Jens Horbach University of Applied Sciences Augsburg Determinants

Jens Horbach University of Applied Sciences Augsburg Determinants of eco-innovation: Theoretical approaches and recent empirical analyses KID Summerschool (Knowledge Dynamics, Industry Evolution, Economic Development), Nice, 2 -8 July 2017

Structure 1. 2. 2. 1 2. 2 3. Introduction Theoretical determinants of eco-innovation Definition of eco-innovation Specificities of eco-innovation 5. Conclusions and research deficits Empirical analyses of the determinants of ecoinnovation 3. 1 Data bases and methods 3. 2 Results of econometric analyses: Stylized facts 3. 3 Two new research areas: Regional spill-overs and staff characteristics

Definition of eco-innovation following the MEI project: The production, assimilation or exploitation of a product, production process, service or management or business methods that is novel to the organization (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives (Kemp, Pearson 2008, p. 7).

Eco-innovation, environmental innovation or sustainable innovation? • Term “eco-innovation” was initially understood as a subset of environmental innovations denoting only those innovations that both lead to a better economic and environmental performance • Environmental or green innovations concentrate on the environmental effects • Sustainable innovation: An eco-innovation is only sustainable if it leads to positive environmental effects for future generations. Often used in the social sciences and by politicians but empirical analyses are nearly not possible (because of the time and the social dimension)

Determinants of eco-innovation Regional spill-overs, Location factors Firm specific factors (e. g. size, energy intensity, green orientation) Technological capabilities (e. g. R&D, information sources) Eco-Innovation Regulation (e. g. strictness, hard or soft policy instruments) Source: Adapted from Horbach et al. (2012). Market characteristics (e. g. competition pressure) Demand pull (e. g. environmental consciousness)

3. 1 Data bases and methods Use of survey data to analyse eco-innovation: Main source: Community Innovation Survey, special modules on eco-innovation in 2009 and 2015, filter questions in earlier waves Advantages • Allow including many different determinants and control variables • Detailed analyses at the firm level

Restrictions • Lack of panel data so that dynamic analyses of ecoinnovation are very rare, one point in time surveys dominate the empirical literature (problems of causality and endogeneity) • Analysis of innovation systems including networks of firms and stakeholders is only partially possible • Limited measurability of latent variables such as the greenness of firms or policy stringency

Typical survey indicators for measuring eco-innovation: • R&D expenditures or an environmentally related R&D budget • Introduction of new or modified environmentally related products • Cleaner production versus end-of-pipe, process versus product innovations • Eco-innovations by type of environmental impact

Innovations with environmental benefits in the CIS questionnaire 2015 An innovation with environmental benefits is a new or significantly improved product (good or service), process, organisational method or marketing method that creates environmental benefits compared to alternatives. • The environmental benefits can be the primary objective of the innovation or a by-product of other objectives. • The environmental benefits of an innovation can occur during the production of a good or service, or during its consumption or use by the end user of a product. The end user can be an individual, another enterprise, the Government, etc.

Patent data • Allow time series analysis reducing endogeneity and causality problems • Restricted to innovation activities that can be patented thus more production oriented • Organizational eco-innovations are not captured but they are crucial for cleaner technologies

Methods • Surveys: Dominance of discrete choice models (e. g. logit, probit, bi-variate probit, multilevel mixed effects models, random effects probit, treatment effects models …) • Patent analysis: Count data models, negative binomial fixed effects models, VAR and VEC …)

Results of econometric analyses: Stylized facts Motivations al. Complying with environmental regulation, strictness of environmental policy (e. g. Jaffe/Palmer 1997, Cleff/Rennings 1999, Bartolomeo 2003, Brunnermeier/Cohen 2003, OECD 2007, Johnstone et 2010, Veugelers 2012, Nesta et al. 2014): Problematic indicators of measuring stringency: • Pollution abatement or compliance expenditures • Monitoring activities • Self perceived stringency • Perceived influence of different policy instruments

Further motivations • Cost savings (OECD 2007, Horbach et al. 2012) • Resource prices (Grupp 1999) • del Improvement of the firm´s image (Bartolomeo 2003, Rio Gonzales 2005) • Environmental impacts (OECD 2007)

„Input“ variables • Existence of a specialized R&D department (Rennings 2003, OECD 2007) • Information sources (Horbach et al. 2013, Cainelli et al. 2015, network activities (Mazzanti/Zoboli 2006) • Person explicitly responsible for environmental concerns (OECD 2007) • Positive influence of environmental management systems (Rennings 2006, OECD 2007, Horbach 2008, Khanna et al. 2009, Cuerva et al. 2014) • Path dependencies: „Innovation breeds innovation“: Positive influence of past firm performance (Mazzanti/Zoboli 2006, Horbach 2008)

Different environmental technology fields, cross-country comparisons • Survey-based analyses by different eco-innovation fields only possible since 2009 (CIS 2008) • Typically end-of-pipe oriented fields (e. g. water purification, air emissions, dangerous substances) are more dependent on regulations whereas cleaner technologies (e. g. energy saving technologies) are motivated by cost-savings • Few cross-country analyses up to now but the results show a high stability of the main determinants across countries (e. g. Horbach 2014)

Two new research areas: Regional spill-overs and staff characteristics as determinants

Specificities of eco-innovations compared to other innovations Dependent variable: ecoinno: 1 Suppliers of environmental goods and services with product or process innovations in 2008, 0 Other innovators Correlates Regional level variables Technological cap. Education 1. 00 (0. 03) GDP 0. 99 (-0. 04) Capstocknew 1. 12 (0. 70) Popdens 0. 99 (-1. 10) Furthereducation 1. 88 (3. 69)** Poverty 1. 08 (1. 92)* Highqual 1. 01 (3. 89)** Sharegreen 1. 03 (0. 84) R&D 1. 42 (2. 10)* Location factors Loc 1 1. 09 (0. 58) Control variables Loc 2 0. 77 (-1. 51) Loc 3 0. 95 (-0. 37) Age 0. 99 (-0. 05) Loc 4 0. 70 (-2. 18)* Competition 1. 48 (3. 02)** Loc 5 1. 65 (2. 64)** Demand 1. 38 (2. 48)** Loc 6 0. 97 (-0. 14) Size 1. 00 (1. 25) Loc 7 1. 00 (0. 02) Loc 8 1. 45 (2. 71)** Loc 9 0. 89 (-0. 76) Loc 10 1. 26 (1. 61) Loc 11 1. 25 (1. 38) Loc 12 0. 81 (-1. 43) Two-Level mixed-effects logistic regression reporting odds ratios. Number of observations: 3297, number of groups: 382. Z-statistics are given in parentheses. Wald Chi 2 (42) = 225. 5. LR test versus logistic regression |Chi 2| = 2. 93. Prob. = 0. 04. +, *, ** denote significance at the 10%, 5% and 1% level, respectively. Source: Horbach (2014). Database: Establishment Panel of the Institute for Employment Research.

• Eco-innovations might be a chance for “disadvantaged“ regions: The attractiveness of the region (value in terms of leisure and residential amenity (loc 4) seems to be more relevant for other innovations, therefore the typical urbanization advantages do not seem to play an important role for eco-innovations whereas eco-innovation is more likely in regions characterized by high poverty rates. • Eco-innovations are more dependent on a good over-regional traffic infrastructure (loc 8) to compensate for the lack of urbanization advantages. • The green orientation of the region does not play the expected positive role for eco-innovations • Eco-innovation needs more research input compared to other innovations: significant relevance of a high-qualified staff for ecoinnovation (highqual), significant need of further education measures (furthereducation), higher importance of R&D activities

Work in progress based on the CIS 2015: Energy turnaround in German firms: What triggers the substitution of fossil energies? (joined work with Christian Rammer from ZEW) • Database: German CIS data for 2014 matched with regional data based on NUTS 3 level and data on more than a million renewable energy plants • Use of multilevel mixed effects probit models (and probit models with clustered standard errors as robustness check)

First results: • A green orientation of a region supports the willingness of firms to implement renewable energy technologies • A high share of solar and biomass in the region is connected with a higher substitution of fossil energy within firms • Organizational innovations are very important to introduce renewable energy innovations within a firm (namely the introduction of new methods organizing business processes, new forms of labor organization, new cooperation arrangements, change of customer relationships, integration of suppliers)

First results (continued): • Energy intensity not significant • Important cooperation partners for the introduction of renewables: firms from the same group of companies, customers from the private sector, private research institutes • Bigger and family owned firms are more likely to introduce renewable energies • Subsidies from "other ministries" such as the environmental ministry are highly relevant

The relevance of personal characteristics and gender diversity for (eco) - innovation activities at the firm-level (see also Horbach, Jens, Jacob, Jojo (2017): The relevance of personal characteristics and gender diversity for (eco) - innovation activities at the firm-level. Results from a linked employer-employee database in Germany. IAB-Discussion 11/2017, Nürnberg, http: //doku. iab. de/discussionpapers/2017/dp 1117. pdf) • Surprisingly, the literature on the determinants of eco-innovation until now has nearly not considered the influence of personal characteristics of a firm’s staff and management. • Project tries to open the "black box" of unexplained heterogeneity among firms: Characteristics of a firm’s personnel (gender, family status, geographical origin, education etc. ) are likely to be crucial in explaining the greenness of a firm. • Special focus on the role of gender diversity of the management and the staff for the realization of eco-innovation activities.

Database: • Linked employer-employee data for about 10, 000 establishments Main results: • Firms characterized by gender mixed first level management board are more likely to introduce ecoinnovations. • A high share of high-paid women in the staff of the firm also triggers eco-innovations. • Specific women's promotion programs seem to support the positive eco-innovation effect of gender-mixed teams

Results (continued): • Organizational innovations matter for the introduction of ecoinnovations: Re-organisation of supply chains and customer relationships, introduction of groupwork and, not surprisingly, environmentally related organizational measures • Innovation input is more important for eco-innovations compared to other innovations supporting results from previous literature

Conclusions and research deficits • Fast growing literature on the determinants of eco-innovation in the past 15 years • Existence of stylized facts even across different countries • Dominance of survey results but very few panel data bases: causality and endogeneity problems • Patent data allow time series analyses but their scope is restricted • Analyses on regional spill-over effects for eco-innovation are rare • A closer look in the „black box“ of management and staff characteristics would be useful

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