consumption of open-cut mine transportation. Life cycle carbon emission factors and power consumption calculation model were established through a Process Analysis- Life Cycle Analysis （PA-LCA）. The following results were obtained： （1） the energy consumption of truck transportation was four to twelve times higher than that of the belt conveyor; （2） the C02 emissions from truck transportation were three to ten times higher than those of the belt conveyor; （3） with the increase in the slope angle for transportation, the ratio of truck to belt conveyor for both energy consumption and carbon emissions gradually decreased; （4） based on 2013 prices in China, the energy cost of transportation using a belt conveyor in open-cut coal mines could save 0.6-2.4 Yuan/（t kin） compared to truck transportation.

Spatial network structure of transportation carbon emission efficiency in China and its influencing factors

Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.

Relationship Between Built Environment, Socio-economic Factors and Carbon Emissions from Shopping Trip in Shenyang City, China

Promoting active travel behavior and decreasing transport-related carbon dioxide （CO2） emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated C02 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was sig- nificant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with sev- eral built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-eeonomic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.

Unearthing the impact of urban sprawl on transport carbon dioxide emissions:Fresh evidence from 274 Chinese cities

Urban sprawl has been a prevailing phenomenon in developing countries like China,potentially resulting in significant carbon dioxide(CO_(2))emissions from the transport sector.However,the impact of urban sprawl on transport CO_(2) emissions(TCEs)is still not fully understood and remains somewhat rudimentary.To systematically investigate how urban sprawl influences TCEs,we employ panel regression and panel threshold regression for 274 Chinese cities(2005-2020),and obtain some new findings.Our results affirm that the degree of urban sprawl is positively associated with TCEs,and this holds true in different groups of city size and geographical region,while significant heterogeneity is observed in terms of such impact.Interestingly,we find urban sprawl nonlinearly impacts TCEs—with an equal increase in urban sprawl degree,TCEs are even lower in cities with larger population size and better economic condition,particularly in East China.Furthermore,the low-carbon city pilot policy shows potential in mitigating sprawl's impact on TCEs.Drawing on our findings,we argue that to achieve the target of TCEs reduction in China by curbing urban sprawl,more priority should be placed on relatively small,less developed,and geographically inferior cities for cost-efficiency reasons when formulating future urban development strategies.

Nonlinear relationship between urban form and transport CO_(2)emissions:Evidence from Chinese cities based on machine learning

Reducing carbon emissions from the transport sector is essential for realizing the carbon neutrality goal in China.Despite substantial studies on the influence of urban form on transport cO_(2)emissions,most of them have treated the effects as a linear process,and few have studied their nonlinear relationships.This research focused on 274 Chinese cities in 2019 and applied the gradient-boosting decision tree(GBDT)model to investigate the nonlinear effects of four aspects of urban form,including compactness,complexity,scale,and fragmentation,on urban transport CO_(2)emissions.It was found that urban form contributed 20.48%to per capita transport CO_(2)emissions(PTCEs),which is less than the contribution of socioeconomic development but more than that of transport infrastructure.The contribution of urban form to total transport CO_(2)emissions(TCEs)was the lowest,at 14.3%.In particular,the effect of compactness on TCEs was negative within a threshold,while its effect on PTCEs showed an inverted U-shaped relationship.The effect of complexity on PTCEs was positive,and its effect on TCEs was nonlinear.The effect of scale on TCEs and PTCEs was positive within a threshold and negative beyond that threshold.The effect of fragmentation on TCEs was also nonlinear,while its effect on PTCEs was positively linear.These results show the complex effects of the urban form on transport CO_(2)emissions.Thus,strategies for optimizing urban form and reducing urban transport carbon emissions are recommended for the future.

Factors affecting electric vehicle acceptance,energy demand and CO_(2)emissions in Pakistan

This work aims to investigate the factors accelerating electric vehicle(EV)acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition.The study further in-vestigates the high EV penetration scenario resulting from the technology acceptance model(TAM's 80%EV)and its impact on energy demand and CO_(2)emissions.The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance.The model under investigation was adapted from the famous Technology-Acceptance Models(TAMs)and modified with other significant predictors evidenced in the literature.Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing.Out of six predictors,only four factors were significant in accelerating the EV transition.Financial policies were found to be highly significant,followed by environmental concern,facilitating conditions and perceived ease of use.The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results.The results highlight the significant in-crease in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 MT instead of 61.6 MT by 2040.

Heterogeneity of Monosymptomatic Resting Tremor in a Prospective Study： Clinical Features, Electrophysiological Test, and Dopamine Transporter Positron Emission Tomography

Background： The relationship between monosymptomatic resting tremor （mRT） and Parkinson＇s disease （PD） Iemains controversial. In this study, we aimed to assess tile function ofpresynaptic dopaminergic neurons in patients with mRT by dopamine transporter positron emission tomography （DAT-PET） and to evaluate the utility of clinical features or electrophysioIogical studies in differential diagnosis. Methods： Thirty-three consecutive patients with toRT were enrolled prospectively. The Unified Parkinson＇s Disease Rating Scale and electromyography were tested before DAT-PET. Striatal asymmetry index （SAI） was calculated, and a normal DATPET was defined as a SAI of 〈15%. Scans without evidence of dopaminergic deficits （SWEDDs） were diagnosed in patients with a subsequent normal DAT-PET and structural magnetic resonance imaging. Results： Twenty-eight toRT patients with a significant reduction in uptake of DAT binding in the striatum were diagnosed with PD, while the remained 5 with a normal DAT-PET scan were SWEDDs. As for UPRDS, the dressing and hygiene score, walking m motor experiences of daily living （Part I1） and motor examination （Part Ill ） were significant different between two groups （P 〈 0.05 and P 〈 0.01, respectively）. Bilateral tremor was more frequent in the SWEDDs group （P 〈 0.05）. The frequency of resting tremor and the amplitude ofpostural tremor tend to be higher in the SWEDDs group （P = 0.08 and P= 0.05, respectively）. Conclusions： mRT is heterogeneous in presynaptic nigrostriatal dopaminergic degeneration, which can be determined by DAT-PET brain imaging. Clinical and electrophysiological features may provide clues to distinguish PD from SWEDDs.

Research on the big data of traditional taxi and online car-hailing:A systematic review

The purpose of this paper is to provide a summary of a quick overview of the latest developments and unprecedented opportunities for scholars who want to set foot in the field of traditional taxi and online car-hailing(TTOC).From the perspectives of peoples(e.g.,passenger,driver,and policymaker),vehicle,road,and environment,this paper describes the current research status of TTOC's big data in six hot topics,including the ridership factor,spatio-temporal distribution and travel behavior,cruising strategy and passenger service market partition,route planning,transportation emission and new-energy,and TTOC's data extensional application.These topics were included in five mainstreams as follows:(1)abundant studies often focus only on determinant analysis on given transportation(taxi,transit,online car-hailing);the exploration of ridership patterns for a multimodal transportation mode is rare;furthermore,multiple aspects of factors were not considered synchronously in a wide time span;(2)travel behavior research mainly concentrates on the commuting trips and distribution patterns of various travel indices(e.g.,distance,displacement,time);(3)the taxi driver-searching strategy can be divided into autopsychic cruising and system dispatching;(4)the spatio-temporal distribution character of TTOC's fuel consumption(FC)and greenhouse gas(GHG)emissions has become a hotspot recently,and there has been a recommendation for electric taxi(ET)in urban cities to decrease transportation congestion is proposed;and(5)based on TTOC and point of interest(POI)multi-source data,many machine learning algorithms were used to predict travel condition indices,land use,and travel behavior.Then,the main bottlenecks and research directions that can be explored in the future are discussed.We hope this result can provide an overview of current fundamental aspects of TTOC's utilization in the urban area.

Distinct potential aerosol masses under different scenarios of transport at a suburban site of Beijing

In order to evaluate the secondary aerosol formation potential at a suburban site of Beijing,in situ perturbation experiments in a potential aerosol mass（PAM） reactor were carried out in the winter of 2014.The variations of secondary aerosol formation as a function of time,OH exposure,and the concentrations of gas phase pollutants and particles were reported in this study.Two periods with distinct secondary aerosol formation potentials,marked as Period Ⅰ and Period Ⅱ,were identified during the observation.In Period Ⅰ,the secondary aerosol formation potential was high,and correlated well to the air pollutants,i.e.,SO2,NO2,and CO.The maximal secondary aerosol formation was observed with an aging time equivalent to about 3 days of atmospheric oxidation.In period Ⅱ,the secondary aerosol formation potential was low,with no obvious correlation with the air pollutants.Meanwhile,the aerosol mass decreased,instead of showing a peak,with increasing aging time.Backward trajectory analysis during the two periods confirmed that the air mass in Period Ⅰwas mainly from local sources,while it was attributed mostly to long distance transport in Period Ⅱ.The air lost its reactivity during the long transport and the particles became highly aged,resulting in a low secondary aerosol formation potential.Our experimental results indicated that the in situ measurement of the secondary aerosol formation potential could provide important information for evaluating the contributions of local emission and long distance transport to the aerosol pollution.