As air pollution tends to be more concentrated near its source and relatively high levels of spatial agglomeration and intra-local business networking characterise Italian territory, the present paper estimated a “spatially” adapted EKC hypothesis. The results confirmed that local income is the more appropriate measure of scale to be considered, and the methodological approach chosen enhanced the geographical dimension of the relationship under study. Overall, our results confirm that even in the “globalisation era” it is still worth looking into
local economies as a crucial dimension of economic development and environmental efficiency.
The empirical findings presented in this study indicate a significant relationship between the evolution of per capita GDP and four different measures of air pollution intensity: CO2 emissions per industrial worker, CH4 emissions per industrial worker, NMVOC emissions per industrial worker, and CO emissions per industrial worker. On performing global regression estimation, a monotonic decreasing relationship emerged for all the pollutants considered. However, the results obtained from assessment of the localized relationship through GWR spatial estimation brought to light the existence of significant heterogeneity at the Italian Nut3 level.
At the local level a quasi-L-shaped “average” relationship predominates in the southern provinces and in some provinces in the central regions (Lazio, Abruzzi, Molise, Tuscany), confirming how misleading it can be to estimate a functional form common to all regions and assume spatial-stationarity. A Kuznets relationship is found in 1996, 2001 and 2005 in the case of CO for the provinces in Puglia, Calabria and Basilicata. Furthermore, for all the pollutants investigated, the localised behaviour of the estimated relationship often affords insight into the emergence of clustered areas where the relationship tends to diverge from the prevailing behaviour. This seems to suggest the existence of more and less
“environmentally efficient” areas which tend to cluster. In this respect it should be also noted that the southern regions often behave very differently from the northern ones, thereby highlighting the role of the local context in development dynamics and, more specifically, the relevance of the dualistic structure of the Italian economy to environmental impact.
Moreover, the GWR estimates highlighted the significant influence of various specific local factors related to the structure of the underlying economic system.
In this respect, the empirical evidence shows that the impact of the “energy intensive” sectors, explicitly introduced into the model as a control variable, is captured mainly at the global level as an “average effect”, thus confirming the extraordinary relevance which had in fact earned them attention in environmental regulation. Actually, the share of energy intensive sectors does not account for the local heterogeneity observed.
The empirical results underline the importance of spatial heterogeneity for environmental pressure. This strong potential impact may depend on either specific features of the sector production specialization of the Italian provinces or the spatial concentration of industrial activities, which seem to reflect the geography of the Italian industrial districts. Clearly, from a policy point of view, the issue of environmental innovation in local systems is particularly important given the high density of firms in certain provinces, which may generate critical damaging local «hot spots» in emission production (Montini and Zoboli, 2004).
This negative environmental feature could be offset if a higher than average innovative propensity were to emerge in these clusters where, by exploiting networking relationships and knowledge spillovers due to proximity. At the same time, even though in absolute terms those areas are “hot spots”, in relative terms, compared with the southern provinces, are relatively more efficient.
References
Agras, J., Chapman, D. (1999) A dynamic approach to the Environmental Kuznets Curve hypothesis, Ecological Economics, 28, 267– 77.
Andreoni, J., Levinson, A. (2001) The simple analytics of the Environmental Kuznets Curve, Journal of Public Economics, 80, 269– 86.
Assocarta (2007) Le attività di Assocarta nel 2007, Roma: Edizione Tecniche Nuove.
Becattini G. (1987) Mercato e forze locali: il distretto industriale. Bologna: Il Mulino.
Bimonte, S. (2002) Information access, income distribution, and the Environmental Kuznets Curve, Ecological Economics, 41,145–56.
Ciriaci D., Palma D. (2008) The Role of Knowledge-based Supply Specialization for Competitiveness: a Spatial Econometric Approach, Papers in Regional Science, Special issue on Spatial Econometrics, 87, 453-75.
Cole, M.A., Rayner, A.J., Bates, J.M. (1997) The Environmental Kuznets Curve:
an empirical analysis, Environment and Development Economics, 2, 401-16.
Cole, M.A. (2004) Trade, the pollution haven hypothesis and environmental Kuznets curve: examining the linkages, Ecological Economics, 48, 71-81.
Dasgupta, S., Laplante, B., Wang, H., Wheeler, D. (2002) Confronting the Environmental Kuznets Curve, Journal of Economic Perspectives, 16, 147–68.
Deacon R., Norman C. (2004) Is the environmental Kuznets curve an empirical regularity?. Economics Working Paper Series, 22-03. University of California at Santa Barbara.
Dinda, S., (2004) Environmental Kuznets curve Hypothesis: a survey, Ecological Economics, 49, 431-55.
Dijkgraaf, E., Vollebergh H.R.J. (1998), Growth and environment—is there a Kuznets curve for carbon emissions? in: Paper presented at the Second ESEE Conference, University of Geneva, Geneva.
European Commission (2007) Contributing to an integrated approach on competitiveness, energy and environment policies, Ensuring future sustainability and competitiveness of European enterprises in a carbon and resource constrained world, Fourth Report, Bruxelles.
Feldman, M. (1994) The geography of innovation. Boston : Kluwer Academic Publishers.
Fingleton B. (2001) Externalities, Economic Geography, and Spatial Econometrics: Conceptual and Modeling Developments, International Regional Science Review, 2, 197-207.
Fotheringham A.S., Brunsdon C., Charlton M. (2002) Geographically weighted regression. The analysis of spatially varying relationships. New York : John Wiley & Sons Ltd.
Greunz, L. (2003) Geographically and Technologically Mediated Knowledge Spillovers between European Regions, The Annals of Regional Science, 37, 657-680.
Galeotti, M. (2007) Economic growth and the quality of the environment: taking stock, Environment, Development and Sustainability, 9, 427-54.
Graziani, A. (1978) The Mezzogiorno in the Italian Economy, Cambridge Journal of Economics, 2, 355-72.
Grossman, G.M., Krueger, A.B. (1993) Environmental impacts of the North American Free Trade Agreement. In: Garber, P. (Eds.) The U.S. –Mexico Free Trade Agreement, pp. 13–56. Cambridge: MIT Press. ,
Grossman, G.M., Krueger, A.B. (1996) The inverted-U: what does it mean?
Environment and Development Economics, 2, 119–22.
Hardle, W. (1990) Applied nonparametric regression. New York: Cambridge University Press.
Hastie T., Tibshirani R., Friedman J. (2001) The Elements of Statistical Learning:
Data Mining, Inference and Prediction. Springer-Verlag. New York.
Holtz-Eakin, D. and T. M. Selden (1995), Stoking the fires? CO2 emissions and economic growth, Journal of Public Economics, 57, 85-101.
ISPRA (2009) Quinto rapporto sulla qualità dell’ambiente urbano, Rome.
ISTAT (2004) Statistiche dei Trasporti, Rome.
ISTAT (2009) Le emissioni atmosferiche delle attività produttive e delle famiglie, Rome
Jaffe A., Newell, R., Stavins, R. (2005) A tale of two market failures: technology and environmental policy, Ecological Economics, 54, 164–74.
Jaffe A., Peterson, S., Portney, P., Stavins, R. (1995) Environmental regulation and the competitiveness of U.S. manufacturing: what does the evidence tell us?, Journal of Economic Literature, 33, 132–63.
Komen, R., Gerking, S., Folmer, H. (1997) Income and environmental R&D:
empirical evidence from OECD countries, Environment and Development Economics, 2, 505–15.
Leung Y., Mei C.L., Zhang W.X. (2000a) Statistical tests for spatial nonstationarity based on the geographically weighted regression model, Environment and Planning A, 32, 9-32.
Leung Y, Mei CL, Zhang WX (2000b) Testing for spatial autocorrelation among the residuals of the geographically weighted regression, Environment and Planning A, 32, 971-890
Loader, C. (1999) Local Regression and Likelihood, Springer. New York.
Maddison, D. (2006) Environmental Kuznets curves: a spatial econometric approach, Journal of Environmental Economics and Management, 51, 218-30.
Maddison, D. (2007) Modelling sulphur emissions in Europe: a spatial econometric approach, Oxford Economic Papers, 59, 726-743.
Magnani, E. (2000) The Environmental Kuznets Curve, environmental policy and income distribution, Ecological Economics, 32, 431–43.
Mazzanti, M., Zoboli, R. (2005) The Drivers of Environmental Innovation in Local Manufacturing Systems, Economia Politica, 22, 399-436.
Onida F. (2004) Se il piccolo non cresce. Piccole e medie imprese italiane in affanno. Bologna: Il Mulino.
Ordás Criado, C. (2007) Temporal and spatial homogeneity in air pollutants panel EKC estimations, Environmental and Resource Economics, forthcoming.
Paci R., Saba E. (1997) The empirics of regional economic growth in Italy, 1951-1993, Contributi di ricerca, Crenos.
Panayotou, T. (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development, ILO, Technology and Employment Programme, Geneva.
Perman R., Stern D. (2003) Evidence from panel unit root and cointegration tests that the Environmental Kuznets Curve does not exist, Australian Journal of Agricultural and Resource Economics, 47, 325-47.
Porter, M.E., Van der Linde, C. (1995) Toward a new conception of the environment-competitiveness relationship, The Journal of Economic Perspectives, 9, 97-118.
Stern, D. I. and M. S. Common (2001) Is there an environmental Kuznets curve for sulfur?, Journal of Environmental Economics and Management, 41, 162-78.
Stern, D. (2000), Applying Recent Developments in Time Series Econometrics to the Spatial Domain, The Professional Geographer, 52:37-49.
Suri, V., Chapman, D. (1998) Economic growth, trade and energy: implications for the environmental Kuznets curve, Ecological Economics, 25,195-208.
Torras, M., Boyce, J.K. (1998) Income, inequality, and pollution: a reassessment of the environmental Kuznets curve, Ecological Economics, 25, 147–60.
Vukina, T., Beghin, J.C., Solakoglu, E.G. (1999) Transition to markets and the environment: effects of the change in the composition of manufacturing output, Environment and Development Economics, 4, 582-98.
Wheeler, D., Tiefelsdorf, M. (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression, Journal of Geographical Systems, 7, 1-28.
Wheeler, D. (2007) Diagnostic tools and a remedial method for collinearity in geographically weighted regression, Environment and Planning, 39, 2464-81.
Table 1
Table 1. Descriptive statistics
Variable Obs Mean Std. Dev. Min Max
Gdp_pc_1991 103 11,32 2,74 6,00 18,00
Gdp_pc_1996 103 14,93 3,87 7,62 23,87
Gdp_pc_2001 103 18,58 4,90 10,03 31,19
Gdp_pc_2005 103 20,57 5,04 11,63 33,61
CO2/N_1991 103 79,37 90,93 13,40 515,98 CO2/N_1996 103 95,63 113,11 13,15 656,26 CO2/N_2001 103 96,85 117,20 12,88 662,07 CO2/N_2005 103 126,57 159,21 7,26 1062,69
CH4/N_1991 103 0,39 0,26 0,06 1,64
CH4/N_1996 103 0,44 0,33 0,06 1,80
CH4/N_2001 103 0,42 0,32 0,03 2,15
CH4/N_2005 103 0,46 0,40 0,02 2,11
NMVOC/N_1991 103 0,38 0,22 0,07 1,12
Gdp_pc= Gdp per capita (euros, thousands); N = workers in industry (thousands);
Emissions (CO2, CH4, NMVOC, CO)= grams;
ETSN= workers in energy intensive sectors (thousands);
Tab 2. Global regression results.
1991 1996 2001 2005 1991 1996 2001 2005
R2 adjusted 0,11 0,11 0,13 0,23 0.23 0.24 0.29 0.31
Intercept 55.93 98.52 99.74 110.41 1.73 1.55 1.84 2,02
(2.37) (3.32) (3.45) ('2.57) (4.89) (3.63) (4.9) (3.69)
Per capita income -2,513 -3.815 -3.34 -4.04 -0.23 -0.0123 -0.135 -0.137
(-1.64) (-2.63) (2.97) (-2.68) (-3.48) (-2.09) (-3.32) (-2.57)
Per capita income2 - - - - 0,00 0,00 0,00 0,00
- - - - (2.95) (1.41) (2.55) (1.92)
Energy intensive sectors 3,02 03:05 3.97 4.92 0.005 0.005 0.006 0.01
(3.098) (2.23) (2.49) 3.66 (1.38) (0.94) (1.09) (2.15)
R2 adjusted 0.6 0.51 0.52 0,48 0.42 0,47 0.34 0.31
Intercept 4.92 6.27 3.82 3.75 1.21 1.23 0.76 0.92
(4.45) (4.72) (4.28) (3.13) (4.64) (4.88) (3.76) (3.09)
Per capita income -0.662 -0.66 -0.32 -0.33 -0.14 -0.99 -0.045 -0.061
(-3.2) (-3.62) (-3.38) (-2.79) (-2.86) (-2.89) (-2.06) (-2.09)
Per capita income2 -0.00 -0.00 -0.00 -0.00 0,00 0,00 0,00 0,00
(-2.34) (-2.75) (-2.55) (2.28) (2.05) (1.91) (1.26) (1.54)
Energy intensive sectors 0.085 0.091 0.084 0.066 0.01 0.011 0.009 0.009
(6.97) (5.27) (6.46) (6.42) (3.76) (3.45) (2.90) (3.34)
Notes: t values in brackets;
CO2 CH4
CO NMVOC
As far as the local estimates of the squared per capita income are concerned, it should be specified that significant local parameter estimates were found different from zero so as to reproduce a quasi-L-shaped behavior in the income-emission relationship.
APPENDIX
Maps 1 and 2. Carbone dioxide: Local t values, ratio of the number of workers in energy intensive sectors (ETS; 2001 and 2005)
CO2 - ets GWR local t 2001
>= 2.58 (27)
1.96 < - <=2.58 (3) 0<-<=1.64 (73)
CO2- ets GWR local t 2005
>2.58 (35) 1.96<-<=2.58 (44) 1.64<-<=1.96 (24)
Maps 3 and 4. Methane: local t values, GDP and GDP2 in 2005.
CH4 - GDP GWR local t 2005
0<-<=1.64 (9) -1.64<-<=0 (59) -1.96<-<=-1.64 (2) -2.58<-<=-1.96 (6)
<-2.58 (27)
CH4 - GDP2 GWR local t 2005
>2.58 (3)
1.96<-<=2.58 (29) 1.64<-<=1.96 (2) 0<-<=1.64 (52) -1.64<-<=0 (17)
Maps 5 and 6 NMVOC: local t values, GDP and GDP2 in 2005.
NMVOC - GDP GWR local t 2005
-1.64<-<=0 (70)
<-2.58 (33)
NMVOC - GDP2 GWR local t 2005
> 2.58 (11) 1.96<-<=2.58 (22) 0<-<=1.64 (70)
Maps 7 and 8 Carbone monoxide: local t values, GDP and GDP2 in 2005.
CO - GDP GWR local t 2005
1.96<-<=2.58 (10) 1.64<-<=1.96 (3) 0<-<=1.64 (25) -1.64<-<=0 (43) -1.96<-<=-1.64 (5) -2.58<-<=-1.96 (14)
<-2.58 (3)
CO - GDP2 GWR local t 2005
1.96 <-<=2.58 (2)
1.64<-<=1.96 (16)
0<-<=1.64 (3)
-1.64<-<=0 (42)
-1.96<-<=-1.64 (27)
-2.58<-<=-1.96 (2)
-2.58 (11)