Abstract
This study explores productivity growth of 65 Austrian biogas plants from 2006 to 2014 using Data Envelopment Analysis. Productivity growth is measured by calculating the Malmquist productivity index, and the contributions of technical change, efficiency change, and scale change to productivity growth are isolated. The results reveal that the average annual productivity growth between 2006 and 2014 is 1.1%. The decomposition of the Malmquist productivity index shows that the annual scale change, technical change, and efficiency change for the average plant is 0.6%, 0.3%, and 0.3%, respectively. These results indicate that the exploitation of returns to scale is a major driver of productivity growth and technical change is rather low. A second-stage regression analysis reveals that rising feedstock prices incentivized efficiency improvements but initial capital subsidies did not have an impact on technical change and productivity growth.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
One anonymous referee suggested to add some critical remarks on the potential of biogas to supply energy in the future and its potential conflict with food production. Muscat et al. (2020) survey the literature on the competition for biomass across food and bioenergy production. Emmann et al. (2013) examine the validity of several points of criticism regarding biogas production. Further critical remarks on the biogas technology are stated and discussed at https://www.biogas-kanns.de/left/Criticisms-and-answers/445l1/. For the potential of biogas to supply energy in the future we refer to IEA (2020).
OECD-Europe comprises all European members of the OECD (not necessarily EU members). In 2012 these were Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey and United Kingdom.
Lovell (2003) criticizes that (i) technical change is measured relative to a CRS-technology and has no economically meaningful interpretation, and (ii) the scale efficiency change component is not measuring the contribution of scale economies to productivity change. Additionally, Ray and Desli (1997) criticize the internal inconsistency of the decomposition introduced by Färe et al. (1994). They argue that if the technology exhibits CRS technical change is measured correctly but no scale effects exist, and any measure of it is misleading.
Recent comprehensive overviews of concepts and models in DEA are provided e.g. in Bogetoft and Otto (2011) and Zhu (2015).
An anonymous referee pointed out that a balanced sample might be problematic if technological progress is related to plant entry and exit. Unfortunately, we have no information on plant exit. Twelve plants entering operation in 2007 are excluded from the balanced sample. Sensitivity checks indicate that the inclusion of the newly commissioned plants would hardly have any effect on the qualitative results and conclusions of our study.
Note that the average capacity utilization rate in our sample is above the sectoral average indicating that the plants in our sample might perform better than the population average.
The main substrate, silo maize, had a slow start in 2012 due to cold weather in May and June. The rainfall was sufficient, however, the comparatively low temperature level slowed down plant growth. This led to delayed and inadequate formed grain maize. The weather reversed in July 2012. Temperature rose and the rain failed (ZAMG, 2013). The heat wave at the end of August led to a prematurely phasing out of the maize plant (Holzkämpfer and Fuhrer, 2015). This combination led to less developed corn cobs and a drop in the sugar and starch contents at the corn. In return, the early stop in growth leads to lower yields and high crude fibre and lignin contents in the plant (Gruber und Hein, 2007).
Supplementary material provides detailed information (histograms) about the frequency distribution of the Malmquist productivity index and its components.
Estimates for t = 2006–2014, t = 2006–2013 and t = 2006–2012 are available on request. The results obtained are similar to the pooled OLS estimates.
The maximum potential electricity output of a biogas plant is ultimately constrained by the electric capacity of the CHP and is reached if the CHP is running with full load throughout the year (8760 h). Multiplying the installed capacity of the CHP (measured in kWel) by 8760 gives the maximum potential electricity output.
We also estimate models with a dummy variable for investment activities and drop (i) the capacity change variable, as well as the (ii) interaction term between capacity change and size of the plant. The results are available in Appendix B, Table 8. The investment dummy is equal to one if investments were undertaken and zero otherwise. While 66% of the biogas plants in the sample increased their capital stock between 2006 and 2012, 34% do not show any investment activity in that period. We find that there is a statistically significant positive relationship between (i) productivity growth and investment activity, as well as (ii) scale change and investments. Unfortunately, we are not able to distinguish between types of investments (e.g. installed electric capacity, heat grid…).
We also test for the effect of changes in feedstock composition on productivity growth and do not find any evidence that it has an impact.
The standard deviation of the efficiency scores is relatively constant over time. It is 0.15, 0.13, 0.13, and 0.13 for the years 2006, 2012, 2013, and 2014, respectively.
We also exploit the cross-sectional variability in feed-in-tariffs (for details see, e.g., Eder and Mahlberg, 2018) to estimate the effect of production subsidies on productivity growth. The estimated regression models suggest that production subsidies do not impact productivity growth.
The percentage of plants without any heat utilisation in the sample declined from 49% in 2006 to 8% in 2014.
Specialized cogeneration plants, which e.g. focus on the generation of electricity and waste substantial amounts of heat, could increase heat output without using additional amounts of primary fuel.
References
Allen K (1999) DEA in the ecological context: an overview. In: Westermann G (ed) Data Envelopment Analysis in the Service Sector. Gabler, Wiesbaden, pp 203–235
Balk BM (2001) Scale efficiency and productivity change. J Prod Anal 15(3):159–183. https://doi.org/10.1023/A:1011117324278
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale efficiencies in data envelopment analysis. Manage Sci 30(9):1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Barros CP (2008) Efficiency analysis of hydroelectric generating plants: a case study for Portugal. Energy Econ 30(1):59–75. https://doi.org/10.1016/j.eneco.2006.10.008
Barros CP, Antunes OS (2011) Performance assessment of Portuguese wind farms: ownership and managerial efficiency. Energy Policy 39(6):3055–3063. https://doi.org/10.1016/j.enpol.2011.01.060
Basu S (1996) Procyclical productivity: increasing returns or cyclical utilization? Q J Econ 111(3):719–751. https://doi.org/10.2307/2946670
Bogetoft P, Otto L (2011) Benchmarking with DEA, SFA, and R. Springer Science+Business Media, New York
Borger B, Kerstens K (2000) The Malmquist productivity index and plant capacity utilization. Scand J Econ 102(2):303–310. https://doi.org/10.1111/1467-9442.00201
Braun, R., Laaber, M., Madlener, R., Brachtl, E., Kirchmayr, R. (2007). Aufbau eines Bewertungssystems für Biogasanlagen – „Gütesiegel Biogas“ Endbericht. Tulln, Austria: Projekt Nr. 804472 im Auftrag des BMVIT (Forschungsprogramm „Energiesysteme der Zukunft“). Available from: https://www.rwth-aachen.de/global/show_document.asp?id=aaaaaaaaaahdapo [accessed 11 October 2020].
Briec W, Peypoch N, Ratsimbanierana H (2011) Productivity growth and biased technological change in hydroelectric dams. Energy Econ 33(5):853–858. https://doi.org/10.1016/j.eneco.2011.01.011
Charnes A, Cooper WW, Rhodes E (1978) Measuring efficiency of decision making units. Eur J Oper Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Cook WD, Tone K, Zhu J (2014) Data envelopment analysis: Prior to choosing a model. Omega 44:1–4. https://doi.org/10.1016/j.omega.2013.09.004
Ðatkov Đ, Effenberger M (2010) Data envelopment analysis for assessing the efficiency of biogas plants: capabilities and limitations. J Process Energy Agric 14(3–4):49–53
Diewert EW, Fox KJ (2017) Decomposing productivity indexes into explanatory factors. Eur J Oper Res 256(1):275–291. https://doi.org/10.1016/j.ejor.2016.05.043
Dyckhoff H, Allen K (2001) Measuring ecological efficiency with data envelopment analysis (DEA). Eur J Oper Res 132(2):312–325. https://doi.org/10.1016/S0377-2217(00)00154-5
Eder A, Mahlberg B (2018) Size, subsidies and technical efficiency in renewable energy production: the case of Austrian Biogas Plants. Energy J 39(1):185–210. https://doi.org/10.5547/01956574.39.1.aede
Eder A, Mahlberg B, Stürmer B. Measuring and explaining productivity growth of renewable energy plants: An empirical study of Austrian biogas plants. Munich Personal RePEc Archiv (MPRA) Paper No. 79826 (28 June 2017, https://mpra.ub.uni-muenchen.de/79826).
Ederer N (2015) Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach. Renew Sustain Energy Rev 42:1034–1046. https://doi.org/10.1016/j.rser.2014.10.071
Emmann CH, Guenther-Lübbers W, Theuvsen L (2013) Impact of Biogas production on the production factors land and labour–current effects, possible consequences and further research needs. Int J Food Sys Dyn 4(1):38–50
Fallahi A, Ebrahimi R, Ghaderi S (2011) Measuring efficiency and productivity change in power electric generation management companies by using data envelopment analysis: a case study. Energy 36:6398–6405. https://doi.org/10.1016/j.rser.2014.10.071
Färe R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84(1):66–83
Färe R, Grosskopf S, Lovell CAK (1985) The measurement of efficiency of production. Kluwer-Nijhoff Publishing, Boston
Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc: Ser A 120:253–281. https://doi.org/10.2307/2343100
Filler G, Odening M, Seeger S, Hahn J (2007) Zur Effizienz von Biogasanlagen. Berichte über Landwirtschaft 85(2):178–194
Gharneh NS, Nabavieh A, Gholamiangonabadi D, Alimoradi M (2014) Productivity change and its determinants: Application of the Malmquist index with bootstrapping in Iranian steam power plants. Utilities Policy 31:114–120. https://doi.org/10.1016/j.jup.2014.10.001
Giannangeli S, Gómez-Salvador R. Evolution and sources of manufacturing productivity growth: Evidence from a panel of European countries. ECB Working Paper Series 2008; No. 914. http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp914.pdf
Gruber, L, Hein W. Ertrag und Futterqualität von Silomais in Abhängigkeit von Vegetationsstadium, Sorte und Standort. In: Brod, H-G (ed.). Landnutzungskonzepte heute und morgen - dargestellt am Beispiel der Region südlicher Oberrhein, 118. VDLUFA-Kongresses. VDLUFA-Schriftenreihe Bd. 62/2007, VDLUFA-Verlag, Darmstadt, pp. 244–259. https://www.vdlufa.de/download/Kongressband-2006-Freiburg.pdf
Heshmati A, Rashidghalam M. Labour Productivity in Kenyan Manufacturing and Service Industries. IZA Discussion Paper 2016; No. 9923. http://ftp.iza.org/dp9923.pdf
Hicks JR (1932) The theory of wages. St. Martins, New York
Holzkämpfer A, Fuhrer J (2015) Wie sich der Klimawandel auf den Maisanbau in der Schweiz auswirkt. Agrarforschung Schweiz 6(10):440–447
IEA. Policy considerations for deploying renewables. Paris: International Energy Agency; 2011. https://www.iea.org/publications/freepublications/publication/Renew_Policies.pdf
IEA. Outlook for biogas and biomethane: Prospects for organic growth. World Energy Outlook special report. Paris: International Energy Agency; 2020. https://www.iea.org/reports/outlook-for-biogas-and-biomethane-prospects-for-organic-growth/the-outlook-for-biogas-and-biomethane-to-2040
IEA. World Energy Outlook 2016. Paris: International Energy Agency; 2016b. https://doi.org/https://doi.org/10.1787/20725302
Iglesias G, Castellanos P, Seijas A (2010) Measurement of productive efficiency with frontier methods: A case study for wind farms. Energy Economics 32(5):1199–1208. https://doi.org/10.1016/j.eneco.2010.03.004
Jaraite J, Di Maria C (2012) Efficiency, productivity and environmental policy: a case study of power generation in the EU. Energy Econ 34(5):1557–1568. https://doi.org/10.1016/j.eneco.2011.11.017
Kumar S, Managi S (2009) Energy price-induced and exogenous technological change: Assessing the economic and environmental outcomes. Resour Energy Econ 31(4):334–353. https://doi.org/10.1016/j.reseneeco.2009.05.001
Kwon OS, Yun WC (2003) Measuring economies of scope for cogeneration systems in Korea: a nonparametric approach. Energy Econ 25(4):331–338. https://doi.org/10.1016/S0140-9883(02)00105-6
Lijo L, Lorenzo-Toja Y, Gonzalez-Garcia S, Bacenetti J, Negri M, Moreira MT (2017) Eco-efficiency assessment of farm-scaled biogas plants. Bioresour Technol 237:146–155
Lovell CAK (2003) The Decomposition of Malmquist Productivity Indexes. J Prod Anal 20(3):437–458. https://doi.org/10.1023/A:1027312102834
Madlener R, Antunes C, Dias L (2009) Assessing the performance of biogas plants with multi-criteria and data envelopment analysis. Eur J Oper Res 14(3–4):49–53. https://doi.org/10.1016/j.ejor.2007.12.051
Mcguckin RH, Streitwieser ML, Doms M (1998) The effect of technology use on productivity growth. Econ Innov New Technol 7(1):1–26. https://doi.org/10.1080/10438599800000026
Morrisson CJ (2012) A Microeconomic Approach to the Measurement of Economic Performance. Springer, New York
Muscat A, de Olde EM, de Boer IJM, Ripoll-Bosch R (2020) The battle for biomass: a systematic review of food-feed-fuel competition. Glob Food Secur 25:100330. https://doi.org/10.1016/j.gfs.2019.100330
Oh DH (2015) Productivity growth, technical change and economies of scale of Korean fossil-fuel generation companies, 2001–2012: a dual approach. Energy Econ 49:113–121. https://doi.org/10.1016/j.eneco.2015.01.009
Oh DH, Lee YG (2016) Productivity decomposition and economies of scale of Korean fossil-fuel power generation companies: 2001–2012. Energy 100:1–9. https://doi.org/10.1016/j.energy.2016.01.004
Oh DH, Oh Y, Lee JD (2017) A sequential global Malmquist productivity index: productivity growth index for unbalanced panel data considering the progressive nature of technology. Empir Econ 52:1651–1674. https://doi.org/10.1007/s00181-016-1104-6
Rácz VJ, Vestergaard N (2016) Productivity and efficiency measurement of the Danish centralized biogas power sector. Renew Energy 92:397–404. https://doi.org/10.1016/j.renene.2016.02.024
Ray S, Desli E (1997) Productivity growth, technical progress and efficiency change in industrialized countries: comment. Am Econ Rev 87(5):1033–1039
IEA. Renewables Information 2016. Paris: International Energy Agency; 2016
Shephard RW (1970) Theory of Cost and Production Functions. Princeton University Press, Princeton
Simões P, Marques RC. On the economic performance of the waste sector. A literature review. Journal of Environmental Management 2012; 106:40–47. http://dx.doi.org/https://doi.org/10.1016/j.jenvman.2012.04.005
Singh SK, Bajpai VK, Garg T (2013) Measuring productivity change in Indian coal-fired electricity generation: 2003–2010. Int J Energy Sect Manage 7(1):46–64. https://doi.org/10.1108/17506221311316470
Skovsgaard L, Klinge JH (2017) Economies of scale in biogas production and the significance of flexible regulation. Energy Policy 101:77–89. https://doi.org/10.1016/j.enpol.2016.11.021
Smith P (1997) Model misspecification in Data Envelopment Analysis. Ann Oper Res 73:233–252. https://doi.org/10.1023/A:1018981212364
Vaz CB, Ferreira AP (2015) Efficiency and Productivity Assessment of Wind Farms. In: Almeida JP, Oliveira JF, Pinto AA (eds) Operational Research IO 2013–XVI Congress of APDIO, Braganca, Portugal, June 3–5, 2013. Springer International Publishing Switzerland, Cham, pp 407–424
Walla C, Schneeberger W (2008) The optimal size for biogas plants. Biomass Bioenerg 32(6):551–557. https://doi.org/10.1016/j.biombioe.2007.11.009
Wirth S, Markard J, Truffer B, Rohracher H (2013) Informal institutions matter: Professional culture and the development of biogas technology. Environ Innov Soc Trans 8:20–41. https://doi.org/10.1016/j.eist.2013.06.002
ZAMG. HISTALP Langzeitklimareihen–Österreich, Jahresbericht 2012. https://www.zamg.ac.at/cms/de/dokumente/klima/dok_news/dok_histalp/jahresbericht-2012 [accessed 2 July 2018]
Zhou P, Ang BW, Poh KL (2008) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189(1):1–18. https://doi.org/10.1016/j.ejor.2007.04.042
Zhu J (2015) Data Envelopment Analysis: A Handbook of Models and Methods. Springer Science+Business Media, New York
Zofío J (2007) Malmquist Productivity Index Decompositions: a unifying framework. Appl Econ 39(18):2371–2387. https://doi.org/10.1080/00036840600606260
Acknowledgements
The authors gratefully acknowledge the valuable comments received from two anonymous referees, Klaus Gugler, Kristiaan Kerstens, Wolfgang Koller, Mikulas Luptacik, members of the Austrian Biogas Network (BiGa-NET), the seminar participants at the Vienna University of Economics and Business as well as the participants at the 9th North American Productivity Workshop (NAPW 2016) in Quebec City, the Annual Conference of the Austrian Economic Association (NOeG 2017) in Linz, the International Association for Energy Economics European Conference 2017 and the 5th IAEE European PhD-Day in Vienna. Special thanks to the Austrian Compost and Biogas Association for providing the data set. The authors would like to thank the Austrian Research Promotion Agency (FFG) for funding the project under project number 845422. The usual disclaimer applies.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendices
Appendix A
Appendix B
Rights and permissions
About this article
Cite this article
Eder, A., Mahlberg, B. & Stürmer, B. Measuring and explaining productivity growth of renewable energy producers: An empirical study of Austrian biogas plants. Empirica 48, 37–63 (2021). https://doi.org/10.1007/s10663-020-09498-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10663-020-09498-y
Keywords
- Data envelopment analysis
- Malmquist productivity index
- Technical change
- Renewable energy sources
- Biogas energy