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Jing Li
Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun 130102, China

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Journal article
Published: 25 August 2021 in Land Use Policy
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The optimization of rural land-use through land consolidation has become an indispensable part of reform in China, significantly impacting rural settlements and their residents. Given the growing social problems due to land consolidation, this study proposes a new land consolidation approach that involves helping rural residents relocate from small settlements to vacant properties in large settlements through a homestead exchange mechanism. We argue that this targeted approach can achieve land-use optimization, without the negative impacts of the conventional approach to land consolidation. To evaluate our idea empirically, we conducted a case study in Jilin Province. The simulation estimated that our approach could increase the effective amount of arable land by 1046.66 km2 and the average size of rural settlements by 51.9%, while decreasing the number of rural settlements by 44.6%. Therefore, our approach can achieve the objective of land-use optimization, despite the highly complex context and varying degrees of rural fragmentation and hollowing.

ACS Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. Reclaiming small to fill large: A novel approach to rural residential land consolidation in China. Land Use Policy 2021, 109, 105706 .

AMA Style

Jing Li, Kevin Lo, Pingyu Zhang, Meng Guo. Reclaiming small to fill large: A novel approach to rural residential land consolidation in China. Land Use Policy. 2021; 109 ():105706.

Chicago/Turabian Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. 2021. "Reclaiming small to fill large: A novel approach to rural residential land consolidation in China." Land Use Policy 109, no. : 105706.

Research article
Published: 01 January 2021 in International Journal of Wildland Fire
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The estimation of post-fire vegetation recovery is essential for forest management and wildfire policy-making. In the last few decades, vegetation indices have been widely used to monitor post-fire vegetation recovery by comparison with the pre-fire state. In this study, vegetation recovery is estimated using Solar-Induced chlorophyll Fluorescence (SIF), which is a by-product of photosynthesis and can reflect the physiological characteristics of a plant. We found that 20 years is insufficient for vegetation recovery, as the SIF within burned areas exhibited a significant increasing trend, which was most notable within the first 6 to 10 years after a wildfire. When comparing the SIF within and outside burned areas, we found that, during the first 3 to 6 years, SIF values outside burned areas were larger than that within burned areas; however, after ~6 years, the SIF within the burned areas exceeded that outside burned areas owing to the different carbon sequestration intensities of different vegetation recovery stages. Field photos of recovering vegetation were then compared with the Enhanced Vegetation Index (EVI) trend within the burned area, and it was found that, although the EVI reached pre-fire levels or stabilised, vegetation recovery was continuing.

ACS Style

Meng Guo; Jing Li; Fangbing Yu; Shuai Yin; Shubo Huang; Lixiang Wen. Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data. International Journal of Wildland Fire 2021, 30, 365 .

AMA Style

Meng Guo, Jing Li, Fangbing Yu, Shuai Yin, Shubo Huang, Lixiang Wen. Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data. International Journal of Wildland Fire. 2021; 30 (5):365.

Chicago/Turabian Style

Meng Guo; Jing Li; Fangbing Yu; Shuai Yin; Shubo Huang; Lixiang Wen. 2021. "Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data." International Journal of Wildland Fire 30, no. 5: 365.

Journal article
Published: 19 February 2020 in Remote Sensing
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Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

ACS Style

Meng Guo; Jing Li; Shubo Huang; Lixiang Wen. Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests. Remote Sensing 2020, 12, 680 .

AMA Style

Meng Guo, Jing Li, Shubo Huang, Lixiang Wen. Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests. Remote Sensing. 2020; 12 (4):680.

Chicago/Turabian Style

Meng Guo; Jing Li; Shubo Huang; Lixiang Wen. 2020. "Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests." Remote Sensing 12, no. 4: 680.

Journal article
Published: 16 October 2019 in Sustainability
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Village hollowing is a growing policy problem globally, but accurately estimating housing vacancy rates is difficult and costly. In this study, we piloted the use of power consumption data to estimate the vacancy rate of rural housing. To illustrate the method used, we took power consumption data in 2014 and 2017 in an area of rural China to analyze the change in housing vacancies. Results indicated that the rural vacancy rates were 5.27% and 8.69%, respectively, while underutilization rates were around 10% in 2014 and 2017. Second, there was significant spatial clustering of vacant rural housing, and the hotspots were mainly distributed in western mountainous areas, whereas villages near urban areas had lower vacancy rates. Third, rural vacancies increased from 2014 to 2017. Compared with other methods, our method proved to be accurate, very cost-effective and scalable, and it can offer timely spatial and temporal information that can be used by policymakers to identify areas with significant village hollowing issues. However, there are challenges in setting the right thresholds that take into consideration regional differences. Therefore, there is also a need for more studies in different regions in order to scale up this method to the national level.

ACS Style

Jing Li; Meng Guo; Kevin Lo. Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data. Sustainability 2019, 11, 5722 .

AMA Style

Jing Li, Meng Guo, Kevin Lo. Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data. Sustainability. 2019; 11 (20):5722.

Chicago/Turabian Style

Jing Li; Meng Guo; Kevin Lo. 2019. "Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data." Sustainability 11, no. 20: 5722.

Journal article
Published: 25 September 2019 in Atmosphere
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The biomass burning model (BBM) has been the most widely used method for estimation of trace gas emissions. Due to the difficulty and variability in obtaining various necessary parameters of BBM, a new method is needed to quickly and accurately calculate the trace gas emissions from wildfires. Here, we used satellite data from the Orbiting Carbon Observatory-2 (OCO-2) to calculate CO2 emissions from wildfires (the OCO-2 model). Four active wildfires in Siberia were selected in which OCO-2 points intersecting with smoke plumes identified by Aqua MODIS (MODerate-resolution Imaging Spectroradiometer) images. MODIS band 8, band 21 and MISR (Multi-angle Imaging SpectroRadiometer) data were used to identify the smoke plume area, burned area and smoke plume height, respectively. By contrast with BBM, which calculates CO2 emissions based on the bottom–top mode, the OCO-2 model estimates CO2 emissions based on the top–bottom mode. We used a linear regression model to compute CO2 concentration (XCO2) for each smoke plume pixel and then calculated CO2 emissions for each wildfire point. The CO2 mass of each smoke plume pixel was added to obtain the CO2 emissions from wildfires. After verifying our results with the BBM, we found that the biases were between 25.76% and 157.11% for the four active fires. The OCO-2 model displays the advantages of remote-sensing technology and is a useful tool for fire-emission monitoring, although we note some of its disadvantages. This study proposed a new perspective to estimate CO2 emissions from wildfire and effectively expands the applied range of OCO-2 satellite data.

ACS Style

Meng Guo; Jing Li; Lixiang Wen; Shubo Huang. Estimation of CO2 Emissions from Wildfires Using OCO-2 Data. Atmosphere 2019, 10, 581 .

AMA Style

Meng Guo, Jing Li, Lixiang Wen, Shubo Huang. Estimation of CO2 Emissions from Wildfires Using OCO-2 Data. Atmosphere. 2019; 10 (10):581.

Chicago/Turabian Style

Meng Guo; Jing Li; Lixiang Wen; Shubo Huang. 2019. "Estimation of CO2 Emissions from Wildfires Using OCO-2 Data." Atmosphere 10, no. 10: 581.

Article
Published: 08 November 2018 in Chinese Geographical Science
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Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982–2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.

ACS Style

Meng Guo; Jing Li; Hong S. He; Jiawei Xu; Yinghua Jin. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982–2015 Time Period. Chinese Geographical Science 2018, 28, 907 -919.

AMA Style

Meng Guo, Jing Li, Hong S. He, Jiawei Xu, Yinghua Jin. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982–2015 Time Period. Chinese Geographical Science. 2018; 28 (6):907-919.

Chicago/Turabian Style

Meng Guo; Jing Li; Hong S. He; Jiawei Xu; Yinghua Jin. 2018. "Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982–2015 Time Period." Chinese Geographical Science 28, no. 6: 907-919.

Research papers
Published: 05 April 2018 in Journal of Spatial Science
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This research examined the link between shopping mobility and travel CO2 emissions among suburban residents in Shenyang, China. We found suburban residents travelled 14.33 km and produced 1111.32 g of CO2 per shopping trip. The high emitters are mostly located at the urban fringe, travelling long distances and having a stronger dependency on cars. Furthermore, we used a binary logistic regression model to discover the main factors statistically significant in explaining private car usage. Evidence from this research indicates the need to formulate sustainable transport policies and reduce carbon emissions through compact urban forms and transit-oriented development.

ACS Style

Jing Li; Kevin Lo; Pingyu Zhang. Shopping mobility and travel carbon emissions among suburban residents: lessons from Shenyang city, China. Journal of Spatial Science 2018, 63, 311 -323.

AMA Style

Jing Li, Kevin Lo, Pingyu Zhang. Shopping mobility and travel carbon emissions among suburban residents: lessons from Shenyang city, China. Journal of Spatial Science. 2018; 63 (2):311-323.

Chicago/Turabian Style

Jing Li; Kevin Lo; Pingyu Zhang. 2018. "Shopping mobility and travel carbon emissions among suburban residents: lessons from Shenyang city, China." Journal of Spatial Science 63, no. 2: 311-323.

Journal article
Published: 30 November 2017 in Sustainability
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This paper quantitatively analyzes the economic resilience of resource-based cities (RBCs) in Northeast China in terms of resistance and recoverability during two economic crises: the Asian financial crisis and the global financial crisis. Moreover, it analyzes the main factors that affected regional resilience. There are three main findings. First, the RBCs in general demonstrated poor resistance during both recessions, but there were variations among the different types of RBCs. Petroleum and metal cities demonstrated the most resistance, whereas coal cities performed the worst. Second, the influential factors affecting economic resilience varied across the two economic cycles, but location advantage, research and development (R and D) intensity, foreign trade dependence ratio, and supporting policies had positive effects on resilience during both economic cycles, while the proportion of employed persons in resource industries had a negative effect. Industrial diversity had a weak and ambiguous effect on resilience. Third, the secondary industry was more resilient during the Asian financial crisis, but the tertiary industry was more resilient during the global financial crisis. This shift may be attributed to both the nature of the crises and the strength of the sectors at the time of the crises.

ACS Style

Juntao Tan; Kevin Lo; Fangdao Qiu; Wenxin Liu; Jing Li; Pingyu Zhang. Regional Economic Resilience: Resistance and Recoverability of Resource-Based Cities during Economic Crises in Northeast China. Sustainability 2017, 9, 2136 .

AMA Style

Juntao Tan, Kevin Lo, Fangdao Qiu, Wenxin Liu, Jing Li, Pingyu Zhang. Regional Economic Resilience: Resistance and Recoverability of Resource-Based Cities during Economic Crises in Northeast China. Sustainability. 2017; 9 (12):2136.

Chicago/Turabian Style

Juntao Tan; Kevin Lo; Fangdao Qiu; Wenxin Liu; Jing Li; Pingyu Zhang. 2017. "Regional Economic Resilience: Resistance and Recoverability of Resource-Based Cities during Economic Crises in Northeast China." Sustainability 9, no. 12: 2136.

Article
Published: 08 September 2017 in Chinese Geographical Science
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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 CO2 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 significant 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 several 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-economic 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.

ACS Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. Relationship between built environment, socio-economic factors and carbon emissions from shopping trip in Shenyang City, China. Chinese Geographical Science 2017, 27, 722 -734.

AMA Style

Jing Li, Kevin Lo, Pingyu Zhang, Meng Guo. Relationship between built environment, socio-economic factors and carbon emissions from shopping trip in Shenyang City, China. Chinese Geographical Science. 2017; 27 (5):722-734.

Chicago/Turabian Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. 2017. "Relationship between built environment, socio-economic factors and carbon emissions from shopping trip in Shenyang City, China." Chinese Geographical Science 27, no. 5: 722-734.

Journal article
Published: 01 July 2017 in Environmental Pollution
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In the summer of 2010, more than 6 hundred wildfires broke out in western Russia because of an unprecedented intense heat wave that resulted from strong atmospheric blocking. The present study evaluated the CO emissions using GOSAT (Greenhouse gases Observing SATellite) data from July 23 to August 18, 2010 for western Russia. The results demonstrated that the GOSAT CAI (Cloud and Aerosol Imager) was well-suited for the identification of smoke plumes and that the GOSAT FTS (Fourier-Transform Spectrometer) TIR (Thermal InfraRed) could be used to calculate the height of the plumes at approximately 800 hPa (1.58 km). Using GOSAT data, we estimated that the 2010 fires in western Russia emitted 255.76 Tg CO. We also calculated the CO emissions by employing the Biomass Burning Model (BBM) for the same study site and obtained a similar result of 261.82-302.48 Tg CO. The present study proposes a new method for the evaluation of CO emissions from a wildfire using remote sensing data, which could be used to improve the knowledge of the burning of biomass at a regional or a continental scale, to reduce the uncertainties in modeling greenhouse gases emissions, and to further understand how wildfires impact the atmospheric carbon cycle and global warming.

ACS Style

Meng Guo; Jing Li; Jiawei Xu; Xiufeng Wang; Hongshi He; Li Wu. CO 2 emissions from the 2010 Russian wildfires using GOSAT data. Environmental Pollution 2017, 226, 60 -68.

AMA Style

Meng Guo, Jing Li, Jiawei Xu, Xiufeng Wang, Hongshi He, Li Wu. CO 2 emissions from the 2010 Russian wildfires using GOSAT data. Environmental Pollution. 2017; 226 ():60-68.

Chicago/Turabian Style

Meng Guo; Jing Li; Jiawei Xu; Xiufeng Wang; Hongshi He; Li Wu. 2017. "CO 2 emissions from the 2010 Russian wildfires using GOSAT data." Environmental Pollution 226, no. : 60-68.

Review
Published: 05 April 2017 in Sensors
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Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

ACS Style

Meng Guo; Jing Li; Chunlei Sheng; Jiawei Xu; Li Wu. A Review of Wetland Remote Sensing. Sensors 2017, 17, 777 .

AMA Style

Meng Guo, Jing Li, Chunlei Sheng, Jiawei Xu, Li Wu. A Review of Wetland Remote Sensing. Sensors. 2017; 17 (4):777.

Chicago/Turabian Style

Meng Guo; Jing Li; Chunlei Sheng; Jiawei Xu; Li Wu. 2017. "A Review of Wetland Remote Sensing." Sensors 17, no. 4: 777.

Journal article
Published: 13 October 2016 in Sustainability
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Resource-based cities face unique challenges when undergoing urban transitions because their non-renewable resources will eventually be exhausted. In this article, we introduce a new method of evaluating the urban transition performance of resource-based cities from economic, social and eco-environmental perspectives. A total of 19 resource-based cities in Northeast China are studied from 2003 to 2012. The results show that resource-based cities in Jilin and Liaoning provinces performed better than those in Heilongjiang province. Liaoyuan, Songyuan and Baishan were ranked as the top three resource-based cities; and Jixi, Yichun and Heihe were ranked last. Multi-resource and petroleum resource-based cities performed better than coal and forestry resource-based cities. We also analyzed the factors influencing urban transition performance using the method of the geographic detector. We found that capital input, road density and location advantage had the greatest effects on urban transition performance, followed by urban scale, remaining resources and the level of sustainable development; supporting policies and labor input had the smallest effects. Based on these insights, we have formulated several recommendations to facilitate urban transitions in China’s resource-based cities.

ACS Style

Juntao Tan; Pingyu Zhang; Kevin Lo; Jing Li; Shiwei Liu. The Urban Transition Performance of Resource-Based Cities in Northeast China. Sustainability 2016, 8, 1022 .

AMA Style

Juntao Tan, Pingyu Zhang, Kevin Lo, Jing Li, Shiwei Liu. The Urban Transition Performance of Resource-Based Cities in Northeast China. Sustainability. 2016; 8 (10):1022.

Chicago/Turabian Style

Juntao Tan; Pingyu Zhang; Kevin Lo; Jing Li; Shiwei Liu. 2016. "The Urban Transition Performance of Resource-Based Cities in Northeast China." Sustainability 8, no. 10: 1022.

Journal article
Published: 22 September 2016 in Energies
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Current literature highlights the role of commercial centers in cities in generating shopping trips and transport carbon emissions. However, the influence of the characteristics of commercial centers on consumer travel behavior and transport carbon emissions is not well understood. This study addresses this knowledge gap by examining shopping trips to eight commercial centers in Shenyang, China, and the CO2 emissions of these trips. We found that the locations and types of commercial centers strongly influence CO2 emissions. CO2 emissions per trip to commercial centers in the suburbs of Shenyang were on average 6.94% and 26.92% higher than those to commercial centers in the urban core and the inner city, respectively. CO2 emissions induced by wholesale centers were nearly three times higher than the lowest CO2 emissions of commercial centers in the inner city. These empirical results enhance our understanding of shopping-related transport carbon emissions and highlight the importance of optimizing urban space structure, in particular, the layout of commercial centers.

ACS Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. Consumer Travel Behaviors and Transport Carbon Emissions: A Comparative Study of Commercial Centers in Shenyang, China. Energies 2016, 9, 765 .

AMA Style

Jing Li, Kevin Lo, Pingyu Zhang, Meng Guo. Consumer Travel Behaviors and Transport Carbon Emissions: A Comparative Study of Commercial Centers in Shenyang, China. Energies. 2016; 9 (10):765.

Chicago/Turabian Style

Jing Li; Kevin Lo; Pingyu Zhang; Meng Guo. 2016. "Consumer Travel Behaviors and Transport Carbon Emissions: A Comparative Study of Commercial Centers in Shenyang, China." Energies 9, no. 10: 765.

Journal article
Published: 15 September 2015 in Energies
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With rising income and the emergence of modern shopping centers in urban China, shopping trips by private car becomes more and more common, leading to higher carbon emissions in the transport sector. Encouraging car owners to shift transport mode from private car to public transport could achieve significant emissions reductions. This study estimate carbon emissions savings by shifting from private cars to public transport for shopping trips in urban China, using Shenyang, one of the largest cities in China, as a case study. Our results show that the average carbon emissions per shopper is 426.9 g, and the carbon emissions on weekends is 13% higher than weekdays. Moreover, shoppers travelling by private car emitted five times more carbon emission than those by public transport. We also found that car ownership gradually increased as accessibility to public transport decreased, and that more car owners chose to travel by private cars than public transport in areas with limited access. This study, thus, highlights the potential for high-quality public transport to reduce the transport sector’s carbon emissions in urban China.

ACS Style

Jing Li; Pingyu Zhang; Kevin Lo; Meng Guo; Mark Wang. Reducing Carbon Emissions from Shopping Trips: Evidence from China. Energies 2015, 8, 10043 -10057.

AMA Style

Jing Li, Pingyu Zhang, Kevin Lo, Meng Guo, Mark Wang. Reducing Carbon Emissions from Shopping Trips: Evidence from China. Energies. 2015; 8 (9):10043-10057.

Chicago/Turabian Style

Jing Li; Pingyu Zhang; Kevin Lo; Meng Guo; Mark Wang. 2015. "Reducing Carbon Emissions from Shopping Trips: Evidence from China." Energies 8, no. 9: 10043-10057.