Incorporating seller/buyer reputation-based system in blockchain-enabled emission trading application

Emission Trading Scheme (ETS) has dual aims to reduce emission production and stimulate adoption of long-term abatement technology. Whilst it has generally achieved its first aim, its issues are hindering the accomplishment of the second. Several solutions have been proposed to improve ETS’s efficacy, yet none of them have considered the advancement of Industry 4.0. This paper proposes a novel ETS model customised for Industry 4.0 integration. It incorporates blockchain technology to address ETS’s management and fraud issues whilst it utilizes a reputation system in a new approach to improve ETS efficacy. Specific design of how the blockchain technology and reputation system are used to achieve these objectives is showed within this paper. The case study demonstrates the inner working of reputation-based trading system—in which reputation signifies participants performance and commitment toward emission reduction effort. Multi-criteria analysis is used to evaluate the proposed scheme against conventional ETS model. The result shows that the proposed model is a feasible scheme and that the benefits of its implementation will outweigh its drawback.

[1]  C. Perthuis.  Carbon markets regulation : the case for a CO2 Central Bank , 2011 .

[2]  Stefano Clò,et al.  Policy options to improve the effectiveness of the EU emissions trading system: A multi-criteria analysis , 2013 .

[3]  P. Quirion.  Complying with the Kyoto Protocol Under Uncertainty: Taxes or Tradable Permits? , 2002 .

[4]  Ali Nejat,et al.  Selection of CO2 mitigation strategies for road transportation in the United States using a multi-criteria approach , 2014 .

[5]  Vasja Roblek,et al.  A Complex View of Industry 4.0 , 2016 .

[6]  Dequn Zhou,et al.  Modeling economic performance of interprovincial CO2 emission reduction quota trading in China , 2013 .

[7]  Oa Us Epa.  Global Greenhouse Gas Emissions Data , 2016 .

[8]  Markus Kraft,et al.  Quantitative tools for cultivating symbiosis in industrial parks; a literature review , 2015 .

[9]  Markus Kraft,et al.  Blockchain technology in the chemical industry: Machine-to-machine electricity market , 2017 .

[10]  Sebastian Mosbach,et al.  Applying Industry 4.0 to the Jurong Island Eco-industrial Park , 2015 .

[11]  L. Friberg.  [Air pollution]. , 1984, Svenska lakartidningen.

[12]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[13]  Ying Fan,et al.  The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment , 2016 .

[14]  Edin Arnautovic,et al.  Bitcoin‐Based Decentralized Carbon Emissions Trading Infrastructure Model , 2015, Syst. Eng..

[15]  Edwin Woerdman,et al.  Energy prices and emissions trading: windfall profits from grandfathering? , 2009 .

[16]  Grischa Perino,et al.  Procrastinating reform: The impact of the market stability reserve on the EU ETS , 2016 .

[17]  Jingjing Jiang,et al.  The construction of Shenzhen׳s carbon emission trading scheme , 2014 .

[18]  Yi-Ming Wei,et al.  An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect , 2010 .

[19]  N. Popiolek,et al.  Multi-criteria analysis of innovation policies in favour of solar mobility in France by 2030 , 2015 .

[20]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[21]  Lixin Miao,et al.  Innovative Carbon Allowance Allocation Policy for the Shenzhen Emission Trading Scheme in China , 2015 .

[22]  Li Zhou,et al.  Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks , 2017 .

[23]  Yan Feng,et al.  Air Pollution, Greenhouse Gases and Climate Change: Global and Regional Perspectives , 2009 .

[24]  Marek Obitko,et al.  Big Data Semantics in Industry 4.0 , 2015, HoloMAS.

[25]  Yim Ling Siu,et al.  Integrated approach to the assessment of CO2e-mitigation measures for the road passenger transport sector in Bahrain , 2017 .

[26]  F. Jotzo,et al.  Price Floors for Emissions Trading , 2010 .

[27]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[28]  Vivian W. Y Tam,et al.  Effectiveness of greenhouse-gas Emission Trading Schemes implementation : a review on legislations , 2016 .

[29]  Frank Venmans.  The effect of allocation above emissions and price uncertainty on abatement investments under the EU ETS , 2016 .

[30]  Pablo del Río González,et al.  Policy implications of potential conflicts between short-term and long-term efficiency in CO2 emissions abatement , 2008 .

[31]  Jules Pretty,et al.  A 2017 Horizon Scan of Emerging Issues for Global Conservation and Biological Diversity. , 2017, Trends in Ecology & Evolution.

[32]  Bernd Bertsche,et al.  Modelling the production systems in industry 4.0 and their availability with high-level Petri nets , 2016 .

[33]  C. Hepburn,et al.  Carbon Trading: Unethical, Unjust and Ineffective? , 2011, Royal Institute of Philosophy Supplement.

[34]  Daniel F. Spulber.  Effluent regulation and long-run optimality , 1985 .

[35]  Tianyu Qi,et al.  Economic impacts of an international carbon market in achieving the INDC targets , 2016 .

[36]  Takayoshi Shinkuma,et al.  Tax versus emissions trading scheme in the long run , 2016 .

[37]  G. Fishelson.  Emission control policies under uncertainty , 1976 .

[38]  M. Weitzman.  Prices vs. Quantities , 1974 .

[39]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[40]  Popi Konidari,et al.  A multi-criteria evaluation method for climate change mitigation policy instruments , 2007 .

[41]  Athanasios V. Vasilakos,et al.  A review of industrial wireless networks in the context of Industry 4.0 , 2015, Wireless Networks.

[42]  A. Kollmuss,et al.  Addressing the risk of double counting emission reductions under the UNFCCC , 2015, Climatic Change.