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.
