This section provides a full description of InfluenceMap’s methodology for scoring entities, as well as a glossary for key terminology employed in our analysis.

We select the companies we score according to their rank in the Forbes Global 2000, not including state-owned enterprises and companies operating in financial services (the latter are not active in climate policy engagement and we are developing alternate methods for measuring their impact on the climate agenda. As of April 2017, we have scored around 200 of the world's largest global corporations.

In addition, InfluenceMap included an analysis of 40 or so external influencers, i.e. a selection of the most powerful organizations representing corporations around the world (excluding climate-focused organizations to avoid preferential selection).

About our Scores

The InfluenceMap Scoring System

To measure and score corporate influence on climate change policy, we have developed a comprehensive process of examining publicly available information through reliable data sources (e.g. legislative consultations, respected press, CDP responses). This information is then used to test a set of queries across data sources for each organization (e.g. position on a carbon tax, energy efficiency standards, etc.).

We score each data cell (data source/query intersection) on a 5-point scale, consistently providing evidence to support our scores. There are 96 such scoring cells in each matrix, and the organization’s (either a corporation or an influencer) organizational score is computed using our proprietary algorithm, which accounts for different cell weightings and irrelevant data sources/queries. The organizational score represents the degree to which the organization is directly influencing climate policy and legislation (through its political messaging and engagement with policymakers). It is expressed as a percentage, with 100% representing very supportive influence over climate policy. Organizational scores are computed for both corporations and influencers (e.g. trade bodies, chambers of commerce, advocacy groups, etc.) in exactly the same manner.

In addition, a corporation will have a relationship score, computed by aggregating the organizational scores of the influencers with which it has relationships, and weighted by both the strength of these relationships and the relative importance of their influencers in the climate change policy arena. This score is also represented as a percentage, on the same scale as the organizational scores. For each corporation, the organizational score and the relationship score are combined to compute an overall rating, placing the corporation in a performance band. There are 20 performance bands from A+ (representing a total score from 95-100%) through to E- (a score of 25-30%), with scores below 25% falling in the red "F" band. A score of 60% or more indicates that the corporation is actively supporting policies and regulations towards a low-carbon future. Conversely, scores lower than 40% indicate obstructing behavior. As different sectors face differing regulatory issues, it is most useful to compare the performance bands of corporations within the same sector. Full details of our method can be found here.

A Metric of Corporate Influence

A corporation can influence the policy process in a number of ways. Our method addresses and measures most of these methods for exerting influence, including indirectly through external agents (e.g. trade associations and advocacy groups). Our scoring is therefore a measure of the extent to which a corporation is supporting or obstructing the climate policy process. It is important to note that the InfluenceMap performance band is a measure of the influence the organization may have on climate policy based on our method. It does not say anything directly about the actual performance of the organization on climate related issues, such as GHG emissions or use of various energy sources, issues of which are extensively analysed and rated by others, e.g. CDP.

A Metric of Regulatory Readiness

Our scores may also be regarded as a measure of a corporation's readiness for a low-carbon regulatory regime, based on the assumption that its support for the low-carbon transition originates from a forward-thinking competitive strategy. For example, a corporation which is actively supporting low-carbon regulations may also be strategically shifting its own activities in this direction. This may provide the corporation with competitive advantage, should legislation shift swiftly to disfavor GHG emissions. Conversely, it is unlikely that a corporation engaging in obstruction of climate change policies will be strategically shifting its activities to support a low-carbon transition.

This analysis may be pronounced in energy sectors, which are expected to be heavily affected by a fast shift to a low-carbon regulatory regime, but also in sectors which are indirectly reliant on fossil-fuels (the automotive, electric utilities and chemicals sectors). Moreover, some research has suggested that political donations are followed by decreased shareholder returns. We will be actively analyzing our results from this regulatory readiness viewpoint with selected partners in the future.

The Engagement Intensity

The engagement intensity is a metric of the extent to which the company is engaging on climate change policy matters, whether positively or negatively. It is a number from 0 (no engagement at all) to 100 (full engagement on all queries/data points. Clearly energy and energy intensive users more affected by climate regulations will have a higher engagement intensity than, for example retailers. So an organization’s score should be looked at in conjunction with this metric to understand the amount of evidence we are using in each case to base our score on.

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Glossary

  • Corporate Influence: We recognise that corporations are manifestly involved in the progress of policy and legislation formation (in all areas, not only climate change) affecting their business and they regard the need to do this as part of their operating model. Research has indicated that this influence likely extends beyond the activities normally associated with the word "lobbying" (e.g. donations to clearly motivated political actors) and includes the domination of the public discourse on climate change science and policy via their hugely powerful and funded messaging tools (e.g. advertising, PR, social media, access to influential meetings) as well as the use of influencers like trade associations and advocacy groups. Therefore a full understanding of climate influence likely needs to analyse funding, messaging, direct involvement in the consultation process, both by the corporations themselves and their agents. Further details of corporate influence can be found here.

  • Climate Policy and Legislation: A process of global, regional and national consensus building, policy formulation and legislation/fiscal measures on climate change has been underway for the last two decades and is on-going. In our assessment of corporate influence, we consider the process from consensus forming on climate science to legislative interventions at various levels and take this process to be climate change policy. We consider regulations/laws and guidelines as well as fiscal interventions such as carbon taxes and continuation of subsidisation of coal, for example. Importantly, we consider amendments to mainstream regulations and fiscal policy motivated by or affecting climate change. See here for more information regarding major climate policies and regulations in the European Union, the United States and Japan.

  • Corporations and their Influencers: In our scoring we distinguish between corporations and the agents they employ to exert influence on their behalf (e.g. trade associations, business forums, think-tanks, etc.). But our scoring methodology is applied consistently to both the corporations and the Influencers. Our system computes an organizational score for each company, based on their direct influence, and a relationship score, linked to their affiliations with indirect influencers and the activities of said influencers. Both scores contribute to their overall rating and the performance band in which they fall.

  • Potential Influence vs. Actual Impact: We stress here that our methodology does not claim to measure actual corporate influence over the policy process. Such an assertion would need to involve vastly more cause-and-effect substantiation, which we do not attempt. Rather we assess and score corporations on a variety of activities that respected authorities (e.g. the UN's Caring for Climate ) have asserted are highly likely to influence policy. On this site when we use the term "corporate influence" it should be regarded in this context.

  • Potential Influence vs. Performance: We note importantly that the InfluenceMap performance band is a measure of the influence the organization has on climate policy. It does not say anything directly about the actual performance of the organization on climate related issues, such as CO2 emissions or use of various energy sources (e.g. coal, wind, nuclear, shale, etc.).

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Data Sources

We apply set criteria for our selection of data sources. Firstly, we aim to ensure as much comparable data as possible across organizations to allow for fair scoring. Secondly, we draw evidence from credible sources (direct company disclosures or respected third party sources). We do not assess data without a credible source (a time-dated web page or a respected media source). Below is a set of criteria that applies to our interrogation of data sources, aimed at providing an objective and consistent method for all organizations.

  1. If a particular query does not apply to a data source (e.g. transparency queries do not apply to external data sources), or if data is unavailable for a particular corporation (e.g. lack of disclosure to CDP or in legislative consultations), we mark the weighting cell with NA (not applicable), and the weighting is set to zero. The data cell’s original weighting is redistributed evenly through other data points for this query.

  2. If no evidence is available (after conducting research) for a particular cell, it is marked NS (not scored), with the same impact on the matrix as NA . Therefore, each organization has its own specific weighting matrix that depends on data availability and relevance for that specific organization. This means that a company will not be impacted by its lack of involvement in policies that do not affect it activities, but it will be more accurately assessed for relevant policies.

  3. Our interrogation of each data source is conducted in a consistent manner for every organization (e.g. using the same terms to search through a set list of data sources, spending a similar amount of time on each organization).

  4. We look at data originating from the last two years, prioritizing more recent evidence. However, older evidence is referred to if it appears that a position has been taken (e.g. a stance on climate science) which remains consistent and unchanged over longer time period. It may also be used to build a chronological sequence of pieces of evidence to demonstrate InfluenceMap’s conclusion on a specific topic.

  5. The English version of an organization’s corporate website is searched using consistent search terms (e.g. "carbon tax"). In the case where an English language data source is unavailable, we make best use of our team’s linguistic ability in translating the data source. Otherwise, we mark the relevant data cells as NS (not scored) if our team is unable to interpret the data source.

The following table summarizes our data sources.

Our Data Sources

Type

Data source

Code

Comments

Organization's promotional information

 

Organization's websites

D1

Main organization website, affiliated websites and major publications (sustainability report, annual report, etc.)

Other direct messaging

D2

Media and other websites controlled (or funded) by the organization, social media (Twitter), direct advertising campaigns, press releases, and initiatives to which the organization has signed up

Voluntary disclosures via third parties

CDP responses

D3

Assessment and scoring of CDP political influence questions (Q2.3)

Disclosure to governments

  

Legislative consultations

D4

Legislative Consultation documents from government sources such as US government, the European Commission and governments of other key regions e.g. Australia and Japan

Financial disclosures

D7

We search 10-K and 20-F SEC filings where available (and non-US equivalents), earnings comments via Edgar Online and Fair Disclosure Wire

EU Transparency Register

D8

Information provided to the voluntary EU Transparency Register

External data (press, creditable websites, etc.)

 

External reporting on the organization

D5

Web searches (the organization's name AND relevant query search terms) in reputable news sources, supported by targeted searches in proprietary databases (LexisNexis)

External reports on CEO messaging

D6

Web searches (CEO's name AND relevant query search terms) in reputable news sources, supported by targeted searches in proprietary databases (LexisNexis)

 

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Assessment

We break down climate change policy into types of legislation, as outlined here. We reason that the formulation of climate change policy commences with scientific research that then enters the policy arena, leading to the implementation of legislation, standards and fiscal measures. Specifically, we draw upon the IPCC's findings on climate science which developed into a general statement of the urgency for human intervention on climate change, now promoted by the UNFCCC in its push for a global treaty. In parallel, many legislative and fiscal interventions are in place or are being proposed at various levels of government. We reason corporations have been interacting with policymakers at all levels and stages of the policy formulation process, using various methods to exert influence (messaging, funding, consultations etc.). Our queries cover all of these stages and are divided into two main categories:

  • *Transparency (T) queries examine the availability of an organization's position on an issue and the accessibility of this information. In the case of corporations, we are searching for their own disclosure on the use of agents or external influencers in the policy arena (e.g. trade associations, advocacy groups, PR agencies). For trade associations, in addition to the organization’s position on a given issue, we are also concerned with disclosure on the roles corporations hold within the organization (e.g. general membership, board membership).

  • Performance (P) queries assess the content of an organization's position on a given issue, relative to the positions expected from the governing body. We also examine any evidence (e.g. from external data sources) that demonstrates an organization’s engagement on a particular policy issue. We cannot rely on an organization’s public messaging to fully assess their actual performance, so for these queries we rely strongly on disclosure to governments and external data sources.

We have a series of twelve queries which we apply across all data sources, constructing a matrix of queries (Q1...Q12) against data sources (D1...D8) for each organization (corporation or influencer). We summarise our set of queries in the table below and note we rely for guidance on accepted organizations such as the UNFCCC and the IPCC to formulate our logic.

Our Queries

Issue categories Queries Code Comments
Climate Science (IPCC position on climate change science) T: Does the organization provide a transparent position supporting the science of climate change? Q1  
P: Is the organization supporting the IPCC demanded response to tackling climate change? Q2 This can be either positive or negative support
Global Treaty (UNFCCC COP process) T: Does the organization provide a transparent position on a global treaty on climate change through the UNFCCC COP process? Q3  
  P: Is the organization supporting a global treaty on climate change through the UNFCCC COP process? Q4  

Climate change policy and legislation

(positions on strands of climate regulations)      

T: Is the organization transparent about their positions on, and engagement with, climate change policy and relevant policymakers? Q5 Q5 can refer to a broader range of climate change policy then covered in Q6-Q11
P: Is the organization supporting policy and legislative measures to address climate change through a carbon tax? Q6  
P: Is the organization supporting policy and legislative measures to address climate change through emissions trading? Q7  
P: Is the organization supporting policy and legislative measures to address climate change through energy efficiency standards and targets? Q8  
P: Is the organization supporting policy and legislative measures to address climate change through renewable energy targets, subsidies and legislation? Q9  
P: Is the organization supporting policy and legislative measures to address climate change through energy policy and measures to transition to a low-carbon energy mix? Q9 We refer to IPCC positions on renewables, coal, oil, gas and nuclear power
P: Is the organization supporting policy and legislative measures to address climate change through GHG emissions standards and targets? Q11  
Disclosure on relationships T: Are corporations being transparent about their business associations and other sources of indirect influence which may impact the climate debate and policy process/ Are trade associations being transparent about the positions corporations hold within their organization? Q12 Corporate transparency as recommended by the UN Caring for Climate (2013)

 

We can now construct a matrix of queries (Q1...Q12) against data sources (D1....D8) for each organization (corporation or influencer). A generic matrix for a particular entity might look as follows (we have skipped out for brevity the intermediate Qs and Ds, indicated by .....). The % (a%, b%, c%...z%) values are the relative weightings we assign to each query/data cell relative to the overall organizational score. While we have a generic weighting matrix applying to all sectors, certain sectors (e.g. automotive, chemicals, energy, utilities) will have a sector specific weighting matrix emphasizing its legislative priorities (e.g. the automotive sector will be weighted more for its influence over GHG emissions standards than energy policy). The weightings are devised from InfluenceMap’s independent research and in consultation with our advisors and external experts.

Generic Weighting Matrix

Query/Data Source D1 D2 D3 ... D8  
Q1 a% b% c% ... v% subtotal %
Q2 d% e% f% ... x% subtotal %
Q3 g% NA NA ... NA subtotal %
... ... ... ... ... ... ...
Q12 j% k% l% ... z% subtotal %
  subtotal % subtotal % subtotal % ... subtotal %

Total

100%

 

If a particular query does not apply to a data source (e.g. transparency queries do not apply to external data sources), or if data is unavailable for a particular corporation (e.g. lack of disclosure to CDP or in legislative consultations), we mark the weighting cell with NA (not applicable) and the weighting is set to zero. The data cell’s original weighting is redistributed evenly through other data points for this query. The matrix will automatically recalibrate to distribute the default weighting equally through the query’s row, so the row’s total weighting is always 100%. In the case that all data sources are NA for a particular query, the weightings are reallocated evenly between the remaining cells in the matrix. Likewise, if no evidence is available (after conducting research) for a particular cell, it is marked NS (not scored), with the same impact on the weightings as NA . So each organization has its own specific matrix depending on data availability and relevance.

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Scoring

The matrix of data source/query cells presents an opportunity for a maximum of 96 (12 x 8) scoring opportunities per organization, but in practice this is less due to the NA (not applicable) cells relevant to a particular organization's matrix. We record five possible outcomes with scores ranging from (-2) to (+2) (-2; -1; 0; 1; 2) depending on an organization’s transparency around particular regulations, their expression of support (or non-support), and the corresponding strength of their engagement with this regulation. Our team also has the option to record a red cell or a blue cell. A red cell can be recorded for an incidence of extreme negative influence on climate policy, not fully expressed through our 5-point scale. A blue cell may be awarded for a notable act of positive influence, not clearly expressed through the scale. The red cells and blue cells present an opportunity to highlight qualitative information that does not fit easily into our quantitative scoring system. The following provides some examples to illustrate this process.

Examples of our Scores

Scoring Details

Examples

Qualitative scores

Points deducted
(-2; -1)
Q2: Evidence of opposition to urgent action (as recommended by the IPCC) to address climate change would score (-2)
Q5: Have disclosed very few details about how they may, or may not, be influencing climate change policy (-1)
Q7: Support emissions trading with major exceptions, advocating for conditions that exceed the sum of the support (-1)
Q9: Evidence of legal action against feed-in tariffs for renewable energy would score (-2)
No points deducted or given (0) Q6: Evidence suggests clear engagement with carbon tax policy, although it is unclear whether their intervention is supportive or obstructive (0)
Q9: Evidence of support for renewable energy legislation with exceptions (i.e. supporting a policy under condition that policy reviews occur annually) would score (0)
Q12: Have disclosed a full list of trade associations memberships, although have not provided any details of the policy positions of the trade associations or how they may be engaging with them (0)
Points given
(+1; +2)
Q2: Evidence of support for GHG emissions reductions with time-scales in line with IPCC recommendations would score (+2)
Q9: Statement of support for renewable energy legislation would score (+1)
Q11: Have taken action (such as sending a letter to a policymaker) in support of GHG emissions standards (+2)
Qualitative scores (optional) Red cell Evidence of direct funding to an obstructive climate change related initiative; evidence of activity directly obstructing the legislative process
Blue cell Evidence of organization taking exceptional initiative to support a specific climate policy or legislative process

 

InfluenceMap provides scoring guidelines for each query/data cell to guide our team as to the precise meaning of the scores (-2; -1; 0; 1; 2) in the specific context of each data cell. We have also provided templates for inputting evidence to ensure our data is internally consistent.

In the case below the total score adds up to 71 (the sum of all the points). Let us assume that the matrix above has three cells with NA . The largest possible number of points is ((12 x 8) -3) x 2 = 186, thus the nominal organizational score is 38%, assuming all cells are equally weighted. In practice they are not and the organisational score will be a weighted average of the scores in the cells. The organization also has one red cell and one blue cell, the details of which will be clearly displayed in its profile page.

Example of an Organization's Scoring Matrix

Query/

DataSource

D1

D2

D3

...

D8

Subtotal

Q1

1

1

2

...

NA

3

Q2

0

-2

NA

...

NA

5

Q3

-2

NA

1

...

2

-1

...

...

...

...

...

...

...

Q12

1

NA

1

...

2

4

Subtotal

3

4

-2

...

4

71

 

For each incidence of scoring in the cells of the matrix, we provide justification in the form of a brief text explanation, supported by dated screenshots of the URLs from which we draw evidence (or scans for non-web data). This is clearly visible by clicking on a particular cell in the matrix. As well as its organizational score, the final rating for a corporation will be impacted by the relationships (R1, R2, R3, etc.) it holds with external agents exerting influence over climate policy, such as trade associations, chambers of commerce and think tanks. Therefore, in addition to its organizational score, a corporation will have a relationship score which we define as a reflection back onto the corporation on the influence exerted by its influencers.

The influencers will themselves have organizational scores, computed in exactly the same manner as for the corporations. These can be labelled O1, O2, O3, etc., also expressed as a percentage. We must also account for the nature of a corporation’s relationship with an influencer, which we document using text and URL references, assigning a strength (S1, S2, S3, etc.) to the relationship (1 = a weak relationship, 10 = a strong relationship). For example a trade association may have 2000 member corporations with 10 of them on its executive committee. The 10 executive committee members would have strength of 8 compared to 3 for the regular members, for example. Our team is provided with guidelines on how to rate the strength of a range of relationships. We define the relative weighting (RW1, RW2, RW2, etc.) as a metric of the level of influence exerted by the influencer with which the corporation holds a relationship, compared to those of other influencers in the global policy arena. We rate these levels of influence against each other on a scale of 1 to 10 (with 10 being very important as an influencer of climate policy). So now we can compute the relationship score with these various metrics in mind.

Example of Relationship Scoring for a Corporation

Relationships (R1, R2....Ri)

Organizational Score of Influencer (O1, O2,...Oi)

Strength of Relationships (S1, S2....Si)

Relative Weighting of Influencer (RW1...RWi)

Sub Totals

R1

50%

4

9

5%

R2

10%

3

10

1%

R3

40%

9

8

36%

R4

90%

5

9

45%

R5

40%

4

5

16%

R6

50%

1

10

5%

Sub Totals

205%

 

 

108%

Normalized Relationship Score

48%

So in this case, based on the six relationships in our database, the corporation in question has a relationship score of 48% (with 0% being the lowest possible score, and 100% the highest). We use the following formula for this computation (where Σi indicates a summation over i).

Relationship Score

We use both the relationship score and the organizational score to compute an overall rating for a corporation as an overall measure of its influence on climate policy. To compute this overall rating we apply the following simple method.

Overall Rating = (Organizational Score x (1-W)) + (Relationship Score x W)

Here the factor W is the relationship weighting and is a value between 0 and 1. It determines the relative impact the relationship score has on the corporation's overall rating. We compute this using an algorithm that incorporates both the values of Si and RWi for the corporation and also the number of relationships Ri. For example, we do not wish a small sample of relationships to unduly impact the overall rating for a corporation.

We stress here that influencing organizations will only have organizational scores under our methodology. Corporations will have organizational scores and relationship scores as noted above, which when combined provide the overall rating that places the corporation in one of 20 performance bands (95-100% = A+; 90-95% = A, 85-90% = A- .......25-30% = E-, with scores below 25% collectively as "F"). Corporations within sectors can be compared against each other by viewing which performance band they fall into, with full breakdown of evidence data easily visible. Influencers can similarly be compared to each other by contrasting their organizational scores.

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