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Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China


  • De Clercq, Djavan
  • Wen, Zongguo
  • Caicedo, Luis
  • Cao, Xin
  • Fan, Fei
  • Xu, Ruifei


Global interest in the conversion of organic biowaste to biomethane is increasing rapidly. As new projects are built, managers must ensure that biomethane engineering processes are operating efficiently. In China, increasing biogas energy output is an integral part of the central government’s 13th Five Year Plan. However, many biogas plants that convert various organic waste types to energy in China operate inefficiently. In this context, the objective of this research is to investigate the determinants of efficiency in a major biogas engineering pilot project that converts municipal, industrial and agricultural waste into biomethane vehicle fuel. The methodology involved applying data envelopment analysis and multiple linear regression to determine factors statistically significant for fluctuations in performance efficiency. The results provided important insights. First, variables statistically significant for the production of primary outputs were isolated. The variables most influential for biomethane production included bagasse input, fish waste input, and cassava input. As for solid fertilizer output, the most influential variables included manure input, fish waste, other input, and FeCl2. Secondly, the surveyed case was found to have significant scale inefficiencies, which has important implications for optimization of industrial scale co-digestion projects. As time progressed, the project experienced decreasing returns to scale, indicating that although overall inputs increased, production per unit of input decreased. Third, specific input/output targets and slacks were computed in order to identify changes required for the project to become efficient at certain points in time over the survey period. Fourth, possible determinants of efficiency were analyzed. The paper concludes with several engineering management and policy suggestions to enhance biomethane conversion efficiency.

Suggested Citation

  • De Clercq, Djavan & Wen, Zongguo & Caicedo, Luis & Cao, Xin & Fan, Fei & Xu, Ruifei, 2017. "Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China," Applied Energy, Elsevier, vol. 205(C), pages 1231-1243.
  • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1231-1243
    DOI: 10.1016/j.apenergy.2017.08.111

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    Cited by:

    1. De Clercq, Djavan & Wen, Zongguo & Fei, Fan, 2019. "Determinants of efficiency in anaerobic bio-waste co-digestion facilities: A data envelopment analysis and gradient boosting approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Lemos, Stella Vannucci & Salgado, Alexandre Pereira & Duarte, Alexandre & de Souza, Marco Antonio Alves & de Almeida Antunes, Fernanda, 2019. "Agroindustrial best practices that contribute to technical efficiency in Brazilian sugar and ethanol production mills," Energy, Elsevier, vol. 177(C), pages 397-411.
    3. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-24, January.
    4. Jingyuan Cai & Liguo Zhang & Jing Tang & Dan Pan, 2019. "Adoption of Multiple Sustainable Manure Treatment Technologies by Pig Farmers in Rural China: A Case Study of Poyang Lake Region," Sustainability, MDPI, Open Access Journal, vol. 11(22), pages 1-18, November.
    5. Garofalo, Pasquale & Campi, Pasquale & Vonella, Alessandro Vittorio & Mastrorilli, Marcello, 2018. "Application of multi-metric analysis for the evaluation of energy performance and energy use efficiency of sweet sorghum in the bioethanol supply-chain: A fuzzy-based expert system approach," Applied Energy, Elsevier, vol. 220(C), pages 313-324.


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