1. Innovation under the Climate Wise Program Schumptoberfest October 22, 2011 Keith Brouhle Grinnell College Brad Graham Grinnell College Donna Harrington University of Vermont

2. Voluntary Environmental Programs  Voluntary Environmental Programs  Encourage firms to reduce toxic releases, waste, and other adverse environmental impacts  Encourage adoption/development of environmentally friendly practices and technology  Improve regulatory compliance  Used with and, sometimes in place of, regulation  Advantages  Low administrative costs  Encourage government/industry collaboration  More cost effective than command and control  Do they work?

3. The Climate Wise Program  The Climate Wise voluntary program ran from 1993 to 2000 under the direction of the EPA and Dept. of Energy.  The program targeted non-utilities in the manufacturing sector, but other entities could also join.  Four broad program objectives: i.Reduce greenhouse gas (GHG) emissions ii.Change the way companies view environmental performance iii.Develop productive partnerships between government and industry iv.Foster innovation

4.  Program focused on innovation in the areas of energy efficiency, renewable energy, and pollution prevention.  Program hoped to spur innovation by  Encouraging broad goals  Allowing organization to identify the most cost-effective ways to reduce GHG emissions  Providing technical assistance  Over the course of the program, 701 entities joined Climate Wise and 353 submitted an Action Plan. Climate Wise and Innovation

5. Objectives of Study 1.Determine the motivations for pledging to participate in the Climate Wise program. 2.Analyze the determinants of innovative behavior and determine whether participants in Climate Wise have significantly higher measures of innovative activity (environmental and non-environmental patents) than non- participants.

6. Background literature  Studies evaluate the effectiveness of VAs using different environmental measures:  Most use release of emissions of toxic chemicals as measure of environmental output (33/50 program, ISO certification, Strategic Goals program).  Other environmental outputs include environmental rating (Sustainable Slopes), fuel/electricity usage (Climate Challenge), and compliance with regulations (33/50, ISO).  We propose to use patents as a measure of environmental innovation.

7. Background literature  The literature that explores the relationship between environmental regulation and innovation includes:  Several studies hypothesize that higher costs of pollution abatement activities (possibly due to stricter regulations) lead to innovation (Lanjow and Mody 1996, Jaffe and Palmer 1997, Brunnermeier and Cohen 2003, Carrión-Flores and Innes 2010).  The impact of command and control regulations on innovation is explored in Bellas (1998), Lange and Bellas (2005), and Popp (2002, 2003, 2006).  Fewer studies have evaluated the relationship between VAs and innovation (working paper by Carrión-Flores, Innes, and Sam (2006)).

8. Conceptual Framework Potential Benefits…  Regulatory relief  Improved reputation w/stakeholders (green consumers, investors and NGOs)  Cost savings/efficiency gains Potential Costs…  costs of GHG mitigation and innovation activities undertaken as a result of participation in Climate Wise. Firms will voluntarily participate/engage in the Climate Wise program when the benefits exceed the costs.

9. Conceptual Framework  Innovativeness  Economies of scale in R&D  Scope for innovation  Market structure and industry traits  Environmental cost savings  Regulatory pressure Why do some firms innovate more than others?

10. The patenting behavior of firm i at time t is where Y it =number of patents D it =participation in the Climate Wise Program X 1it =vector of exogenous explanatory variables Empirical Model As with any standard treatment problem, estimation of the above equation may lead to biased results if the treatment is not random. Do factors that affect the decision to participate in Climate Wise also affect innovation?

11. Potential solutions to non-random treatment? 1.If one believes treatment occurs on observables, use a matching approach 2.If one believes treatment occurs on unobservables, use an IV approach Our default is to use an IV approach. Empirical Model

12. The net benefits from participating in Climate Wise for firm i at time t are D it * = β 2 X 2it + ε 2it where D it * = net benefits from participating in Climate Wise. The net benefits are not observable, so we estimate D it =F( β 2 X 2it ) + μ it where D it = 1 if D it * > 0 or 0 otherwise.

13. Explanatory Variables We organize our set of explanatory variables for the participation and innovation equations into the following categories:  Firm specific environmental variables  Firm specific market and financial variables  Industry variables  Location variables

14. Explanatory Variables  Firm specific environmental variables  Firm specific market and financial variables  Industry variables  Location variables

15. Explanatory Variables  Firm specific environmental variables  Superfund  TRI Releases  Violations  Spills  Firm specific market and financial variables  Industry variables  Location variables

16. Explanatory Variables  Firm specific environmental variables  Firm specific market and financial variables  Return on Assets  Profit  Assets to Equity; Debt to Assets  R&D  Employees  Patent stock  Industry variables  Location variables

17. Explanatory Variables  Firm specific environmental variables  Firm specific market and financial variables  Industry variables  Final good  Concentration  New Capital Expenditures  Industry dummies  Location variables

18. Explanatory Variables  Firm specific environmental variables  Firm specific market and financial variables  Industry variables  Location variables  Total Energy Price  Green Energy Program  Fuel Mix Disclosure  State regulatory stringency (Levinson index)  Climate Wise Partner State

19.  Sample:Firms that are part of the Corporate Environmental Profile Database (CEPD) from Risk Metrics as well as the NBER Patent database and Compustat.  Sample size: 10,998 firm-year observation from 1,738 unique firms.  Sample period: 1993 to 2003 Data and Sample

20. Summary Statistics  Looking at the characteristics of participants, Climate Wise participants were…  In industries that impact the environment (petroleum & chemicals, construction, electric utility sectors)  Dirty in the past (higher TRI releases; more past violations; more spills; more Superfund sites)  Close to consumers (final good producer)  And innovative (more R&D; more patents)

21. Results: What influences participation? Dependent variable: Climate Wise participationCoefficient Environmental TRI Releases 0.036*** (0.012) Superfund 0.015*** (0.004) Violations 0.233** (1.119) Spills -0.087* (0.045) Financial R&D 0.002*** (0.0004) Return on Assets 0.008*** (0.003) Location Levinson 1.379*** (0.508) Climate Wise Partner State 0.500** (0.205) Industry Final Good 0.427*** (0.136)

22. Results: What influences participation?  Among environmental variables:  Most are significant: TRI Releases, Superfund and Violations positively influence participation; Spills negative influences participation.  Among financial variables:  R&D and Return on Assets positively influence participation.  Among location variables:  Levinson and CW Partner significantly influence participation (positive).  Among industry variables:  Final Good and industry dummies positively influence participation Note, year dummies and EPA region dummies are significant too.

23. Results: What influences patenting?  With results from participation model, we can calculate for each firm the probability that they will join Climate Wise in each year.  This predicted probability serves as our instrument in our patenting equations.  We investigate the impact of Climate Wise participation on innovation behavior (patents).  We consider patents in the environmental area (Carrión-Flores and Innes 2010), non-environmental area, and total patents.  We estimate fixed effects panel Poisson models.

24. Results: What influences patenting? Env Patents Climate WisePredicted CW participation 0.0254 (0.100) Environmental Violations -0.004 (0.019) TRI Releases 0.060*** (0.003) Spills -0.008 (0.006) Financial R&D 0.001*** (0.00005) Patent stock 0.001*** (0.00003) Employees 0.637*** (0.011) Debt to Equity -0.006*** (0.001)

25. Results: What influences patenting? Env Patents Location Fuel mix disclosure 0.131*** (0.019) Energy price 0.022** (0.007) Number of programs 0.008 (0.005) Industry Final good -0.169*** (0.025) Concentration ratio 0.00004 (0.0004) New capital expenditures 0.031*** (0.002)

26. Results: What influences patenting? Env PatentsNon-Env Patents Climate WisePredicted CW participation 0.0254 (0.100) 0.286*** (0.047) Environmental Violations -0.004 (0.019) -0.168*** (0.007) TRI Releases 0.060*** (0.003) 0.056*** (0.001) Spills -0.008 (0.006) -0.036*** (0.004) Financial R&D 0.001*** (0.00005) 0.001*** (0.00002) Patent stock 0.001*** (0.00003) 0.001*** (0.000003) Employees 0.637*** (0.011) 0.492*** (0.004) Debt to Equity -0.006*** (0.001) -0.005*** (0.0002)

27. Results: What influences patenting? Env PatentsNon-Env Patents Location Fuel mix disclosure 0.131*** (0.019) 0.132*** (0.008) Energy price 0.022** (0.007) -0.068*** (0.003) Number of programs 0.008 (0.005) 0.006*** (0.001) Industry Final good -0.169*** (0.025) -0.275*** (0.011) Concentration ratio 0.00004 (0.0004) 0.005*** (0.0002) New capital expenditures 0.031*** (0.002) 0.004*** (0.0005)

28. Results: What influences patenting?  Across patenting, we consistently find a significant influence of  Environmental factors: TRI Releases, Superfund  Financial factors: R&D, patent stock, size, debt to equity  Location factors: fuel mix disclosure  For environmental patenting, we note  Spills, violations, and concentration ratio are not as important  Total energy price has an different impact  Interestingly, the impact of the Climate Wise program varies:  Positively influences overall patenting and regular patenting, but no detectible influence on environmental patenting.

29. Conclusions  Objective 1: Determine the motivations for pledging to participate in the Climate Wise program.  Participants in Climate Wise were more likely to …  have higher toxic releases, more violations and responsible for Superfund site,  be from a dirty economic sector that is a final good producer,  spend more on R&D and have higher return on assets,  and be located in a Climate Wise Partner state.

30. Conclusions  Objective 2: Analyze the determinants of innovative behavior and determine whether participants in Climate Wise have significantly higher measures of innovative activity (environmental and non- environmental patents) than non-participants.  Patenting is associated with…  spending on R&D, past stock of patents,  the scope of a firm’s exposure to environmental issues,  a firm’s financial health  as well as whether the firm pledged to participate in Climate Wise.