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.
Similar notions include environmentally sensitive productivity, green total-factor productivity, environmental performance, and various forms of eco-efficiency.
- 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.
Also known as environmental production technology.
- 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.
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.
- 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.
- 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.
The measures are analogous to units of transportation measurement, such as passenger-kilometer and freight-kilometer.
- 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.
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.
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.
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.
Type-approval data on the CO2 emissions of new passenger cars are normally measured in g/km.
- 16.
See https://www.trafi.fi/en/road/taxation/vehicle_tax/structure_and_amount_of_tax for more information.
References
Aigner D, Lovell CK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6(1):21–37.
Aigner DJ, Chu SF (1968) On estimating the industry production function. The American Economic Review 58(4):826–839.
Alvarez R, Weilenmann M (2012) Effect of low ambient temperature on fuel consumption and pollutant and CO2 emissions of hybrid electric vehicles in real-world conditions. Fuel 97:119–124.
André M, Joumard R, Vidon R, Tassel P, Perret P (2006) Real-world European driving cycles, for measuring pollutant emissions from high- and low-powered cars. Atmospheric Environment 40(31):5944–5953.
Aparicio J, Barbero J, Kapelko M, Pastor JT, Zofío JL (2017) Testing the consistency and feasibility of the standard Malmquist-Luenberger index: Environmental productivity in world air emissions. Journal of Environmental Management 196:148–160.
Arabi B, Munisamy S, Emrouznejad A, Shadman F (2014) Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist–Luenberger index measurement. Energy Policy 68:132–145.
Arabi B, Doraisamy SM, Emrouznejad A, Khoshroo A (2017) Eco-efficiency measurement and material balance principle: An application in power plants Malmquist Luenberger index. Annals of Operations Research 255(1–2):221–239.
Berndt ER (1990) Energy use, technical progress and productivity growth: A survey of economic issues. Journal of Productivity Analysis 2(1):67–83.
Blackwelder B, Coleman K, Colunga-Santoyo S, Harrison JS, Wozniak D (2016) The Volkswagen Scandal. URL http://scholarship.richmond.edu/robins-case-network/17/.
Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica: Journal of the Econometric Society pp 1393–1414.
Chang TP, Hu JL (2010) Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China. Applied Energy 87(10):3262–3270.
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. European Journal of Operational Research 2(6):429–444.
Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management 51(3):229–240.
Coelli T, Lauwers L, Van Huylenbroeck G (2007) Environmental efficiency measurement and the materials balance condition. Journal of Productivity Analysis 28(1–2):3–12.
Dings J (2013) Mind the Gap! Why official car fuel economy figures don’t match up to reality. Tech. rep., URL https://www.transportenvironment.org/publications/mind-gap-why-official-car-fuel-economy-figures-donE28099t-match-reality.
Du J, Chen Y, Huang Y (2017) A Modified Malmquist-Luenberger Productivity Index: Assessing Environmental Productivity Performance in China. European Journal of Operational Research.
Emrouznejad A, Yang Gl (2016) CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index. Energy Policy 96:397–410.
European Parliament, the Council (2009) Regulation (EC) No 443/2009 of the European Parliament and the Council of 23 April 2009 setting emission performance standards for new passenger cars as part of the Community’s integrated approach to reduce CO2 emissions from light-duty vehicles. Url http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:140:0001:0015:EN:PDF.
Färe R, Grosskopf S (2000) Theory and application of directional distance functions. Journal of Productivity Analysis 13(2):93–103.
Färe R, Grosskopf S (2006) New directions: Efficiency and productivity, vol 3. Springer Science & Business Media.
Färe R, Primont D (1995) Multi-output production and duality: Theory and applications. Kluwer Academic Publishers, Boston.
Färe R, Grosskopf S, Lovell CK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics pp 90–98.
Färe R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review pp 66–83.
Färe R, Grosskopf S, Norris M (1997) Productivity growth, technical progress, and efficiency change in industrialized countries: Reply. The American Economic Review pp 1040–1044.
Färe R, Grosskopf S, Hernandez-Sancho F (2004) Environmental performance: An index number approach. Resource and Energy economics 26(4):343–352.
Färe R, Grosskopf S, Noh DW, Weber W (2005) Characteristics of a polluting technology: Theory and practice. Journal of Econometrics 126(2):469–492.
Førsund FR (2008) Good modelling of bad outputs: Pollution and multiple- output production. Tech. rep., Memorandum//Department of Economics, University of Oslo.
Hailu A, Veeman TS (2000) Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959–1994: An input distance function approach. Journal of Environmental Economics and Management 40(3):251–274.
He F, Zhang Q, Lei J, Fu W, Xu X (2013) Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs. Energy Policy 54:204–213.
Kaya Y, Yokobori K (1998) Environment, energy and economy: Strategies for sustainability. Tech. rep., Aspen Inst., Washington, DC (United States); Brookings Institution, Washington, DC (United States).
Keshvari A (2017) A penalized method for multivariate concave least squares with application to productivity analysis. European Journal of Operational Research 257(3):1016–1029.
Kortelainen M, Kuosmanen T (2007) Eco-efficiency analysis of consumer durables using absolute shadow prices. Journal of Productivity Analysis 28(1–2):57–69.
Kuosmanen T (2008) Representation theorem for convex nonparametric least squares. The Econometrics Journal 11(2):308–325.
Kuosmanen T (2012) Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model. Energy Economics 34(6):2189–2199.
Kuosmanen T (2013) Green productivity in agriculture: A critical synthesis. Tech. rep., Report prepared for the OECD Joint Working Party on Agriculture and the Environment.
Kuosmanen T, Kortelainen M (2012) Stochastic non-smooth envelopment of data: Semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis 38(1):11–28.
Kuosmanen T, Johnson A, Saastamoinen A (2015) Stochastic nonparametric approach to efficiency analysis: A unified framework. In: Data Envelopment Analysis, Springer, pp 191–244.
Lin B, Fei R (2015) Regional differences of CO2 emissions performance in Chinas agricultural sector: A Malmquist index approach. European Journal of Agronomy 70:33–40.
Liu X, Zhou D, Zhou P, Wang Q (2017) Dynamic carbon emission performance of Chinese airlines: A global Malmquist index analysis. Journal of Air Transport Management 65:99–109.
Lovell CK (2003) The decomposition of Malmquist productivity indexes. Journal of Productivity Analysis 20(3):437–458.
Meeusen W, Van den Broeck J (1977) Efficiency estimation from Cobb- Douglas production functions with composed error. International Economic Review pp 435–444.
Meng M, Niu D (2012) Three-dimensional decomposition models for carbon productivity. Energy 46(1):179–187.
Ministry of Industry and Information Technology of the People’s Republic of China (2015) Explanatory notes regarding fuel consumption limits for passenger cars (in Chinese). Url http://chinaafc.miit.gov.cn/n2257/n2260/c97720/content.html.
Nishimizu M, Page JM (1982) Total factor productivity growth, technological progress and technical efficiency change: Dimensions of productivity change in Yugoslavia, 1965–78. The Economic Journal 92(368):920–936.
OECD (2011) Towards green growth: Monitoring progress. OECD, Paris.
Ray SC, Desli E (1997) Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. The American Economic Review 87(5):1033–1039.
Rødseth KL (2016) Environmental efficiency measurement and the materials balance condition reconsidered. European Journal of Operational Research 250(1):342–346.
Rødseth KL (2017) Axioms of a polluting technology: A materials balance approach. Environmental and Resource Economics 67(1):1–22.
Shao Y, Wang S (2016) Productivity growth and environmental efficiency of the nonferrous metals industry: An empirical study of China. Journal of Cleaner Production 137:1663–1671.
Shen Z, Boussemart JP, Leleu H (2017) Aggregate green productivity growth in OECDs countries. International Journal of Production Economics 189:30–39.
Shepard RW (1953) Cost and production functions. Princeton University Press, Princeton.
Shepherd RW (1970) Theory of cost and production functions. Princeton University Press, Princeton.
Song M, Zheng W, Wang S (2017) Measuring green technology progress in large-scale thermoelectric enterprises based on Malmquist–Luenberger life cycle assessment. Resources, Conservation and Recycling 122:261–269.
Sueyoshi T, Goto M, Wang D (2017) Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces. Energy Economics 67:554–571.
Vehicle Certification Agency of the United Kingdom Department for Transport (2016) Explanatory notes regarding CO2, fuel consumption testing and more. Url http://www.dft.gov.uk/vca/fcb/new-car-fuel-consump.asp.
Yang Z, Bandivadekar A (2017) 2017 Global update: Light-duty vehicle green- house gas and fuel economy standards. Tech. rep., International Council on Clean Transportation.
Yang Z, Mock P, German J, Bandivadekar A, Lah O (2017) On a pathway to de-carbonization—A comparison of new passenger car CO2 emission standards and taxation measures in the G20 countries. Transportation Research Part D: Transport and Environment.
Yao X, Guo C, Shao S, Jiang Z (2016) Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach. Applied Energy 184:1142–1153.
Yu C, Shi L, Wang Y, Chang Y, Cheng B (2016) The eco-efficiency of pulp and paper industry in China: An assessment based on slacks-based measure and Malmquist–Luenberger index. Journal of Cleaner Production 127:511–521.
Yu Y, Choi Y, Wei X, Chen Z (2017a) Did China’s regional transport industry enjoy better carbon productivity under regulations? Journal of Cleaner Production 165:777–787.
Yu Y, Qian T, Du L (2017b) Carbon productivity growth, technological innovation, and technology gap change of coal-fired power plants in China. Energy Policy 109:479–487.
Zhang C, Liu H, Bressers HTA, Buchanan KS (2011) Productivity growth and environmental regulations-accounting for undesirable outputs: Analysis of china’s thirty provincial regions using the Malmquist–Luenberger index. Ecological Economics 70(12):2369–2379.
Zhang N, Choi Y (2013) Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis. Energy Economics 40:549–559.
Zhang N, Zhou P, Kung CC (2015) Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis. Renewable and Sustainable Energy Reviews 41:584–593.
Zhang S, Wu Y, Liu H, Huang R, Un P, Zhou Y, Fu L, Hao J (2014) Real-world fuel consumption and CO2 (carbon dioxide) emissions by driving conditions for light-duty passenger vehicles in China. Energy 69:247–257.
Zhou P, Ang B, Han J (2010) Total factor carbon emission performance: A Malmquist index analysis. Energy Economics 32(1):194–201.
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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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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