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Environmental performance, manufacturing sectors and firm growth: structural factors and dynamic relationships

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Abstract

This paper investigates the correlation between sectoral environmental performance and firm growth, using a large data set of 61,219 Italian manufacturing firms and sector environmental–economic accounts. Specifically, the paper investigates the extent to which the correlation between (past) emissions intensity and environmental regulation influences firms’ economic growth. Our results show a trade-off: structurally, higher emissions intensity gives firms more freedom and relaxes the constraints on growth. This is a trade-off which rejects our hypothesis that economic and environmental performance might dynamically be positively correlated. However, although lower emissions intensity does not pay in terms of promoting turnover, there seems to be a non-linear relation characterising the economic growth–environmental performance relationship. Transforming the prevalent trade-off in a possible joint dynamics in which poor environmental performances hampers firm growth and investment in greener technologies might be associated with positive economic performance, thus becomes possible.

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Notes

  1. The concepts are linked to the concept of environmental productivity. In this paper we use the notion of ‘emission intensity’ to proxy for environmental performance.

  2. Business competitiveness within the green economy is highlighted as a key issue: ‘Transforming the economy onto a resource-efficient path will bring increased competitiveness and new sources of growth and jobs through cost savings from improved efficiency, commercialisation of innovations and better management of resources over their whole life cycle’ (p. 4), and ‘We could reduce that amount (use of resources) whilst increasing production and competitiveness. Moreover, improving the re-use of raw materials through greater ‘industrial symbiosis’ (where the waste of some firms is used as a resource for others) across the EU could save €1.4bn a year and generate €1.6bn in sales’ (p. 6).

  3. Studies that use the same approach as ours include Salama (2005), who analyses British companies’ environmental and economic performance relationship in a panel setting, and a set of studies that use real environmental performance to study the co-evolutionary/inverse correlation effects between firm performance (turnover and profits) and firms’ emissions intensity, such as Earnhart and Lizal (2006, 2007, 2010). This second set of papers is interesting but exploits a limited 1996–1998 panel for Czech firms, of around 400 units. We have extended the scale of study using a national dataset that captures the dynamic as the lagged effect of environmental performances, defined as emission intensity (on the value generated by the firm), on productivity and turnover growth.

  4. Note that, at least in the European Union (EU), NAMEA data come from emissions sector sources, based on observation of a sample of establishments, from which firm-based data are extracted. NAMEA has the advantage that it provides integrated data on emissions, employment and value added. Also, Italy, Spain and Germany are the only countries with for which NAMEA data are available for an extended period. The long panel series for Italy allows recovery of robust and significant ‘period averages’, while cross-section yearly data would be flowed and volatile. The inventory of industry firms’ emissions related to the European Emissions Trading System (ETS) operational since 2005 [ETS 2003 EU Directive, see Borghesi et al. 2012)] and the IPPC 2008 Directive, should provide emissions data that could be used for future studies and merged with other economic datasets.

  5. The difficulty of obtaining firm-based data on environmental performance and merged data on economic–environmental performance, is discussed by Cole and Elliott (2007) in their study of the employment effects of the environmental strategies of manufacturing firms in the UK.

  6. Note that disaggregating the effect by different periods (e.g. 1990–1994 and 1995–1999) does not affect the results.

  7. The main source of data on sector-pollutants is NAMEA, published by ISTAT (Italian National Statistical Institute, http://www.istat.it). NAMEA is based on real observations of point emission sources, year on year. EUROSTAT launched for its first EU27 NAMEA covering 2000–2006 (EUROSTAT 2011) in March 2011 (Costantini et al. 2012).

  8. Average units of pollutant produced per employee in the branch. Since it is based on quantity not value, it can be taken as an indicator of ‘technical emissions efficiency’, which reflects the production technology of the branch. Given the level of aggregation of NAMEA production branches, emission on employee indicators (E/N) may also reflect composition effects, i.e. the combination of different E/N in, e.g., different industries in the NAMEA branch DK, namely ‘Machinery’ (see Mazzanti and Zoboli 2009a).

  9. Cole et al. (2009) deal with the same issue and, along similar lines, integrate data on workers’ wages with data on firm pollution available only at sector level. Our econometric analyses include the usual cluster correction (Wooldridge 2003, 2006) to take into account of merged sector-firm data.

  10. See Table 5 in the appendix for more insights.

  11. We use turnover rather than productivity for two reasons. First, it is coherent with most of the Gibrat literature. Second, though it might be interesting to investigate the environmental and economic efficiency correlation (Mazzanti and Zoboli 2009b), the period 2000–2004 is atypical since it was a period in Italy when there was decrease or stagnation in labour productivity in many industrial sectors, due in part to an increase in the workforce and employment (mainly women, immigrants, atypical contracts) and low growth in value added.

  12. See Harris and Trainor (2005), who analyse manufacturing sectors in a panel framework to study the relationship between growth and size, rejecting Gibrat’s law in all observed cases. Other recent work dealing with measures other than or in addition to employment size are Dunne and Hughes (1994), Delmar et al. (2003), Audretsch et al. (2004), Del Monte and Papagni (2003), which study Italian manufacturing firms in 1989–1997. A detailed and comprehensive survey of this literature can be found in Santarelli et al. (2006).

  13. In regressions we associate quartiles to Q1 (1st quartile, low emissions intensity), Q2 (2nd, medium-low), e.g. Q1_SO x VA is the dummy representing the 1st quartile. Note that each regression excludes one of the quartiles to avoid perfect collinearity. The estimation of all the regressions (e.g. excluding Q1_SO x VA, Q2_SO x VA, Q3_SO x VA and Q4_SO x VA) allows us to derive a ranking for the relationship between structural emission intensity and economic performance.

  14. Sector branches that witnessed a decrease in the CO2/VA ratio in 1999 compared to 1990 are: DB, DD, DG, DH, DJ. We could not apply the dynamic dummy exercise for the other two pollutants—for SO x all sectors produced less intense emissions in 1999 compared to 1990; for NO x only two sectors showed no decrease thus a dummy variable would be unbalanced.

  15. However, all show lower emissions per value added in 1999 compared to 1990.

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Authors and Affiliations

  1. University of Padova, Padova, Italy

    Giulio Cainelli

  2. Department of Economics and Management, University of Ferrara, Ferrara, Italy

    Massimiliano Mazzanti

  3. Catholic University of Milan and CERIS CNR, Milan, Italy

    Roberto Zoboli

  4. CERIS CNR, Milan, Italy

    Giulio Cainelli

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  1. Giulio Cainelli
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  2. Massimiliano Mazzanti
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  3. Roberto Zoboli
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Correspondence to Massimiliano Mazzanti.

Appendix

Appendix

Table 9 Classification of manufacturing activities
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Cainelli, G., Mazzanti, M. & Zoboli, R. Environmental performance, manufacturing sectors and firm growth: structural factors and dynamic relationships. Environ Econ Policy Stud 15, 367–387 (2013). https://doi.org/10.1007/s10018-013-0064-8

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