1. Eco-innovation and sustainability performance: Priorities for developing statistics
Gjalt Huppes
Department of Industrial Ecology
CML, Leiden University
Conclusions from the
ECODRIVE and EXIOPOL projects
EUROSTAT E3 meeting
Brussels, 12 February 2008

2. Outline Statistics follow demand from policy & theory
Dynamics in a time specified systems view
From micro activities to macro performance
Typology of eco-innovation indicators
Data sources transformed into information: one framework
Measuring sustainability performance: BEYOND ESA95
Linking better to relevant theoretical notions

3. Statistics follow demand from policy & theory (1)
Sustainability as policy aim
What is sustainability: elements
economic performance and welfare distribution
environmental impacts and environmental quality
social performance (stability, creativity, …) not here
How to get there: Theory & Policy
Are we getting there: Data & Information

4. Statistics follow demand from policy & theory (2)
Sustainability as policy aim
What is sustainability: elements
economic performance and welfare distribution
environmental impacts and environmental quality
social performance (stability, creativity, …) not here
How to get there: Theory & Policy
Are we getting there: Data & Information

5. Statistics follow demand from policy & theory (3)
Sustainability as policy aim
What is sustainability: elements
How to get there: Theory & Policy
Three levels:
knowledge processes as modelled
stages, structure: criteria?
economic processes as modelled (2x)
socio-economic processes as modelled
Proof of all pudding: in the eating
Are we getting there: Data & Information

6. Eco-innovation as dynamics of society
Economic Growth and Decoupling as essential ingredients of moving towards Sustainability:
Improved Environmental Performance
Improved Economic Performance
 Eco-innovation Performance, as dynamics
Central questions:
How is society doing?
How is society developing?
Can we improve development?
 Main factors for improved Eco-innovation Performance

7. Eco-innovation performance
Performance as result of socio-economic processes
Performance dimensions on sustainability:
Environmental performance
Economic performance
[Social performance]
Performance dimensions:
Macro level performance as ultimate measure for meso and micro level contributions
Improvement in time: Growth and Decoupling
Improvement in ratio economic-environmental: Eco-Efficiency

8. Explaining dynamics (1)
Knowledge based growth increasingly important, capital investment technicality
Characteristics of knowledge as a good essentially different from traditional good:
Non-rivalness
Excludability limited but variable
Knowledge creation essential
Institutions regulating the use of knowledge essential

9. Explaining dynamics (2)
Types of knowledge: main dimension
Propositional, generalisable knowledge
mostly freely available, globally
Prescriptive knowledge on technologies
Protected to some extent, for some time
Institutions regulating knowledge
Privatisation:
Intellectual ownership rights, enforceable secrecy
Public availability:
Publish or perish
Financing rules and financing

10. Explaining dynamics (3)
Intellectual property rights: trade offs
High protection gives high incentive,
monopolistic aspect dominant
High protection prevents broad use
Monopolists don’t like to share their profits
High transaction cost
Protection through law; further differentiations
Duration of patent, duration of copyright
Patentable knowledge, broad or small
Protecting big business or small inventors?

11. Explaining dynamics (4)

12. Systematic analysis (1)
Performance first
Factors for performance grouped
Societal structure as a basis: Economy, Culture, Institutions, Polity/Policy
Two levels in Economy:
Society as a whole: MACRO level
Activities creating the macro level: MICRO
Disaggregation: sectors, installations (adding up)
Units of action: firms, technologies (not adding up)
Primate in policy, in this analysis, three main types of policy, towards the three subsystems of society

13. Systematic analysis (2): main causal lines, dotted feedback

14. Time frames involved in innovation
Knowledge types; knowledge cycles
Basic institutions for knowledge creation
Knowledge creation: science
Knowledge creation: technology
Knowledge creation: market introduction
Knowledge creation: market diffusion
Empirical 1: Kondratiev
years, driving fast for years, then slowing down
Empirical 2: Hirooka

15. Time frames involved in innovation: Hirooka: Electronics

16. Time frames involved in innovation: Hirooka: Electronics

17. From micro level activities to macro level performance
Eco-innovations at micro level do not add up to societal eco-innovation performance.
Embedding mechanisms:
Physical constraints (rare metals, land, ..)
Broader market mechanisms
Macro-economic structural effects
Technicalities for adding up: performance per year required: not CBA, not LCA, not LCC

18. Typology of indicators: Performance & five types of predictive indicators

19. Typology of indicators: Performance & five types of predictive indicators
0. Economic and environmental performance of society: absolute decoupling
Economic and environmental performance: disaggregation: increased eco-efficiency
Economic and environmental performance: micro level drivers in technologies and firms
Predictive cultural indicators
Predictive institutional indicators
Predictive policy indicators (3 main types)
Non-predictive associated variables as indicators

20. Data transformed into information: One framework
Performance indicators
Indicators predictive for performance
Prediction requires model
Models quantified to semi-quantified
What to do: main mechanisms not quantified
Data supporting model development?

21. The big model of societal dynamics (1)
Convergence: -institutional factors dominant -knowledge increasingly important -several types of knowledge -dynamics based on
Stages of development spread out in time: typical 4 decades from basic development to large scale implementation; all embedded in broader scientific and practical knowledge, non-rival; not-excluded

22. The big model of societal dynamics (2)
Measurement of indicators vs filling of models
Indicator types from ECODRIVE
Discussion at Eurostat (Chaired by mr Ritola): hardly anything possible, go for project based data gathering
consistent time series difficult

23. The big model of societal dynamics (3): Five types of Indicators
Performance indicators of Economy Macro & Meso: Economic, Environmental & Combined
Predictive Economy indictors: Business, Markets, Technologies, Products
Predictive indicators Cultural
Predictive indicators Institutional
Predictive indicators Political

24. Five main types of indicators
Performance indicators of Economy Macro & Meso: Economic, Environmental & Combined
Predictive Economy indictors: Business, Markets, Technologies, Products
Predictive indicators Cultural
Predictive indicators Institutional
Predictive indicators Political

25. Performance indicators of Economy, Macro & Meso: Economic
GDP
Ratio with labour, inhabitants: -Labour Productivity -Income per head of population
Meso: Sectors according to NACE V2, level 5/x
Gross Value Added per sector
Ratio with labour (not with inhabitants) -Sectoral Labour productivity
Turnover per sector (possibly), as ‘cradle-to-gate Value Added’
(Not-yet-eco)-Innovation as: % Growth per year

26. Performance indicators of Economy, Macro & Meso: Environmental (1)
Environmental mechanisms/aspects considered:
Global warming
Acidification / Eutrophication
Human toxicity
Ecotoxicity
Abiotic resource depletion
Non-renewable energy resource depletion
Biotic depletion
xxx
Aggregated environmental score
Land use
Data availability: now limited beyond CO2, CH4, N20, SOx, NOx, NHx
Urgent necessity for expansion, as time series!

27. Performance indicators of Economy, Macro & Meso: Environmental (2)
Environmental mechanisms:
Limited now effectively to:
CO2, CH4, N20  GWP (substantially covered)
SOx, NOx, NHx/3  Acidification, Eutrophication (partially)
Urgent necessity for expansion well beyond ESA95, as time series, retrofitting in NACE V1.1 and V2!
[EXIOPOL framework] [JRC-IPTS preliminary datasets:
Broadest NAMEA set: As; Cd; CFC; CH4; CO; CO2; Cr; Cu; HCFC; HFC; Hg; N2O; NH3; Ni; NMVOC; NOx; Pb; PFC; PM10; Se; SF6; SOx; Zn.

28. Performance indicators of Economy, Macro & Meso: Environmental (3)
Year-on-year: environmental scores Per mechanism, possibly further disaggregated to underlying factors, possibly aggregated to overall environmental score
Macro:
Contribution to environmental mechanism by Society
Contribution to environmental mechanism by Total Consumption. Life Cycle Approach
Meso:
Contribution to environmental mechanism by Sector (NACE v2, as detailed as possible, ideally 600-sectors level)
Contribution to environmental mechanism by Grouping of Consumption Activities (Food; Housing); …) Life Cycle Approach: GLOBAL PERSPECTIVE! Consistent UPSTREAM DATA required
Eco-Performance dynamics: % change per year

29. Performance indicators of Economy, Macro & Meso: Economic & Environmental ‘scores together’
Eco-Innovation as performance dynamics Improvement (preferably) as economic growth with absolute decrease in environmental impact  decoupling; first empirically
Macro:
Economic score and Contribution to environmental mechanism by Society
Economic score and Contribution to environmental mechanism by Total Consumption. Life Cycle Approach
Meso: Economic score (VA) and Contribution to environmental mechanism by Sector k Score of Sector k on Env Mechanism i in Year j
Volume of consumption and Contribution to environmental mechanism by Consumption Grouping (Food; Housing); …) Life Cycle Approach

30. Performance indicators of Economy, Macro & Meso: Economic& Environmental Scores Combined
Year-on-year ratios: environmental intensity ratios = eco-efficiency scores
Derived from basic environmental and basic economic scores
Macro:
Environmental Intensity of Society Per mechanism, possibly further disaggregated to underlying factors, possibly aggregated to overall mechanism
Environmental intensity of Total Consumption (full life cycle).
Meso:,
Environmental Intensity of Sector i (NACE v2)
Environmental Intensity of Consumption Groupings (Food; Housing); …)
Eco-Innovation dynamics: decrease in environmental intensity ratios

31. Performance of Economy Micro level: Economic and environmental performance in pairs together: for Firm & Product
Firm:
Value Added and Direct Environmental Scores (Sum = Sector/Society)
Turnover and cradle-to-gate environmental score (EU/global)
Product volume (pieces, kg, kW, ..?) and cradle-to-gate environmental score
Product:
Life cycle cradle-to-grave environmental scores, per product, per grouping of consumption activities (‘1000km car driving’)

32. Derived predictive indicators: Cultural
Availability of knowledge on actual and expected eco-innovation performance (#6)
Values conducive to economic innovation (#7)
Values conducive to eco-innovation (#4)

33. Derived predictive indicators: Cultural Availability of knowledge on actual and expected eco-innovation performance (#6)
Knowledge with stakeholders
C1 Measurement of Eco-Innovation progress, EU Remark: Survey based; Pro Inno linked?
C2 Eco-Innovation research input volume Remark: Definition and data availability?
C3 Business availability of EI performance knowledge. Remark: Number of firms yes/no, or more quantified measure?
C4 National Eco-Innovation indicators used? Remark: Repeated use of Inno-view survey
Knowledge on relevant factors to improve eco-innovation
C5 Research output volume as indication of progress Remark: Bibliometric analysis
C6 Knowledge transfer mechanisms: research-business-government linkages; volume of network activities; business volume(-share) of networking firms Remark: ETAP; CIP; Inno-Policy Trendchart related

34. Derived predictive indicators: Cultural (2) Values conducive to economic innovation (#7)
C7 Innovation values within firms.
Remark: Interesting to develop, related to Citizen values
C8- Innovation values with SMEs, measured as: innovation in-house;
C13 active networking; volume of inno expenditure; early stage venture capital financing; ICT expenditure; organisational focus on inno
Remark: already available

35. Derived predictive indicators: Cultural (3) Values conducive to eco-innovation (#4)
C14 Values for eco-innovation in firms
Remark: Eco-innovation attitudes in Innobarometer?
C15 General environmental attitudes in personnel of firms Remark: Measured as private membership of eco-organisations
C16 General environmental attitudes in the market
Remark: Measured by eco-innovative purchasing behaviour, but how?
C17 Market support for eco-innovation Remark: Measured by green procurement, volume share in total purchasing

36. Derived predictive indicators: Cultural Questions
There seems to be much overlap between adjoining programmes: Coordination, reduction?
There is an inherent softness in the concepts involved, as “conducive to” is not easily quantified. Further development would be useful.
In many instances, there is information at national level, different between countries, and at the EU level. Some soft coordination would be useful.
Comparability between countries would be very useful as a soft incentive, but many measures like membership of green organisations, may have different background in many countries not related to eco-innovation attitude.
There is some overlap with institutional aspects, like ‘having a green purchasing programme.
Where: Preferential treatment of research with higher eco-innovation potential?

37. Derived predictive indicators: Institutional
Market indicators for innovation (#1)
Market indicators for eco-innovation (#4)
Systematic internalisation through environmental regulation and law (#4)
Intellectual property rights (#2)
Organisation and volume eco-inno R&D (#1)
Organised pre-competitive knowledge exchange (#3)

38. Derived predictive indicators: Institutional (1) Market indicators for Innovation and for Eco-Innovation (#1)
Innovation
I1 Global competitiveness index
Eco-Innovation
I2 Nr and value of environment focused investment funds
I3 Venture capital availability for eco-innovation
I4 Reporting requirements on environmental performance
I5 Availability of environmental specialists
Remark: As subcategory of Innovation Scoreboard?

39. Derived predictive indicators: Institutional (2) Systematic internalisation through environmental regulation and law (#4)
I6 Nr of prosecutions for breaches of regulations
Remark: compliance is to be measured
I7 Value of fines for breaches of regulations
I8 Incentive structure of environmental policy, between technology binding and eco-innovation incentives.
I9 Value of environmentally regulative taxes and similar payments
I10 Effective implementation of environmental liability law, as value of damage payment

40. Derived predictive indicators: Institutional (3) Intellectual property rights (#2) Organisation and volume eco-inno R and R&D (#1)
Intellectual property rights
I11 Degree of satisfaction
I12 Environment related patents: number licensed; total amount of license payments
Remark: to Culture?
I13 Incentives for eco-license patenting
Organisation and volume of Research and R&D in eco-innovation
I14 Recognisable place for eco-innovation financing
Remark: split off from general innovation research measurement

41. Derived predictive indicators: Institutional (3) Organised pre-competitive knowledge exchange (#3)
I15; I16; I17: see comparable series in 8a, 8b, and 8c

42. Derived predictive indicators: Political
Strategic significance of eco-innovation policy
P1 Integration of eco-innovation objectives in all policies, nut just environmental policy, per member state
P2 Eco-innovation targets specified
P3 Eco-innovation strategies specified
Volume of eco-innovation policy
P4 Quality of eco-innovation policy
Remark: difficult operationalisation
P5 Resources available for eco-innovation policy in terms of financial resources and manpower.
Quality of eco-innovation policy
P6 Eco-innovation quality process.
Remark: as subclass of innovation policy quality; InnoPolicy Trend Chart.

43. Derived predictive indicators: Political Questions
Convincingness of indicators limited still; improvements by more clear framework?
More independent lines to institutional development; cultural development; and direct economic measures?
Policy consistency and policy integration studies, linked to impact assessment studies
Impact assessment more focused on environmental aspects, more elaborate modelling (eg: Biofuel)?

44. The small model of Sustain-dynamics: Performance and envIOA (1)
Is the same as performance indicators in the big model!
insight and prediction by detailing: sectoral trends; decomposition analysis; contribution analysis; etc.
From micro data to sustainability performance:
improving on ESA95

45. Data sources for additional environmental variables in envIOA, other than NAMEA-air
Emissions air
as reported by EMEP (see table 4)
EMEP
as reported by UNFCCC
UNFCCC
as reported by EPER[1] (see table 4)
EPER
Incidentally: RAINS, GAINS, national PRTRs
Emission water
as reported by EPER (20 substances)
Incidentally: national PRTRs
Emission soil
metals (Cd, Cu, Zn, Pb, Hg, Cr, Ni), pesticides and nutrients (N, P) to agricultural soil
manure, fertilizer and pesticide consumption (Eurostat, 2006; FAO, 2006 ) combined with composition data
Hazardous
waste generation
as reported in “Generation of waste by economic sector and households”
Eurostat environment and energy statistics (Eurostat, 2006)
Wastebase of ETC (ETC/RWM, 2006)
Waste generation
[1] EPER data only for 2001

46. Data sources for additional environmental variables in envIOA, other than NAMEA-air
Water consumption
as reported in “Water consumption by supply category and by sector”
Eurostat environment and energy statistics (Eurostat, 2006)
Waste water generation
as reported in “Generation and discharge of waste water”
Extraction of a-biotic resources
compounds, minerals and fossil fuels as reported by USGS (Ag, Al, Au, Co,Cr, Cu, Fe, Ir, Mn, Mo, Ni, Os, P, Pb, Pd, Pt, Rh, Ru, Sn, U, Zn, Coal, lignite, crude oil, tar sands, oil shales, natural gas,
commodity statistics (USGS, 2006)
Voet et al., 2005.
ores, minerals and fossil fuels as reported by Eurostat (Agglomerated Iron ores and pyrites, Copper ores and concentrates, Nickel ores and concentrates, Aluminium ores and concentrates  etc.)
production statistics (Eurostat, 2006)
Extraction of biotic resources
natural wood, fish
FAOstat (FAO, 2006)
Land occupation
as reported in CLC1990, CLC2000[1]
Corine Landcover database CLC1990 and CLC2000 (EEA, 2006)

47. Four Conclusions on eco-innovation as improved sustainability performance
Basic data linked, now disparate, non-specified samples, if samples at all:  based data linked in terms of environmental & economic aspects, as single three dimensional sample
or explicit model based on single samples
Environmental data expanded
Detailed framework for all statistics
Make & use table as base data carrier

48. Four Conclusions on eco-innovation as improved sustainability performance
Basic data
Environmental data:  to be expanded substantially, as impact data linked to activities creating them
Resources and Material flows  use NACE v.2 framework for systematic data gathering

49. Four Conclusions on eco-innovation as improved sustainability performance
Basic data linked
Environmental data expanded
Detailed framework required for virtually all applications use NACE V.2 at level four
even if sampling is representative at a higher level only
Make & use table as base data carrier

50. Four Conclusions on eco-innovation as improved sustainability performance
Basic data linked
Environmental data expanded
Detailed framework: NACE V.2 level four
Make & use table as base data carrier  squared tables theoretical construct; -good for economic analysis, -NOT for sustainability analysis -base statistics open to several kinds of modelling

51. Eco-innovation as a process
Knowledge processes as modelled
stages, structure, process
 See MEI-project