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Spatial Distribution of Energy Consumption and Carbon Emission of Regional Logistics

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  • Fan Xiao

    (School of Information Engineering Institute, Shanghai Maritime University, Shanghai 200135, China
    Logistics Research Center, Shanghai Maritime University, Shanghai 200135, China)

  • Zhi-Hua Hu

    (Logistics Research Center, Shanghai Maritime University, Shanghai 200135, China)

  • Ke-Xin Wang

    (School of Economics and Management, Shanghai Maritime University, Shanghai 200135, China)

  • Pei-Hua Fu

    (School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

Facing serious energy-related constraints and environmental stress, the development of the green logistics industry is restricted by degrees of logistics energy utilization and carbon emissions. Considering different logistics spatial distributions, this paper uses the degree of regional logistics energy utilization and the spatial distribution of carbon emissions as two indicators of green logistics to investigate the regional differences and changes in spatiotemporal logistics energy efficiency. We firstly measure the regional logistics in terms of energy consumption and carbon emission, then further measure the logistics by energy intensity and carbon intensity. Based on these four indicators, the relations between spatiotemporal logistics and regional logistics development are analyzed. Through studying the spatial and temporal evolution trends of the above indicators, we found that a certain convergence exists. Finally, based on the analysis, the suggestions for energy saving and emission reduction are proposed according to regional conditions. The results benefit to narrow the efficiency gap between regions and achieve the goal of improving logistics energy efficiency.

Suggested Citation

  • Fan Xiao & Zhi-Hua Hu & Ke-Xin Wang & Pei-Hua Fu, 2015. "Spatial Distribution of Energy Consumption and Carbon Emission of Regional Logistics," Sustainability, MDPI, vol. 7(7), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:7:p:9140-9159:d:52590
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    Cited by:

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    2. Xueping Tao & Ping Wang & Bangzhu Zhu, 2016. "Measuring the Interprovincial CO 2 Emissions Considering Electric Power Dispatching in China: From Production and Consumption Perspectives," Sustainability, MDPI, vol. 8(6), pages 1-12, May.
    3. Zhang, Cheng & Zhou, Xinxin & Zhou, Bo & Zhao, Ziwei, 2022. "Impacts of a mega sporting event on local carbon emissions: A case of the 2014 Nanjing Youth Olympics," China Economic Review, Elsevier, vol. 73(C).
    4. Tamás Bányai, 2018. "Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions," Energies, MDPI, vol. 11(7), pages 1-25, July.
    5. Caiquan Bai & Yuehua Mao & Yuan Gong & Chen Feng, 2019. "Club Convergence and Factors of Per Capita Transportation Carbon Emissions in China," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    6. Xiaopeng Guo & Jiaxing Shi & Dongfang Ren & Jing Ren & Qilin Liu, 2017. "Correlations between air pollutant emission, logistic services, GDP, and urban population growth from vector autoregressive modeling: a case study of Beijing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(2), pages 885-897, June.
    7. Miroslav Stefanov, 2018. "Features of Compressed Natural Gas Physical Distribution: A Bulgarian Case Study," Logistics, MDPI, vol. 2(3), pages 1-21, September.
    8. Li, Wei & Sun, Wen & Li, Guomin & Cui, Pengfei & Wu, Wen & Jin, Baihui, 2017. "Temporal and spatial heterogeneity of carbon intensity in China's construction industry," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 162-173.
    9. Lei Yang & Yiji Cai & Jiahui Hong & Yongqiang Shi & Zhiyong Zhang, 2016. "Urban Distribution Mode Selection under Low Carbon Economy—A Case Study of Guangzhou City," Sustainability, MDPI, vol. 8(7), pages 1-22, July.
    10. Hao Zhang & Xin Sun & Kailong Dong & Lianghui Sui & Min Wang & Qiong Hong, 2022. "Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    11. Lijun Liu & Zhixin Long & Chuangchuang Kou & Haozeng Guo & Xinyu Li, 2023. "Evaluation of the Environmental Cost of Integrated Inbound Logistics: A Case Study of a Gigafactory of a Chinese Logistics Firm," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
    12. Guo-Ling Jia & Rong-Guo Ma & Zhi-Hua Hu, 2019. "Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    13. Gao, Jingzhe & Xiao, Zhongdong & Cao, Binbin & Chai, Qiangfei, 2018. "Green supply chain planning considering consumer’s transportation process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 311-330.
    14. Cejun Cao & Congdong Li & Qin Yang & Fanshun Zhang, 2017. "Multi-Objective Optimization Model of Emergency Organization Allocation for Sustainable Disaster Supply Chain," Sustainability, MDPI, vol. 9(11), pages 1-22, November.
    15. Sören Lauenstein & Christoph Schank, 2022. "Design of a Sustainable Last Mile in Urban Logistics—A Systematic Literature Review," Sustainability, MDPI, vol. 14(9), pages 1-14, May.

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