Innovation under theClimate Wise Program

SchumptoberfestOctober 22, 2011

Keith BrouhleGrinnell College

Brad GrahamGrinnell College

Donna HarringtonUniversity of Vermont

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?

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

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

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.

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.

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)).

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.

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?

The patenting behavior of firm i at time t is

where Yit = number of patents Dit= participation in the Climate Wise

ProgramX1it= vector of exogenous explanatory

variables

itititit XDY 111

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?

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

Empirical Model

The net benefits from participating in Climate Wise for firm i at time t are

Dit* = β2 X2it + ε2it

where Dit* = net benefits from participating in

Climate Wise.

The net benefits are not observable, so we estimate

Dit = F(β2X2it) + μit

where Dit = 1 if Dit* > 0 or 0 otherwise.

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

Explanatory Variables

Firm specific environmental variables

Firm specific market and financial variables

Industry variables

Location variables

Explanatory Variables

Firm specific environmental variables Superfund TRI Releases Violations Spills

Firm specific market and financial variables

Industry variables

Location variables

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

Explanatory Variables

Firm specific environmental variables Firm specific market and financial variables

Industry variables Final good Concentration New Capital Expenditures Industry dummies

Location variables

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

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

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)

Results: What influences participation?

Dependent variable: Climate Wise participation

Coefficient

Environmental

TRI Releases 0.036***(0.012)

Superfund 0.015***(0.004)

Violations 0.233**(1.119)

Spills -0.087*(0.045)

FinancialR&D 0.002***

(0.0004)

Return on Assets 0.008***(0.003)

LocationLevinson 1.379***

(0.508)

Climate Wise Partner State 0.500**(0.205)

Industry Final Good 0.427***(0.136)

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.

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.

Results: What influences patenting?

Env Patents

Climate Wise

Predicted CW participation

0.0254(0.100)

Environmental

Violations-0.004(0.019)

TRI Releases0.060***(0.003)

Spills-0.008(0.006)

Financial

R&D0.001***(0.00005)

Patent stock0.001***(0.00003)

Employees0.637***(0.011)

Debt to Equity-0.006***(0.001)

Results: What influences patenting?

Env Patents

Location

Fuel mix disclosure0.131***(0.019)

Energy price0.022**(0.007)

Number of programs0.008

(0.005)

Industry

Final good-0.169***(0.025)

Concentration ratio0.00004(0.0004)

New capital expenditures0.031***(0.002)

Results: What influences patenting?

Env Patents

Non-Env Patents

Climate Wise

Predicted CW participation

0.0254(0.100)

0.286***(0.047)

Environmental

Violations-0.004(0.019)

-0.168***(0.007)

TRI Releases0.060***(0.003)

0.056***(0.001)

Spills-0.008(0.006)

-0.036***(0.004)

Financial

R&D0.001***(0.00005)

0.001***(0.00002)

Patent stock0.001***(0.00003)

0.001***(0.000003)

Employees0.637***(0.011)

0.492***(0.004)

Debt to Equity-0.006***(0.001)

-0.005***(0.0002)

Results: What influences patenting?

Env Patents

Non-Env Patents

Location

Fuel mix disclosure0.131***(0.019)

0.132***(0.008)

Energy price0.022**(0.007)

-0.068***(0.003)

Number of programs0.008

(0.005)0.006***(0.001)

Industry

Final good-0.169***(0.025)

-0.275***(0.011)

Concentration ratio0.00004(0.0004)

0.005***(0.0002)

New capital expenditures0.031***(0.002)

0.004***(0.0005)

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.

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.

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.