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Is There a Long-Term Relationship Between Agricultural GHG Emissions and Productivity Growth? A Dynamic Panel Data Approach

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Abstract

The paper adopts a single-country regional panel dataset to analyse the long-term relationship between agricultural greenhouse gases (GHG) emissions and productivity growth and, consequently, to assess emissions sustainability. The hypothesis of emission sustainability is assessed by estimating alternative panel model specifications with conventional and GMM estimators applied to the highly heterogeneous Italian regional agriculture, whose methane and nitrous oxide emissions are properly reconstructed for the periods 1951–2008 and 1980–2008. The modelling approach and the empirical specification include the environmental Kuznets curve (EKC) as one of the possible outcomes. Results suggest that, when a significant relationship between agricultural GHG emissions and productivity growth occurs, it is often monotonic and, though sustainability is accepted for some GHG, no univocal robust evidence of the EKC emerges across the different specifications, estimators and periods. Policy implications of this empirical evidence are finally drawn.

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Notes

  1. Compared to other studies, these estimates are even optimistic. For example, using different methodologies, the Worldwatch Institute (Goodland and Anhang 2009) estimates that the contribution of agriculture to GHG global emissions may currently exceed 50 %. FAO (2006) states that the livestock sector alone is responsible for 18 % of all GHG production.

  2. Though it might be insufficient to achieve the global emission targets established at the international level where, in fact, a substantial reduction of GHG emissions is required over the next decades (OECD 2008), this definition of sustainability remains an interesting benchmark especially if this condition is met, on a global scale, with a growing level of agricultural production.

  3. In fact, this decomposition of emission growth is a very simple adaptation of the well-known IPAT model (Holdren and Ehrlich 1974; Kaya 1990; Borghesi and Vercelli 2009).

  4. The IPCC actually includes six sources but one of them, source 4E-Prescribed burning of Savannas, is not relevant in Italy, therefore it will not be considered here.

  5. This is the case of the Implied Emission Factor, IEF; see Sect. 4 for clarifications.

  6. The relevance of the intra-sectoral composition effect is emphasised in a recent study for the EU Commission on climate change mitigation options focusing on the food sector (Faber et al. 2012). The difference in emission per unit of agricultural product is used to identify changes in dietary choices that could contribute to reduce emissions from agriculture and food production.

  7. Some studies propose the use of TFP within an EKC framework to analyse the long-term relationship between environmental indicators and economic growth (Chimeli and Braden 2005; Khanna and Plassmann 2007).

  8. The pro-cyclical nature of agricultural TFP is well-known in the literature and also with specific reference to the Italian case here considered (Esposti 2000, 2011).

  9. This different behaviour of \(VA_{t}/L_{t}\) and TFP over time is reflected in their respective stochastic properties. As will be shown in Sect. 7.1, while \(VA_{t}/L_{t}\) is stationary within the panel, the agricultural TFP shows a unit root.

  10. Assuming stationarity around the mean of \(g_{Lt}\) and \(g_{Pt}\) does not imply stationarity around the mean of \(p_{t}\) and \(\ln L_{t}\). However, if \(p_{t}\) and \(\ln L_{t}\) are stationary around a deterministic trend or are I(1) processes, it follows that \(g_{Lt}\) and \(g_{Pt}\) are stationary around the mean. These stochastic properties will be confirmed, for the case under analysis, in Sects. 5.3 and 7.1.

  11. In the specific case under investigation here (Italian regions over period 1951–2008 or 1980–2008), the assumptions \(g_{Pt} >0\) and \(g_{Lt} <0\) are fully consistent with observed data (see Sect. 5).

  12. Conditions \(h\le \frac{\lambda }{\gamma }- 1\) and \(h\le \frac{\lambda }{\gamma }\) can be empirically assessed by taking the average \(\lambda \) and \(\gamma \) observed within the sample under investigation (see next sections).

  13. In the present application \(\hbox {N}=20\) and \(\hbox {T}= 29\) and 58 for the short and long time series, respectively (see below).

  14. In performing model estimation, the one-lag specification has been chosen among alternative AR(p) specifications according to the AIC (Akaike Information Criterion).

  15. For the sake of simplicity, in the case of \(\hbox {CH}_{4}\), “short series” here identifies the 1980–2008 period, “long series” the 1951–2008 period (see Sect. 5 for details). By aggregating the emission series of \(\hbox {CH}_{4}\) and \(\hbox {N}_{2}\hbox {O}\) on the basis of their Global Warming Potential (GWP), it is also possible to reconstruct the whole agricultural GHG emission expressed in terms \(\hbox {CO}_{2}\)eq. for the period 1980–2008. All the analysis and estimates here proposed have been also performed for this aggregate \(\hbox {CO}_{2}\)eq. series. As they simply combine what separately obtained for \(\hbox {CH}_{4}\) and \(\hbox {N}_{2}\hbox {O}\), these results are not reported here but available upon request.

  16. This range of variation of the Italian regional sample is delimited by the lower bounds Umbria-1951 (for \(p_t\)) and Liguria-2008 (for \(e_{kit}^{V}\)) and the upper bounds Toscana-2008 (for \(p_{t}\)) and Valle d’Aosta-1951 (for \(e_{kit}^{V}\)). The same behaviour of \(e_{kit}^{V}\) can be observed for \(e_{kit}^{L}\); for space limitation this latter case is not reported here but can be obtained upon request.

    Fig. 2
    figure 2

    Range of variation of yearly E/VA (g per 1995 US $) and VA/L (thousand 1995 US $ per labour unit) in the Italian regional panel 1951–2008 (grey area) and in selected countries in the last decade (2000–2005). Source: Elaborations on FAO, IPCC and Agrefit (Rizzi and Pierani 2006) data

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  17. For \({VA_t}/{L_t}\), country data are taken from FAO and are expressed in 1995 US $ per unit of agricultural population. To make data comparable in Fig. 2, also Italian regional data for \(VA_{t}\) have been expressed in 1995 US $. Data on country–level \(E_{t}\) come from UNFCCC submissions. While agricultural emission data are regularly collected and released for Appendix countries (Kyoto Protocol) following the IPCC guidelines, for non-Appendix countries (therefore, less developed, developing and emerging countries) comparable emission data are infrequent. However, some of these updated and comparable data are provided by the voluntary national communications to the UNFCCC. Besides USA (2005), these data concern China (2005), Brazil, India and Congo (2000). Therefore, these are the countries here considered.

  18. Although field burning of crop residues (4F) is forbidden in Europe, Italy is one of few countries that still reports figures from this minor source category.

  19. \(\hbox {CO}_{2 }\)and fluorine gas emissions from these sources are negligible and \(\hbox {CO}_{2}\) emissions and removals from land use, land-use change and forestry, are reported under another Category (LULUCF) and estimated with a different methodology.

  20. More details on this top–down methodology can be found in De Lauretis et al. (2009).

  21. Agrefit database is freely available at www.agriregionieuropa.it.

  22. In Italy, area cultivated adopting single aeration is a very little share of the total rice area, and this practice has been mainly used since 1990.

  23. It is worth noticing that the IEFs used in both the top–down and the bottom–up reconstruction are not available at regional level. The national IEFs are applied to the respective regional activity data to obtain all regional emission series. It follows that it is not possible to distinguish at the regional level the variation of GHG emissions coming from intra-sectoral composition and that generated by technological change, this latter being mostly expressed by a variation of the IEF associated to a given activity.

  24. VA is expressed in million € at 1995 constant prices, while \(L\) is expressed in thousand units.

  25. Nonetheless, Liu et al. (2006), Hong and Wagner (2008), Galeotti et al. (2009) remind that investigating the cointegration relationship under an inverted-U shape relationship may be not trivial, especially within a panel, as the alleged relation is not linear by definition.

  26. See Granger and Hallman (1988) and Liu et al. (2006) about the properties of monotonic or polynomial transformations of series with known order of integration.

    Table 3 Panel unit-root test (CIPS test) on EKC variables
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  27. It is also worth reminding that the adopted panel test (CIPS) is not simply the combination of individual ADF tests as it includes the cross-sectional dependence (therefore, the individual CADF tests) (Pesaran 2007).

  28. (Karlsson and Lothgren (2000), p. 254) recommend, for large T (say, \(T> >\hbox {N}\)), to jointly consider individual and panel unit-root tests to carefully assess stationarity properties of the panel (see also Baltagi 2005, p. 247).

  29. Due to space limitations, these regional test results cannot be reported here for all variables. They are available upon request.

  30. As mentioned, \(p_{t}\) differs from the total factor productivity \((TFP_{t})\) which is much more pro-cyclical (see Table 8) and, consequently, it is non-stationary within the panel and in most regions (Esposti 2011).

  31. The Augmented Dickey-Fuller (ADF)-GLS test is a modified ADF test with significantly greater power than the conventional ADF test, especially under near unit-root processes and small sample size. Unlike the ADF test, in the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test the null hypothesis is that the series is I(0), while in the alternative hypothesis, is I(1). Therefore, the KPSS test is expected to reveal those series that the conventional ADF test tends to accept as I(1) while, in fact, they are only near unit-root processes.

  32. GMM estimation has been obtained using all admitted lags as instruments. To maintain consistency and robustness of the estimated standard errors, only the one-step GMM estimation is here performed (Arellano 2003).

  33. In all GMM estimations the Hansen–Sargan test confirms that the selection of instruments is appropriate, while the Arellano-Bond autocorrelation test accepts the adopted dynamic specification as first order correlation is observed but no second order correlation. The results of all these tests are available upon request. In the case of \(\hbox {CH}_{4}\) (long series), for instance, test results are the following (\(p\) value in parenthesis). For specification (5a), the Sargan test is 892.12 (0.79), the Arellano-Bond test is -2.25 (0.02) for the first order autorcorrelation and 1.36 (0.17) for the second order autorcorrelation. For specification (5b), the Sargan test is 827.25 (0.98), the Arellano-Bond test is \(-\)1.696 (0.09) for the first order autorcorrelation and 1.026 (0.30) for the second order autorcorrelation.

  34. In Tables 6 and 7 estimates reported for \(\hbox {CH}_{4}\)(long series) concerns the whole period (1951–2008) but the SCs are assessed in the two subperiods (1951–1979 and 1980–2008).

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  1. INEA (National Institute for Agricultural Economics), Via Nomentana, 41 00161 , Rome, Italy

    Silvia Coderoni

  2. Department of Economics and Social Sciences, Università Politecnica delle Marche, Ancona, Italy

    Roberto Esposti

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  1. Silvia Coderoni
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Correspondence to Silvia Coderoni.

Appendix

Appendix

See Tables 8, 9, 10, 11 and Fig. 2.

Table 8 Average annual growth rate of the regional agricultural labour productivity (VA/L) and total factor productivity (TFP) (1951–2002)
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Table 9 Agricultural production specialization of across countries and within the Italian regional panel: % on the total agricultural gross production value
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Table 10 Regional Cross sectionally augmented ADF (CADF) test (\(\hbox {H}_{0}\): unit root) on \(p_{t}\)
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Table 11 Regional augmented Dickey–Fuller GLS test (ADF GLS; \(\hbox {H}_{0}\): unit root) and Kwiatkowski, Phillips, Schmidt e Shin unit root tests on price series (KPSS; \(\hbox {H}_{0}\): no unit root) on \(e_{CH_{4} (long)}^{V}\) regional series
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Coderoni, S., Esposti, R. Is There a Long-Term Relationship Between Agricultural GHG Emissions and Productivity Growth? A Dynamic Panel Data Approach. Environ Resource Econ 58, 273–302 (2014). https://doi.org/10.1007/s10640-013-9703-6

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