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Environmental sustainability in the city of Shanghai municipal solid waste treatment system: an integrated framework of artificial neural network (ANN) and LCA-emergy methodology

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Abstract

The municipal solid waste treatment system is a vital part of sustainable urban development and needs to obtain ecological finance compensation according to the national standard. In this paper, the environmental sustainability compensation mechanism of Shanghai City has been reevaluated, calculated, and analyzed based on LCA-emergy methodology in order to compare and judge whether the current compensation mechanism and standard are qualified. The results demonstrate that (1) emergy amount of incineration accounts for approximately 50% of the total emergy, followed by emergy amount of gas pollution (33%), emergy amount of recycling (6%), emergy amount of discard (5%), emergy amount of landfill (4%), and emergy amount of storage (2%). Compared with emergy sustainability index, emergy amount of landfill (1.620) has the best level of sustainability, followed by emergy amount of incineration (1.307), emergy amount of discard (1.302), and emergy amount of storage (0.752). (2) The new emergy ecological subsidy standard should be 1045.14 $/t in Shanghai City, but the current national standard is 82.2 $ (about 12.7:1), which has caused a lot of negative impact on the ecological environment in Shanghai City. Meanwhile, the error variation of each factor is analyzed by artificial neural network. In order to balance the gap between them, some positive measures and policies should be adopted in the municipal solid waste treatment system, including raising subsidies for waste disposal, improving the efficiency of the recyclable garbage disposal, and enhancing the use proportion of renewable energy. The study of this paper provides a quantitative reference for city managers and government departments, which is conducive to improving the treatment efficiency of municipal solid waste treatment system for city managers and government departments.

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Abbreviations

\(UE{V}_{s_j}\) :

transformity of all elements

Q j :

amount of different elements

EAD:

emergy amount of discard

EAS:

emergy amount of storage

EAL:

emergy amount of landfill

EAI:

emergy amount of incineration

EAR:

emergy amount of recycling

EAG:

emergy amount of gas pollution

E dust :

emergy of dust

E ESA :

emergy amount of storage

α :

emergy to humans per year

U i :

annual mass of air pollutants

v :

local annual average wind speed

EEL:

ecological environment loss

UEV MSWTS :

the level of environmental damage

ϕ ECM :

environmental compensation criterion

β :

standard compensation coefficient

ξ ECM :

unit ecological compensation cost

μ :

penalty coefficient

δ t :

unit treating pollution costs

E land :

emergy of land

E water :

emergy of water

DALY:

impact element

M i :

dilution air mass

s i :

the acceptable concentration

T w :

unit emergy value of wind

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Authors and Affiliations

  1. School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang, 212100, China

    Junxue Zhang & Pengyuan Ren

  2. M.E. Rinker, Sr. School of Construction Management, College of Design, Construction and Planning, University of Florida, Gainesville, FL, 32603, USA

    Ashish T. Asutosh & He Zhang

  3. School of Mechanical engineering, **’an Jiaotong University, **’an, 710049, China

    Yanyan Yan

  4. Department of Marine Engineering, Tian** Maritime College, Tian**, 300350, China

    Yan Zhang

  5. School of Art, Anhui University of Finance and Economics, Bengbu, 233030, China

    Guobin Wei

  6. School of Design, Shanghai Jiaotong University, Shanghai, 200240, China

    Cheng Ma

  7. Tourism and Urban-Rural Planning College, Chengdu University of Technology, Chengdu, 610059, China

    Yaling Shi

  8. College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao, 266520, China

    Yuchen Gao

  9. School of Fine Arts, Yunnan Normal University, Kunming, 650500, China

    **angdong Yan

  10. School of Civil Engineering and Architecture, Anhui University of Technology, Ma’ an shan, 243032, China

    Kun **ng

  11. School of Civil Engineering and Architecture, Chuzhou University, Chuzhou, 239099, China

    Yuanliang Dong

  12. School of Architecture, Soochow University, Suzhou, 215123, China

    Guodong Wu

  13. College of Architecture, Sanjiang University, Nan**g, 210012, China

    Lan Liu

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  1. Junxue Zhang
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Zhang, J., Asutosh, A.T., Zhang, H. et al. Environmental sustainability in the city of Shanghai municipal solid waste treatment system: an integrated framework of artificial neural network (ANN) and LCA-emergy methodology. Arab J Geosci 15, 1271 (2022). https://doi.org/10.1007/s12517-022-10537-0

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