Network structure and influence of the climate Network ... › original › nature-assets › n... · nomics center gmu lec, global climate coalition, global warming policy foun-dation,

Network Structure and Influence of theClimate Change Counter-Movement

Online Methods

Contents

Social Network Data Collection and Methods . . . . . . . . . . . . 2

Text Data Collection and Methods . . . . . . . . . . . . . . . . . . . 7

Supplementary Tables and Figures . . . . . . . . . . . . . . . . . . . 11

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Network structure and influence of the climate change counter-movement

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2875

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

© 2015 Macmillan Publishers Limited. All rights reserved

http://dx.doi.org/10.1038/nclimate2875

Social Network Data Collection and Methods

The population of 164 climate change counter-movement organizations were

aggregated from prior peer-reviewed research and archival analysis of individual

organization websites and material. First, and most importantly, the majority

of organizations were identified from a recently published comprehensive census

of climate counter-movement organizations and funding (1 ). I derived 118

organizations from this particular peer-reviewed census. I then added other

organizations that have appeared in other published research on climate change

(2–5 ), as well as lists from reputable non-profits (6–7 ). Lastly, I then conducted

an exhaustive content and link analysis of organization websites using the

Internet Archive, which led me to more organizations. For example, the

Cooler Heads Coalition, which is an affiliate of the Competitive Enterprise

Institute, lists many organizations on their website. I only included these linked

organizations (and the organizations they link, etc.) if their organizational

material indirectly or directly (1) contained overt arguments climate change

about uncertainty, (2) overtly opposed mitigation of carbon emissions, and (3)

contained information contrary to scientific consensus on climate change issue.

From this process of in-depth primary and secondary source triangulation, I

added 46 organizations to the original list, for a total of 164.

It is important to note that some of these organizations were much more

involved than others in the climate change counter-movement. Some are still

active participants, while others have become defunct or no longer overtly

support contrarian efforts. But for the organizations who no longer overtly

participate, the individuals who participated in them often move on to other

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organizations, which are traced through the social network connections in

Figures 1 and 2 of the manuscript. So while I follow previous peer-reviewed

research to cast a broad net of inclusion in a cross-section of time (1993-2013),

one of the outcomes of this study it to actually narrow down who is more

central, and the role of corporate funding in this process.

I also collected variables about each organization, and present frequency

statistics in Table S1. In most cases the mission statement of each organization

was available from IRS 990 forms or from the organization itself. Using this

information, I coded organizations into three straightforward categories: (1)

advocacy, (2) think tank, (3) foundation, trade association, and other. I also

use mission statements and archival material to classify each organization

according to whether or not they are solely focused on climate change, or

if they focus on multiple issues. For example, the Center for the Study of

Carbon Dioxide and Global Change, a 501(c)(3), is solely focused on climate

change, whereas the Cato Institute has been very involved in climate change,

but has many other issues of concern. Thus, I coded dichotomously based

on the scope of their mission (1=Only Climate Change, 0=Multiple Political

Issues). I record the mean assets of each organization using IRS data from

GuideStar, the National Center for Charitable Statistics, and the Foundation

Center. Twenty six of the organizations were not registered with the IRS, and

thus I contacted the organization directly, or was unable to record asset data.

The sparsity of the data meant that the mean of the assets for all available

years was the most reliable indicator.

Academic researchers have had a very difficult time tracking flows of eco-

3


nomic resources between members of this network, especially in recent years

because of foundations like DonorTrust, which enables contributors to give

to specific causes while at the same time shielding the identity of the donor

(1,8 ). Reliably assessing the influence of corporate benefactors has thus been

extremely difficult. To overcome this difficulty, I rely on indicators of giving

from two corporate and philanthropic actors in American politics: ExxonMobil

(EM) and Koch family foundations (KFFs). With regard to their influence, my

argument is that donations from these corporate benefactors is an indicator

of entry into a powerful network of influence. They are reliable indicators

of a much larger effort of corporate lobbying in the climate change counter-

movement. Less important is how much or how often organizations receive

money from these actors, but whether they are part of this sub-network of po-

litical influence at all. Prior research has suggested this type of influence using

historical analysis (3,5 ), but has never been tested quantitatively, especially

in relationship to the full network and population of texts. Thus, I create a

dichotomous variable recording whether or not an organization has received

money from either EM or the KFFs from 1993-2013. The data for this variable

come from IRS records aggregated from GuideStar, the National Center for

Charitable Statistics, the Foundation Center, peer-reviewed funding data (1 ),

and reputable non-profit reports (6–7 ). 51 percent of organizations received

funding from these corporate benefactors (Table S1).

In addition to these attributes, it is equally important to reliably measure

the relational connections between the organizations themselves. I created

an affiliation network (9–10 ) whereby I identified 4,556 individuals with ties

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to these organizations. This was an exhaustive process that aggregated data

from many different sources. I first began by using GuideStar and IRS data to

record every board member in the organization for the last five years. This was

an important first step, but only provided one formal dimension of ties between

organizations. Using the website and archival material described above, I

recorded all names of individuals with past involvement in an organization. For

example, the Heartland Institute publishes a list of hundreds of speakers that

have served at their annual conference on climate change—many of whom are

linked to other organizations in myriad ways. In addition to primary source

material, I also collected relational data from reputable non-profit organizations

(7 ). These primary and secondary sources of data provided a rich source of

overlapping social, political, economic, and scientific ties. Such relationships

include serving as a scientific consultant for a think tank, speaking at a

conference, political candidates who have publicly supported climate contrarian

organizations, individuals who have publicly given and received donations to

these groups, individuals who publicly represent an organization in the media,

scientists who have signed climate contrarian petitions, and so on. All ties

share in common the fact that an individual has, at one time, supported an

organization in one way or another. I recognize that there are varying degrees of

support, but given the intricate nature of this movement, I reduce complexity

and focus on the dichotomous measure rather than creating a network of

weighted ties. For example, senator James Inhofe—–a chairman on the federal

Committee on Environment and Public Works—–was an honoree in 2004 at

the Annapolis Center for Science-Based Public Policy and a co-plaintiff in

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a lawsuit with the Competitive Enterprise Institute. Or, the scientist Fred

Singer—who was part of a report criticizing EPA research on second hand

smoking—and is now an active climate contrarian, with his own think tank

(Science & Environmental Policy Project), as well as serving as a researcher,

board member, and consultant for various conservative think tanks who have

received money from EM. But other individuals may not be as overtly engaged

in climate change issue, and perhaps simply sit on the board of an organization,

or spoke once at a conference. But capturing these subtle relationships and

varying degrees of participation goes well beyond interlocking board members,

toward a more socially oriented, qualitatively rich, and empirically accurate

depiction of the different ways in which this network operates. In order to

ensure that there were no duplicate names, I cleaned all titles (e.g. ‘Mr’,

‘Dr’), all middle initials, alternate spellings (e.g. Chris vs. Christopher), and

adjudicated any misspellings. This was all completed by hand on all 4,556

names.

I calculated betweenness centrality in the one-mode network using the

igraph package in R. This is the most appropriate measure for my purposes

because I am interested in which organizations have the most power within

the network with regard to flows of information and resources. As noted

in the manuscript, I found statistically significant differences (P < 0.01) in

betweenness centrality for organizations with ties to corporate benefactors

(mean = 0.020) versus those without ties (mean = 0.009). Fig. S1 presents a

boxplot visualizing these differences.

6


Text Data Collection and Methods

Because most of these organizations were very concerned with disseminating

uncertainty about climate change, their material was extensive and accessible.

For all organizations that produced texts (N=120) I collected documents

containing “climate change” or “global warming” between 1993 and 2013.

These include the entirety of press releases, published papers, website articles,

scholarly research, policy studies, and conference proceedings transcripts. This

massive collection effort was streamlined with customized Python programming

scripts for each organization, that scraped, parsed, and organized text from their

websites, including historical and online archives (i.e. The Internet Archive).

Images and PDFs of text, especially from earlier years, were scanned and

converted to plain text using optical character recognition software. The total

population of texts includes 40,785 separate documents summing over 39 million

words, with an average document length of 957 words.

News media texts were collected from the LexisNexis database. I collected

and parsed every article published between 1993-2013 containing the keywords

“climate change” or “global warming” from three major news sources. To ensure

representativeness, I chose three sources that span the American political

spectrum: the left-leaning New York Times (14,943 articles), the right-leaning

Washington Times (5,030 articles), and the centrist USA Today (2,635 articles).

I then collected the full text of every public Presidential address (verbal

and written) mentioning “climate change” or “global warming” from 1993-2013,

resulting in 1,930 documents. These data come from the The American Presi-

dency Project at the University of California, Santa Barbara, and include all

7


public messages, statements, speeches, and news conference remarks. Alongside

these data, I obtained from the United States Government Printing Office full

texts of every verbal proceeding and debate on the floor of Congress mention-

ing “climate change” or “global warming” from 1993-2013, resulting in 5,856

documents.

My application of computational text-analysis uses latent semantic similarity

methods. As I mention in the manuscript, there is a huge body of literature

on latent semantic methods that spans linguistics, computer science, natural

language processing, psychology, and political science (for an in-depth and

technical overview, see (11–14 ). This general analytical approach for leveraging

text similarity (e.g. cosine similarity, plagiarism detection, and other related

methods) follows recent interest in computational text methods in political

science (15–16 ) and sociology (17 ). In latent semantic similarity analysis,

an algorithm is applied to estimate the underlying structure and meaning of

texts by examining the relationships between sets of texts and the terms they

contain. One advantage of this approach is that it normalizes by document

length, which is important given the variation in word count between all of the

climate change texts. Gomaa and Fahmy (18 :p.14) describe this technique in

succinct detail:

“Latent semantic analysis (LSA) is the most popular technique of

Corpus-Based similarity. LSA assumes that words that are close in

meaning will occur in similar pieces of text. A matrix containing

word counts per paragraph is constructed from a large piece of text

and a mathematical technique called singular value decomposition

8


(SVD) is used to reduce the number of columns while preserving

the similarity structure among rows. Words are then compared by

taking the cosine of the angle between the two vectors formed by

any two rows.”

Using the lsa package in R, I calculated cosine similarity coefficients for

every organization by comparing every text in a given year to every text in that

same year in the news media, presidential discourse, and congressional discourse.

Cosine similarity coefficients range between 0 (no similarity) and 1 (perfect

similarity). My calculations include standard tf-idf weights. Temporal order is

organized at the yearly level, and thus I aggregate all of these coefficients into

yearly means, to assess change between 1993-2013. I then use these coefficients

to predict differences between organizations in their similarity scores with the

news media texts in particular (Manuscript Table 1). I control for the year

the texts were written and the similarity means were calculated, as well as

the full set of alternate explanatory variables such as the mission focus of an

organization, organization type, organization assets, network centrality, and the

year the organization was founded. Per the helpful suggestion of one reviewer,

I also include a control for the baseline similarity score, which measures an

organization’s initial similarity score.

Lastly, one reviewer pointed me toward the KonFound-it! tool to assess

the robustness of the causal inferences. For more on this tool, and its use, see

(19–21 ). Using the final model in Table 1, this tool suggest that 44 percent

of the estimated effect would need to be due to bias for it to invalidate the

inference linking semantic similarity with ties to corporate benefactors. As one

9


of the reviewers notes, this is more robust than two-thirds of the education

studies evaluated in Frank et al. 2015 (19 ).

10


Supplementary Tables and Figures

0.001

0.100

Organizations

Cent

rality

No EM / KFF Ties

EM / KFF Ties

Figure 1: Statistically significant differences (P < 0.01) in network centrality betweenorganizations who received funding from EM/KFFs versus organizations who did not receivefunding from EM/KFFs.

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Table 1: Frequency Statistics for Organizational Variables

Variable Mean SDReceived Corporate Funding (EF/KFFs) = 1 .512 .501Assets 16.9 million 69.5 millionBetweenness Centrality (Normalized) .013 .019Type: Advocacy .372 .439Type: Think Tank .427 .491Type: Foundation/Trade Association/Company .201 .351Narrow Mission Focus = 1 .305 .462Year Founded 1983 26.13

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164 organizations aggregated from previous peer-reviewed censusand archival research. Some organizations are no longer involvedin the climate contrarian movement, and some are no longer in ex-istence. When necessary, some organization names were formattedand abbreviated for analysis. 51 percent of these organizations re-ceived funding from EM/KFFs.60 PLUS ASSOCIATION, ACCURACY IN MEDIA, ACTION INSTITUTE FOR THE STUDY OF RE-

LIGION AND LIBERTY, ADVANCEMENT OF SOUND SCIENCE CENTER INC, ALEXIS DE TOC-

QUEVILLE INSTITUTION, AMERICAN COAL FOUNDATION, AMERICAN COALITION FOR CLEAN

COAL ELECTRICITY, AMERICAN CONSERVATIVE UNION FOUNDATION, AMERICAN COUNCIL

FOR CAPITAL FORMATION, AMERICAN COUNCIL ON SCIENCE AND HEALTH, AMERICAN EN-

ERGY ALLIANCE, AMERICAN ENERGY FREEDOM CENTER, AMERICAN ENTERPRISE INSTI-

TUTE FOR PUBLIC POLICY RESEARCH, AMERICAN FARM BUREAU FEDERATION, AMERI-

CAN FRIENDS OF INSTITUTE OF ECONOMIC AFFAIRS, AMERICAN FUEL AND PETROCHEMI-

CAL MANUFACTURERS, AMERICAN GAS ASSOCIATION, AMERICAN LEGISLATIVE EXCHANGE

COUNCIL, AMERICAN NATURAL GAS ALLIANCE INC, AMERICAN PETROLEUM INSTITUTE,

AMERICAN POLICY CENTER, AMERICAN SPECTATOR FOUNDATION, AMERICAN TRADITION

INSTITUTE, AMERICANS FOR A LIMITED GOVERNMENT INC, AMERICANS FOR BALANCED

ENERGY CHOICES, AMERICANS FOR PROSPERITY, AMERICANS FOR TAX REFORM, ANNAPO-

LIS CENTER FOR SCIENCE BASED PUBLIC POLICY INC, ASSOCIATION OF GLOBAL AUTOMO-

BILE MANUFACTURERS INC, ATLANTIC LEGAL FOUNDATION, ATLAS ECONOMIC RESEARCH

FOUNDATION ATLAS, AUSTRALIAN CLIMATE SCIENCE COALITION, CAPITAL RESEARCH CEN-

TER AND GREENWATCH, CASCADE POLICY INSTITUTE, CATO INSTITUTE, CENTER FOR

AMERICAN AND INTERNATIONAL LAW, CENTER FOR DEFENSE OF FREE ENTERPRISE, CEN-

TER FOR SECURITY POLICY INC, CENTER FOR STRATEGIC AND INTERNATIONAL STUD-

IES, CENTER FOR STUDY OF CARBON DIOXIDE AND GLOBAL CHANGE, CENTRE FOR NEW

EUROPE, CHAMBER OF COMMERCE OF UNITED STATES OF AMERICA, CHARLES KOCH IN-

STITUTE, CITIZENS FOR A SOUND ECONOMY NOW FREEDOMWORKS, CITIZENS FOR AF-

FORDABLE ENERGY INC, CO2 IS GREEN INC, COALITION FOR AMERICAN JOBS, COALITION

FOR VEHICLE CHOICE INC, COLLEGIANS FOR CONSTRUCTIVE TOMORROW, COMMITTEE

FOR CONSTRUCTIVE TOMORROW, COMMUNICATIONS INSTITUTE, COMPETITIVE ENTER-

PRISE INSTITUTE, CONSUMER ALERT INC, CONSUMER ENERGY ALLIANCE INC, CONSUMERS

ALLIANCE FOR GLOBAL PROSPERITY, COOLER HEADS COALITION, CORNWALL ALLIANCE

FOR THE STEWARDSHIP OF CREATION, DCI GROUP, DEFENDERS OF PROPERTY RIGHTS,

DONORS TRUST DONORS CAPITAL FUND, EDISON ELECTRIC INSTITUTE, ENERGY MAKES

AMERICA GREAT, ENVIRONMENTAL CONSERVATION ORGANIZATION, ENVIRONMENTAL LIT-

ERACY COUNCIL, EXXONMOBIL, FEDERALIST SOCIETY FOR LAW AND PUBLIC POLICY STUD-

IES, FEDERATION FOR AMERICAN COAL ENERGY AND SECURITY, FRASER INSTITUTE, FREE

ENTERPRISE ACTION INSTITUTE FREE ENTERPRISE EDUCATION INSTITUTE, FREEDOM AC-

TION, FREEDOMWORKS FOUNDATION, FREEDOMWORKS INC, FRONTIERS OF FREEDOM IN-

13


STITUTE INC, GEORGE MARSHALL INSTITUTE, GEORGE MASON UNIVERSITY LAW AND ECO-

NOMICS CENTER GMU LEC, GLOBAL CLIMATE COALITION, GLOBAL WARMING POLICY FOUN-

DATION, GREENING EARTH SOCIETY, HEARTLAND INSTITUTE, HERITAGE FOUNDATION,

HOOVER INSTITUTION ON WAR REVOLUTION AND PEACE STANFORD UNIVERSITY, HUD-

SON INSTITUTE, ILLINOIS POLICY INSTITUTE, INDEPENDENCE INSTITUTE, INDEPENDENT

COMMISSION ON ENVIRONMENTAL EDUCATION, INDEPENDENT INSTITUTE, INDEPENDENT

PETROLEUM ASSOCIATION OF AMERICA, INDEPENDENT WOMENS FORUM, INDUSTRIAL EN-

ERGY CONSUMERS OF AMERICA, INITIATIVE FOR PUBLIC POLICY ANALYSIS, INSTITUTE

FOR BIOSPHERIC RESEARCH, INSTITUTE FOR ENERGY RESEARCH, INSTITUTE FOR LIBERTY,

INSTITUTE FOR REGULATORY SCIENCE, INSTITUTE FOR STUDY OF EARTH AND MAN, INSTI-

TUTE OF HUMANE STUDIES GEORGE MASON UNIVERSITY, INSTITUTE OF PUBLIC AFFAIRS,

INTERMOUNTAIN RURAL ELECTRIC ASSOCIATION, INTERNATIONAL CLIMATE AND ENVI-

RONMENTAL CHANGE ASSESSMENT PROJECT, INTERNATIONAL CLIMATE SCIENCE COALI-

TION, INTERNATIONAL COUNCIL FOR CAPITAL FORMATION, INTERNATIONAL POLICY NET-

WORK, INTERNATIONAL REPUBLICAN INSTITUTE IRI, JAMES MADISON INSTITUTE FOR PUB-

LIC POLICY STUDIES INC, JOHN LOCKE FOUNDATION INC, JUNKSCIENCE DOT COM, KNOWL-

EDGE AND PROGRESS FUND, KOCH FOUNDATIONS COMBINED, KOCH INDUSTRIES, LAND-

MARK LEGAL FOUNDATION, LEXINGTON INSTITUTE, LINDENWOOD UNIVERSITY, LOCKE IN-

STITUTE, MACKINAC CENTER, MANHATTAN INSTITUTE FOR POLICY RESEARCH INC, MEDIA

RESEARCH CENTER INC, MERCATUS CENTER INC GWU, MOUNTAIN STATES LEGAL FOUNDA-

TION, NATIONAL ASSOCIATION OF MANUFACTURERS OF USA, NATIONAL BLACK CHAMBER

OF COMMERCE, NATIONAL CENTER FOR POLICY ANALYSIS, NATIONAL CENTER FOR PUBLIC

POLICY RESEARCH INC, NATIONAL COUNCIL FOR ENVIRONMENTAL BALANCE, NATIONAL

ENVIRONMENTAL POLICY INSTITUTE, NATIONAL LEGAL CENTER FOR PUBLIC INTEREST,

NATIONAL MINING ASSOCIATION, NATIONAL PETROLEUM COUNCIL, NATIONAL POLICY FO-

RUM, NATIONAL RURAL ELECTRIC COOPERATIVE ASSOCIATION, NATIONAL TAXPAYERS

UNION, NATIONAL TAXPAYERS UNION FOUNDATION, NATIONAL WILDERNESS INSTITUTE,

NEW ZEALAND CLIMATE SCIENCE COALITION, OKLAHOMA COUNCIL OF PUBLIC AFFAIRS

INC, OREGON INSTITUTE OF SCIENCE AND MEDICINE, PACIFIC LEGAL FOUNDATION, PA-

CIFIC RESEARCH INSTITUTE FOR PUBLIC POLICY, PEABODY ENERGY, PLANTS NEED CO2

ORG, PROPERTY AND ENVIRONMENT RESEARCH CENTER, REASON FOUNDATION, RESPON-

SIBLE RESOURCES, SCIENCE AND ENVIRONMENTAL POLICY PROJECT, SCIENCE AND PUB-

LIC POLICY INSTITUTE, SHOOK HARDY AND BACON LLP, SMALL BUSINESS SURVIVAL COM-

MITTEE, SMITHSONIAN ASTROPHYSICAL OBSERVATORY WILLIE SOON AND SALLIE BALIU-

NAS, SOUTHEASTERN LEGAL FOUNDATION INC, SOVEREIGNTY INTERNATIONAL INC, STATE

POLICY NETWORK, STATISTICAL ASSESSMENT SERVICE STATS, TECH CENTRAL SCIENCE

FOUNDATION, TEXAS PUBLIC POLICY FOUNDATION, THOMAS JEFFERSON INSTITUTE FOR

PUBLIC POLICY, TS AUGUST, UNITED FOR JOBS, US RUSSIA BUSINESS COUNCIL, VIRGINIA IN-

STITUTE FOR PUBLIC POLICY, WASHINGTON LEGAL FOUNDATION, WASHINGTON POLICY

CENTER, WEIDENBAUM CENTER ON THE ECONOMY GOVERNMENT AND PUBLIC POLICY

14


CENTER FOR THE STUDY OF AMERICAN BUSINESS, WESTERN FUELS ASSOCIATION, WORLD

AFFAIRS COUNCILS OF AMERICA, WORLD CLIMATE REPORT

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Documents

Network structure and influence of the climate Network ... › original › nature-assets › n... · nomics center gmu lec, global climate coalition, global warming policy foun-dation,