Abstract
With the concerns of ecological and circular economy along with sustainable development, reverse logistics has attracted the attention of enterprise. How to achieve sustainable development of reverse logistics has important practical significance of enhancing low carbon competitiveness. In this paper, the system boundary of reverse logistics carbon footprint is presented. Following the measurement of reverse logistics carbon footprint and reverse logistics carbon capacity is provided. The influencing factors of reverse logistics carbon footprint are classified into five parts such as intensity of reverse logistics, energy structure, energy efficiency, reverse logistics output, and product remanufacturing rate. The quantitative research methodology using ADF test, Johansen co-integration test, and impulse response is utilized to interpret the relationship between reverse logistics carbon footprint and the influencing factors more accurately. This research finds that energy efficiency, energy structure, and product remanufacturing rate are more capable of inhibiting reverse logistics carbon footprint. The statistical approaches will help practitioners in this field to structure their reverse logistics activities and also help academics in developing better decision models to reduce reverse logistics carbon footprint.








Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Accorsi R, Versari L, Manzini R (2015) Glass vs. plastic: life cycle assessment of extra-virgin olive oil bottles across global supply chains. Sust 7:2818–2840
Agrawal S, Singh RK, Murtaza Q (2015) A literature review and perspectives in reverse logistics. Resour Conserv Recy 97:76–92
Ali HS, Law SH, Zannah TI (2016) Dynamic impact of urbanization, economic growth, energy consumption, and trade openness on CO2 emissions in Nigeria. Environ Sci Pollut Res 23:12435–12443
Allwood JM, Ashby MF, Gutowski TG, Worrell E (2013) Material efficiency: providing material services with less material production. Philos Trans R Soc A 371:20120496
Awasaki T, Yamada T, Itsubo N, Inoue M (2015) Multi criteria simulation model for lead times, costs and CO2 emissions in a low-carbon supply chain network. Procedia CIRP 26:329–334
Bazan E, Jaber MY, Zanoni S (2016) A review of mathematical inventory models for reverse logistics and the future of its modeling: an environmental perspective. Appl Math Model 40:4151–4178
Bouzon M, Govindan K, Taboada-Rodriguez CM (2016) Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resour Conserv Recy 108:182–197
Choudhary A, Sarkar S, Settur S, Tiwari MK (2014) A carbon market sensitive optimization model for integrated forward-reverse logistics. Int J Prod Econ 164:433–444
Coskun S, Ozgur L, Polat O, Gungor A (2016) A model proposal for green supply chain network design based on consumer segmentation. J Clean Prod 110:149–157
Cucchiella F, Adamo ID, Lenny Koh SC, Rosa P (2015) Recycling of WEEEs: an economic assessment of present and future e-waste streams. Renew Sust Enger Rev 51:263–272
Das K, Chowdhury AH (2012) Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning. Int J Prod Econ 135:209–221
Demirel NO, Gokcen H (2008) A mixed integer programming model for remanufacturing in reverse logistics environment. Int J Adv Manuf Tech 39:1197–1206
Diabat A, Salem AM (2015) An integrated supply chain problem with environmental considerations. Int J Prod Econ 164:330–338
Ding N, Yang JX (2015) Life cycle inventory analysis of fossil energy in China. China Environ Sci 35:1592–1600
Dominic CY, Raymond RT, Denny KS (2008) Carbon and footprint-constrained energy planning using cascade analysis technique. Energy 33:1480–1488
Druckman A, Jackson T (2009) The carbon footprint of UK household 1990-2004: a socio-economically disaggregated, quasi-multi-regional input-output model. Ecol Econ 68:2066–2077
Du F, Evans GW (2008) A bi-objective reverse logistics network analysis for post-sale service. Comput Oper Res 35:2617–2634
Fahimnia B, Sarkis J, Eshragh A (2015) A tradeoff model for green supply chain planning: a leanness-versus-greenness analysis. Omega 54:173–190
Fargione J, Hill J, Tilman D, Polasky S, Hawthorne S (2008) Land clearing and the bio-fuel carbon debt. Science 319:1235–1238
Farhani S, Ozturk I (2015) Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res 22:15663–15676
Garnett T (2009) Livestock-related greenhouse gas emissions: impacts and options for policy makers. Environ Sci Pol 12:491–503
Glen PP, Edgar GH (2008) CO2 embodied in international trade with implications for global climate policy. Environ Sci Pol 42:1401–1407
Godichaud M, Amodeo L (2015) Efficient multi-objective optimization of supply chain with returned products. J Manuf Syst 37:683–691
Johnson E (2008) Disagreement over carbon footprints: a comparison of electric and LPG forklifts. Energ Policy 36:1569–1573
Kannan D, Diabat A, Alrefaei M, Govindan K, Yong G (2012) A carbon footprint based reverse logistics network design model. Resour Conserv Recy 67:75–79
Kenny TK, Gray NF (2009) Comparative performance of six carbon footprint models for use in Ireland. Environ Impact Asses 29:1–6
Kitzes J, Peller A, Goldfinger S, Wackernagel M (2007) Current methods for calculating national ecological footprint accounts. Sci Environ Sust So 4:1–9
Krikke H (2011) Impact of closed-loop network configurations on carbon footprints: a case study in copiers. Resour Conserv Recy 55:1196–1205
Laurent A, Olsen SI, Hauschild MZ (2010) Carbon footprint as environmental performance indicator for the manufacturing industry. CIRP Ann-Manuf Techn 59:37–40
Linton JD, Klassen R, Klassen V (2007) Sustainable supply chains: an introduction. J Oper Manag 25:1075–1082
Liobikiene G, Dagiliute R (2016) The relationship between economic and carbon footprint changes in EU: the achievements of the EU sustainable consumption and production policy implementation. Environ Sci Pol 61:204–211
Liu D (2014) Network site optimization of reverse logistics for E-commerce based on genetic algorithm. Neural Comput Appl 25:67–71
Ma CM, Li SC, Ge QS (2014) Greenhouse gas emission factors for grid electricity for chinese provinces. Resour Sci 36:1005–1012
Manglaa SK, Govindanb K, Luthrac S (2016) Critical success factors for reverse logistics in Indian industries: a structural model. J Clean Prod 129:608–621
Markaki M, Belegri-Roboli A, Sarafidis Y, Mirasgedis S (2017) The carbon footprint of Greek households (1995–2012). Energ Policy 100:206–215
Metz B, Davidson O, Bosch P, Dave R, Meyer L (2007) Climate Change 2007: Mitigation of climate change. Cambridge University Press, New York NY
Mohajeri A, Fallah M (2016) A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: a case study. Transport Res D-TR E 48:425–450
Niknejad A, Petrovic D (2014) Optimization of integrated reverse logistics networks with different product recovery routes. Eur J Oper Res 238:143–154
Nikolaou IE, Evangelinos KI, Allan S (2013) A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. J Clean Prod 56:173–184
Ohlan R, Hazards N (2015) The impact of population density, energy consumption, economic growth and trade openness on CO2 emissions in India. Environ Sci Pollut Res 79:1409–1428
Pan ZH (2010) The new technology of producing high purity active magnesium oxide with external heating rotary kiln and recycling carbon dioxide. Environ Prot Circ Econ 30:42–44
Pattara C, Raggi A, Cichelli A (2012) Life cycle assessment and carbon footprint in the wine supply chain. Environ Manag 49:1247–1258
Peters GP (2010) Carbon footprints and embodied carbon at multiple scales. Curr Opin Env Sust 2:245–250
Ravi V (2014) Reverse logistics operations in automobile industry: a case study using SAP-LAP approach. Glob J Flex Syst Manag 15:295–303
Schanes K, Giljum S, Hertwich E (2016) Low carbon lifestyles: a framework to structure consumption strategies and options to reduce carbon footprints. J Clean Prod 139:1033–1043
Sovacool BK, Brown MA (2010) Twelve metropolitan carbon footprints: a preliminary comparative global assessment. Energ Policy 38:4856–4869
Sun Q, Zhou XZ (2016a) Dynamic analysis for regional carbon footprint coupling and influencing factors. Oxid Commun 39:1462–1477
Sun Q, Zhou XZ (2016b) Robust reverse logistics network design for the waste of electrical and electronic equipment (WEEE) under recovery uncertainty. J Environ Biol 37:1153–1165
Sun Q, Shen YZ, Li SJ (2015) Robust optimization for multi-product two stage reverse logistics reproduction network. Comput Eng Appl 51:18–23
The national development and reform commission on climate change department (2007) The people’s republic of China National Greenhouse Gases Inventory. Beijing, China
Tristram OW, Gregg M (2002). A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture:Comparing tillage practices in the United States. Agric Ecosyst Environ 91: 217–232.
Vahabzadeh AH, Asiaei A, Zailani S (2015) Green decision-making model in reverse logistics using FUZZY-VIKOR method. Resour Conserv Recy 103:125–138
Weidema BP, Thrane M, Christense P, Schmidt J, Lokke S (2008) Carbon footprint. J Ind Ecol 12:3–6
Yang CH, Lee KC, Chen HC (2016) Incorporating carbon footprint with activity-based costing constraints into sustainable public transport infrastructure project decisions. J Clean Prod 133:1154–1166
Yang D, Zeng DH, Zhao Q (2009) Soil contamination by magnesite dusts: its mechanism s and phytoremediation. Chin J Ecol 28:1891–1896
Zhang S, Lee KM, Chan HK, Choy KL, Wu Z (2015a) Swarm intelligence applied in green logistics: a literature review. Eng Appl Artif Intell 37:154–169
Zhang Q, Zheng D, Xu XS (2015b) Factor decomposition analysis on the energy carbon footprint ecological pressure change in China. J Arid Land Resour Environ 29:41–46
Zubelzu S, Álvarez R, Hernández A (2015) Methodology to calculate the carbon footprint of household land use in the urban planning stage. Land Use Policy 48:223–235
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: Philippe Garrigues
Rights and permissions
About this article
Cite this article
Sun, Q. Research on the influencing factors of reverse logistics carbon footprint under sustainable development. Environ Sci Pollut Res 24, 22790–22798 (2017). https://doi.org/10.1007/s11356-016-8140-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-016-8140-9


