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Environmental Productivity Growth in Consumer Durables

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Energy, Environment and Transitional Green Growth in China

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Abstract

A large number of studies on environmental productivity have appeared across various sub-disciplines of economics as well as in other related disciplines such as operations research and engineering. In these studies, the production units of interest are usually plants or firms, sectors or industries, regions, and countries. To our knowledge, however, only one previous study considers environmental performance of consumer durables. This is somewhat surprising because, during their use phase, consumer durables such as passenger cars and home appliances are in fact production units that consume energy and resources to provide services for consumers, and hence are also contributors to various environmental pollutants. This chapter aims to develop an environmental productivity index specially designed for consumer durables. To this end, we first analyze the particular features of consumer durables compared to conventional production units. Based on these features, we elaborate how to model the production activity during the use phase of consumer durables; and then we present an overview of the existing approaches to measuring environmental productivity change and describe how they can be applied in the current context. Finally, we use a unique Finnish data set of passenger cars to illustrate the interpretation of the proposed index.

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Notes

  1. 1.

    Similar notions include environmentally sensitive productivity, green total-factor productivity, environmental performance, and various forms of eco-efficiency.

  2. 2.

    Alternative interpretations of environmental productivity exist: In addition to environmental costs, economic inputs (such as capital, labor) may also be included in the denominator.

  3. 3.

    Also known as environmental production technology.

  4. 4.

    Kuosmanen (2013) uses the term StoNED (stochastic nonparametric envelopment of data), but in fact, he only applies CNLS (which is the first stage of StoNED) and does not proceed to further steps.

  5. 5.

    For example, fuel consumption of passenger cars on roads are affected by driving behavior, vehicle maintenance, and ambient conditions (e.g., temperature, road, traffic flow, altitude, weather, etc.) (VCA 2016). As a result, even given cars of the same make and model, the actual amounts of fuel consumption may vary significantly.

  6. 6.

    Estimation of real-world fuel consumption and CO2 emissions of consumer durables, passenger cars in particular, has attracted a lot of attention. See for example (Alvarez and Weilenmann 2012; André et al. 2006; Dings 2013; Zhang et al. 2014).

  7. 7.

    An exception is that some real-world data at the aggregate level (e.g., total amount of fuel/electricity consumption) may be available for consumer durables and those data can be used to estimate other related aggregate measures (e.g., total CO2 emissions). As such, environmental productivity analysis might be able to be performed at the aggregate level (sector, region, or country).

  8. 8.

    See (Yang and Bandivadekar 2017; Yang et al. 2017), for example, for overviews of CO2 emission/fuel consumption standards for new passenger cars in different countries.

  9. 9.

    In fact, the air pollutant emissions of a passenger car are also associated with a broad range of factors such as the vehicle technology (e.g., end-of-pipe abatement) and maintenance, fuel quality, driving behavior, and ambient conditions. See (VCA 2016) for a more detailed discussion.

  10. 10.

    The measures are analogous to units of transportation measurement, such as passenger-kilometer and freight-kilometer.

  11. 11.

    See the websites for more information: https://www.eia.gov/tools/faqs/faq.php?id=307&t=11, https://ec.europa.eu/clima/policies/transport/vehicles/cars.

  12. 12.

    See Rødseth (2017) for a more general discussion on the consistency between the general axioms of the production theory and the materials balance principle.

  13. 13.

    The direct proportionality assumption implicitly assumes that all fuel in the tank is burned into CO2. It is possible that only part of the measured amount of fuel is burned while the other is wasted, and in this circumstance, Axioms 4 and 7 hold. Nevertheless, this possibility can be ruled out under type-approval test conditions or in the case that CO2 emissions data are estimated by multiplying the measured amount of fuel by a constant emissions factor.

  14. 14.

    Färe et al. (1994) proposes a third component: scale efficiency change under variable returns to scale (VRS). Yet, no consensus has been reached on the derivation and interpretation of this component. See (Färe et al. 1997; Ray and Desli 1997) for a critical exchange on this topic and (Lovell 2003) for a more detailed discussion.

  15. 15.

    Type-approval data on the CO2 emissions of new passenger cars are normally measured in g/km.

  16. 16.

    See https://www.trafi.fi/en/road/taxation/vehicle_tax/structure_and_amount_of_tax for more information.

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Acknowledgements

The author is indebted to Timo Kuosmanen for his valuable guidance, advice, and comments, which greatly improved the presentation of this chapter. I am also grateful to Knox Lovell for his constructive comments and suggestions; to Ruizhi Pang, Xuejie Bai, and Knox Lovell for the nice invitation to contribute to this edited volume; to Abolfazl Keshvari for his kind assistance in computation; and to the participants at the 2016 Asia-Pacific Productivity Conference and the 7th Helsinki Workshop on Efficiency and Productivity Analysis for helpful suggestions. This research was financially supported by the Sustainable Transitions of European Energy Markets (STEEM) project and the Foundation for Economic Education (LSR) (no. 160358). The trip to the 2016 Asia-Pacific Productivity Conference was financially supported by the HSE Foundation (no. 4-759). These financial supports are greatly acknowledged. Of course, the usual disclaimers apply.

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  1. Aalto University School of Business, 00100, Helsinki, Finland

    Xun Zhou

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Editors and Affiliations

  1. College of Economic and Social Development, Nankai University, Tianjin, China

    Ruizhi Pang

  2. College of Economic and Social Development, Nankai University, Tianjin, China

    Xuejie Bai

  3. School of Economics, University of Queensland, Brisbane, Queensland, Australia

    Knox Lovell

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Zhou, X. (2018). Environmental Productivity Growth in Consumer Durables. In: Pang, R., Bai, X., Lovell, K. (eds) Energy, Environment and Transitional Green Growth in China. Springer, Singapore. https://doi.org/10.1007/978-981-10-7919-1_4

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