IEFE, Milan, february 11, 2011 Environmental performance, innovation and regional spillovers Valeria Costantini (Università di Roma III) Massimiliano Mazzanti (Università di Ferrara e CERIS-CNR) Anna Montini (Università di Bologna e CERIS-CNR)
Research questions � Environmental performances (emission intensity, EM/VA), the role of: � regional productive structures � (sectoral) efficiency � sectoral labour productivity � sectoral technological innovation � sectoral technological innovation � technological/innovation spillovers � environmental regulation � environmental spillovers � Geo-framework: � Italian regions (20) 2 V.Costantini, M.Mazzanti, A.Montini - Environmental Performance and Regional Innovation Spillovers
Sectoral decoupling analysis using NAMEA � Framed in Environmental Kuznets Curves / IPAT models (Martinez Zarzoso, 2009) Study of sectoral drivers, time effects � Merge with innovation, trade, policy data � Dynamics of (joint) economic and environmental productivities � � Italian NAMEAs (1990-2007 for air emissions) are a good playground Mazzanti & Zoboli (2009), Ecological Economics � Mazzanti, Montini and Zoboli (2008), Economic System Research Mazzanti, Montini and Zoboli (2008), Economic System Research � Mazzanti and Montini (2010), Ecological Economics Mazzanti and Montini (2010), Ecological Economics � Marin and Mazzanti (2011), J. of Evolutionary Economics � • EU Femia, Moll, Watson (2006), shift-share in NAMEA EU + input-output + emissioni indirette � Forthcoming: A full Eurostat EU27 NAMEA 2000-2006 � (Actually: Eurostat Namea x19 countries x12air emissions x80sectors �
Regional NAMEA analyses � Studies using Reg-NAMEA are still less developed (Stauverman, 2007) � In Italy: � E.g. recent applications to Lazio and Emilia Romagna (Bonazzi - Sansoni, 2008; Mazzanti, Montini and Zoboli, 2007, EFEA; Mazzanti Sansoni, 2008; Mazzanti, Montini and Zoboli, 2007, EFEA; Mazzanti & Montini, 2010, Ecological Economics) � Costantini, Mazzanti and Montini, 2010, FEEM, provide spatial and innovation related analyses on ISTAT 2005 Regional NAMEA using shift-share and econometrics � ISTAT will release full 2000 REG-NAMEA by early 2011, thus allowing panel based and spatial analysis
Methods � Shift-Share analysis (2005, 20 regions, 24 sectors, 10 pollutants) � to decompose the source of change of the emission intensity (EM/VA) into: � regional specific productive structures/specializations (the share ) � the efficiency feature (the shift between regional and national efficiency) � a covariance effect between the previous two � a covariance effect between the previous two � Modelling emission intensity (cross-section 2005, 19 regions, 11 sectors, GHG, ACID) � Emission-demand model to identify inn. and env. drivers � Environmental spillovers � Technological/innovation spillovers (patents based indicator) 5
Modelling emission intensity 1 ln r = α r + β r + β r + β r + ε r e Y t p 2 3 k k k k k k − − − [adapted from Cole et al.2005; Medlock and Soligo (2001)] r = α r + β r + β r + β r + β r + β r + ε r e lp es t ts p 1 2 3 4 5 k k k k k k k k − + − − − where: e kr , pollutant emissions for k-sector in the r-region scaled with e kr , pollutant emissions for k-sector in the r-region scaled with � � region/sector value added α kr , region and sector specific effects � lp kr , labour productivity � es kr , environmental spillovers � t kr , internal (regional) technology � ts kr , innovation spillovers (inter-regional and intra-sector) � p kr , environmental policy � ε k r, error term � 6
Emissions’ intensities � (Main) Data � Italian regional NAMEA data, 2005 (Istat, 2009) � 20 regions � 24 productive sectors � environmental pressures, 10 pollutants (direct emissions) • GHG (CO 2 , N 2 O, CH 4 ) - globally distributed GHG (CO , N O, CH ) - globally distributed • ACID (NO x , SO x , NH 3 ) – local (neighb. regions) � economic data (ValAd, HouEx, FullTimeEJ) � Electricity consumption by sector (TERNA) � Indirect emissions (considered in the model) 7
CO2 and Sox emission intensity (kgx1M€ of value added, increasing order) Region CO 2 Region SO X Trentino Alto Adige 136 Trentino Alto Adige 39 Campania 141 Valle d’Aosta 45 Valle d’Aosta 153 Abruzzo 69 Piedmonte 185 Campania 78 Lazio 204 Lomba rdy 99 Marche 206 Lazio 101 Lombardy 209 Marche 108 Abruzzo 258 Piedmonte 108 Veneto 267 Calabria 123 Emilia Romagna Emilia Romagna 270 270 Basilicata Basilicata 224 224 Tuscany 278 Emilia Romagna 226 ITALY 301 Molise 276 Calabria 307 Veneto 300 Umbria 342 ITALY 315 Friuli Venezia Giulia 353 Tuscany 349 Basilicata 430 Umbria 373 Liguria 472 Friuli Venezia Giulia 539 Sicily 547 Puglia 859 Molise 689 Liguria 886 Sardinia 824 Sicily 1,347 Puglia 971 Sardinia 1,530 8
Innovation (spillovers) � The role of regional innovation (technological) spillovers � tech. learning and knowledge spilloves have a centripetal force fostering agglomeration patterns (Kyriakopoulou and Xepapadeas, 2009) � Domestic (internal) effect ( t ) � Inter-regional intra-sector effect ( ts ) � Measure � Sectoral innovation intensities: patents (wo specific env. purposes – further research ) to VA ratios research ) to VA ratios � Five years average (2000-04) for patents by sector (proxy of the innovation stock at sectoral level) � Geographical distances and economic structure similarity matter � Localization economies associated with the concentration of a particular sector in the (neighbouring) regions � Data � Patents (REGPAT-Eurostat from OECD PATSTAT) � ad hoc codification of IPC codes according to NACE (manufacturing) codes 9
Innovation spillovers (measure) The relative specialization index (RSI) : r IT t t = r RSI k k r t where is the five-years average of patents to valued added k k n n t ∑ ∑ ratios for each k-th sector and r-th region, while is the r IT ITk t t q ∑ same measure at the national level, as = r k k t t ITk k 1 1 k = k = 1 r = The bilateral innovation spillovers ( ) for each k-th sector from the s-th Region to the rs ts k r-th Region un-weighted by the geographical distance are expressed as: . − 1 r − s RSI RSI ∀ s ≠ r k k rs = ⋅ s ts t k k r + s RSI RSI k k The resulting ( 20 x 20 ) matrix of spillovers for each k-th sector (with a vector of 0 in the diagonal dimension ) ∀ s = r 10 V.Costantini, M.Mazzanti, A.Montini - Environmental Performance and Regional Innovation Spillovers
Innovation spillovers (spatial weights) � Several alternative criteria to transform geo-distances into spatial weights � Binary contiguity concept (D 1 ): assumes interregional spillovers take place only between direct neighbours (comon border) ( ) n ∑ with w rs = 1 only if s neighbouring r r = rs D ts ts w 1 k k rs 1 , s = s ≠ r � k-nearest neighbours (D 2 ): thresold distance, 300km � inverse distances (D 3 ): the intensity of influences between any two regions diminishes continuosly with increasing distances 11
Environmental spillovers � The role of extra regional environmental regulation on regional environmental performance � environmental policy acts as a centrifugal force - increasing compliance costs reduce the convenience to localize industrial activities in that region � emissions produced by neighbouring Regions may represent the role of economic agglomeration phenomena in explaining environmental of economic agglomeration phenomena in explaining environmental performances (Gray and Shadbegian, 2007) � concentration of dirty activities into circumscribed geo-areas � Measure � Emission intensity of the surrounding regions � environmental spillovers as the sum of sectoral emissions per unit of value added from the other regions (eks) valid for ∀ s ≠ r weighted by distances D 1 , D 2 , D 3 12
Environmental regulation � Proxied by � The stringency of the environmental regulatory framework • the incidence of environmental regulation on average regional income (Costantini and Crespi 2008) • Public expenditure 4env.protection may be considered as a proxy of the (regional) WTP of citizens to preserve natural environment � Measure(s) � Measure(s) � 3 alternative public expenditure (regional) measures • Current exp. 4environmental protection activities • Capital exp. 4environmental protection activities • R&D exp. 4environmental protection activities � Data Expenditures 4environmental protection activities (Istat, 2007) � � by region 13
Diagnostic checks � Spatial dependence � LM lag and LM error tests � Potential endogeneity of regional innovation � Hausman test � Hausman test 14
Empirical evidence (i) The geographical distribution of environmental performance in the Italian regions in the Italian regions
i. Shift-Share analysis (only direct emissions) • industry mix effect (other things being equal): - industrial regional specialization matters - more industrialized N regions are (obv.) penalized • efficiency effect (given an homogeneous industry mix across regions): - NW regions perform well - some N regions perform bad (es. FVG) 16
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