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.

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.

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.

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.