Carbon markets, carbon prices and innovation:Evidence from Interviews with Managers∗

Ralf Martin†, Mirabelle Muûls ‡and Ulrich Wagner§

Preliminary

Abstract

Based on interviews with almost 800 manufacturing firms in six Europeancountries, this study develops a firm level indicator of “clean” that is climatechange related innovation and explores the impact of the EU Emissions Trad-ing System on such innovation. We find that the majority of firms engagein some form of climate change related innovation although this is primarilyprocess rather than product innovation. We also find significant differences inthe propensity to innovate between countries, even after controlling for poten-tially different industrial structures. Further, we find that firms expecting lessgenerous allocations of free permits in the third phase of the EUETS tend toinnovate more. We explore if this a causal effect of variations in allocationsacross sectors by devising a regression discontinuity design. We find a discretedrop in innovation effort around the thresholds - in terms of carbon and tradeintensity - implied by EU Commission rules for free allocation. This suggeststhat allocating permits for free causes a reduction in clean innovation. Thiscontradicts the hypothesis that permit allocation has only a distributional butno effect on real behaviour. We suggest that requiring firms to buy permitsin an auction has an important signalling effect for senior management, whichdoes not occur under free allocation.

JEL Classification: D22, O31, Q48, Q54

∗The authors thank Morgan Bazilian and Karsten Neuhoff for useful comments and suggestions.†Centre for Economic Performance and Grantham Research Institute on Climate Change and

the Environment, London School of Economics and Political Science, Houghton Street, LondonWC2A 2AE, phone: 020 7955 6975, email: r.martin@lse.ac.uk‡Grantham Institute for Climate Change, Imperial College London, South Kensington Campus,

London SW7 2AZ and Research Associate, Centre for Economic Performance, London School ofEconomics and Political Science, phone: 020 7594 8452, email: m.muuls@imperial.ac.uk§Universidad Carlos III Madrid, Department of Economics, Calle Madrid, 126 28903 Getafe,

Spain and Research Associate, Centre for Economic Performance, London School of Economicsand Political Science, phone: +34 (0) 916 24 8488, email: uwagner@eco.uc3m.es

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1 Introduction

In advanced economies, the industrial sector is directly responsible for about a thirdof greenhouse gas (GHG) emissions (IEA, 2009). Understanding the drivers andbarriers that affect company behavior related to climate change is therefore essentialto design effective policies for reducing emissions, to prevent dangerous levels ofglobal warming. At the same time, private-sector firms are also the key players whenit comes to investments in research and development (R&D); “clean” innovationis critical for achieving the transition to a low-carbon economy. The relationshipbetween “clean” R&D investments and climate change policy becomes all the morerelevant if the fruits of these investments spill over across international bordersand help to reduce emissions in countries with few or no climate change policies.Currently, some view this possibility as the main justification for European climatechange policy, given that EU emissions reductions alone would not be sufficient toprevent dangerous global climate change from occurring.

To date, the empirical evidence on what firms are doing (or not) to curb GHGemissions is rather limited and has been collected through either mail surveys orcase studies (McKinsey and Ecofys (2005), Kenber et al. (2009)). This paper exam-ines the link between climate change regulation, investment and innovation, usingdetailed survey data for a representative sample of firms. The data were collectedby interviewing managers on climate-change and energy related issues using a novelmethod that circumvents various types of bias that plague more traditional surveyformats. We applied this approach to a sample of approximately 800 manufacturingfirms in six European countries from August to October 2009.

Based on this dataset, we obtain a number of new descriptive results regardingthe behavior of businesses related to climate change. It appears that most (70%)firms are engaged in some formal or informal R&D, with the aim of curbing emis-sions and/or energy consumption (“clean process innovation”). A smaller proportion(40%) is also pursuing “clean product innovation”; i.e. R&D with the aim of de-veloping products that can help customers to reduce their emissions. There aresignificant differences between countries when it comes to clean innovation. Ac-cording to our study, most active on product innovation is Germany, on processinnovation is France with lowest levels of innovative activities observed in Hungaryand Poland. Also, this study shows that firms expect carbon prices to be consider-ably higher in the future, compared with current levels in the EU ETS. We find anaverage expected carbon price of €40 for the post-2012 trading period. And finally,ompared to the current trading period (Phase II, from 2008 to 2012), firms expectthe imposition of tighter caps for Phase III, starting in 2013. The proportion offirms reporting that their allowance allocation does not imply a binding emissionslimit falls from 40% in Phase II to less than 10% in Phase III.

Apart from descriptive statistics, we use regression analysis to analyze the effectof climate policy on innovation at the firms in our sample. We provide two piecesof evidence in support of a causal link between company-specific caps – i.e. theamount of allowances companies receive for free in the EU ETS – and “clean” R&D

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by firms. First, we find a significant positive association between the expectationsfirms hold about the future stringency of their cap and “clean” innovation. Thisrelationship is robust to including a broad range of control variables.Second, wefind that firms within the EU ETS which are just below the thresholds establishedfor free allowances are engaging more strongly in climate change related productinnovation than firms that are just above the threshold (and thus will continue toreceive free allowances). There is a discontinuity in both expected stringency aswell as “clean” innovation at the thresholds that are implied by the latest set ofcriteria that the European Commission has proposed for allocating free emissionsallowances after 20121. This result suggests that the ongoing practice within theEU ETS, of generously allocating allowances for free to manufacturing sectors leadsto less innovation than would otherwise be the case.

The remainder of this paper is organized as follows: Section 2 describes the process ofinterviewing managers about various aspects of company behavior related to climatechange. Section 3 provides summary statistics of our innovation measures as wellas regarding future expectations. Section 4 examines the link between innovation,future expectations and climate policy. Section 5 concludes.

2 Interviewing managers

2.1 Interview Methodology

Our survey builds upon and substantially extends previous work on climate changepolicies and management practices by Martin et al. (2010). We conduct structuredtelephone interviews with managers at randomly selected manufacturing facilitiesin Belgium, France, Germany, Hungary, Poland and the UK. The interview setupfollows the management survey design pioneered by Bloom and van Reenen (2007),in that the interviewer engages interviewees in a dialogue with open questions thatare meant not to be answered by “yes” or “no”. On the basis of this dialogue, theinterviewer then assesses and ranks the company along various dimensions. Notethat our setup adopts a double-blind strategy: interviewees do not know that theinterviewers are scoring their answers and interviewers do not know performancecharacteristics of the firm they are interviewing. This interview format is designedto avoid several sources of bias common in conventional surveys (Bertrand andMullainathan, 2001). For instance, experimental evidence shows that a respondent’sanswers can be manipulated by making simple changes to the ordering of questions,to the way questions are framed, or to the scale on which respondents are supposedto answer. By asking open-ended questions and by delegating the task of scoring theanswers to the interviewer, we seek to minimize cognitive bias of this type. Possiblecognitive bias on the part of the interviewers can be controlled for using interviewer-fixed effects in the regression analyses. Another common observation with survey

1The Commission takes this decision for each sector based on whether it is sufficiently (i) carbonintensive, (ii) trade intensive or (iii) both.

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Table 1: Interview response rates by country

Refused

Belgium 134 131 85 46 178 47 0.74France 141 140 92 48 238 98 0.59Germany 139 138 95 43 337 199 0.41Hungary 69 69 37 32 90 21 0.77Poland 78 78 57 21 140 62 0.56UK 209 205 63 142 468 264 0.44Total 770 761 429 332 1451 691 0.52

# of Interviews

# of Firms Interviewed

# of ETS Firms

Interviewed

# of Non ETS Firms

Interviewed

Total Firms Contacted

Response Rate

Notes: There are more interviews than interviewed firms as we conducted severalinterviews with different partners in a small number of firms.

data is that respondents are tempted to report attitudes or patterns of behaviorthat are socially desirable but may not reflect what they actually think and do.This problem may be exacerbated in situations where respondents do not have adefinite attitude toward the issues they are asked about but are reluctant to admitthat. Our research design addresses this issue in two ways. First, the interviewerstarts by asking an open question about an issue and then follows up with morespecific questions, or asks for some examples in order to evaluate the respondent’sanswer as precisely as possible. Second, the results of the interviews are then linkedto independent data on economic performance, as a validation exercise.

2.2 Interview Practice

Using the ORBIS database maintained by Bureau Van Dijk we obtained contact de-tails for 44,605 manufacturing firms in Belgium, France, Germany, Hungary, Polandand the UK2. We randomly selected companies from that list to solicit an interview.To ensure sufficient coverage of firms subject to the EU ETS (hereafter, EU ETSfirms), we also sampled manufacturing firms at random from the Community Inde-pendent Transaction Log (CITL) in these countries. Interviewers made “cold calls”to production facilities (not head offices), gave their name and affiliation with theLondon School of Economics and then asked to be put through to the environmentalmanager. In the case of EU ETS firms, interviewers asked for the person responsiblefor the EU ETS, as it is listed in the CITL. Table 1 reports the number of calls madeand various statistics about the response rates.

An ordinal scale of 1 to 5 was adopted to measure various management practicesrelated to climate change. For each aspect of management ranked in this way, inter-viewers were instructed to ask a number of open questions. Questions were orderedsuch that the interviewer started with a fairly open question about a topic andthen probed for more details in subsequent questions, if necessary. The goal was tobenchmark the practices of firms according to a few common criteria. For instance,

2For more details on the survey, see Anderson et al. (2011)

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rather than asking the manager for a subjective assessment of the management’sawareness of climate-change issues, we gauged this by how formal and far-reachingthe discussion of climate-change topics is in current management. To verify the con-sistency of the interviewer’s scoring, a subset of randomly selected interviews wasdouble-scored by a second team member who listened in. The interviews seeks togather information on both the effectiveness and the competitiveness effects of cli-mate change policies, particularly of the EU ETS, in a random sample of Europeanmanufacturing firms. The questionnaire (see appendix) is divided in four sections.The first section examines the current and anticipated future effects of the EU ETS.The second section deals with prices for energy and CO2, competition and otherexternal drivers of climate-change related management practices. The third sec-tion inquires about specific measures that were adopted by firms and others whichwere considered but eventually discarded. The last section gathers information onrelevant company characteristics.

3 Descriptive evidence from interviewing managers

3.1 Clean Innovation

An important path for firms’ future GHG emissions abatement is to innovate: thecompany can either invest in finding cleaner production processes or develop newand cleaner products, thereby reducing their customers’ future emissions. Figure1 shows the distribution of the scores for clean process and product innovation3.An example of clean process innovation could be the development of a less energy-consuming way to transform limestone into quicklime. Clean product innovationwould for example be the invention of a new tyre with which cars consume lesspetrol. We see that almost 70% of firms are engaged in some form of clean processinnovation; only 40% engage in clean product innovation. Figure 2 shows that cleanproduct innovation is most likely to occur in firms that are also conducting cleanprocess innovation.

Figure 3 examines whether there are differences in clean innovation between coun-tries in our sample. There is clearly a gap between Western and Central/EasternEuropean countries, with Poland and Hungary lagging behind, both in terms ofprocess and of product R&D. France emerges as the leader in process, Germanyin product R&D. The second panel of Figure 3 explores whether these differencesare driven by differences in the specialization of the various economies across sec-tors. The figures report average differences between countries while controlling forthe 3-digit sector4. This makes the differences found in the first panel even more

3The clean process and product innovation measures are throughout this paper taken from thesurvey responses and we hereafter use interchangeably the term innovation and R&D.

4We use the NACE rev. 1.1 classification of the ORBIS data

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Figure 1: Distribution of the Clean Innovation Scores

(a) Clean Process Innovation (b) Clean Product Innovation

Notes: The figures show a histogram of the scores for each of the two types of cleaninnovation reported by interviewed firms.

Figure 2: The relative frequency of clean process and product innovation

Notes: The figure shows a histogram of the types of clean innovation reported by inter-viewed firms.

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Figure 3: Differences in clean innovative activities between countriesNo industry controls

(a) Process Innovation (b) Product innovation3-digit industry controls

(c) Process Innovation (d) Product innovationNotes: Each graph shows the average difference - conditional on noise controls - between firms fromdifferent countries in terms of the interview scores for process and product innovation describedin Figure 1. Stars indicate if these deviations are statistically significant and at what significancelevel: ***=1%, **=5%, *=10%.

pronounced, suggesting that they are not driven by differences in industrial com-position.

Figure 4 reveals sizeable differences in clean innovation across sectors and betweentypes of innovation. In Table 2, we use this information to group sectors accordingto their focus on clean technologies. Only three sectors – Glass, Other Mineralsand TV & Communication – have above-average scores in both product and processinnovation.

3.2 What firms expect

Investment decisions depend on the expectations held by investors. In order to elicitthis information, the interview included a number of questions on expectations.This section reports on our findings, starting with an examination of differences in

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Figure 4: Differences in clean innovative activities between industries

(a) Process Innovation (b) Product innovationNotes: Each graph shows the average difference - conditional on noise controls - between firmsfrom different sectors in terms of the interview scores for process and product innovation describedin Figure 1. Stars indicate if these deviations are statistically significant and at what significancelevel: ***=1%, **=5%, *=10%.

Table 2: Sectors according to their focus on clean innovation

Clean Product Innovatonbelow average above average

below average

above average

Clean Process Innovaton

- Ceramics; - Fabricated Metals- Food & Tobacco- Publishing - Textle Leather

- Machinery & Optcs;- Other Basic Metals;- Vehicles

- Cement; - Chemicals & Plastc;- Fuels;- Iron & Steel; - Wood & Paper

- Glass- Other Minerals- TV & Communicaton

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Figure 5: Distribution of price expectations

Notes: The figure shows a histogram of the price of a tonne of CO2 that interviewed firmsexpect on the EU ETS market.

these expectations between firms, countries and sectors. Subsequently, we discussexpectations about the future of the EU ETS.

3.2.1 Prices

Figure 5 reports the distribution of the expected price of emitting one tonne ofcarbon dioxide in 2020. Forecasts range between 0 and 500. The median pricereported is €30, the mean €40. There are substantial differences, not only betweendifferent firms but also between countries and sectors, as shown in Figure 6. Frenchfirms expect a much higher price, of €50 on average, whereas UK firms expect amore modest €28, on average. Across sectors, the highest price is expected by firmsin TV and Communication, with an average of €78, the lowest by firms in the Fuelssector, at close to €23. There is also a sizeable variation across sectors and countrieswith respect to the mere existence of a price expectation. Figure 7 shows that morethan 80% of interviewed Polish managers actually have a carbon price expectationversus less than 5% of the Hungarian managers that we interviewed. At the sectorlevel, the highest proportion of firms with an expectation occurs in the Glass sector(55%), whilst the lowest percentage is only 18%, in Machinery and Optics. We alsoasked interviewees about their knowledge of the current price of a tonne of CO2 onthe EU ETS, as a way of gauging their awareness of the market and the potentialintegration of this price in investment and trading decisions. Again, there is a lot ofvariation between sectors and countries, as shown in Figure 8, with Poland and theFuels industry exhibiting the largest shares of managers aware of the carbon price.

3.2.2 Expected stringency of EU ETS

We also asked firms about their expectations regarding the stringency of the cap onemissions implied by their participation in Phase III of the EU ETS. Put differently,we wanted to know how hard it would be for them to limit their future emissions to

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Figure 6: Price expectations across industries and countries

(a) Across countries (b) Across sectorsNotes: The figures show average carbon price expectations for 2020 for all interviewed firms thatreported price expectations (i.e. both EU ETS and non EU ETS firms).

Figure 7: Existence of Price expectations

(a) Across countries (b) Across sectorsNotes: The figures show what proportion of firms in a country or a sector reported a carbon priceexpectation in our sample.

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Figure 8: Knowledge about current prices

(a) Across countries (b) Across sectorsNotes: The figures show what proportion of firms in a country or a sector knew the current carbonprice in the EU ETS.

the amount they receive in free allowances (or how expensive if they do not reducetheir emissions, and need to buy allowances). A firm’s response to this questionwould depend on (i) how costly it would be to reduce its emissions, on (ii) howmany allowances it receives for free and on (iii) the price they expect on the marketand therefore the expected overall allocation. We asked a similar question regardingthe stringency of the cap imposed by the current Phase II of the trading system.Figure 9 reports on the resulting scores. Firms clearly expect more stringent capsfor Phase III. The proportion of firms answering that the cap they receive will allowthem to continue under business-as-usual terms declined, from almost 40% to lessthan 10%. That said, few firms expect, even for Phase III, that fundamental changeswould be needed in order to meet their cap.

4 What is driving investment in climate-change re-lated innovation?

Having described in section 3 firms’ investment in R&D related to climate changeand their expectations for the future, this section, examines whether there is a linkbetween the two . Table 3 reports results from regressions of the process innovationscore whereas Table 4 reports the equivalent regressions for product innovation.

In column 1 we start first by regressing the innovation scores on a set of dummyvariables indicating whether a firm is part of the EU ETS in either Phase II or IIIor both. For process innovation this does not lead to a significant coefficient. Forproduct innovation we find a coefficient that is significant at 10%, however only forparticipation in Phase 2. Hence there is no strong evidence that ETS firms in generaldiffer in their innovativeness from non-ETS firms. In column 2 we add both priceexpectations and expectations about the future stringency of the firm level cap asexplanatory variables. We find positive coefficients for both variables and with both

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Figure 9: Expected stringency and current perceived stringency

(a) Trading Phase II (b) Trading Phase IIINotes: The figure for Trading Phase II shows how stringent firm-specific caps are perceived tobe. The figure for Trading Phase III shows what stringency firms expect for the Trading Phasestarting in 2013

types of innovation. However, we only get significant coefficients for the expectedstringency of the cap. Thus it seems that the stringency of the emissions cap is morerelevant for R&D decisions than the price. This is an interesting suggestion, as asimple model of company behavior would tend to predict the opposite: since the EUETS is a cap-and-trade system the only thing that should matter for firms’ alloca-tion and investment decisions is the (expected) emissions price. Company-specificcaps should only be relevant for determining the distribution of rents that emergefrom imposing scarcity on a formerly free good (GHG pollution). The notion thatallocation decisions are independent of the distribution of allowances has been re-ferred to as the “independence property” of emissions trading (?, Hahn and Stavins,2010).

An alternative explanation for the correlation between stringency and innovationcould be that we are picking up reverse causality or biases due to omitted variables.For example, a firm’s perception of stringency is certainly influenced by the avail-ability of cheap technological solutions to reduce emissions. When cheap solutionsare not available, a firm is likely to respond that the cap is more stringent. There-fore, such a firm might be more likely to conduct some R&D in response to highercarbon prices. In this case, a positive relationship between R&D and stringencymight emerge due to unobserved heterogeneity. We proceed by accounting for thisin a number of ways: First, by adding more control variables. Second, by examiningwhat happens when we include current (i.e. Phase II) perceived stringency, ratherthan future stringency. Third, we use process innovation as a control for un-observedfactors in the product innovation regressions. Fourth, rather than using the surveybased stringency measures we exploit variations in cap stringency implied by theEU Commission rules for continued free allocation – instead of the requirement topurchase allowances through auctioning. We discuss each approach in turn.

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Table 3: Regressions of process innovation score

(1) (2) (3) (4) (5) (6) (7) (8)Dependent Variable. Process Innovation ScoreAnticipation of ETS Stringency in Phase 3 0.177** 0.107 0.122 0.144**

(0.079) (0.079) (0.076) (0.071)ETS Stringency in Phase 2 0.093* 0.078* 0.125**

(0.049) (0.046) (0.057)

0.100 -0.041 0.038 0.015

(0.074) (0.063) (0.063) (0.094)

0.064 0.115*** 0.071 0.078 0.139*** 0.103**

(0.048) (0.038) (0.051) (0.049) (0.037) (0.046)Multinational Enterprise 0.140 0.161 0.339 0.172 0.184 0.379*

(0.148) (0.132) (0.228) (0.144) (0.130) (0.228)R&D Intensive Facility 0.140 0.171* 0.039 0.166 0.197** 0.076

(0.106) (0.091) (0.147) (0.107) (0.095) (0.146)Employment 0.244*** 0.199*** 0.162** 0.254*** 0.213*** 0.187***

(0.046) (0.036) (0.072) (0.047) (0.037) (0.067)In ETS in Phase 2 -0.237 -0.285 -0.316 -0.129 . -0.490* -0.317 .

(0.198) (0.198) (0.241) (0.235) . (0.275) (0.278) .In ETS in Phase 3 0.225 -0.064 0.046 0.061 . 0.322 0.308 .

(0.304) (0.340) (0.350) (0.345) . (0.329) (0.357) .In ETS in Phase 2 and 3 0.388 0.128 -0.053 0.004 . 0.109 0.185 .

(0.359) (0.401) (0.392) (0.380) . (0.381) (0.378) .Observations 732 732 731 731 342 731 731 342

0.28 0.31 0.40 0.30 0.33 0.38 0.28 0.32Cluster 153 153 153 153 91 153 153 913-digit sector controls yes yes yes no no yes no noNoise Controls yes yes yes yes yes yes yes yes

CO2 Price expectation by 2020

ln(CO2 Price)

CO2 Intensity in 2008

ln(CO2/EMP)

ln(emp)

R2

Notes: All columns estimated by OLS with standard errors are in parentheses under coefficientestimates clustered by sector. The dependent variable is the interview score for process innovation.CO2 intensity data are derived from the CITL database while employment is taken from ORBIS. Allother variables are derived from the interviews. Noise controls include interviewer, country, time,day and month of the interview and manager background fixed effects. Stars indicate statisticalsignificance level: ***=1%, **=5%, *=10%.

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Table 4: Regressions of product innovation score

Notes: All columns estimated by OLS with standard errors are in parentheses under coefficientestimates clustered by sector. The dependent variable is the interview score for product innovation.CO2 intensity data are derived from the CITL database while employment is taken from ORBIS. Allother variables are derived from the interviews. Noise controls include interviewer, country, time,day and month of the interview and manager background fixed effects. Stars indicate statisticalsignificance level: ***=1%, **=5%, *=10%.

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4.1 More control variables

Column 2 in Tables 3 and 4 already includes 3-digit industry dummies. In column3 we add further controls, such as the size, CO2 intensity, foreign ownership, etc.of a firm. We note that the stringency coefficient remains positive and significantfor product innovation. For process innovation it remains positive but is no longersignificant. For completeness we also examine what happens when we drop industrycontrols in column 4 and when we restrict the sample to only firms that are particip-ating in Phase III5. For product innovation, the stringency-innovation relationship isrobust in all of these specifications. The results for process innovation are less clear-cut. The stringency coefficient remains insignificant when dropping sector controlsbut becomes significant when restricting to the ETS sample.

4.2 Using current stringency

Up until Phase II emission allowances have by and large been allocated for free toemitters. Current plans for Phase III stipulate that some firms will have to purchasean increasing fraction of their allowances through an auction or on the open market.Hence, if the relationship between stringency and innovation is driven by variationsin the cap rather than abatement cost heterogeneity, we expect that the relationshipis weaker or non-existent with current stringency. Again, this turns out to be thecase for product innovation where the current stringency coefficient is not significantin columns 6 to 8. For process innovation, the reverse seems to be the case with thecurrent stringency coefficient being positive and significant.

4.3 Using process innovation as a control variable

Product innovation should be less affected by any confounding variation that mightaffect the relationship between stringency and innovation. This is because producingand researching the design of wind turbines, for example, does not necessarily gen-erate knowledge that helps to reduce the emissions from producing wind turbines.Even so, we cannot rule out that there are complementarities in conducting processand product R&D. To account for that, Table 4 includes an additional column wherewe include process R&D as an additional regressor6. The future stringency relation-ship remains significant at 10% in column 9 and leads to an estimate comparable insize when including process R&D as an additional control variable7.

5Note that we have the expected stringency score variable only for firms that are regulated bythe EU ETS in Phase III. Thus in all columns the stringency effect is identified only from thosefirms. Including the other firms can be useful, however, to identify the impact of variables oninnovation that are relevant for all firms, such as sector, size or noise controls.

6If variations in the cap are driving both process and product R&D, this would likely lead toa downward bias in the estimated coefficient on stringency. But if the "cap" effect is stronger forproduct R&D, we should still be able to detect an effect.

7The results of Tables 3 and 4 are robust to including a control of whether the firm is tradingon the EU ETS or not.

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Figure 10: Placebo and real thresholds

Notes: The Figure plots interviewed firms in the Value at Stake (y-axis) and Trade Intensity(x-axis) space according to the industry they belong to. The three lines correspond to theEU criteria for exemption of auctioning in Phase III and to two placebo threshold linesused in our analysis.

4.4 The effect of the EU Commission rules

As a further check of the robustness of the relationship between stringency andinnovation, we exploit the link between exogenous variations in free allowance al-location, rather than relying on the self-reported stringency score8. As mentionedbefore, Phase III will see radical changes in the number of allowances that are al-located for free to firms. Under current proposals, the European Commission willexempt certain 4-digit industries from allowance auctioning, based on two statistics,trade intensity (TI) and CO2 intensity (VaS) 9. Firms in a 4-digit industry will beexempt if the industry’s TI or VaS rates exceed 30%, or if the sector simultaneouslyexceeds a 10% threshold for TI and a 5% threshold for VaS. Figure 10 plots the firmsin our sample in the VaS-TI space, along with a line (labeled “actual”) indicatingthose thresholds.

However, in Tables 5 and 6, we examine whether there is a discrete jump in both thereported expected stringency as well as innovation as we move over the thresholds

8Of the three factors underlying a firm’s perception of stringency (see Section 3.2.2), we focushere on point (ii), the amount of permits the firm receives for free.

9The Value at Stake is defined as the ratio between the sum of the direct and indirect costs offull auctioning and the gross value added of a sector. The direct costs are calculated as the valueof direct CO2 emissions (using a proxy price of €30/t CO2), and the indirect costs capture theexposure to electricity price rises.

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implied by the European Commission criteria10. We also test if such discrete jumpsare a feature of our data rather than the cause of exemptions from auctioning,by defining two “placebo” thresholds, which are also indicated in Figure 10. Onethreshold is slightly lower, the other slightly higher than the actual threshold. Weexperimented with a range of values here. The specific lines in Figure10 are definedso as to ensure that 5% of firms fall within the band on either side of the differentcut-off values. Let us consider first the regression results reported in Table 5, wherefuture stringency – derived from our survey - is the dependent variable. In column1, we include indicator variables that are equal to 1 if a firm exceeds any of thesethresholds as explanatory variables. We see that there is a negative and significantcoefficient for exceeding the actual threshold (“Exempt after 2012”) and a positivecoefficient for exceeding the upper threshold. In column 2, we include trade intensity(TI) and CO2 intensity (VaS) in addition as explanatory variables. This has littleeffect on the threshold parameters. As we include quadratic terms, however, incolumn 3, the actual threshold effect becomes stronger and more significant, whereasthe placebo effect becomes smaller and loses significance11. Moreover, we find incolumn 4 that there is no such threshold effect when looking at the current stringencyscore.

Table 6 shows the results from similar regressions for the innovation variables. Wefind a negative point estimate of passing the actual threshold for process innovation,but it is not statistically significant at conventional levels. On the other hand, we finda very clear and statistically significant threshold effect for product innovation. Thecoefficient estimate implies that passing the threshold for free allowance allocationimplied by the EU criteria leads to a drop in innovation on the order of 1 score point,on our score scale ranging from 1 to 5. As this corresponds roughly to 1 standarddeviation, we conclude that the effect of stringency on product innovation is alsoeconomically significant. In column 5, we see that this result is robust to includingonly the actual threshold. Column 6 reports negative point estimates for the placebothreshold when the actual threshold is excluded from the regression. This reflectsbias from omitting the relevant threshold as well as more noisy estimates, as theactual threshold is measured with error. Figure 11 illustrates the results of theregression in column 5: there is a clear discontinuity in product innovation impliedby the thresholds that define free allocation of allowances.

In sum, we find robust evidence that product innovation responds to the expectedstringency of the EU ETS. There is no clear evidence that the same is true forprocess innovation. An explanation for this could be that having to pay for allrequired emissions allowances has an important signaling function for companies indrawing the attention of more senior management levels to the issue of emissions. Itis only then that firms would take the more strategic decision to engage in productlines that are climate-change related, as opposed to process innovation, which is

10It is easy to think of reasons why trade and energy intensity might be correlated with thedegree of innovation a firm is undertaking, irrespective of these criteria. Indeed, in Figure 11 wefind that there is an inverse u-shaped relationship with respect to energy intensity for both productand process innovation.

11We also experimented with higher order terms, which gave similar results.

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Table 5: What drives expectations?

Notes: All columns estimated by OLS with standard errors are in parentheses under coefficientestimates clustered by sector. The dependent variable is the interview score for expected stringencyin columns (1) to (3) and for current stringency of EU ETS in column (4). Sectoral CO2 intensityand trade intensity are derived from EUROSTAT data. CO2 intensity data are derived from theCITL database while employment is taken from ORBIS. All other variables are derived from theinterviews. Stars indicate statistical significance level: ***=1%, **=5%, *=10%.

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Table 6: The effect of exemptions on innovation

(1) (2) (3) (4) (5) (6)Dependant Variable Process Innovation Score Product Innovation ScoreExempt after 2012 -0.294 -0.301 -1.106** -1.229** -0.927***

(0.291) (0.305) (0.454) (0.468) (0.340)Placebo Exempt (lower) 0.445** 0.390* 0.249 0.255 -0.240

(0.194) (0.226) (0.188) (0.256) (0.340)Placebo Exempt (upper) 0.120 0.195 0.664 0.424 -0.212

(0.288) (0.299) (0.521) (0.513) (0.404)

0.393 0.696 0.666 5.237 6.475* 2.498(0.740) (2.501) (0.560) (3.439) (3.517) (3.617)

Sectoral Trade Intensity (TI) -0.716 -1.964 -0.140 2.244 3.946* 2.238(0.432) (1.754) (0.609) (2.250) (2.284) (2.285)

VaS x VaS -1.563 -5.155 -6.642 -2.710(3.312) (4.303) (4.335) (4.680)

VaS x TI 4.299 -7.068* -7.297* -4.131(4.073) (3.628) (3.994) (3.782)

TI x TI 1.457 -1.968 -3.322 -2.285(1.688) (2.101) (2.285) (2.193)

0.098 0.094 0.032 0.013 0.009 0.010

(0.062) (0.063) (0.056) (0.055) (0.056) (0.059)Multinational 0.446* 0.423* 0.564*** 0.547*** 0.520** 0.530**

(0.233) (0.240) (0.191) (0.201) (0.202) (0.211)Site does R&D 0.041 0.046 0.173 0.140 0.128 0.133

(0.171) (0.174) (0.133) (0.130) (0.133) (0.125)0.192*** 0.187*** 0.112 0.113 0.117 0.120(0.064) (0.064) (0.068) (0.069) (0.071) (0.074)

Share of Competitors outside EU 0.288 0.338* 0.176 0.178 0.186 0.182(0.194) (0.202) (0.246) (0.231) (0.229) (0.234)

Observations 342 342 342 342 342 342R2 0.28 0.28 0.24 0.25 0.25 0.22

Sectoral CO2 Intensity (VaS)

Firm level CO2 Intensity

[lnCO2/EMP]

Employment [lnEMP]

Notes: All columns estimated by OLS with standard errors are in parentheses under coefficientestimates clustered by sector. The dependent variable is the interview score for process innovationin columns (1) to (3) and for product innovation in column (4). Sectoral CO2 intensity andtrade intensity are derived from EUROSTAT data. CO2 intensity data are derived from theCITL database while employment is taken from ORBIS. All other variables are derived from theinterviews. Stars indicate statistical significance level: ***=1%, **=5%, *=10%.

19

Figure 11: Discontinuity in Product Innovation

 Notes: The figure gives a graphical impression of the regression results in column 5 of Table 6. Thehorizontal plane represents the CO2 and Trade Intensity Space. The vertical axis measures theinnovation score. We see that innovation responds to both Trade and CO2 intensity with a positivelinear and negative quadratic form. The figure shows the discontinuous decline in innovation thatis implied by the thresholds which allow for free allocation of allowances.

arguably more gradual. In such a scenario there could be information barriers thatprevent senior management from seeing these opportunities unless the issuance oftight, company-specific emissions caps forces them to focus on climate change issues.

5 Conclusion

This paper investigates climate-change related investment behavior among manufac-turing firms in Europe on the basis of approximately 800 interviews with managersin six European countries. We start by looking at climate-change related innovationand find that most (70%) firms are equally engaged in formal or informal R&D withthe aim of curbing emissions and/or energy consumption. A smaller proportion(40%) is also pursuing “Clean product innovation”; i.e. R&D with the aim of devel-oping products that can help customers to reduce their emissions. Almost all firmsthat report product R&D also report process R&D. We find that firms expect futurecarbon prices to be considerably higher than current levels in the EU ETS, averaging€40 in 2020. Compared to the current 2nd trading period, firms expect the impos-ition of tighter caps for the post-2012 EU ETS period. Whereas in Phase II 40% offirms reported that the EU ETS cap imposed on them was not binding, this propor-tion reduces to less than 10% for the post-2012 period. Firms that expect a morestringent EU ETS cap in Phase III are more likely to engage in product innovation.Such a correlation could potentially imply that in a trading system the allocationof emissions allowances might not be independent of other real factors, such as theinvestment in R&D. We examine this hypothesis further by examining whether there

20

is a discontinuity in innovative activity at the thresholds implied by the EuropeanCommission criteria for exemptions from auctioning post-2012. We find that firmsthat narrowly qualified for exemption from auctioning were conducting significantlyless product innovation than firms that narrowly missed being exempted from auc-tioning. This finding is consistent with the hypothesis that having a tight carbonbudget has an important signalling effect for firms in that it draws the attention ofhigher levels of management to the matter of GHG emissions. This in turn might bethe trigger of the decision to engage in climate-change related product R&D. On thewhole, our results support the view that allocating fewer allowances for free wouldlead to a stronger innovation response in an otherwise identical emissions tradingsystem.

ReferencesAnderson, B., Leib, J., Martin, R., McGuigan, M., Muuls, M., de Preux, L., and

Wagner, U. J. (2011). Climate change policy and business in europe: Evidencefrom interviews with managers. Technical report.

Bertrand, M. and Mullainathan, S. (2001). Do people mean what they say implica-tions for subjective survey data. American Economic Review Papers & Proceed-ings, 91(2):67–72.

Bloom, N. and van Reenen, J. (2007). Measuring and explaining management prac-tices across firms and countries. Quarterly Journal of Economics, CXXII(4):1351–1406.

Hahn, R. W. and Stavins, R. N. (2010). The effect of allowance allocations on cap-and-trade system performance. Social Science Research Network Working PaperSeries.

Kenber, M., Haugen, O., and Cobb, M. (2009). The effects of eu climate legislationon business competitiveness: A survey and analysis. Climate & Energy PaperSeries, The German Marshall Fund of the United States.

Martin, R., Muûls, M., De Preux, L. B., and Wagner, U. J. (2010). Anatomy of aparadox: Management practices, organizational structure and energy efficiency.CEP Discussion Paper Series, 1039.

McKinsey and Ecofys (2005). Review of emissions trading scheme: Survey high-lights. Technical report, European Commission Directorate General for Environ-ment, Brussels.

21

A Questionnaire

22

   

 1  App

endix:  Que

stionn

aire  

  Que

stions  

Values  

Coding

 descriptio

n  

I.  Introd

uctio

n     1.  A  bit  ab

out  y

our  b

usiness  

(a)  Is  y

our  firm

 a  m

ultin

ationa

l?  If  yes,  w

here  is  th

e  he

adqu

arters?  

(b)  O

n  ho

w  m

any  prod

uctio

n  sites  do  you  op

erate  (globa

lly)?    

(c)  H

ow  m

any  of  th

ese  sites  are  situated

 in  th

e  EU

?  (d)  H

ow  m

any  of  th

ese  sites  are  situated

 in  th

e  UK/B/FR

/...?  

 

no,  list  of  cou

ntrie

s,  dk,  rf    

“No”,  if  n

ot  a  m

ultin

ationa

l;    cou

ntry  whe

re  headq

uarters  is  located

 if  a  

multin

ationa

l  nu

mbe

r,  dk,  rf  

numbe

r,  dk,  rf  

 

Num

ber  o

f  site

s  globa

lly  (a

pproximate  if  un

sure)  

Num

ber  o

f  site

s  in  the  EU

   

numbe

r,  dk,  rf  

Num

ber  o

f  site

s  in  curren

t  cou

ntry    

 

  2.  A  bit  ab

out  y

ou  

(a)  Job

 title

 text  

 (b)  T

enure  in  com

pany  

numbe

r,  rf  

 (c)  T

enure  in  current  post  

numbe

r,  rf  

 (d)  M

anagerial  backgroun

d  commercial,  techn

ical,  law

,  other    

 

  3.  EU  ETS  involvem

ent  

As  you

 might  kno

w,  the

 Europ

ean  Union

 Emiss

ions  Trading  

System

 (referred  to  as  E

U  ETS,  h

ereafter)  is  a

t  the

 heart  of  

Europe

an  clim

ate  chan

ge  policy.  

(a)  Is  y

our  c

ompa

ny  (o

r  parts  th

ereo

f)  regulated  un

der  the

 EU  

ETS?    

(b)  Since  whe

n?  

(c)  H

ow  m

any  of  you

r  Europ

ean  bu

siness  s

ites  a

re  covered

 by  the  

EU  ETS?  

 

no,  list  of  years  200

5-­‐20

09,  yes  dk  

year,  d

k,  rf  

 

numbe

r,  dk,  rf  

 

 

   

   

Que

stions  

Values  

Coding

 descriptio

n  

4.  Site

 locatio

n  For  single  plan

t  firm

s  and

 interviewees  b

ased  at  a

 produ

ction  site:  

Could  you  tell  me  the  po

stcode

 of  the

 business  s

ite  whe

re  you

 are  

based?  

For  m

ulti-­‐plan

t  firm

s  where  th

e  interviewee  is  located  at  a  non

-­‐prod

uctio

n  site:  

Some  of  th

e  qu

estio

ns  I  am

 going  to

 ask  you

 next  a

re  sp

ecific  to  a  

prod

uctio

n  site  with

in  you

r  firm

.  Please  choo

se  a  particular  

prod

uctio

n  site  an

d  an

swer  m

y  qu

estio

ns  fo

r  the

 particular  site  

througho

ut  th

e  interview.  The

 site  sh

ould  be  the  on

e  you  know

 be

st,  the

 largest  o

ne,  o

r  the

 one

 nearest  to

 you

.  If  you

 are  in  th

e  EU

 ETS,  p

lease  pick  a  site  covered

 by  the  EU

 ETS.    Co

uld  you  tell  

me  the  po

stcode

 of  the

 cho

sen  site?  

text  

Records  the

 postcod

e  

  II.  Im

pact  of  E

U  ETS  

  5.  EU  ETS  strin

gency  (If  not  an  EU

 ETS  firm

,  con

tinue

 with

 que

stion  9)  

(a)  H

ow  to

ugh  is  the  em

issions  cap

/quo

ta  currently  im

posed  by  th

e  EU

 ETS  on  your  produ

ction  site?  

(b)  C

an  you

 describe  some  of  th

e  measures  y

ou  put  in  place  to

 comply  with

 the  cap?  

1-­‐5,  dk,  rf,  n

a  Low  

Cap  is  at  business  a

s  usual.  

Mid  

Some  ad

justmen

ts  se

em  to

 have  taken  place,  how

ever  nothing  which  

led  to  fu

ndam

ental  cha

nges  in  practices;  e.g.  insulation,  etc.  

High  

Measures  w

hich  led  to  fu

ndam

ental  cha

nges  in  produ

ction  processes;  

e.g.  fu

el  sw

itching;  rep

lacemen

t  of  e

ssen

tial  plant  and

 machine

ry.  

 

(c)  W

hat  is  the

 ann

ual  cost  b

urde

n  of  being  part  o

f  the

 EU  ETS?  

For  e

xample,  m

onito

ring,  verificatio

n  an

d  tran

saction  costs;  th

e  cost  

of  buying  pe

rmits  or  red

ucing  em

issions.  

If  the  man

ager  does  n

ot  und

erstan

d  the  qu

estio

n:  

Imagine  your  installatio

n  was  not  part  o

f  the

 EU  ETS  th

is  year,  w

hat  

cost  sa

ving  wou

ld  you

r  firm

 do?  

numbe

r  

percen

tage  

 

Absolute  num

ber  

Or  p

ercentage  of  ann

ual  ope

ratin

g  cost  

 

  6.  EU  ETS  m

anag

emen

t  Ask  on

ly  m

ulti-­‐plan

t  firm

s:  

site,  other  site,  n

ationa

l  firm

,    

   

Que

stions  

Values  

Coding

 descriptio

n  

Is  EU  ETS  com

pliance  man

aged

 on  the  prod

uctio

n  site  or  

elsewhe

re?  

europe

an  firm

,  dk,  rf,  n

a  

  7.  ETS  trad

ing  

(a)  In  March  of  this  y

ear  (i.e.  b

efore  the  compliance  process),  w

hat  

was  you

r  allowan

ce  position

 on  this  site?  

(b)  W

ere  you  short  o

r  lon

g  in  allowan

ces?  

long,  sho

rt,  b

alan

ced,  dk,  rf,  n

a  text  

 

  If  the  man

ager  hap

pens  to

 men

tion  the  de

tailed  nu

mbe

r  of  a

llowan

ces,  m

ake  a  

note  of  it  in  this  fie

ld.  

 

(c)  B

efore  the  compliance  process  in  Ap

ril,  d

id  you

 buy  or  sell  

allowan

ces  o

n  the  market  o

r  over  the

 cou

nter  from

 other  firm

s?  

(d)  If  n

ot,  w

hy  not?  

buy,  se

ll,  both,  no:  only  trad

ing  

durin

g  compliance  pe

riod,  no:  no  

need

,  no:  im

age  concerns,  n

o:  

tran

saction  costs,  no:  other,  d

k,  rf,  

na  

 

(e)  If  yes,  h

ow  freq

uently?  

daily,  w

eekly,  m

onthly,  q

uarterly,  

bi-­‐ann

ual,  yearly,  d

k,  rf,  n

a    

(f)  In

 April  this  yea

r,  wha

t  was  you

r  position

 after  

the  compliance  process?  

   

If  an

swers  "

long

":  Did  you

 ban

k  pe

rmits  fo

r  future  

years?  W

hy?  

banking  to  emit  more  in  fo

llowing  

years,  ban

king  to

 sell  at  a  highe

r  ETS  pe

rmit  price  in  fu

ture,  b

anking  

dk  why,  lon

g  for  p

ooling,  dk,  rf,  n

a  

Banking  reason

.  

If  an

swers  "

balanced/com

pliant"  or  "short":  D

id  you

 bo

rrow

 permits  from

 next  y

ear's  allowan

ce?  Why?  

borrow

ing  to  emit  less  in  fo

llowing  

years,  borrowing  to  buy  at  a

 lower  

ETS  pe

rmit  price  in  fu

ture,  

borrow

ing  to  be  compliant,  

borrow

ing  dk  why,  rf,  dk,  n

a    

Borrow

ing  reason

.  Note:  Only  choo

se  "bo

rrow

ing  to  be  compliant"  if  the  

man

ager  is  very  short  sighted  and

 doesn't  seem

 to  und

erstan

d  he  will  

eventually  have  to  eith

er  emit  less  or  b

uy  permits  

If  an

swers  "

short":  W

hy  did  you

 remain  short?  

short  for  poo

ling,  sh

ort  a

nd  paid  

fine,  other,  rf,  dk,  n

a  Short  reason.  

text  

If  “other”:  why?  

 

(g)  H

as  th

is  site  exchan

ged  em

ission  pe

rmits  with

 other  

installatio

ns  belon

ging  to

 you

r  com

pany  th

at  are  part  o

f  the

 EU  

ETS?  (p

ooling)  

yes,  no,  rf,  d

k,  na  

 

 

   

Que

stions  

Values  

Coding

 descriptio

n  

8.  Rationa

lity  of  m

arket  b

ehaviour  

(a)  H

ow  do  you  de

cide

 how

 man

y  pe

rmits  to

 buy  or  sell  or  trade

 at  

all?  

(b)  D

id  you

 base  this  de

cisio

n  on

 any  fo

recast  abo

ut  pric

es  and

/or  

energy  usage?  

(c)  D

id  you

 trad

e  pe

rmit  revenu

e  off  a

gainst  emiss

ion  redu

ction  

costs  in  your  plann

ing  on

 this  iss

ue?  

1-­‐5,  dk,  rf,  n

a  Low  

Take  th

eir  p

ermit  allocatio

n  as  a  ta

rget  to

 be  met  as  s

uch  an

d  do

 not  

take  into  accou

nt  th

e  price  of  permits  or  the

 cost  o

f  aba

temen

t.  Just  se

ll  if  there  is  a  surplus  o

r  buy  if  th

ere  is  a  de

ficit.  

Mid  

Are  in  th

e  process  o

f  learning  ho

w  th

e  market  w

orks  and

 in  th

e  first  

years  d

id  not  have  an

y  market  d

riven

 attitu

de,  b

ut  now

 have  someo

ne  

in  cha

rge  of  m

anaging  the  ETS  so  as  to  minim

ize  compliance  cost.  This  

person

 has  experience  in  fina

ncial  m

arkets  and

 sometim

es  interacts  

with

 the  prod

uctio

n  man

ager.  

High  

Compa

ny  has  a  th

orou

gh  und

erstan

ding  of  the

 site-­‐spe

cific  CO2  

abatem

ent  cost  curve.  Trading  is  used  as  a  to

ol  to

 redu

ce  com

pliance  

cost  and

 to  gen

erate  extra  revenu

es  from

 excess  a

batemen

t.  Moreo

ver,  

compa

ny  fo

rms  e

xpectatio

ns  abo

ut  permit  price  an

d  re-­‐optim

izes  

abatem

ent  cho

ice  if  ne

cessary.  Trade

r  resorts  to

 futures  a

nd  derivatives  

to  m

anage  ETS  pe

rmits  as  a

 fina

ncial  asset.  

 

  9.  Anticipation  of  pha

se  III  

(a)  D

o  you  expe

ct  to

 be  pa

rt  of  the

 EU  ETS  from

 201

2  on

wards?  

If  no

t,  continue  with

 question  10

 yes,  no,  dk,  rf,  n

a    

(b)  H

ow  strin

gent  do  you  expe

ct  th

e  ne

xt  pha

se  of  the

 EU  ETS  (from  

2012

 to  202

0)  to

 be?  

(c)  W

ill  it  be  tough  for  y

our  firm

 to  re

ach  such  a  ta

rget?  Ca

n  you  

describ

e  some  of  th

e  measures  y

ou  wou

ld  have  to  put  in  place?  

(d)  D

o  you  be

lieve  th

e  allowan

ces  w

ill  be  distrib

uted

 through  an

 au

ctioning  m

echa

nism

?  (e)  Is  it  likely  that  sa

nctio

ns  fo

r  non

-­‐com

pliance  will  becom

e  more  

strin

gent?  

1-­‐5,  dk,  rf,  n

a  Low  

Cap  for  p

hase  III  is  a

nticipated

 to  be  compa

rable  to  business  a

s  usual.  

The  man

ager  believes  the

re  will  be  no

 add

ition

al  sa

nctio

ns  and

 that  

they  will  re

ceive  the  pe

rmits  fo

r  free.  

Mid  

Phase  III  is  likely  to

 trigger  som

e  ad

justmen

ts,  h

owever  nothing  th

at  will  

lead

 to  fu

ndam

ental  cha

nges  in  practices.  O

nly  a  sm

all  part  o

f  permits  

will  be  au

ctione

d  an

d  sanctio

ns  are  not  expected  to  be  very  high.  

High  

The  presen

ce  of  stron

g  sanctio

ns,  exten

sive  use  of  auctio

ning  and

 more  

strin

gent  ta

rgets  in  Ph

ase  III  is  anticipated

.  It  is  likely  to  im

ply  the  

adop

tion  of  m

easures  w

hich  will  lead

 to  fu

ndam

ental  cha

nges  in  

prod

uctio

n  processes.  It  m

ight  also

 imply  the  closure  of  th

e  plan

t,  or  

redu

ndan

cy  of  m

ore  than

 20%

 of  e

mploymen

t.    

(f)  Do  you  expe

ct  to

 tran

sfer  unu

sed  (ban

ked)  ERU

s  or  C

ERs  from  

Phase  II  to  Pha

se  III  ?  

EUAs,  ERU

s,  CER

s,  EUAs  and

 ERU

s,  

EUAs  and

 CER

s,  ERU

s  and

 CER

s,  all  

 

   

Que

stions  

Values  

Coding

 descriptio

n  

Note:  ERU

s  are  Emiss

ion  Re

duction  Units  stem

ming  from

 Joint  

Implem

entatio

n  projects.  C

ERs  a

re  Certified  Em

ission  Re

ductions  

stem

ming  from

 Clean

 Develop

ment  M

echa

nism

 projects.  

three,  no,  dk,  rf,  n

a  

  10.  A

waren

ess  

(a)  A

re  clim

ate  chan

ge  to

pics  disc

ussed  with

in  you

r  business?  Can

 you  give  examples?  

(b)  A

re  clim

ate  chan

ge  re

lated  iss

ues  formally  disc

ussed  in  

man

agem

ent  m

eetin

gs?  Ca

n  you  give  examples?  

(c)  D

o  your  strategic  ob

jectives  m

entio

n  clim

ate  chan

ge?  

(d)  D

id  you

 com

miss

ion  repo

rts  o

r  studies  on  ho

w  clim

ate  chan

ge  

will  affe

ct  you

r  business?  

1-­‐5,  dk,  rf,  n

a  Note:  Give  minim

um  sc

ore  of  3  to

 ETS  firm

s  and

 probe  dire

ctly  fo

r  4  or  5

,  skipping

 (a)  a

nd  (b

).  Low  

Don't  k

now  if  th

reat  or  o

pportunity.  N

o  aw

aren

ess.  

Mid  

Some  aw

aren

ess  b

acked  up

 by  eviden

ce  th

at  th

is  is  be

ing  

form

ally  disc

ussed  by  m

anagem

ent.  

High  

Eviden

ce  th

at  clim

ate  chan

ge  is  an  im

portan

t  part  o

f  the

 bu

siness  s

trategy.  

 

Men

tione

d  po

sitive  im

pact:  

yes,  no  

 

  III.  Prices  

  11a  En

ergy  price  expe

ctations  

By  how

 man

y  pe

rcen

t  do  you  expe

ct  ene

rgy  prices  to

 go  up

 or  d

own  

by  202

0?  

percen

tage,  d

k,  rf  

percen

tage,  d

k,  rf  

percen

tage,  d

k,  rf  

 

Expe

cted

 pric

e  chan

ge  in  percent  of  tod

ay's  price.  

Note:  This  p

rice  includ

es  th

e  effect  of  current  and

 future  clim

ate  chan

ge  policies  

on  th

e  energy  price.  

Upp

er  bou

nd  on  expe

cted

 pric

e  chan

ge  –  re

cord  only  if  interviewee  m

entio

ns  

it.  

Lower  bou

nd  on  expe

cted

 pric

e  chan

ge  –  re

cord  only  if  interviewee  m

entio

ns  

it.  

 

  11b  Ca

rbon

 price  expe

ctations  

(a)  A

s  you

 might  kno

w,  the

 EU  has  com

mitted

 to  re

ducing  

greenh

ouse  gas  emiss

ions  by  20

%-­‐30%

 over  the

 next  d

ecad

e.  W

hat  

price  do

 you

 expect  to  pa

y  for  e

mitting  on

e  tonn

e  of  CO2  in  202

0?  

percen

tage,  d

k,  rf  

Expe

cted

 pric

e  in  Euros  per  to

n  of  CO2.  

percen

tage,  d

k,  rf  

Or  e

xpected  price  chan

ge  in  percent  of  tod

ay's  price.  

yes,  no,  rf,  d

k  Kn

ows  tod

ay's  price  of  CO2.  

 

(b)  W

hat  p

rice  do

 you

 expect  in  the  worst-­‐case  scen

ario?  

 Upp

er  bou

nd  in  Euros  per  to

n  of  CO2.  

   

Que

stions  

Values  

Coding

 descriptio

n  

(c)  W

hat  p

rice  do

 you

 expect  in  the  be

st-­‐case  scen

ario?  

 Lower  bou

nd  in  Euros  per  to

n  of  CO2.  

  12.  Future  im

pact  of  carbo

n  pricing  

(a)  D

o  you  expe

ct  th

at  governm

ent  e

fforts  to  pu

t  a  pric

e  on

 carbo

n  em

issions  will  fo

rce  you  to  outsource  parts  of  the

 produ

ction  of  th

is  bu

siness  s

ite  in  th

e  foreseeable  future,  o

r  to  close  do

wn  completely?  1-­‐5,  dk,  rf  

Low  

No  im

pact  of  this  k

ind.  

Mid  

Significant  re

duction  (>10

%)  in  prod

uctio

n/em

ploymen

t  due

 to  

outsou

rcing.  

High  

Complete  close-­‐do

wn.  

 

(b)  W

hat  carbo

n  price  do

 you

 associate  with

 this  

scen

ario?(Assume  that  you

 wou

ld  have  to  pay  fo

r  all  allowan

ces.)  

Note:  The  price  relates  to  the  scenario  given  und

er  (a

).  If  an

swered  

"no  im

pact"  un

der  (a),  skip  this  qu

estio

n.  

numbe

r,  dk,  rf,  na

 Eu

ros  p

er  to

n  

(c)  H

ow  wou

ld  you

r  answer  to

 the  previous  que

stions  cha

nge,  if  you

 received

 a  free  allowan

ce  fo

r  80%

 of  you

r  current  emiss

ions?  

Note:  If  answered  "no

 impa

ct"  un

der  (a),  skip  this  qu

estio

n.  

1-­‐5,  dk,  rf,  n

a  Low  

No  im

pact  of  this  k

ind.  

Mid  

Significant  re

duction  (>10

%)  in  prod

uctio

n/em

ploymen

t  due

 to  

outsou

rcing.  

High  

Complete  close-­‐do

wn.  

 

(d)  N

ote:  Only  ask  if  an

swered  "no

 impa

ct"  un

der  (a).  

At  wha

t  carbo

n  price  level  w

ould  you

 be  forced

 to  close  you

r  plan

t  dow

n?  

If  the  man

ager  has  no  idea

 or  say

s  it  w

ould  need  to  be  very  high,  

try  differen

t  pric

es,  starting  high

,  for  example:  If  you

 had

 to  pay  

200  Eu

ros/ton  of  carbo

n,  wou

ld  you

 need  to  close  dow

n?  

numbe

r,  dk,  n

a  Eu

ros  pe

r  ton

 

(e)  H

ow  did  you

 reach  this  conclusio

n?  

(f)  How

 con

crete  are  the  plan

s  for  outsourcing  or  c

losure?  

1-­‐5,  dk,  rf,  n

a  Low  

Gut  fe

eling  of  th

e  man

ager.  

Mid  

Respon

se  is  based

 on  a  plau

sible  argum

ent.  For  e

xample,  interviewee  

discusses  a

vailable  techno

logical  options  and

 associated  cost  and

 relates  the

m  to

 profit  m

argins.  

High  

Commiss

ione

d  a  de

tailed  stud

y  of  aba

temen

t  options  and

 associated  

cost  (in-­‐ho

use  or  externa

l).  

 

(g)  W

hat  fraction  of  an  en

ergy  pric

e  or  carbo

n  price  increase  can

 you  pa

ss  on  to  you

r  customers?  

percen

tage,  d

k,  rf  

 

  IV.  C

ompe

tition  and  custom

ers  

   

Que

stions  

Values  

Coding

 descriptio

n  

  13.  Com

petitors  

(a)  C

an  you

 tell  me  the  nu

mbe

r  of  firm

s  in  the  world  which  

compe

te  with

 you

 in  one

 or  m

ore  local  m

arkets?  

Note:  For  m

ulti-­‐prod

uct  m

ulti-­‐plan

t  firm

s  refer  to

 the  market  for  

the  prod

ucts  created  on  the  current  site

 referred  to

 during  this  

interview.  For  instan

ce,  for  m

ulti-­‐plan

t  firm

s  start  th

e  qu

estio

n  with

 "For  the  produ

cts  p

rodu

ced  at  th

e  prod

uctio

n  site,  can

 you

 tell  me  ..."  

numbe

r,  dk,  rf  

 

(b)  H

ow  m

any  of  th

em  are  located  with

in  th

e  EU

?  nu

mbe

r,  dk,  rf  

 (c)  H

ow  m

any  of  th

em  are  located  in  you

r  cou

ntry?  

numbe

r,  dk,  rf  

 (d)  Location  of  m

ain  compe

titor  (cou

ntry)  

list  o

f  cou

ntrie

s,  dk,  rf,  n

a    

(e)  D

o  you  know

 in  which  cou

ntry  you

r  main  compe

titor  doe

s  most  o

f  its  produ

ction?  

same,  EU,  n

on-­‐EU,  list  of  

coun

tries,  dk,  rf,  n

a    

  14.  Location  of  Customers  

(a)  Sha

re  of  sales  exported  (to  the  EU

 and

 the  rest  of  the

 world)  

percen

tage,  d

k,  rf  

 (b)  Sha

re  of  sales  exported  to  EU  cou

ntrie

s  pe

rcen

tage,  d

k,  rf  

 (c)  A

re  you

r  produ

cts  s

old  mainly  to  con

sumers  o

r  to  othe

r  bu

sinesses?  

B2B,  fina

l  customer,  d

k,  rf  

 

  15.  Customer  pressure  

(a)  A

re  you

r  customers  c

oncerned

 abo

ut  you

r  GHG  emiss

ions?  

(b)  H

ow  do  they  voice  th

is  concern?  

(c)  D

o  your  customers  req

uire  hard  da

ta  on  your  carbo

n  em

issions?  

1-­‐5,  dk,  rf  

Low  

"B2C

"  -­‐  N

ot  aware  that  emiss

ions  perform

ance  is  of  significan

t  con

cern  

to  con

sumers  o

f  the

ir  prod

uct.  

"B2B

"  -­‐  N

ot  aware  that  businesses  the

y  supp

ly  to

 are  con

cerned

 abo

ut  

the  em

issions  of  the

 plant;  q

uality  an

d  price  are  the  on

ly  con

siderations.  

Mid  

"B2C

"  -­‐  T

he  business  is  a

ware  of  th

e  im

portan

ce  of  clim

ate-­‐chan

ge  

issue

s  in  gene

ral  and

 so  are  con

scious  th

at  th

eir  c

ustomers  m

ay  

consider  GHG  perform

ance  to

 be  im

portan

t,  althou

gh  th

ey  do  no

t  expe

ct  or  req

uire  data  as  proof.  

"B2B

"  -­‐  C

ustomers  s

et  ISO  140

01  as  a

 precond

ition

 to  su

ppliers.  

   

Que

stions  

Values  

Coding

 descriptio

n  

Eviden

ce  of  e

nviro

nmen

tal  com

pliance  is  requ

ested,  but  details  of  

emiss

ions  figures  a

re  not  re

quire

d.  

High  

"B2C

"  -­‐  B

eing  se

en  to

 redu

ce  GHG  emiss

ions  is  th

ought  to  be

 impo

rtan

t  in  th

e  pu

rcha

sing  de

cisio

ns  of  the

 firm

's  consum

ers.  This  h

as  been  

determ

ined

 by  market  research  or  con

sumers  h

ave  voiced

 their  c

oncern  

through  othe

r  means.  C

ustomers  a

lso  ask  fo

r  certified  da

ta  on  

emiss

ions  during  prod

uctio

n  or  usage.  A

 customer-­‐friend

ly  sy

stem

 to  

recognize  the  be

st  produ

cts  in  term

s  of  e

nergy  efficiency  is  often  

available  in  th

e  market  (e.g.  EU  ene

rgy  efficiency  grad

e  for  h

ome  

appliances).  

"B2B

"  -­‐  C

ustomers  a

sk  fo

r  evide

nce  of  externa

l  validation  of  GHG  

figures.  C

ustomers  req

uest  inform

ation  on

 carbo

n  em

issions  as  p

art  o

f  their  o

wn  supp

ly  cha

in  carbo

n  au

ditin

g.  Customers  c

onform

 to  PAS

 20

50  or  o

ther  nationa

l  stand

ard  in  carbo

n  foot-­‐prin

ting  an

d  so  re

quire

 de

tailed  inform

ation  on

 a  re

gular  b

asis.  

 

  16  Clim

ate  chan

ge  re

lated  prod

uct  inn

ovation  

(a)  G

loba

lly,  is  y

our  c

ompa

ny  currently  trying  to

 develop

 new

 prod

ucts  th

at  help  your  customers  to  redu

ce  GHG  emiss

ions?  

(b)  C

an  you

 give  exam

ples?  

(c)  W

hat  fraction  of  you

r  Research  &  Develop

men

t  fun

ds  are  used  

for  tha

t?  (Less  tha

n  10

%,  m

ore  than

 10%

?)  

1-­‐5,  dk,  rf  

Low  

No  efforts  to  de

velop  clim

ate  chan

ge  re

lated  

prod

ucts.  

Mid  

Some  efforts  b

ut  it  is  not  th

e  main  ob

jective  of  

the  firms  R

&D  efforts.  

High  

The  firm  is  fo

cusin

g  all  produ

ct  R&D  efforts  

on  clim

ate  chan

ge.  

 

  V.  M

easures  

  17.  Ene

rgy  mon

itorin

g  

   

Que

stions  

Values  

Coding

 descriptio

n  

(a)  H

ow  detailed  is  your  m

onito

ring  of  ene

rgy  usage?    

(b)  H

ow  often

 do  you  mon

itor  y

our  e

nergy  usage?  Since  whe

n?  

(c  )  De

scrib

e  the  system

 you

 have  in  place.  

1-­‐5,  dk,  rf  

Low  

No  mon

itorin

g  ap

art  from  looking  at  th

e  en

ergy  bill.  

Mid  

     Eviden

ce  of  e

nergy  mon

itorin

g  as  opp

osed

 to  looking  at  th

e  en

ergy  bill,  

i.e.  the

re  is  so

me  consciou

sness  a

bout  th

e  am

ount  of  e

nergy  be

ing  used

 as  a  business  o

bjectiv

e.  How

ever,  d

iscussio

ns  are  irregular  a

nd  not  part  

of  a  structured

 process  and

 are  m

ore  freq

uent  with

 pric

e  rises.    Not  

more  than

 qua

rterly  m

onito

ring  of  ene

rgy.    

High  

   Energy  use  is  m

easured  an

d  mon

itored  constantly  and

 is  on  the  agen

da  

in  re

gular  p

rodu

ction  meetin

gs.  Ene

rgy  use  in  th

e  plan

t  is  d

ivided

 up  in  

space  (by  prod

uctio

n  line,  m

achine

 or  sim

ilar)  and

 mon

itored  over  time  

(daily,  h

ourly

 or  c

ontin

uously).  Th

e  am

ount  of  e

nergy  rather  th

an  th

e  cost  is  fo

cused  on

.      

2000

 and

 earlier,  list  o

f  years  

2001

-­‐201

0,  dk,  rf,  n

a  Start  d

ate  (put  “na

”  if  score  is  “1”)  

 

  18.  Targe

ts  on  en

ergy  con

sumption  for  m

anag

emen

t  (a)  D

o  you  ha

ve  any  ta

rgets  o

n  en

ergy  con

sumption  which  

man

agem

ent  h

as  to

 observe?  (e.g.  kWh  of  electric

ity)  

no  ta

rgets,  re

lativ

e  qu

antity  

targets,  absolute  qu

antity  targets,  

absolute  and

 relativ

e  qu

antity  

targets,  only  expe

nditu

re  ta

rgets,  

dk,  rf  

Type

 

(b)  C

an  you

 describe  some  of  th

e  challenges  you

 face  in  m

eetin

g  the  

targets?  

(c)  H

ow  often

 do  you  meet  the

se  ta

rgets?  Do  you  think  they  are  

tough?  

Note:  If  th

e  man

ager  re

plies  they  ha

ve  EU  ETS/CCA

 targets,  ask  

"have  these  been  tran

slated  into  internal  ta

rgets  for  m

anag

ement?"  

1-­‐5,  dk,  rf  

Low  

No  targets.  

Mid  

Targets  e

xist  but  se

em  easy  to  achieve.  

High  

 Eviden

ce  th

at  ta

rgets  a

re  hard  to  achieve.  D

etailed.  

 

(d)  B

y  ap

proxim

ately  ho

w  m

uch  do

es  th

is  requ

ire  re

ducing  you

r  curren

t  ene

rgy  consum

ption  in  th

e  ne

xt  5  years  (1

0%,  25%

,  50%

)?  

Note  the  tim

etab

le  fo

r  the  ta

rget  (e

.g.  5  years  or  o

ther  num

ber  g

iven  by  

interviewee).  

percen

tage,  d

k,  rf,  n

a  nu

mbe

r,  dk,  rf,  na

   

  Horizon

 (num

ber  o

f  years)  

 

(e)  Since  whe

n  do

 you

 have  these  targets?  

2000

 and

 earlier,  list  o

f  years  

2001

-­‐201

0,  dk,  rf,  n

a    

   

Que

stions  

Values  

Coding

 descriptio

n  

  19.  G

HG  m

onito

ring  

(a)  D

o  you  explicitly  mon

itor  y

our  G

HG  emiss

ions?    Since  whe

n?  

(b)  H

ow  do  you  estim

ate  your  GHG  emiss

ions?  

(c)  A

re  you

r  GHG  estim

ates  externa

lly  validated

?    

1-­‐5,  dk,  rf  

Low  

No  specific  GHG  m

onito

ring.  

Mid  

Detailed  en

ergy  m

onito

ring  with

 clear  evide

nce  for  carbo

n  accoun

ting  

(at  least  firm

 level).  M

anager  is  aware  that  ene

rgy  fig

ures  need  to  be  

scaled

 by  carbon

 intensity

.  High  

Carbon

 accou

nting  of  both  direct  and

 indirect  emiss

ions  (sup

ply  chain  

emiss

ions).  External  validation  of  GHG  figures.  

 

 20

00  and

 earlier,  list  o

f  years  

2001

-­‐201

0,  dk,  rf,  n

a  Start  d

ate  (put  “na

”  if  score  is  “1”)  

  20.  Targe

ts  on  GHG

 emission

s  for  m

anag

emen

t  (a)  D

o  you  ha

ve  any  ta

rgets  o

n  GHG  emiss

ions  which  

man

agem

ent  h

as  to

 observe?    

no  ta

rgets,  dire

ct  emiss

ions,  

indirect  and

 dire

ct,  d

k,  rf  

 

(b)  C

an  you

 describe  some  of  th

e  challenges  you

 face  in  m

eetin

g  the  

targets?  

(c)  H

ow  often

 do  you  meet  the

se  ta

rgets?  Do  you  think  they  are  

tough?  

Note:  If  th

e  man

ager  re

plies  they  ha

ve  EU  ETS/CCA

 targets,  ask:  

Have  these  be

en  tran

slated  into  internal  ta

rgets  for  m

anagem

ent?  

1-­‐5,  dk,  rf  

Low  

 No  targets  for  GHG  emiss

ions.  

Mid  

There  is  some  aw

aren

ess  o

f  the

 con

tribution  of  differen

t  ene

rgy  sources  

and  prod

uctio

n  processes  to  em

issions,  b

ut  th

is  is  a  second

ary  

consideration  to  cost  focused

 ene

rgy  targets.  The

re  is  so

me  de

gree  of  

difficulty

 in  th

e  targets.    

HIgh  

There  are  sepa

rate  ta

rgets  for  GHGs,  distinct  from

 ene

rgy  use.  GHG  

emiss

ions  are  a  KPI  (K

ey  Perform

ance  In

dicator)  fo

r  the

 firm

.  The

 contrib

ution  of  each  en

ergy  so

urce  and

 the  prod

uctio

n  process  to  GHG  

emiss

ions  is  kno

wn  an

d  suggested  im

provem

ent  p

rojects  for  th

e  prod

uctio

n  are  assessed

 on  their  p

oten

tial  impa

ct  on  carbon

 as  w

ell  as  

energy  efficien

cy.  

 

(d)  B

y  ap

proxim

ately  ho

w  m

uch  do

 these  targets  req

uire  you

 to  re

duce  

your  emiss

ions  in  th

e  ne

xt  5  years  (1

0%,  25%

,  50%

)  com

pared  their  

curren

t  level?  

Note  the  tim

etab

le  fo

r  the  ta

rget  (e

.g.  5  years  or  o

ther  num

ber  g

iven  by  

interviewee)  

percen

tage,  d

k,  rf,  n

a  nu

mbe

r,  dk,  rf,  na

   

  Horizon

 (num

ber  o

f  years)  

 

   

Que

stions  

Values  

Coding

 descriptio

n  

(e)  W

hen  did  you  start  h

aving  targets  o

n  GHG  emiss

ions?  

2000

 and

 earlier,  list  o

f  years  

2001

-­‐201

0,  dk,  rf,  n

a    

  21.  Targe

t  enforcemen

t  (a)  W

hat  h

appe

ns  if  ene

rgy  consum

ption  or  GHG  emiss

ion  targets  

are  no

t  met?    

(b)  D

o  you  pu

blicize  targets  a

nd  ta

rget  achievemen

t  with

in  th

e  firm  

or  to

 the  pu

blic?  Ca

n  you  give  examples?    

(c)  A

re  th

ere  fin

ancial  con

sequ

ences  in  case  of  n

on-­‐achievemen

t?    

(d)  Is  the

re  a  bon

us  fo

r  target  a

chievemen

t?  

1-­‐5,dk,rf  

Low  

No  targets  o

r  miss

ing  targets  d

o  no

t  trig

ger  a

ny  re

spon

se.    

Mid  

Both  ta

rget  achievemen

t  and

 non

-­‐achievemen

t  are  internally  and

 externally  com

mun

icated

.    

High  

Target  non

-­‐achievemen

t  leads  to

 fina

ncial  con

sequ

ences  interna

lly  

and/or  externa

lly;  including  pen

altie

s,  e.g.  staff  do

es  not  get  bon

us.  

 

  22.  Emission

-­‐red

ucing  mea

sures  

(a)  C

an  you

 tell  me  wha

t  measures  y

ou  have  ad

opted  in  order  to

 redu

ce  GHG  emiss

ions  (o

r  ene

rgy  consum

ption)  on  this  site?  

DO  NOT  PR

OMPT

 with

 the  list  if  d

oesn't  ha

ve  an  idea,  rathe

r  ask:  

Have  you  bo

ught  any  new

 equ

ipmen

t,  or  have  you  chan

ged  the  way  

you  prod

uce?  

List  of  tickboxes  

I.  Heatin

g  an

d  cooling:  

1-­‐  Optim

ised  use  of  process  heat  

2-­‐  M

odernisatio

n  of  coo

ling/refrigeration  system

 3-­‐  Optim

isatio

n  of  air  cond

ition

ing  system

 4-­‐  Optim

isatio

n  of  exhau

st  air  system

 and

/or  d

istric

t  heatin

g  system

 II.  M

ore  clim

ate-­‐friend

ly  ene

rgy  gene

ratio

n  on

 site:  

1-­‐  In

stallatio

n  of  com

bine

d  he

at  and

 pow

er  (C

HP)  plant  /  cogene

ratio

n  2-­‐  Biogas  feed-­‐in  in  local  com

bine

d  he

at  and

 pow

er  plant  or  d

omestic  gas  grid

 3-­‐  Switching  to

 natural  gas  

4-­‐  Exploita

tion  of  re

newab

le  ene

rgy  source  

III.  M

achine

ry:  

1-­‐  M

odernisatio

n  of  com

pressed  air  system  

2-­‐  Other  indu

stry-­‐spe

cific  produ

ction  process  o

ptim

isatio

n/machine

 upgrade

 3-­‐  Produ

ction  process  inn

ovation  

IV.  Ene

rgy  man

agem

ent:  

1-­‐  In

trod

uctio

n  of  ene

rgy  man

agem

ent  system  

2-­‐  Sub

metering  /  u

pgrade

 of  a

n  existing  en

ergy  m

anagem

ent  system  

3-­‐  (E

xterna

l)  En

ergy  aud

it  4-­‐  In

stallatio

n  of  timers  a

ttache

d  to  m

achine

ry  

5-­‐  In

stallatio

n  of  (d

e-­‐)cen

tralise

d  he

ating  system

s  

   

Que

stions  

Values  

Coding

 descriptio

n  

V.  Other  m

easures  o

n  prod

uctio

n  site:  

1-­‐  M

odernisatio

n  of  lightin

g  system

 2-­‐  Ene

rgy-­‐efficient  site  exten

sion/im

proved

 insulatio

n/introd

uctio

n  of  building  

man

agem

ent  

3-­‐  Employee  awaren

ess  c

ampa

igns  and

 staff  trainings  

4-­‐  Non

-­‐techn

ical  re

organisatio

n  of  produ

ction  process  

5-­‐  In

stallatio

n  of  ene

rgy-­‐efficient  IT-­‐system  

6-­‐  Im

proved

 waste  m

anagem

ent/recycling  

VI.  B

eyon

d  prod

uctio

n  on

 site:  

1-­‐  In

trod

uctio

n  of  clim

ate-­‐friend

ly  com

muting  sche

me  

2-­‐  Con

sideration  of  clim

ate-­‐related  aspe

cts  in  investmen

t  and

 purchase  

decisio

ns  

3-­‐  Con

sideration  of  clim

ate-­‐related  aspe

cts  in  distrib

ution  

4-­‐  Customer  edu

catio

n  prog

ramme  

5-­‐  Participation  in  carbo

n  offsettin

g  sche

mes  

 

(b)  W

hich  one

 of  the

se  m

easures  a

chieved  the  largest  carbo

n  saving?  

measure  cod

e  Fill  in  th

e  code  correspon

ding

 to  th

e  measure  in  (a

)  (e.g.  II-­‐4  fo

r  “Exploitatio

n  of  

renewab

le  energy  source”).  

(c)  B

y  ho

w  m

uch  did  this  measure  re

duce  you

r  total  ene

rgy  

consum

ption?  

percen

tage,  d

k,  rf,  n

a    

(d)  B

y  ho

w  m

uch  did  this  measure  re

duce  you

r  total  GHG  

emiss

ions?  

percen

tage,  d

k,  rf,  n

a    

(e)  W

hat  m

otivated

 the  ad

optio

n  of  th

ese  measures?  

 EU

 ETS,  ene

rgy  cost  sa

ving  /  high  

profita

bility,  pollutio

n  redu

ction,  

repu

tatio

n,  customer  pressure,  

employee  initiative,  pub

lic  

investmen

t  sup

port,  com

pliance  

with

 regulatio

n,  com

pliance  with

 expe

cted

 future  re

gulatio

n,  other,  

dk,  rf,  na

 

Main  motivation  (select  o

nly  ONE)  

text  

Other  m

otivation  (if  not  in  tick  boxes,  o

r  secon

d)  

 

(f)  How

 did  you

 learn  ab

out  this  m

easure?  

consultant,  governm

ent,  

custom

er,  sup

plier,  em

ployee,  

R&D  project,  compe

titor,  o

ther,  

Tick  m

ore  than

 one

 option,  if  differen

t  sou

rces  m

entio

ned  

   

Que

stions  

Values  

Coding

 descriptio

n  

dk,  rf,  na

 

(g)  W

hen  did  you  im

plem

ent  this  m

easure?  

2000

 and

 earlier,  list  o

f  years  

2001

-­‐201

0,  dk,  rf,  n

a    

  VI.  Inn

ovation,  barrie

rs  to

 investmen

t  and

 managem

ent  

  23.  Clim

ate  chan

ge  re

lated  process  inn

ovation  

(a)  D

o  you  de

dicate  staff  tim

e  an

d/or  fina

ncial  resou

rces    to  fin

ding  

new  ways  o

f  red

ucing  the  GHG  emiss

ions  at  y

our  facility?  Did  you  

commiss

ion  an

y  stud

ies  for  th

at  purpo

se?  

(b)  C

an  you

 give  exam

ples?    

(c)  W

hat  fraction  of  you

r  firm

's  glob

al  Research  &  Develop

men

t  fund

s  are  used  for  tha

t?  (less  tha

n  10

%,  m

ore  than

 10%

?)  

Note:  This  d

oes  n

ot  includ

e  expenses  fo

r  staff  training

s  or  e

nergy  

mon

itorin

g,  but  actua

l  inn

ovation.  

1-­‐5,  dk,  rf  

Low  

No  R&

D  resources  c

ommitted

 to  re

ducing  GHG  emiss

ions.  

Mid  

Eviden

ce  of  R

&D  projects  to

 redu

ce  emiss

ions.    

High  

Eviden

ce  th

at  th

is  kind

 of  R

&D  is  an

 impo

rtan

t  com

pone

nt  in  th

e  compa

ny's  R&

D  po

rtfolio  (5

 or  h

ighe

r).  

 

  24.  B

arrie

rs  to

 ado

pting  en

ergy-­‐efficiency  investmen

ts  

(a)  C

an  you

 give  on

e  exam

ple  of  a  m

easure  to

 enh

ance  ene

rgy  

efficiency  which  was  con

sidered

,  but  eventua

lly  not  ado

pted

?  List  of  tickboxes  

Same  list  a

s  for  que

stion  22

a.  

(b)  W

hich  payba

ck  time  was  re

quire

d  in  th

e  econ

omic  evaluation  

of  th

is  measure?  

numbe

r,  dk,  rf,  na

 “Years”;  if  in  m

onths,  put  equ

ivalen

t  in  years,  e.g.  record  6  mon

ths  a

s  0.5.  

(c)  Is  this  p

ayba

ck  time  longer  or  sho

rter  th

an  th

e  on

e  ap

plied  to  

non-­‐en

ergy  re

lated  measures  to  cut  costs?  

1-­‐5,  dk,  rf,  n

a  Low  

Longer,  i.e.  m

uch  less  strin

gent  

Mid  

Equa

l  

High  

Shorter,  i.e.  m

uch  more  strin

gent  

 

(d)  If  d

ifferen

t:  why?  

text    

 

(e)  W

as  uncertainty  abo

ut  fu

ture  pric

es  or  regulation  im

portan

t  for  the

 decision

 to  re

ject?  

no,  yes_p

rices,  yes_regulation,  

yes_bo

th,  d

k,  rf,  n

a    

(f)  W

hat  o

ther  fa

ctors  w

ere  influ

entia

l  in  the  de

cisio

n?  

text  

 

   

Que

stions  

Values  

Coding

 descriptio

n  

(g)  H

as  th

e  curren

t  econo

mic  dow

nturn  affected

 you

r  investm

ent  

crite

ria  fo

r  clean

 techno

logies?  How

?  no

,  favors  c

lean

,  favou

rs  other,  

more  strin

gent  overall,  less  

strin

gent  overall,  dk,  rf,  n

a  

 

  25.  Further  re

ductions  

(a)  B

y  ho

w  m

uch  (in

 percentage  po

ints)  cou

ld  you

 -­‐  at  current  

energy  pric

es  -­‐  furthe

r  red

uce  your  current  GHG  emiss

ions  

with

out  com

prom

ising  you

r  econo

mic  perform

ance?  (i.e.  how

 much  more  em

ission  redu

ction  could  be

 achieved  with

out  

increasin

g  costs)  

percen

tage,  d

k,  rf  

 

(b)  If  so,  why  have  you  no

t  implem

ented  these  measures  y

et?    

text  

 

(c)  W

hat  further  GHG  emiss

ion  redu

ction  (in

 percentage  po

ints)  

wou

ld  be  techno

logically  possib

le  (a

lthou

gh  not  necessarily  at  no  

extra  cost)?    

 

percen

tage,  d

k,  rf  

Notes:  A

ssum

ing  that  produ

ction  stays  c

onstan

t  and

 that  no  processes  a

re  

being  ou

tsou

rced

.  This  s

hould  no

t  include

 emiss

ion  redu

ction  achieved

 by  

switching  to

 rene

wab

le  electric

ity.  Include

 emiss

ions  re

ductions  th

rough  

combine

d  he

at  and

 pow

er  how

ever.    

  26.  M

anag

er  re

spon

sible  for  C

limate  Ch

ange

 issues  

(a)  A

t  the

 man

agem

ent  level,  w

ho  is  re

spon

sible  fo

r  dealing  with

 clim

ate  chan

ge  policies  a

nd  ene

rgy  an

d  po

llutio

n  redu

ction  in  th

e  firm  nationa

lly?  Wha

t  is  the

 official  job  title?  

Note:  If  se

veral,  ask  for  h

ighest-­‐ran

king

.  If  n

obod

y,  put  title

 “no

 clear  respo

nsibility”.  

text  

Job  title  of  the

 man

ager    

(b)  H

ow  fa

r  in  the  man

agem

ent  h

ierarchy  is  th

is  man

ager  below

 the  CE

O?  (figure  out  th

rough  sequ

entia

l  que

stioning  if  necessary)  

CEO,  n

umbe

r,  no

 clear  

respon

sibility,  d

k,  rf  

No  of  peo

ple  be

tween  CE

O  and

 Man

ager,  e.g.  if  rep

orts  dire

ctly  to

 CEO

,  put  0  

(c)  H

as  th

ere  recently  been  a  chan

ge  in  re

spon

sibilitie

s  for  clim

ate  

chan

ge  issues?  Whe

n?  

(d)  H

ow  fa

r  in  the  man

agem

ent  h

ierarchy  was  th

is  man

ager  below

 the  CE

O?  (figure  out  th

rough  sequ

entia

l  que

stioning  if  necessary)  

 

no  cha

nge,  list  of  years  200

0-­‐20

10,  

yes  d

k  year,  d

k,  rf  

 

CEO,  n

umbe

r,  no

 clear  

respon

sibility,  d

k,  rf  

 

text  

Record  past  m

anager  title

 if  m

entio

ned,  but  do  no

t  prompt  fo

r  it.  

 

 

   

Que

stions  

Values  

Coding

 descriptio

n  

VI.  Firm

 Characteristics  

  27.  Firm/Plant  Details  

(a)  H

ow  m

any  pe

ople  are  employed

 in  th

e  firm  globa

lly  (including  

this  coun

try)?  

Note:  If  a  m

ultin

ationa

l,  ask  for  the  who

le  group

's  nu

mber.  

numbe

r,  dk,  rf  

 

(b)  H

ow  m

any  pe

ople  doe

s  the

 firm

 employ  in  you

r  cou

ntry?  

numbe

r,  dk,  rf  

 (c)  H

ow  m

any  pe

ople  are  employed

 at  the

 current  site?  

numbe

r,  dk,  rf  

 (d)  A

nnua

l  Ene

rgy  Bill-­‐An

nual:  

numbe

r,  dk,  rf  

  percen

tage,  d

k,  rf,  n

a  pe

rcen

tage,  d

k,  rf,  n

a    

  Do  not  ask,  but  in  case  interviewee  does  n

ot  kno

w  th

e  ab

solute  num

ber  a

nd  

answ

ers  w

ith  one  of  the  fo

llowing:  

Energy  cost  a

s  percentage  of  tu

rnov

er

Energy  cost  a

s  percentage  of  c

osts

 

(e)  T

otal  ann

ual  run

ning  costs  (w

age  cost  +  m

aterials,  includ

ing  

energy):  

 

numbe

r,  dk,  rf  

 

 Answered

 (d)  a

nd  (e

)  at  the

 site  level  or  a

t  the

 com

pany  level?  

site,  com

pany,  n

a    

(f)  Doe

s  you

r  com

pany  purchase  rene

wab

le  pow

er?  

yes,  no,  dk,  rf  

Note:  Do  no

t  include  electricity

 generated  on  site.  

(g)  D

oes  this  s

ite  do  an

y  prod

uct  R

 &  D?  

Note:  Do  no

t  dwell  on  this  qu

estio

n,  m

ake  a  judg

ement  from  first  

answ

er.  

yes,  no,  dk,  rf  

 

(h)  Is  M

arketin

g  for  y

our  p

rodu

cts  d

one  from

 this  site?  

Note:  Do  no

t  dwell  on  this  qu

estio

n,  m

ake  a  judg

ement  from  first  

answ

er.  

yes,  no,  dk,  rf  

 

(i)  Doe

s  this  s

ite  have  an

 enviro

nmen

tal  m

anagem

ent  system  (ISO

 14

000)?  

yes,  no,  dk,  rf    

 

  VII.  Co

untry-­‐specific  po

licies  

  UNITED  KINGDOM  

   

Que

stions  

Values  

Coding

 descriptio

n  

  UK.1  Participation  in  voluntary  governm

ent  clim

ate  change  policies  

(a)  A

re  you

 aware  of  volun

tary  governm

ent  schem

es  to

 help  

busin

esses  red

uce  GHG  pollutio

n?  

(b)  W

hich  one

s?  

(c)  A

re  you

 participating  in  any?  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

 

Carbon

 Trust  Online  To

ols  (Be

nchm

arking  Too

ls,  Action  Plan

 Too

l)  Whe

n?  

Carbon

 Trust  Ene

rgy  Au

dit  o

r  Advice?  (C

Taud

it)  

Inno

vatio

n  gran

ts  from

 the  Ca

rbon

 Trust?  Whe

n?  

Carbon

 Trust  Stand

ard  

Enha

nced

 Cap

ital  A

llowan

ce  sc

heme?  (E

CA)  

 

  UK.2  Participation  in  Clim

ate  Change  agreement  

(a)  Is  y

our  com

pany  (o

r  parts  th

ereo

f)  subject  to  a  UK  Clim

ate  

Chan

ge  Agreemen

t?  

(b)  Since  whe

n?  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

 

(c)  H

ow  strin

gent  is  th

e  target  im

posed  by  th

e  CC

A?  

(d)  C

an  you

 describe  some  of  th

e  measures  y

ou  had

 to  put  in  place  

to  com

ply  with

 the  cap?  

1-­‐5,  dk,  rf,  n

a  Low  

No  targets.  

Mid  

Targets  e

xist  but  se

em  easy  to  achieve.  

High  

Eviden

ce  th

at  ta

rgets  a

re  hard  to  achieve.  D

etailed  de

scrip

tion  of  

serio

us  problem

s  in  achieving  targets.  

 

((e)  D

id  you

 buy  or  sell  emiss

ion  rig

hts  v

ia  th

e  UK  ETS?  

no  becau

se  of  image  concerns,  n

o  be

cause  no

 cap

acity

,  no  othe

r,  bo

ught,  sold,  both,  dk,  rf,  n

a  

 

  BELGIUM  

B.1  Participation  in  indu

stry  agreemen

ts  (a

ccords  de  

Bran

che/Be

chmarkcon

vena

nten

)  (a)  Is  y

our  c

ompa

ny  (o

r  parts  th

ereo

f)  subject  to  an

 indu

stry  

agreem

ent?  

(b)  Since  whe

n?  

no,  list  of  years  200

1-­‐20

09,  d

k,  rf,  

na  

 

(c)  H

ow  strin

gent  is  th

e  target  im

posed  by  th

e  agreem

ent?  

1-­‐5,  dk,  rf,  n

a  Low  

No  targets.  

   

Que

stions  

Values  

Coding

 descriptio

n  

(d)  C

an  you

 describe  some  of  th

e  measures  y

ou  had

 to  put  in  place  

to  com

ply  with

 the  cap?  

Mid  

High  

 

Targets  e

xist  but  se

em  easy  to  achieve.  

Eviden

ce  th

at  ta

rgets  a

re  hard  to  achieve.  D

etailed  de

scrip

tion  of  

serio

us  problem

s  in  achieving  targets.  

 

B.2  Do

 you

 ben

efit  from

 any  ta

x  redu

ction  from

 the  Fede

ral  

governmen

t  becau

se  of  investm

ents  th

at  re

duce  ene

rgy  

consum

ption/loss?  If  yes,  whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

B.3  Brussels:  H

ave  you  ha

d  a  gran

t  for  an  en

ergy  aud

it  or  advice  

finan

ced  by  th

e  Brussels  region

?  If  yes,  whe

n?  

Walloon

:  Have  you  ha

d  an

y  en

ergy  aud

it  (AMURE

)  or  a

dvice  

finan

ced  by  th

e  Walloon

 region

?  If  yes,  whe

n?  

Flan

ders:  H

ave  you  received

 any  advice  or  ene

rgy  au

dit  finan

ced  

by  VLA

O  (V

laam

s  Agentscha

p  Ond

erne

men

)?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

B.4  Brussels:  H

ave  you  be

nefited

 from

 an  investmen

t  sub

sidy  

from

 the  Brussels  region

 for  improving  your  building's  o

r  prod

uctio

n  process's

 ene

rgy  efficiency  ?  If  yes,  whe

n?  

Walloon

:  Have  you  ha

d  a  gran

t  from  th

e  en

ergy  fu

nd  of  the

 Walloon

 region

 for  improving  your  building's  o

r  produ

ction  

process's

 ene

rgy  efficiency?  If  yes,  w

hen?  

Flan

ders:  H

ave  you  received

 an  ecolog

ical  grant  (E

cologiprem

eie)  

of  th

e  Flem

ish  re

gion

 for  improving  your  building's  o

r  produ

ction  

process's

 ene

rgy  efficiency?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

B.5  Flan

ders:  D

o  you  ha

ve  a  heat  a

nd  pow

er  certificate  from

 the  

Flem

ish  re

gion

 (warmtekrachtcertificaat)?  If  yes,  since  whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

  FRANCE  

F1.  A

re  you

 part  o

f  the

 AER

ES  (A

ssociatio

n  de

s  entreprise

s  pou

r  la  

rédu

ction  de

 l'effet  d

e  serre)  and

 have  sig

ned  up

 to  volun

tary  

GHG  emiss

ion  redu

ctions?  If  yes,  since  whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

F2.  H

ave  you  ha

d  a  gran

t  for  an  en

ergy  aud

it  or  advice  fin

anced  by  

ADEM

E?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

F3.  H

ave  you  be

nefited

 from

 a  “FO

GIM

E”  gua

rantee  fo

r  loa

ns  you

 ha

ve  ta

ken  to  invest  into  ene

rgy  efficiency  im

provem

ents  or  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

   

Que

stions  

Values  

Coding

 descriptio

n  

emiss

ion  redu

ctions  ?  If  yes,  w

hen?  

F4.  H

ave  you  be

nefited

 from

 a  grant  from

 ADE

ME  for  improving  

your  building's  o

r  produ

ction  process's

 ene

rgy  efficiency  ?  If  yes,  

whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

  GERM

ANY  

  G.1  Renewable  Energy  Sources  Act  

(a)  In  previous  year,  ha

ve  you

 been  gran

ted  a  discou

nt  on  your  

energy  cost  w

hich  re

duces  the

 ene

rgy  cost  app

ortio

nmen

t  em

bodied

 in  th

e  Re

newab

le  Ene

rgy  Sources  A

ct?  

no,  yes,  d

k,  rf,  n

a    

(b)  H

ave  you  ap

plied  for  the

 disc

ount  (a

lso)  in  20

09?  

no,  yes,  d

k,  rf,  n

a    

 (c)  D

id  th

e  certificatio

n  process  req

uire  you

 to  upgrade

 you

r  en

ergy  m

anagem

ent  system?  

Note:  Since  200

9  the  ap

proval  of  the  disc

ount  is  su

bject  to  the  

certificatio

n  of  you

r  energy  man

agem

ent  system  by  30

 June  200

9.    

yes,  no  up

grad

e  ne

cessary,  no  ha

d  certificate  before,  dk,  rf,  n

a    

 

  G.2  Public  support  program

mes  

Have  you  pa

rticipated

 in  pub

lic  su

pport  p

rogram

s  aim

ed  at  saving  

energy  or  a

t  red

ucing  GHG  emiss

ions?  

   

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  Clim

ate  initiative  

 no

,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  ER

P  En

vironm

ent  a

nd  Ene

rgy  Efficiency  Prog

ramme    

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  Grant  fo

r  ind

epen

dent  ene

rgy  au

dit  from  fo

nds  for  ene

rgy  efficiency  in  SME  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  Provision

 of  cut-­‐rate  investmen

t  credit  from  fo

nds  for  ene

rgy  efficiency  in  SME  

to  im

plem

ent  ide

ntified

 ene

rgy-­‐saving  m

easures    

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  Supp

ort  schem

e  of  a  fe

deral  state  

text  

Other  

 

     

 

   

Que

stions  

Values  

Coding

 descriptio

n  

  HUNGARY  

H1.  Have  you  received

 governm

ent  sup

port  fo

r  any  of  you

r  investmen

ts  to

 redu

ce  emiss

ions  or  implem

ent  e

nergy  efficiency  

measures  o

r  increase  the  use  of  re

newab

les?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  Kö

rnyezetvéd

elmi  A

lap  Cé

lelőirá

nyzat  

H2.(a)  H

ave  you  received

 EU  fu

nds  to  supp

ort  a

ny  of  you

r  investmen

ts  to

 redu

ce  emiss

ions  or  implem

ent  e

nergy  efficiency  

measures  o

r  increase  the  use  of  re

newab

les?  If  yes,  w

hen?  

(b)  If  yes,  for  which  Ope

rativ

e  Prog

ram;    which  call  for  propo

sal?    

 

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  

KEOP,  KIOP,  ERF

A,  dk,  rf,  n

a    

     

H3.  Have  you  received

 fund

ing  from

 the  Norwegian  Fund

 for  

supp

ort?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a  EG

T  és  Norvég  Fina

nszírozási  Mecha

nizm

usok  program

 

  POLAND

 P.1  Do

 you

 use  th

e  sectoral  inform

ation  brochu

res  p

ublishe

d  by  

the  Ministry  of  E

nviro

nmen

t  tha

t  include

 the  inform

ation  ab

out  

the  be

st  available  techno

logies  fo

r  differen

t  econo

mic  activity

?  Since  whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

P.2  Have  you  ever  ta

ken  a  techno

logical  credit  p

rovide

d  by  th

e  Techno

logical  Credit  F

und?  If  yes.  w

hen?  

 

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

P.3  Have  you  ever  been  co-­‐fina

nced

 or  h

ave  taken  a  preferen

tial  

cred

it  from

 the  Nationa

l  Fun

d  of  Enviro

nmen

tal  Protection  an

d  Water  M

anagem

ent,  Ba

nk  of  E

nviro

nmen

tal  Protection  an

d  EkoFun

d?  If  yes,  w

hen?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

P.4  Have  you  ever  ben

efite

d  from

 the  subven

tions  and

 tax  

redu

ctions  from

 the  go

vernmen

t  for  enviro

nmen

tal  purpo

ses?    If  

yes,  whe

n?  

no,  list  of  years  200

1-­‐20

09,  yes  dk  

year.  d

k,  rf,  n

a    

  VIII.  Post  Interview

 

   

Interview  duration  (m

ins)  

numbe

r  Minutes  

   

Que

stions  

Values  

Coding

 descriptio

n  

Interviewers'  im

pressio

n  of  interviewee's  reliability  

1-­‐5,  dk,  rf  

Low  

Mid  

High  

 

Some  know

ledge  ab

out  h

is  site,  and

 no  know

ledge  ab

out  the

 rest  of  the

 firm.  

Expe

rt  kno

wledge  ab

out  h

is  site,  and

 some  know

ledge  ab

out  the

 rest  of  

the  firm.  

Expe

rt  kno

wledge  ab

out  h

is  site  an

d  the  rest  of  the

 firm

.    

Interviewee  se

emed

 con

cerned

 abo

ut  clim

ate  chan

ge  

1-­‐5,  dk,  rf  

Low  

Mid  

High  

 

Not  con

cerned

.  Somew

hat.  

Very  con

cerned

.    

Interviewee  se

emed

 skep

tic  abo

ut  action  on

 clim

ate  chan

ge  

1-­‐5,  dk,  rf  

Low  

Mid  

High  

 

Not  sk

eptic  at  a

ll.  

Somew

hat  skeptic.  

Very  sk

eptic.  

 

Men

tione

d  othe

r  clim

ate  chan

ge  re

lated  po

licies  

text  

 Moa

ned  a  lot  a

bout  high  en

ergy  pric

es  

no,  a  little,  a  lot  

 Num

ber  o

f  tim

es  interview  neede

d  to  be  resche

duled  

numbe

r    

Seniority

 of  interview

ee  

Director,  V

P/Gen

eral  M

anager,  

Plan

t/Factory  Man

ager,    

Man

ufacturin

g/Prod

uctio

n  Man

ager,  (En

vironm

ental),  Health

 &  Safety  Man

ager,  Techn

ician  

 

Age  of  interviewee  

Note:  Do  no

t  ask,  guess!  

numbe

r    

Gen

der  o

f  interview

ee  

male,  fe

male  

 Interview  langua

ge  

English

,  French,  German

,  Dutch,  

Hun

garia

n,  Polish