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Diffusion of Cement Kiln Co-Processing of Contaminated Soil in Selected Provinces of China: Engineering Practices, Modeling, and Driving Factors

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  • Tian Liang

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Bin Yang

    (Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Chenning Deng

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Ping Du

    (Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Tuqiang Wang

    (Beijing Building Materirals Academy of Sciences Research, Beijing 100041, China)

  • Hongxing Zhou

    (Chongqing Taifu Environmental Protection Technology Group Co., Ltd., Chongqing 401147, China)

  • Panpan Wang

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Jingjing Yu

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Aizhong Ding

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China)

  • Fujun Ma

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Qingbao Gu

    (State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Fasheng Li

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

Abstract

Promoting the diffusion of remediation technologies is an attractive solution to environmental protection and urban sustainability challenges. To better understand technology diffusion, we reviewed the engineering practices of cement kiln co-processing (CKC) of contaminated soil and obtained diffusion parameters using the Bass model in three provinces of China. Our results show that CKC has been adopted for the disposal of multiple contaminants and that the optimal feed rate of contaminated soil is 4–5%. The obtained diffusion parameters can be used to analyze and predict CKC diffusion. Driving factors analysis suggest that CKC diffusion is regulation-driven and obeys the S-curve pattern. Policies at the national level shape the basic pattern of the diffusion curve, while local policies, market scales, and contaminant types produce variations in diffusion rates across provinces. Results also reveal that the co-processing quota management on contaminated soil has little impact on CKC adoption. This study provides insights into contaminated soil remediation technology diffusion and the effectiveness of environmental policy implementation at home and abroad.

Suggested Citation

  • Tian Liang & Bin Yang & Chenning Deng & Ping Du & Tuqiang Wang & Hongxing Zhou & Panpan Wang & Jingjing Yu & Aizhong Ding & Fujun Ma & Qingbao Gu & Fasheng Li, 2022. "Diffusion of Cement Kiln Co-Processing of Contaminated Soil in Selected Provinces of China: Engineering Practices, Modeling, and Driving Factors," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14887-:d:969369
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    References listed on IDEAS

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    1. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.
    2. Ukrit Suksanguan & Somsak Siwadamrongpong & Thanapong Champahom & Sajjakaj Jomnonkwao & Tassana Boonyoo & Vatanavongs Ratanavaraha, 2022. "Structural Equation Model of Factors Influencing the Selection of Industrial Waste Disposal Service in Cement Kilns," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    3. Mohan, Preeya & Strobl, Eric & Watson, Patrick, 2021. "Innovation, market failures and policy implications of KIBS firms: The case of Trinidad and Tobago's oil and gas sector," Energy Policy, Elsevier, vol. 153(C).
    4. Maria Letizia Bertotti & Giovanni Modanese, 2019. "On the evaluation of the takeoff time and of the peak time for innovation diffusion on assortative networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 25(5), pages 482-498, September.
    5. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    6. Usha Rao, K. & Kishore, V.V.N., 2009. "Wind power technology diffusion analysis in selected states of India," Renewable Energy, Elsevier, vol. 34(4), pages 983-988.
    7. Lim, Hyungsoo & Jun, Duk Bin & Hamoudia, Mohsen, 2019. "A choice-based diffusion model for multi-generation and multi-country data," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 163-173.
    8. Kumar, Anjani & Takeshima, Hiroyuki & Thapa, Ganesh & Adhikari, Naveen & Saroj, Sunil & Karkee, Madhab & Joshi, P.K., 2020. "Adoption and diffusion of improved technologies and production practices in agriculture: Insights from a donor-led intervention in Nepal," Land Use Policy, Elsevier, vol. 95(C).
    9. Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
    10. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    11. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    12. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    13. Stijn van Ewijk & Will McDowall, 2020. "Diffusion of flue gas desulfurization reveals barriers and opportunities for carbon capture and storage," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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