IDEAS home Printed from https://ideas.repec.org/a/wly/sustdv/v33y2025i3p4718-4731.html
   My bibliography  Save this article

The Influence of Environmental Variables on the Carbon Performance of Water Companies Across Time

Author

Listed:
  • Maria Molinos‐Senante
  • Alexandros Maziotis

Abstract

One of the main challenges that water companies face is to reduce carbon footprint in their transition to carbon neutrality. Past research assessing carbon performance of water companies has mostly ignored heterogeneity among the water companies evaluated. To overcome this limitation, this study employed a parametric metafrontier approach to assess and compare carbon performance of a sample of English and Welsh water companies, embracing water and sewerage companies (WaSCs) and water‐only companies (WoCs). This method allows for a comparison of the carbon performance of these two types of companies and analyses the impact of environmental variables on their performance. It was evidenced that water treatment complexity, main source of raw water, and population density significantly influence carbon performance of water companies. The average carbon efficiency for WaSCs was 0.816, indicating marginally superior performance compared to WoSCs, which had an average carbon efficiency of 0.803. Regarding carbon productivity between 2011 and 2020, WoSCs demonstrated an annual improvement in carbon performance of 2.9%, while WaSCs showed an annual decrease in carbon productivity of 4.2%. The insights gained from this study are highly significant for policymakers focused on transitioning the water industry toward net‐zero carbon emissions.

Suggested Citation

  • Maria Molinos‐Senante & Alexandros Maziotis, 2025. "The Influence of Environmental Variables on the Carbon Performance of Water Companies Across Time," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(3), pages 4718-4731, June.
  • Handle: RePEc:wly:sustdv:v:33:y:2025:i:3:p:4718-4731
    DOI: 10.1002/sd.3375
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sd.3375
    Download Restriction: no

    File URL: https://libkey.io/10.1002/sd.3375?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yujiro Hayami, 1969. "Sources of Agricultural Productivity Gap Among Selected Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(3), pages 564-575.
    2. Ananda, Jayanath & Hampf, Benjamin, 2015. "Measuring environmentally sensitive productivity growth: An application to the urban water sector," Ecological Economics, Elsevier, vol. 116(C), pages 211-219.
    3. Hampf, Benjamin & Ananda, Jayanath, 2015. "Measuring environmentally sensitive productivity growth: An application to the urban water sector," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77008, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    5. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    6. Fatima Zahra Kherazi & Dongying Sun & Jan Muhammad Sohu & Ikramuddin Junejo & Hafiz Muhammad Naveed & Asadullah Khan & Sonia Najam Shaikh, 2024. "The role of environmental knowledge, policies and regulations toward water resource management: A mediated‐moderation of attitudes, perception, and sustainable consumption patterns," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 5719-5741, October.
    7. Jayanath Ananda & Dong-hyun Oh, 2023. "Assessing environmentally sensitive productivity growth: incorporating externalities and heterogeneity into water sector evaluations," Journal of Productivity Analysis, Springer, vol. 59(1), pages 45-60, February.
    8. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    9. Lizhan Cao & Zhongying Qi & Junxia Ren, 2017. "China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    10. Susaeta, Andres & Sancewich, Brian & Klizentyte, Kotryna & Soto, Jose & Joshi, Omkar, 2024. "Profit efficiency in the provision of ecosystem services in the Cross Timbers forests," Land Use Policy, Elsevier, vol. 136(C).
    11. Oh, Dong-hyun, 2010. "A metafrontier approach for measuring an environmentally sensitive productivity growth index," Energy Economics, Elsevier, vol. 32(1), pages 146-157, January.
    12. Delgado, A. & Rodriguez, D.J. & Amadei, C.A. & Makino, M., 2024. "Water in Circular Economy and Resilience (WICER) Framework," Utilities Policy, Elsevier, vol. 87(C).
    13. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    14. Qiang Wang & Xiaowei Wang & Rongrong Li, 2024. "The impact of globalization on the decoupling of water consumption and economic growth in BRICS and N11 countries—Linear and nonlinear approaches," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 755-776, February.
    15. Jin, Qianying & Basso, Antonella & Funari, Stefania & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2024. "Evaluating different groups of mutual funds using a metafrontier approach: Ethical vs. non-ethical funds," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1134-1145.
    16. Victoria Wojcik & Harald Dyckhoff & Marcel Clermont, 2019. "Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts?," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 559-595, December.
    17. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    18. Ramón Sala-Garrido & Manuel Mocholí-Arce & María Molinos-Senante & Alexandros Maziotis, 2021. "Comparing Operational, Environmental and Eco-Efficiency of Water Companies in England and Wales," Energies, MDPI, vol. 14(12), pages 1-14, June.
    19. Rita, Rui & Marques, Vitor & Bárbara, Diogo & Chaves, Inês & Macedo, Pedro & Moutinho, Victor & Pereira, Mariana, 2023. "Crossing non-parametric and parametric techniques for measuring the efficiency: Evidence from 65 European electricity Distribution System Operators," Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wensheng Wang & Yuting Jia, 2024. "Scenario Analysis of CO 2 Reduction Potentials from a Carbon Neutral Perspective," Sustainability, MDPI, vol. 16(10), pages 1-16, May.
    2. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    3. Liu, Xiaohong & Yang, Jiangjiang & Xu, Chengzhen & Li, Xingchen & Zhu, Qingyuan, 2023. "Environmental regulation efficiency analysis by considering regional heterogeneity," Resources Policy, Elsevier, vol. 83(C).
    4. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    5. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    6. Jayanath Ananda & Dong-hyun Oh, 2023. "Assessing environmentally sensitive productivity growth: incorporating externalities and heterogeneity into water sector evaluations," Journal of Productivity Analysis, Springer, vol. 59(1), pages 45-60, February.
    7. Lin, Boqiang & Xu, Mengmeng, 2018. "Regional differences on CO2 emission efficiency in metallurgical industry of China," Energy Policy, Elsevier, vol. 120(C), pages 302-311.
    8. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    9. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    10. Alexandros Maziotis & Ramon Sala‐Garrido & Manuel Mocholi‐Arce & Maria Molinos‐Senante, 2024. "Estimating the impact of carbon inefficiency and overuse of energy on the economics of water companies: A case study for England and Wales," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(4), pages 3601-3611, August.
    11. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    12. Lizhan Cao & Zhongying Qi & Junxia Ren, 2017. "China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    13. Zhang, Ning & Wang, Bing, 2015. "A deterministic parametric metafrontier Luenberger indicator for measuring environmentally-sensitive productivity growth: A Korean fossil-fuel power case," Energy Economics, Elsevier, vol. 51(C), pages 88-98.
    14. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    15. Qunwei Wang & Ye Hang & Jin‐Li Hu & Ching‐Ren Chiu, 2018. "An alternative metafrontier framework for measuring the heterogeneity of technology," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(5), pages 427-445, August.
    16. Wang, Qunwei & Su, Bin & Zhou, Peng & Chiu, Ching-Ren, 2016. "Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach," Energy Economics, Elsevier, vol. 56(C), pages 475-482.
    17. Du, Limin & Hanley, Aoife & Wei, Chu, 2015. "Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis," Energy Economics, Elsevier, vol. 48(C), pages 217-229.
    18. Thanh Pham Thien Nguyen & Son Hong Nghiem & Eduardo Roca & Parmendra Sharma, 2016. "Efficiency, innovation and competition: evidence from Vietnam, China and India," Empirical Economics, Springer, vol. 51(3), pages 1235-1259, November.
    19. Adwitiya Gupta & Rashmi Shukla & Rudra Sensarma, 2025. "Ownership structure and bank efficiency in India: new evidence from a meta-frontier approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1713-1737, April.
    20. Zhang, Yue-Jun & Sun, Ya-Fang & Huang, Junling, 2018. "Energy efficiency, carbon emission performance, and technology gaps: Evidence from CDM project investment," Energy Policy, Elsevier, vol. 115(C), pages 119-130.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:sustdv:v:33:y:2025:i:3:p:4718-4731. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1719 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.