Environmental production and productivity growth: evidence from european paper and pulp manufacturing

Abstract

The production and manufacturing sector is one of the primary factors that affects the environment, which has been a very important topic of recent studies. Many approaches are employed to reduce the impact of production on the environment. More recently, carbon-abatement technology and activities have been introduced into the production processes to reduce carbon emissions, such as the implementation of emission trading programs in many industrial sectors, including the paper and pulp sector. Nevertheless, the costs of abatement activities will result in a certain level of sacrifice in productivity growth, when the inputs are reallocated from good output production to abatement activities to maintain bad output under the regulatory limit. However, how and the extent to which such technology will affect productivity remain unclear. Therefore, it is worth investigating the opportunity cost of introducing such technology. In this paper, we offer new empirical evidence by studying panel data on 17 EU member states from 1995 to 2006. Productivity changes are calculated using a data envelopment directional distance function with and without adapting the carbon-abatement technology in the paper and pulp production. The results support our concern about the potential opportunity cost of introducing carbon-abatement technology, which leads to a decline in productivity growth. In addition, industrial production is not operating efficiently; on average it moves further away from the efficient production frontier over time.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    See Färe and Primont (1995) for a discussion.

  2. 2.

    The usual strong disposability of good outputs condition: \( \left( {y,b} \right) \in P\left( x \right)\, {\text{and }}\,y^{\prime} \le y \,{\text{imply}}\,\left( {y^{\prime},b} \right) \in P\left( x \right) \) This condition implies that a reduction of the good outputs is feasible without a simultaneous reduction of the bad outputs.

  3. 3.

    See Shephard and Färe (1974).

  4. 4.

    See Shephard (1970).

  5. 5.

    See Krautzberger and Wetzel (2012) for a similar approach.

  6. 6.

    As noted by Shestalova (2003), technological regress can be reasonably explained for sectors such as mining, whereas in most industrial sectors technology progresses or at least remains unchanged. In the paper and pulp manufacturing sector, we expect a technological progress in the EU27 countries.

  7. 7.

    Note that if the observed data for observation \( k^{\prime} \) in period \( t + 1 \) are located above the frontier in period \( t \) the linear program for the mixed period directional distance function \( \vec{D}_{o}^{t} \left( {t + 1} \right) \) yields an infeasible solution.

  8. 8.

    Note that the derectional distance function for Model 2 is \( \vec{D}_{o}^{t} \left( {x^{{t,k^{\prime}}} ,y^{{t,k^{\prime}}} , 0;y^{{t,k^{\prime}}} ,0} \right) = \hbox{max} \theta \) in which the bad outputs are excluded. That means the linear programming is to optimize solely the good outputs for given inputs.

  9. 9.

    Switzerland and the nine other member states of the European Union could not be included in the analysis because of missing data.

  10. 10.

    GDP deflators are used due to incomplete industry-specific deflators in the OECD Structural Analysis database.

  11. 11.

    This variable choice follows the gross output concept of productivity measurement appropriate when analysing firm or industry level data. For a detailed comparison of gross output based and value-added based productivity measures see the “OECD Manual on Measuring Productivity” (OECD 2001).

  12. 12.

    The 5% depreciation rate is a country average derived from diverse sources such as Abadir and Talmain (2008). Testing the robustness of our estimations, we also applied a 3% and a 10% depreciation rate. The results reveal no significant differences. The information on the gross fixed capital formation was drawn from the STAN.

  13. 13.

    NACE stands for ‘Nomenclature statistique des activités économiques dans la Communauté européenne’.

  14. 14.

    All reported indices are geometric means. Please refer Sect. 2 for an economic interpretation.

References

  1. Abadir, K., & Talmain, G. (2008). Depreciation rates and capital stocks. The Manchester School, 69(1), 42–51.

    Article  Google Scholar 

  2. Aiken, D. V., Färe, R., Grosskopf, S., & Pasurka, C. A. (2009). Pollution abatement and productivity growth: Evidence from Germany, Japan, the Netherlands, and the United States. Environmental & Resource Economics, 44(1), 11–28.

    Article  Google Scholar 

  3. Arabi, B., Doraisamy, S. M., 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.

    Article  Google Scholar 

  4. Asif, M., Muneer, T., & Kelley, R. (2007). Life cycle assessment: A case study of a dwelling home in Scotland. Building and Environment, 42(3), 1391–1394.

    Article  Google Scholar 

  5. Barla, P. (2007). ISO 14001 certification and environmental performance in Quebec’s pulp and paper industry. Journal of Environmental Economics and Management, 53(3), 291–306.

    Article  Google Scholar 

  6. Beamon, B. M. (1999). Designing the green supply chain. Logistics Information Management, 12(4), 332–342.

    Article  Google Scholar 

  7. Carlsson, D., D’Amours, S., Martel, A., & Rönnqvist, M. (2009). Supply chain planning models in the pulp and paper industry. INFOR: Information Systems and Operational Research, 47(3), 167–183.

    Google Scholar 

  8. Chambers, R. G., Chung, Y., & Färe, R. (1996a). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419.

    Article  Google Scholar 

  9. Chambers, R. G., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2), 351–364.

    Article  Google Scholar 

  10. Chambers, R. G., Färe, R., & Grosskopf, S. (1996b). Productivity growth in APEC countries. Pacific Economic Review, 1(3), 181–190.

    Article  Google Scholar 

  11. Chan, H. K., Wang, X., White, G. R. T., & Yip, N. (2013). An extended fuzzy-AHP approach for the evaluation of green product designs. IEEE Transactions on Engineering Management, 60(2), 327–339.

    Article  Google Scholar 

  12. Chan, H. K., Yee, R. W. Y., Dai, J., & Lim, M. K. (2016). The moderating effect of environmental dynamism on green product innovation and performance. International Journal of Production Economics, 181(Part B), 384–391.

    Article  Google Scholar 

  13. Chen, J., & Xiang, D. (2018). Carbon efficiency and carbon abatement costs of coal-fired power enterprises: A case of Shanghai, China. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.09.087.

    Article  Google Scholar 

  14. Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.

    Article  Google Scholar 

  15. Clift, R., & Wright, L. (2000). Relationships between environmental impacts and added value along the supply chain. Technological Forecasting and Social Change, 65(3), 281–295.

    Article  Google Scholar 

  16. Cooper, J. S., & Fava, J. A. (2008). Life-cycle assessment practitioner survey: Summary of results. Journal of Industrial Ecology, 10(4), 12–14.

    Article  Google Scholar 

  17. Eurostat. (2011). What are AEA and what are they for? https://ec.europa.eu/eurostat/web/environment/emissions-of-greenhouse-gases-and-air-pollutants/air-emissions-accounts. Accessed 18 Dec 2018.

  18. Färe, R., Grosskopf, S., & Pasurka, C. A. (2001). Accounting for air pollution emissions in measures of state manufacturing productivity growth. Journal of Regional Science, 41(3), 381–409.

    Article  Google Scholar 

  19. Färe, R., Grosskopf, S., & Pasurka, C. A. (2007a). Environmental production functions and environmental directional distance functions. Energy, 32(7), 1055–1066.

    Article  Google Scholar 

  20. Färe, R., Grosskopf, S., & Pasurka, C. A. (2007b). Pollution abatement activities and traditional productivity. Ecological Economics, 62(3–4), 673–682.

    Article  Google Scholar 

  21. Färe, R., & Primont, D. (1995). Multi-Output Production and Duality: Theory and Applications. Boston: Kluwer Academic Publishers.

    Google Scholar 

  22. Hailu, A., & Veeman, T. S. (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.

    Article  Google Scholar 

  23. Handfield, R. B., Walton, S. V., Seegers, L. K., & Melnyk, S. A. (1998). Green’ value chain practices in the furniture industry. Journal of Operations Management, 15(4), 293–315.

    Article  Google Scholar 

  24. Hawkins, T., Hendrickson, C., Higgins, C., Matthews, H. S., & Suh, S. (2007). A mixed-unit input-output model for environmental life-cycle assessment and material flow analysis. Environmental Science and Technology, 41(3), 1024–1031.

    Article  Google Scholar 

  25. Hsu, C.-C., & Lo, S.-L. (2017). The potential for carbon abatement in Taiwan’s steel industry and an analysis of carbon abatement trends. Renewable and Sustainable Energy Reviews, 69, 1312–1323.

    Article  Google Scholar 

  26. Huang, Y., Liu, L., Ma, X., & Pan, X. (2015). Abatement technology investment and emissions trading system: a case of coal-fired power industry of Shenzhen, China. Clean Technologies and Environmental Policy, 17(3), 811–817.

    Article  Google Scholar 

  27. Kainuma, Y., & Tawara, N. (2006). A multiple attribute utility theory approach to lean and green supply chain management. International Journal of Production Economics, 101(1), 99–108.

    Article  Google Scholar 

  28. Koroneos, C., Roumbas, G., Gabari, Z., Papagiannidou, E., & Moussiopoulos, N. (2005). Life cycle assessment of beer production in Greece. Journal of Cleaner Production, 13(4), 433–439.

    Article  Google Scholar 

  29. Krautzberger, L., & Wetzel, H. (2012). Transport and CO2: Productivity growth and Carbon Dioxide Emissions in the European commercial transport industry. Environmental & Resource Economics, 53, 435–454.

    Article  Google Scholar 

  30. Lamming, R., & Hampson, J. (1996). The environment as a supply chain management issue. British Journal of Management, 7(s1), S45–S62.

    Article  Google Scholar 

  31. Lopes, E., Dias, A., Arroja, L., Capela, I., & Pereira, F. (2003). Application of life cycle assessment to the Portuguese pulp and paper industry. Journal of Cleaner Production, 11(1), 51–59.

    Article  Google Scholar 

  32. Mo, J. L., Schleich, J., & Fan, Y. (2018). Getting ready for future carbon abatement under uncertainty–key factors driving investment with policy implications. Energy Economics, 70, 453–464.

    Article  Google Scholar 

  33. OECD. (2001). Measuring productivity – OECD manual: Measurement of aggregate and industry-level productivity growth. https://www.oecd-ilibrary.org/industry-and-services/measuring-productivity-oecd-manual_9789264194519-en. Accessed 18 Dec 2018.

  34. OECD. (2011a). STAN industry ISIC rev. 3 (2011 edition). https://doi.org/10.1787/data-00029-en. Accessed 18 Dec 2018.

  35. OECD. (2011b). Purchasing power parities for GDP 2011. In Economics: Key tables from OECD. https://doi.org/10.1787/2074384x-2011-table11. Accessed 18 Dec 2018.

  36. Oh, D.-H., & Heshmati, A. (2010). A sequential Malmquist-Luenberger productivity index: environmentally sensitive productivity growth considering the progressive nature of technology. Energy Economics, 32(6), 1345–1355.

    Article  Google Scholar 

  37. Peng, J., Yu, B.-Y., Liao, H., & Wei, Y.-M. (2018). Marginal abatement costs of CO2 emissions in the thermal power sector: a regional empirical analysis from China. Journal of Cleaner Production, 171, 163–174.

    Article  Google Scholar 

  38. Pokhrel, D., & Viraraghavan, T. (2004). Treatment of pulp and paper mill wastewater—a review. Science of the Total Environment, 333(1–3), 37–58.

    Article  Google Scholar 

  39. Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment Part 1: Goal and scope and inventory analysis. International Journal of Life Cycle Assessment, 13(4), 290–300.

    Article  Google Scholar 

  40. Reich, M. C. (2005). Economic assessment of municipal waste management systems—case studies using a combination of life cycle assessment (LCA) and life cycle costing (LCC). Journal of Cleaner Production, 13(3), 253–263.

    Article  Google Scholar 

  41. Sarkis, J. (2003). A strategic decision framework for green supply chain management. Journal of Cleaner Production, 11(4), 397–409.

    Article  Google Scholar 

  42. Shephard, R. W. (1970). Theory of Production Functions. Princeton: Princeton University Press.

    Google Scholar 

  43. Shephard, R. W., & Färe, R. (1974). The law of diminishing returns. Journal of Economics, 34(1–2), 69–90.

    Google Scholar 

  44. Shestalova, V. (2003). Sequential Malmquist indices of productivity growth: an application to OECD industrial activities. Journal of Productivity Analysis, 19(2–3), 211–226.

    Article  Google Scholar 

  45. Stoppato, A. (2008). Life cycle assessment of photovoltaic electricity generation. Energy, 33(2), 224–232.

    Article  Google Scholar 

  46. Sundarakani, B., de Souza, R., Goh, M., Wagner, S. M., & Manikandan, S. (2010). Modeling carbon footprints across the supply chain. International Journal of Production Economics, 128(1), 43–50.

    Article  Google Scholar 

  47. Szabó, L., Soria, A., Forsström, J., Keränen, J. T., & Hytönen, E. (2009). A world model of the pulp and paper industry: Demand, energy consumption and emission scenarios to 2030. Environmental Science & Policy, 12(3), 257–269.

    Article  Google Scholar 

  48. Thompson, G., Swain, J., Kay, M., & Forster, C. F. (2001). The treatment of pulp and paper mill effluent: a review. Bioresource Technology, 77(3), 275–286.

    Article  Google Scholar 

  49. Walton, S. V., Handfield, R. B., & Melnyk, S. A. (1998). The green supply chain: Integrating suppliers into environmental management processes. Journal of Supply Chain Management, 34(2), 2–11.

    Google Scholar 

  50. Wang, X., Chan, H. K., Yee, R. W. Y., & Diaz-Rainey, I. (2012). A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of Production Economics, 135(2), 595–606.

    Article  Google Scholar 

  51. Weinzettel, J., Reenaas, M., Solli, C., & Hertwich, E. G. (2009). Life cycle assessment of a floating offshore wind turbine. Renewable Energy, 34(3), 742–747.

    Article  Google Scholar 

  52. Yung, W. K. C., Chan, H. K., Wong, D. W. C., So, J. H. T., Choi, A. C. K., & Yue, T. M. (2012). Life cycle assessment of a personal electronic product subject to the energy-using product directive. International Journal of Production Research, 50(5), 1411–1423.

    Article  Google Scholar 

  53. Zhang, H. C., Kuo, T. C., Lu, H., & Huang, S. H. (1997). Environmentally conscious design and manufacturing: a state-of-the-art survey. Journal of Manufacturing Systems, 16(5), 352–371.

    Article  Google Scholar 

  54. Zhang, T., & Matthews, K. (2012). Efficiency convergence properties of Indonesian banks 1992–2007. Applied Financial Economics, 22(17), 1465–1478.

    Article  Google Scholar 

  55. Zhang, N., & Xie, H. (2015). Toward green IT: Modeling sustainable production characteristics for Chinese electronic information industry, 1980–2012. Technological Forecasting and Social Change, 96, 62–70.

    Article  Google Scholar 

  56. Zhu, Q., Sarkis, J., & Geng, Y. (2005). Green supply chain management in China: Pressures, practices and performance. International Journal of Operations & Production Management, 25(5), 449–468.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hing Kai Chan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Chan, H.K. & Zhang, T. Environmental production and productivity growth: evidence from european paper and pulp manufacturing. Ann Oper Res (2018). https://doi.org/10.1007/s10479-018-3126-2

Download citation

Keywords

  • GREEN manufacturing
  • Environmental management
  • Productivity
  • Data envelopment analysis