Why does public climate change agreement matter?

Meghdad Rahimian

Department of Economics

Western University

April 29, 2019

Author Note

Ph.D. Student, Department of Economics, Western University, Social Science Centre Room

4064, email:mrahim4@uwo.ca.

I want to thank my supervisor Victor Aguiar for his great support in developing this paper.

We have also benefitted from valuable discussions with Salvador Navarro and Nouri Najjar.

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Abstract

There is substantial public disagreement on fundamental knowledge of climate change among

Americans. Regardless of the reasons, estimating the impact of the public climate change

agreement (PCCA) on the choice of greenhouse-gas (GHG) emission of industrial facilities in

the U.S. is difficult because of endogeneity and omitted variable bias. We use variations in

the short-term temperature anomalies as an instrumental variable for PCCA across the U.S.

counties in 2014 and 2016. PCCA is strongly negatively related to GHG emission generated

by extensive emitting facilities in the US: one percentage point increase in PCCA rises the

likelihood of GHG emission reduction by 0.6 percentage point in the following year. We

exclude other channels through which temperature anomaly may affect GHG emission. The

annual negative welfare consequences of the extra emitted GHGs exceed ten billion dollars

for US citizens, and the adverse impact on the global environment is even more substantial,

exceeding seventy billion dollars. This damage could have been avoided by rectifying the

information gap regarding climate change among Americans.

Keywords: Climate Change, Global warming, Public climate change awareness.

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1. Introduction

Almost all (97%) of the scientific publications that express a position on climate change

endorse the news that climate change is happening and human activities are the leading cause

for it (Cook et al. 2013). Public climate change agreement, hereafter PCCA, refers to the

extent of public agreement with the fundamental scientific facts regarding climate change.

The Intergovernmental Panel has asserted these facts on Climate Change (IPCC)1. These are

(i) climate change is happening, (ii) climate change has mostly caused by human activities,

and (iii) there is a scientific consensus regarding anthropogenic climate change or the so-

called "global warming." Although "IPCC view is widely shared across countries and barely

challenged" (Bruggemann and Engesser, 2016), such statements “have translated poorly into

the public arena" in the US (Boykoff and Boykoff, 2007). Several public opinion surveys

indicate a large disagreement with fundamental knowledge of climate change among

Americans. For instance, according to the Yale Climate Change Communication Program2, in

2018, only 70% of Americans agree that climate change is happening, 57% agree that it

caused mostly by human activities, and just less than a half (49%) agree with the statement

"most scientists think that global warming is happening." In contrast, the Measures of PCCA

are higher among many developed and developing countries3.

The U.S. citizens are not excluded from the effects of climate change. The U.S. National

Oceanic and Atmospheric Administration (NOAA) announced that 2017 was the costliest

year for the natural disasters in the U.S. Storms, fires, floods, and heat caused enormous

human suffering and hundreds of millions of dollars in destruction. However, the US is

among the largest emitting countries. In 2016, the U.S. economy was responsible for

generating more than 15% of the total annual carbon dioxide (CO2) emissions in the world4.

The average CO2 emission per capita in the U.S. is almost 2.5 as much as the average emitted

in the other OECD-European countries5 (20.71 vs. 8.30 tCo2e). Nevertheless, efforts to

regulate CO2 emissions are continuing at a slow pace, facing enormous resistance in many

cases. For example, the Clean Power Plant Act which was considered to be the most severe

government intervention to reduce greenhouse gas (GHG) emissions from the electric

generation industry faced several court challenges from state authorities after introduction in

2015 and finally abolished in 2017.

The main idea of climate change mitigation policies is to internalize the negative

externalities of GHG emissions generated in the economy. These policies often impose

significant costs on emitters and effectively constraint consumption or production decisions

by increasing the cost of carbon. However, the core concept of mitigation policies relies on

the scientific facts that climate change is happening and the GHG emissions of human

1 IPCC is the United Nations body for assessing the science related to climate change.

2 To gain a visual sense of geographic variation in opinions on climate change at state and county

scales in the U.S, we refer to the Yale Public Opinion Map at the following address:

http://climatecommunication.yale.edu/visualizations-data/ycom-us-2018/ 3 According to Gallup Polls conducted in 127 countries in 2007 and 2008, the percentage of people

who say global warming is the result of human activities in the U.S. was 49%. In comparison, the figures were 91% (Japan), 92% (South Korea), Greece (84%), 81% (Argentina), 80% (Brazil), 79%

(Portugal). 4 Global Greenhouse Gas Emissions Data, Environmental Protection Agency (EPA) report, 2017

(https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data). 5 Data extracted from Organization for Economic Cooperation and Development (OECD)

(https://stats.oecd.org)

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activities mostly cause it. In this perspective, mitigation policies can be welfare improving.

Otherwise, if we believe that climate change is not happening, then there is no need for any

policies, in the first place.

Moreover, if we believe that climate change is a natural phenomenon, then there is no

point in limiting human activities to confront climate change. In this worldview, mitigation

policies are inefficient and socially harmful policies. Instead, it would be more reasonable to

pursue adaptation policies which aim to prepare us to adapt o the consequences of climate

change.

Whether to choose no policy, adaptation policies or mitigation policies, are all influenced

by public opinion on climate change. In general, public opinion plays a vital role within the

socio-political context of policymaking. The state of public support or opposition affects

successful enactment of regulations, taxes, and subsidies. Public stand on climate change

mitigation policies depends on public beliefs about the causes and effects of climate change.

Mitigation policies may fail if the public does not appropriately perceive the underlying

reasons and potential benefits. In contrast, high levels of public agreement on the exact

causes and effects of climate change enhance public support for higher environmental

standards and justify the extra costs imposed by mitigation policies, thus limiting GHG

emissions generated in the economy.

High levels of PCCA can boost policymaking efforts to limit GHG emissions in many

ways. For instance, high PCCA can change public voting behavior in favor of electing

greener politicians (Herrnstadt and Muehlegger 2014) or foster the formation of collective

actions to improve environmental standards and legitimize the adoption of more ambitious

environmental policies (Adger 2003). Environmental regulations and policies affect

production decisions of high emitting facilities by increasing the marginal and the average

cost of production directly (e.g., in the case of demand-and-control policies) or indirectly

(e.g., in the case of cap-and-trade systems).

The pivotal role of PCCA in the process of policymaking has motivated a growing body of

research to highlight the mechanisms underlying the significant lack of public agreement with

the scientific facts of climate change observed in the U.S6. However, the existing literature

does not adequately address the magnitude of the impact of the increase in PCCA on the

GHG emission reduction and the consequent welfare improvement for US citizens. The

intricacy of the underlying socio-political mechanisms complicates an empirical approach to

study the relation between PCCA and GHG emissions outcome. For example, lobbying

efforts of special interest groups try to influence policymakers directly and impede progress

in the enactment of higher emission standards. These unobservable efforts also include

funding campaigns to induce public “doubt” about scientific facts on climate change through

media. As a result, special interest groups may affect public opinion and GHG emission

levels at the same time. In contrast, political campaigns of politicians who support the cause

may drive PCCA and GHG emission level in the opposing direction, simultaneously.

6 Many studies such as Nisbet and Myers (2007), Boykoff and Boykoff, (2007), Sampei and Aoyagi-

Usui (2008), Schmidt et al. (2013), Bruggemann and Engesser (2016), and Shapiro (2016) try to

explain the causes and the effects of existing climate change denial and skepticism in the US. The role

of “special interest” such as oil and gas companies and “media coverage” has gained more popularity among other explanations. We discuss these ideas in more details.

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This paper proposes an empirical model to estimate the impact of PCCA variation on the

GHG emission per capita of extensive emitting facilities (i.e., facilities emitting more than

25000 metric tons of CO2 equivalent emissions per year) from all industries across the US.

We use exogenous variation in the (lagged) temperature anomaly as an instrumental variable

to identify the impact of PCCA on the GHG emission per capita of the following year, at the

county level. This identification strategy makes it credible to infer that the association

between variation in the PCCA and the emitted GHG is a causal relationship rather than

merely a correlation.

This paper uses the county level estimates of PCCA provided by Yale Climate Change

Communication (YCCC) comprehensive public opinion surveys in 2014 and 2016. To our

knowledge, YCCC provides the only publicly available estimates of PCCA in several layers

at the county, state, and country levels. This feature enables us to incorporate geographical

heterogeneity of PCCA across counties and time in the model. YCCC has recorded an

increase in PCCA of the Americans from 2014 to 2016. The average PCCA has shifted by

20% (from 41% to 49%) during this period. However, the average PCCA of Americans did

not significantly change from 2016 to 2018.

We find that PCCA variation is significantly related to the GHG per capita emission of the

following years across mainland US counties. The relationship between PCCA and the

reduction in the observed GHG emissions of US direct emitters is evident: one percentage

point increase in PCCA reduces the GHG emission of industries by 0.6 percentage points.

This relation is robust across a range of regression specifications, including those with

county-, state- and time- fixed effects. We extend the results by focusing on the effect of

PCCA variation on GHG emissions of the electric generation industry since 76% of the total

GHG reduction between 2015 and 2017 has occurred in this sector. We refine the model by

controlling for several additional variables that specifically affect GHG emissions of power

plants. These variables include the effect of Clean Power Plant Act, changes in the relative

input prices (i.e., natural gas to coal) over time and across the regions identified by North

American Electric Reliability Corporation (NERC)7. We find that the PCCA impact on the

power plants clean-up is six times larger.

Finally, we introduce a measure of PCCA gap and perform a back-of-envelope calculation

to estimate the welfare damage of the existing public misperception of climate change

science, using different measures of the social cost of carbon (SSC). The estimated net

present value of the global GDP loss of the excess GHG emission generated in the US varies

between 70 to 350 billion dollars, depending on the choice of existing values for SSC. Filling

the PCCA gap in the US could avoid the damage.

2. Measurements: data and related discussions

2.1. Public Climate Change Agreement (PCCA)

This paper uses the Yale climate change communication program's (YCCC) surveys to

measure PCCA. The YCCC conducts studies on Americans public opinion and behavior.

Every two year, YCCC publishes estimations of Americans' climate change beliefs, risk

7 The NERC regions represent electric market regions.

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perceptions, and policy preferences at the state and county levels8. The beliefs category

specifically questions individuals’ agreement on the three fundamental scientific statements

of climate change asserted by IPCC. These are (i) climate change is happening, (ii) climate

change has mostly caused by human activities, and (iii) there is a strong scientific consensus

regarding anthropogenic climate change or the so-called "global warming." Currently, three

YCCC surveys (2014, 2016, and 2018) are publicly available. Table 1, summarizes the

evolution of Americans’ climate change believes from 2014 to 2018. In 2018, only 70% of

According to YCCC, Americans agree that climate change is happening, 57% agree that

human activities mostly cause it, and just less than a half (49%) agree with the statement

"most scientists think that global warming is happening." Figure 1 illustrates the distribution

and the evolution of PCCA across the US counties from 2014 to 2016. The cumulative

distribution of PCCA has shifted significantly, during this period.

According to Yale Climate Opinion Maps (YCOM), "American opinions about global

warming vary widely depending on where people live." Figure 2 graphically communicates

the geographical dispersion of Americans' perception of climate change. Americans' PCCA

vary widely depending on where they reside. The coastal areas experienced a relatively

higher increase in PCCA than the middle regions, by visual comparison of the 2014 and 2016

maps. Meanwhile, these areas are more vulnerable to the consequences of climate change

such as intensified hurricanes, floods or heatwaves.

We use the proportion of people agreeing with the IPCC’s third statement (“Most

scientists think that global warming is happening”) as the main measure of PCCA. An

important implication of agreeing with IPCC facts is to support adopting appropriate

strategies to alleviate climate change impacts. There are mainly two strategies to confront

climate change: mitigation9 10. The prerequisite of supporting any of the two strategies is to

comply with the fact that climate change is occurring. Supporting mitigation strategies (i.e.,

adopting environmental regulations aim to limit GHG emissions in the economy) entails

more requirements. It needs to agree with the scientific fact that global warming is caused

mostly by human activities”, as oppose to accepting natural causes for climate change. Thus,

agreeing with each of the IPCC’s statements involves different levels of mindfulness needed

to support appropriate policies to mitigate climate change impacts.

Public opinion plays a vital role within the socio-political context of policymaking. The

state of public support or opposition determines successful enactment of regulations, taxes,

and subsidies that aim to limit GHG emissions generated in the economy. Public stand on

climate change mitigation policies depends on the public beliefs about climate change causes

and effects. On the one hand, these policies often impose significant costs on emitters and

effectively constraint consumption or production decisions by manipulating the prices and

8 As a part of YCCC program, Yale Climate Opinion Maps (YCOM) uses a multilevel regression

with post-stratification (MRP) on a large national survey dataset (n>18,000 for 2014 and >22000 for

2016 and 2018), along with social and spatial population characteristics to derive the estimates. The

error estimates based on 95% confidence intervals is approximately six percentage points at the state level and eight percentage point at the county level, including the error in the initial national surveys.

9 According to IPCC 2014 assessment report “mitigation generally involves reductions in human

emissions of greenhouse gases (GHGs)…Mitigation includes reducing energy demand by increasing

energy efficiency, phasing out fossil fuels by switching to low-carbon energy.” 10

According to the United Nations Framework Convention on Climate Change (UNFCCC)

Glossary of Climate Change Acronyms, “Adaptation seeks to reduce the vulnerability of social and biological systems to relatively sudden change and thus offset the effects of global warming."

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marginal production costs. On the other hand, policy interventions aim to mitigate the effects

of climate change. The results of scientific studies should be communicated clearly to the

general audience; otherwise, people cannot correctly identify the potential causes and effects

of climate change. In other words, a high level of PCCA is necessary to gain public support

and to justify the extra costs imposed by mitigation policies.

PCCA is a relatively accurate predictor of public stand upon adopting further mitigation

policies. There is a strong correlation between PCCA measures and a few public policy

preferences reported by YCCC 2014 and 2016. Table 2 illustrates the results of the first-

difference estimation of regressing a measure of public policy support, namely the proportion

of the people agreeing with "setting strict CO2 limit on existing coal-fired power plants", on

PCCA. Changes in local PCCA level can explain almost 80% of the total changes in the

public policy support. The similar result applies to most of public policy support measures

reported by YCCC (results not shown). These public policies include: funding research into

renewable energy sources, regulating CO2 as a pollutant, requiring utilities to produce 20%

of electricity from renewable sources, and schools should teach about global warming.

Besides, PCCA can be a plausible proxy for the “stringency of the local institutions." The

quantity and the quality of environmental policies adopted to address climate change vary

tremendously across the states in the U.S.; however, these differences are far from being

random. For example, the number and types of programs supporting the development of

renewable energy are heterogeneous across the states. These programs typically provide i)

technical support, ii) regulatory policies such as setting a target for the share of renewables in

the electricity production (Sousa et al.,2014), and iii) financial incentives such as tax cut and

subsidies. The stringency of renewable energy policies at the state level varies by the level of

PCCA across the states. Figure 2 shows the relation between PCCA and the variations

observed in the quantity and type of renewable energy programs at the state level11. We sort

the states based on their PCCA level in 2016 in 6 different bins and compute the average

amount of each type of programs for each bin. It indicates that higher state-wide PCCA

corresponds to a higher number of all types of programs. The number of renewables

programs in top PCCA bin is almost four times as the lowest bin on average. The quantity of

all types of programs is higher in the top PCCA bins, including regulating programs, which

are more likely to have a higher influence on agents' behavior.

The existing literature uses several different measures to refer to the level of public

awareness concerning climate change. These measures typically include general public

awareness (i.e., the proportion of people who claim they have a fair amount of knowledge

about climate change) or measures of risk perception (e.g., the proportion of people

acknowledge that climate change is currently affecting them or will affect them or their

generations) or combinations of both measures. These measures appear extensively in the

literature of climate change, especially for cross-country analysis (e.g., Hagen et al., 2016)12

.

However, these measures of public opinion raise several conceptual concerns when it comes

to analyzing the policy impact of public opinion regarding climate change mitigation.

11 The North Carolina Clean Energy Technology Center (http://www.dsireusa.org/) provides public

access to information on thousands of policies and incentives for renewable energy and energy

efficiency. We analyzed more than 6400 state- and local-wide programs, starting from 2000 to 2017

to proceed with the graph shown in figure 2. 12 Also, Herrnstadt and Muehlegger (2014) use Google search activities to proxy the heterogeneity

of public awareness within the U.S., most probably because surveys like YCCC was not available at the time.

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The general public awareness measure can poorly represent the state of the US public opinion

on climate change. Nisbet and Myers (2007) review on dozens of public opinion surveys on

climate change awareness reports that awareness of the topic of climate change was universal

among Americans in 2006, however, the public perception of the causes of climate change is

not aligned with the scientific knowledge of the issue. Although, scientific view of climate

change is "widely shared across countries and barely challenged" (Bruggemann and

Engesser, 2016), nevertheless, it is not the case for the U.S. There is a substantial

misperception regarding climate change among Americans. In other words, claiming to know

about climate change does not necessarily translate into agreeing with scientific facts about

climate change (Boykoff and Boykoff, 2007). Appendix.1 provides more details about the

underlying mechanism that may lead to a public misperception regarding climate change.

Furthermore, risk perception measures cannot distinguish between those who agree with

human-made causes versus those who accept natural causes for climate change. The latter

group may prefer adaptation over mitigation strategies. Thus, risk perception measures are

not necessarily informative to reconcile the level of public support for climate change

mitigation, in the US.

2.2. Temperature variation

In this paper, we use lagged temperature variations provided by Climate at a Glance

System (CGS), to construct instrumental variables for PCCA. NOAA has developed the CGS

for analysis of monthly temperature and rainfall data across the US. The records include

several parameters, local options, and analyses13. The primary temperature variation indicator

in this research is "temperature anomaly." Temperature anomaly is 12-month average

temperature minus mean temperature of the last hundred years (i.e., 1901-2000). In other

words, temperature anomaly represents the difference between occurring temperature and the

long-term average or “the expected temperature."

According to the U.S Climate Change Indicators (2016), the temperature average grew

with higher than the global growth rate in the U.S. mainland during the past four decades

(from 0.29°F to 0.46°F per decade), and it is expected to continue the growth. The

temperature growth is not homogeneous across the US. While some parts of the South-east

have seen small changes, the North and the West have experienced temperature increases.

Table 3 summarizes the temperature variation indicators constructed based on the NOAA

reported observations. In 2013, the average temperature anomaly in the contagious U.S. was -

0.4°F (colder than the hundred-year average) while this amount was 1.8°F (warmer than the

hundred-year average) in 2015.

A growing body of literature finds a strong relationship between temperature innovations

and individual and public perception of climate change. For instance, Donner and McDaniels

(2013) find a strong relation between previous 3-12 months temperature anomalies and

perceiving climate change is happening. In a separate study, Hamiltone and Stampone (2013)

find stronger effects of temperature anomaly of closer in time. We find a strong correlation

between different measures of lagged temperature innovations and variety of PCCA

measures, which is in line with the results of previous literature. Table 4 illustrates the

correlation of PCCA measures with lagged and current temperature anomalies across the U.S.

13 Climate at a Glance system provides adjusted temperature observations. The adjustments

account for “the artificial effects introduced into the climate record” (e.g., instrument changes, station relocation, and urbanization).

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counties and across time. Temperature variations can partially predict innovations of PCCA in

the U.S. during 2014 and 2016.

Many studies try to explain the reason behind the high correlation between temperature

anomaly and the public perception of climate change. Whitemarsh et al. (2009) relate this

strong correlation to the misunderstanding between the concepts of weather and climate by

the non-expert. In other words, people tend to judge about validity of climate change based

on temperature innovations. Hence, public perception of the temperature anomalies can

affect the public believes regarding climate change (Myers et al., 2013). "It is thus plausible

that changing meteorological conditions may influence aggregate public perceptions over

time" (Whitemarsh et al., 2015). We use this line of reasoning, among other reasons, to justify

using lagged and current temperature anomalies as an instrumental variable for PCCA to

quantify the PCCA impact on the next years' emission of facilities. Appendix 2 provides other

perspectives on the relation between weather variation and PCCA.

2.3. GHG emission of large direct industrial emitters

Environmental Protection Agency (EPA) tracks GHG emission of large emitters in the

U.S. through the Greenhouse Gas Reporting Program (GHGRP)14

, since 2010. The program

covers more than 85-90% of the total US industrial GHG emissions, recorded in the US GHG

inventory. GHGRP requires over 8000 large emitters (>25000 MT CO2 eq. per year) to

report their GHG emission such as from 41 different sources. The GHGRP uses global

warming potential (GWP) values provided in IPCC’s assessment reports. These values are

used to calculate reporters’ emissions in carbon dioxide equivalent (CO2e). It excludes

emissions from agricultural sources and land use changes such as forestry. GHGRP mainly

includes emissions reported by suppliers (e.g., mobile sources, fuel use at stationary sources

with small emissions, and industrial gases) and direct emitters (such as power plants,

industrial facilities, and landfills). In this paper, we use the emission data reported by direct

emitters, which represents almost half of the total US GHG emissions in the inventory. Direct

emitters are required to report total GHG emissions that take place at their facility from the

processes covered by the program15

. The program provides data in national, state, local,

sector, and facility-specific levels.

In this paper, we study the effect of PCCA on the GHG emission of "direct emitters"

registered in GHGRP. To construct a comparable index of GHG emission of counties, we use

GHG emission per capita index which is a standard measure of emission intensity. The total

direct emission for each county is calculated and divided by the population of the county. The

index is in terms of annual Metric Ton of CO2 equivalent per capita. At the county level, over

40% of U.S. mainland counties are not hosting any large direct emitters16. These counties

may still have minor direct emitters as well as GHG emission from other sources such as

transportation, agriculture, and forestry. However, we omit these counties in the analysis.

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Since 1990, EPA has been collecting GHG emission data through a complementary program the Inventory of U.S.

Greenhouse Gas Emissions and Sinks (the Inventory). The program reflects GHG emission from all sources and forestry sinks. The data are reported on a national level and categorized by sector.

15 I did not include suppliers. Suppliers of fossil fuel products such as petroleum, natural gas, and industrial gases report

the total GHG quantity that would result from the complete combustion, oxidation or use of the covered products they

supply to the economy. The reason is that most suppliers sell their products in a large area that may contain several county or states.

16 The total number of the mainland counties is 3144, however, only 1875 of these counties host at least one sizeable

emitting facility; that is 60% of the total amount of counties.

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Table 5 summarizes the primary industries and the top 10 states which contribute the most

to the GHG emission in 2015 and 2017. Approximately 65% of the total direct GHG

emissions are related to the power plants. Also, power plants contributed nearly 76% of the

total direct GHG emission reduction from 2014 to 2016. Texas by itself generates 13.5

percent of the total direct GHG emission in the U.S.

Table 6 summarizes the descriptive statistics of the main variables for the sample of 1875

counties, including direct GHG emission measures. The total GHG emission reduced by

9.1%, from 3.01 (Billion MT CO2 e) in 2015 to 2.73 (Billion MT CO2 e) in 2017. The GHG

emission per capita has experienced a similar decrease, from 60.1 (MT CO2e per capita) to

54.1 (MT CO2e per capita). The number of average emitting facilities in a county also has

dropped from 6 to 5.3.

In sum, a relatively large-scale clean-up occurred in direct facilities emission from 2015 to

2017. More than three-quarter of the clean-up happened in the electricity generation industry.

Manufacturing production had almost zero growth (Figure 4) over this period. So, factors

other than scale contributed to the clean-up in manufacturing. Changes in the composition of

production across- or within- industries, from more to lower emission intensive products, and

technological changes, toward more environmentally friendly productions can explain the

complete changes for this period.

The electricity generation industry experienced an 11% reduction in GHG emission during

the same period. Again, changes in the industry output or scale are not the main contributor to

the clean-up, electricity generated reduced from 4.08 to 4.03 billion MWh (1.2%) during this

period. However, the composition variation can explain the majority of changes. Figure 5,

illustrates the increasing trend of renewables shares in power production, taking the lead from

2015 as the prominent source of energy in the US. Technological progress which is reducing

the cost of production from the renewable sources played a crucial role in this regard.

Meanwhile, coal-fired power plants keep on replacing with the cleaner natural gas power

plants. In the past decade, access to the natural gas, due to the revolutionary expansion of the

natural gas network of pipelines in the US, along with decreasing relative input price of

natural gas to coal was the main market forces driving the switching. In the robustness

section, we focus on the power industry and try to control these market mechanisms to

identify the effects of PCCA through changes in environmental regulations.

2.3. Other determinants of PCCA

The remaining data are drawn mainly from US Census Bureau and US Energy Information

Administration (EIA). The main control variables include median income and poverty

measures (drawn from US Census Bureau, Poverty estimates); educational attainment and

urban influence code (drawn from US Census Bureau, Censuses of Population, and the 2012-

16 American Community Survey 5-yr average.); major oil and gas producer (from US EIA);

political leaning (from Presidential Election Vote shares).

3. Estimation Framework

The primary motivation of this paper is to quantify the impact of PCCA variation on the

industries clean-up decisions in the U.S. Americans' claimed a dramatic change of their

attitude toward climate change in the period of 2014 to 2016. Moreover, the PCCA measures

indicate a tremendous heterogeneity across the U.S. states and counties, as well. The main

idea here is to use the cross-local and across time variations of PCCA to identify the short-

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term impact of PCCA on GHG emission variation of extensive direct emitting facilities in the

U.S. Equation 1 presents the structural model designed to estimate the immediate effect of

PCCA on the GHG emission level of the very next year. The panel data includes county-level

variables of two periods 2014 and 2016.

𝐺𝐻𝐺 = 𝑎 + 𝜆 + 𝛾 𝑃𝐶𝐶𝐴 + 𝑋 𝛽 + 𝑢 (1)

The variable of interest, 𝑃𝐶𝐶𝐴 , is the public climate change agreement at the county i in

the year t. It is measured by the percentage share of the people living in a county agree with

the statement “there is a scientific consensus about human-made climate change.” The

dependent variable, 𝐺𝐻𝐺 , is the log of total GHG emission per capita (MT CO2 eq. per

capita) of large direct emitting facilities aggregated at the county level in the very next year.

The county fixed effects, 𝑎 , is included in some specifications to capture time-invariant

characteristics of the counties that may be related to the GHG emission of facilities they are

hosting. These characteristics include an array of spatial and socio-economic features such as

measures of education, urbanization, political leaning, and production specialization.

Furthermore, the model also controls for time differentials of cross-states variations, 𝜆 .

The variable can capture heterogeneous institutional changes across the states. The quantity

and the quality of environmental regulations adopted at the state level, or say the "stringency

of institutions" directly affect producers' clean-up decisions. We discussed in this paper that

stringency of institutions vary across the states and positively correlate with the average level

of PCCA in the state. Every year, new sets of environmental regulations are hired by the

states' authorities to modify the previous regulations or two address new issues. These

institutional innovations occur differently across the states. The model accounts for these

types of state-wide institutional changes across time. Finally, the model controls for time-

varying county-level characteristics 𝑋 , such as income level and the poverty ratio.

In order to consistently estimate the returns of PCCA variations, we have to make sure that

PCCA variation in the presented model is independent of the possible omitted variables and

the unobservable factors. Independence assumption is a strong assumption in our case, since

the chain of reactions, starting form changes in PCCA ending in changes in GHG emission,

includes many factors of influence which can drive the outcome while meaningfully affect

public opinion. For example, lobbying efforts of interest groups, on the one hand, tend to

undermine the public interest effect on GHG emission level by directly influencing

policymakers toward biased decisions. On the other hand, these efforts also include an array

of funding to support opposing views of climate change through mass media17. Although the

literature suggests measures for quantifying the media coverage of climate change, however,

we could not find a valid measure of media coverage that takes into account the degree of

media bias toward rejecting scientific knowledge of climate change and how audience frame

it. As a result, we expect that PCCA variation is not orthogonal to the variation of the error

term, 𝑢 which includes the effects of unobservable such as special interests and omitted

variables such as effective media coverage.

This paper uses a lagged of local temperature anomalies to instrument the innovations in

PCCA at the county level, to alleviate the endogeneity arise from selection and omitted

variables. Lagged-temperature anomaly is the temperature deviation of the last year’s average

temperature from the average temperature level of the last hundred years (1900 to 2000). We

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. We have discussed the effect of media coverage and special interests in the previous section.

12

build on the findings of previous literature on the relation between weather variation and

individuals’ believes regarding climate change.

In the current paper, we used the two-stage-least-square (2SLS) method to estimate the

short-term impact of PCCA variation on GHG emission per capita of the next year. Equation

2 presents the first stage, where the endogenous variable, PCCA, is projected on the set of all

exogenous variables including the IVs, 𝑇𝑒𝑚𝑝 , and 𝑇𝑒𝑚𝑝 . The “1” subscript denotes

the first stage equation here. The disturbance term is allowed to be correlated across periods

for the same county in all regressions. Equation 3 is the second stage and mainly the rewrite

of equation-1. At this stage, instead of PCCA, we use the projection of PCCA from the first

stage regression to estimate the impact.

𝑃𝐶𝐶𝐴 = 𝑎 + 𝜆 + 𝑐 𝑇𝑒𝑚𝑝 + 𝑐 𝑇𝑒𝑚𝑝 + 𝑋 𝛽 + 𝑢 (2)

𝐺𝐻𝐺 = 𝑎 + 𝜆 + 𝛾 𝑃𝐶𝐶𝐴 ̂ + 𝑋

𝛽 + 𝑢 (3)

We focus principally on the GHG emission per capita of county i in year t (𝐺𝐻𝐺 ) which

already host one or more large direct emitting facilities, according to GHGRP database.

Temperature deviations, as captured in lagged and current temperature anomaly

(𝑇𝑒𝑚𝑝 ,𝑇𝑒𝑚𝑝 ) is used to instrument PCCA variation (𝑃𝐶𝐶𝐴 ) in the first stage, with

other county characteristics (𝑋 ) controlled for. Results are broadly similar – although

somewhat weaker- if (lagged) temperature differences or (lagged) temperature levels are used

as instrumental variables for PCCA instead (results not shown). We perform IV panel data

analysis both with county fixed effects and without. In some specifications of the model

entail county fixed effects, so we suggest utilizing the fixed-effect (within) estimator with

instrumental variable (IV) to estimate the coefficients for those cases.

The first stage relationship between temperature deviation and PCCA is strongly positive:

temperature anomalies are significantly related to PCCA variations at over 95 percent

confidence regressing on pooled data with county controls (regression 1 in Table 7), and this

relationship is robust to the inclusion of state-time fixed effects (regression 2), and county

fixed effects (regression 3). As we discussed in the previous section, 3-12 month temperature

anomalies affect the public perception of climate change. Although, temperature variation and

climate change are different concepts, however, especially non-experts tend to judge about

validity of climate change based on comparisons of current temperature with some

“expected” levels. Moreover, positive temperature anomaly may accompany by the

occurrence of heat waves, long summers and short winters which affect people perception

about global warming. In the case of lagged-temperature anomalies, our claim is people may

perceive the last year’s temperature deviation from its expected level (the temperature of the

last hundred years) as clues to verify the occurrence of climate change18.

The positive and approximately linear first-stage relationship is presented graphically in

figure 4 using a nonparametric Fan local regression method with Epanechnikov Kernel. We

examined verity of other instrumental variables, including lagged temperature deviation and

18 We also used other measures of temperature variations that showed the similar results not

reported in this paper. For instance, lagged-temperature growth works in the same way. The

difference between these measures is the definition of expected temperature or the base year’s

temperature. The expected temperature in temperature growth is the last year’s temperature while the

base temperature in the computation of temperature anomaly is the average temperature of the last century, from 1900 to 2000.

13

lagged percentage temperature deviation. In these cases, the coefficient estimates are positive

as expected and statistically significant (regression not shown). However, these instruments

are weaker than the ones we select for our specification. Also, higher levels of lagged

temperature anomaly are associated with significantly less GHG emission per capita of the

extensive direct emitting facilities at the county level. The non-parametric relation between

lagged temperature anomaly and GHG emission per capita is negative and roughly linear

(Figure 5).

The second-stage equation estimates the impact of PCCA variation on the GHG emission

per capita. We performed both IV-2SLS estimation. The temperature instrument is somewhat

weak (The F-statistics is 7.2 and 7.8 in regressions 6 and 7). The 2SLS with weak instrument

yield bias estimates and incorrect confident intervals. To be conservative with our inference,

we report Finlay and Magnusson (2009) conditional likelihood ratio confidence intervals that

allow for clustering. We clustered the standard errors since the error terms correlate among

those counties in the same state19. Also, to deal with the county-fixed effects, we report the

estimates using the within estimator20.

4. Main Results

Table 8 reports the relation of PCCA level and the next year's GHG emission per capita, at

the county level. Regression 1 to 4 presents this relation without using instrumental variable

and regression 5 to 7 reports the results for the second stage using an instrumental variable to

address potential endogeneity issues. PCCA has a statistically significant negative correlation

with the next year's GHG emission per capita. The negative relation is robust for all

regressions presented in Table 8.

In this paper, we have access to a standard short panel, since each county is observed

precisely for two time-periods. One can think of our model as the standard OLS or IV

regression with 2*1874 observations (regressions 1). As a result, the model is more

informative than a one-period cross-section. The only difference is that we need to take into

account the possible dependence of observations across time. In our model presented in

equation (2), we assume that (i) the 𝑎 ′𝑠 are independent of 𝑢 ′𝑠; (ii) the 𝑎 ′𝑠, and 𝑢 ′𝑠 are

i.i.d across both county and time; and (iii) 𝑎 is independent of 𝑋 for all i,j,t. These strong

assumptions allow us to run GLS (regression 2) or 2SLS (regression 5) over the panel with

random effects.

Moreover, panel data allows us to track the behavior of counties over time and correct for

unobserved county specific time-invariant heterogeneity. Thus we may relax the strong

assumption that 𝜖 is independent of 𝑋 for all i,j,t. This allows us to run regression over the

difference of variables across time which eliminates any county specific fixed effects

(regressions 3, 4, 6 and 7). With county fixed effects, the regression coefficient is driven by

how the variation occurs over time within each county.

The OLS regression on the pooled data with county controls (regression 1) illustrates a

sizeable negative relation between PCCA and GHG emission per capita across the counties in

the U.S. The high correlation in the cross-section vanishes as we add a time dimension to our

linear specification. We control for cross-state and across time fixed effects (regression 2) and

19

The confidence intervals are computed using –rivtest- command for Stata. 20 Note that first-difference estimator would give precisely the same results since we have only two

periods in the panel.

14

county fixed effects (regression 3), separately and together (regression 4). These state-time

fixed effects capture a large proportion of the negative co-variation between PCCA and GHG

emission per capita, both cross-country and across-time period. We add state-time fixed

effects in our specifications mainly to account for institutional differences across the states.

Nevertheless, the quantity and quality of the regulations (e.g., cap-and-trade systems in

California and seven other states) that may constrain producers' decisions on how much to

generate GHG emissions vary across the states, and also it varies by the time.

Education (measured by the percentage of adults holding college or higher degrees) has a

significant negative relation with the amount of emission produced across the counties.

Political characteristics of a county have a significant relation with the amount of emission

generated across the counties. Notably, we can observe that counties with higher political

polarization (measured by the difference between the percentage of republican and democrat

voters) emit are more likely to generate higher levels of emissions. Other county

characteristics such as the level of urbanization and specialization in mining and

manufacturing are significantly and positively corresponds to GHG emission per capita

variations across the counties. Note that the mentioned county characteristics are relatively

time-invariant; hence they are part of the county fixed effects. Thus, these variables disappear

in any specification we control for county fixed effects (regressions 3, 4, 6, and 7).

Regression 3 in Table 8 shows the results of the panel data regression with county fixed

effects. Panel data fixed effect analysis rules out any socio-economic and spatial

characteristics of a county that may contribute to GHG emissions differences across counties.

As we reported in table 6, the regression coefficient in the presence of county-level fixed

effects is small but significantly identified. We cannot infer causal relationships between

PCCA and GHG emission per capita based on this regression since we did not account for

possible correlation of PCCA with potential time-varying omitted or unobserved variables.

We identify the causal impact by using an instrumental variable.

Regressions 4-7 in Table 8 report the second stage of the 2SLS estimation in the presence

of the instrumental variable. We use lagged and current temperature anomaly to instrument

PCCA21. The choice of lagged variations plausibly eliminates the effect of temperature

anomalies on GHG emissions via channels other than PCCA. Also, temperature anomalies

show serial correlation for groups of neighboring counties. To eliminate any bias induced due

to these regional serial correlations, we further control for current temperature anomaly and

adjust the estimated standard deviations for potential clustering effect at the state level.

In regressions 5, coefficients for county characteristics including education, primary oil

production, political leaning and differences, urbanization, mining and manufacturing

specialization are statistically significant at 95 percent confident or above. Counties hosting

extensive oil and gas, manufacturing and mining activities, generate much higher GHG per

capita emission in comparison to the other counties. The impact of higher education level on

the GHG per capita is negative. On the one hand, as we showed in Table 7, individuals with

higher education are more likely to realize the scientific foundations of global warming. Thus

it is highly expected that more educated people would support higher emission standards to

21 There is already a time lag between the measure of PCCA and GHG emission per capita in our

specifications. County PCCAs are observed in 2014 and 2016, while GHG emissions are related to the

facility reports in 2015 and 2017. So, in principle, the lagged temperature anomaly corresponds to

weather observations in 2013 and 2015, and current temperature anomaly relates to 2014 and 2016 observations.

15

limit emitting facilities which reduce GHG emission per capita in the local economy. On the

other hand, the concentration of highly educated individuals in a county may also translate

into producers’ access to the higher quality of human capital, which in turn can positively

impact the productivity of facilities thus reducing GHG emission for the same level of

production.

Finally, regression 7 indicates that PCCA significantly negatively impacts the GHG per

capita emission of major direct emitting facilities. We expect if PCCA level in a county

increase for 1 percent, the corresponding GHG emission per capita reduces for almost 0.6

percent, on average. Using instrumental variable as an identification strategy makes it

credible to infer that the association between variation in the PCCA and the emitted GHG is a

causal relationship rather than merely a correlation.

5. Potential violations of the exclusion restriction The average temperature in the US is not growing monotonically. Although the CO2

emission released to the atmosphere every year is known as the main responsible for global

warming, the yearly amount of emissions barely correlates with the short-term temperature

variations. Zickfeld et al. (2012) suggest that "global mean temperature change is

proportional to cumulative CO2 emissions, independent of the timing of those emissions."

The independence of temperature anomaly and the annual CO2 emissions in the short-run has

an important implication for our analysis; specifically, it eliminates the likelihood of reverse

causality, from GHG emission to temperature anomaly.

While it is intuitively plausible that variations in temperature anomaly are exogenous, it

must also satisfy the exclusion restriction: temperature anomaly innovations should affect per

capita GHG emissions of large direct emitters only through PCCA. A severe violation of the

exclusion restriction is the possibility that high levels of temperature anomaly might directly

affect GHG emission reduction independently of economic conditions. For instance, heat

waves may immediately increase the electricity consumption for cooling or low-pressure

waves may immediately increase the consumption for heating purposes. To cross-out any

immediate effect of temperature variation on energy demand, we used the lagged temperature

anomalies. Also, we showed that the relation between the lagged temperature anomalies with

the GHG emission is negative, which based on the energy demand scenario we expect to see

a positive correlation between these variables. Also, we controlled for the current temperature

anomaly in our specifications.

Finally, the identification strategy chosen in this paper may not work for other regions of

the world especially for most of the European countries mainly because the climate change

information gap is not significant in comparison to the U.S. But, it may work for countries

with a similar issue. After all, the disinformation campaigns, initially launched in the US, are

followed in Canada, Australia, and New Zealand (Dunlap and McCright, 2011). Also, it

cannot be used for a developing country, since, the requirements of economic structure

(agriculture or manufacturer-based economy) or weak institutional structures may not let

small variations in public climate change awareness effect GHG emission level.

6. Robustness

Power generation facilities were responsible for almost 65% of the total GHG emitted

from extensive direct emitting facilities. Moreover, 76% of the GHG emission reduction

16

occurred during 2015 and 2017 happened in power plants. The electricity generation industry

experienced an 11% reduction in GHG emission during the same period. These facts indicate

that the power generation industry requires a separate focus. In this section, we focus on the

power industry and try to quantify the impact of PCCA on the clean-up of the power industry.

Using GHGRP data for 2015 and 2017, we isolate all power plants along with their North

America Industry Classification (NAIC) code22. Only 739 counties host at least a sizeable

emitting power plant23.

Change in the industry output level (i.e., scale factor) is not the main contributor to the

clean-up since the electricity generated in this period has experienced a minor reduction from

4.08 to 4.03 billion MWh (1.2%). However, changes in the composition of input sources can

explain the majority of variations. Figure 5, illustrates the increasing trend of the renewables

share in power production. Renewables took the lead as the prominent source of energy in the

US, from 2015. Technological progress thus decreasing the cost of production from the

renewables is the main reason for the observed trend.

Meanwhile, coal-fired power plants keep on replacing with the cleaner natural gas power

plants. In the past decade, access to the natural gas expanded due to the revolutionary

development of the natural gas network of pipelines in the US. Also, relative unit heat price

of natural gas to coal was decreasing in recent years, marked natural gas a cost-effective

alternative for coal.

Other than market forces, new environmental regulations and policies catalyzed the

switching trend. The introduction of the Clean Power Plant (CPP) at the federal level in 2015

could potentially affect the outcome of the GHG emissions of the industry. CPP aimed to

reduce greenhouse-gas (GHG) emissions from the electricity generation industry by 30

percent relative to 2005 levels in fifteen years. The plan had three pillars: reducing emissions

from coal-fired power plants, increase utilizing renewable sources, and energy conservation.

In 2015, the coal-fired power plants generated 30% of the total US electricity, but they were

responsible for 70% of the GHG emissions of the whole sector. The interim goal of the CPP

was to immediately retire coal-fired power plants and switch to natural gas as a primary

source of power generation. CPP required each state to meet a specific average emission

performance rate (EPR)24 target. CPP set the threshold in a way that states with higher shares

of coal-fired power plants in electricity generation would affect the most25. States were free to

plan how to comply with the new emission standards26. The long-term vision of CPP was

persuading wind energy or other alternative renewable sources. The abolished in 2017 due to

the court challenges of 18 states (Appendix 3).

22

Codes under 22111 to 22118 dedicates to electric power generation utilities. 23

The state of Vermont does not have any large emitting power plants, thus we exclude this state

from our analysis. 24 EPR measures the amount of GHG emissions released as a by-product of 1 MWh electricity

generated by a power plant (lb CO2 e/MWh).

25 Typically the EPR of a coal-fired power plant is above 2000 lbCO2/MWh while the required

average EPR of the states was set between 1000 to 1300 lbCO2/MWh. 26

States could use low carbon technologies from available sources to substitute for their coal generation. Also, they could develop a plan in coordination with other states.

17

Equation 4, is the modified specification to analyze the PCCA impact size in the electricity

generation industry. We check the robustness of the PCCA impact by adding more controls.

Input prices and average heat rate of the power plants changes across the North American

Electric Reliability Corporation (NERC) regions and over time. To control for these

variations, we add NERC-time-specific fixed effect, 𝜃 , to the specification. Counties with

higa her initial share of coal electricity were more likely to be affected by the introduction of

CPP, 𝐶𝑆 = )27. Also, the effect can be heterogeneous for the counties located in the

states that accepted the CPP versus those located in the states that challenged the act in the

court28. Again, we use temperature anomalies to instrument the PCCA variations the model.

𝐺𝐻𝐺 = 𝑎 + 𝜆 + 𝜃 + 𝜂𝐶𝑆 = ) + 𝛾 𝑃𝐶𝐶𝐴 ̂ + 𝑋

𝛽 + 𝑢 (4)

The first and second stage estimation results are illustrated in Table 9 and Table 10,

respectively. Since the grid networks connect under North American Electric Reliability

Corporation (NERC) regions, we report the standard errors clustered at the NERC level. The

effect of PCCA is more pronounced: a percentage increase in PCCA leads to almost 3 percent

decrease of the GHG emission per capita of power plants in the following year. The results

are robust to adding time and regional-time fixed effects.

7. The welfare impact

In this section, we aim to analyze the economic significance of the existing PCCA gap in

the US. First, we propose different measures to size the PCCA gap, based on the US public

opinion estimates in 2018 (Figure 5). Next, using the estimated PCCA elasticity of GHG

emission of direct emitters (𝜀 = .6 ), we perform a back-of-envelope calculation to evaluate

the welfare impact of PCCA gap under different counterfactual scenarios. We use different

estimates of the social cost of carbon (SSC)29

to monetize the adverse effects of climate

change30

.

27 𝐶𝑆 is the county coal share of in electricity generation in 2015. = ) is the time indicator

which is 1 for 2017 and 0 for 2015. 28 Note, that we aim to know if the instrumental variables and the estimated impact of PCCA are

still reliable by adding different controls. The goal is not to identify the effects of the CPP act. The

additional variables cannot appropriately reconcile the CPP effect, since it is unlikely to believe that

the power plants are randomly assigned to the CPP, thus 𝜂 in the equation 4 is not estimated

consistently due to endogeneity raised from possible selection. 29 The SSC is “the change in the discounted value of economic welfare from an additional unit of

CO2 – equivalent emission, and it is the most important economic concept in the economics of climate change.” (Nordhaus, 2017).

30 The EPA’s report in 2016, Climate Change indicator in the United States, mentions that “Long-term changes in climate can directly or indirectly affect many aspects of society in potentially disruptive ways. For example, warmer average temperatures could increase air conditioning costs and affect the spread of diseases like Lyme disease, but could also improve conditions for growing some crops. More extreme variations in weather are also a threat to society. More frequent and intense extreme heat events can increase illnesses and deaths, especially among vulnerable populations, and damage some crops. While increased precipitation can replenish water supplies and support agriculture, intense storms can damage property; cause loss of life and population displacement; and temporarily disrupt essential services such as transportation, telecommunications, energy, and water supplies.”

18

Figure 6 illustrates the public beliefs of Americans on climate change. In our analysis, we

used the percentage people agreeing with the statement “most scientists think global warming

is happening,” as the primary measure of PCCA in the US. Based on this measure, the total

PCCA gap in the US is 51%. This considerable gap consists of four major parts: awareness

gap, denial gap, skepticism gap, and knowledge gap (Table 11). 16% of the American claim

they are not aware of global warming is happening, which constitute the “awareness gap.”

Also, 14% reject the fact that global warming is happening, forming the “denial gap.” From

70% of Americans, who believe global warming is happening only 57% believe that human

activities mostly cause it. In other words, 13% of Americans are skeptical of the

anthropogenic nature of global warming, forming the skepticism gap. Finally, among the 57%

who believe the human-made nature of global warming, 49% have further knowledge that

there is a scientific consensus on global warming. That is 8% do not possess the knowledge,

forming the knowledge gap (Table 11).

The next step is to choose the measure of SSC. A large body of literature tries to monetize

the consequences of climate change. Governmental bodies typically use the SSC estimates of

an integrated assessment model (IAM) to assess the environmental policy effects. IAMs

estimate the reductions in GDP as a consequence of different climate change scenarios. These

models simulate time paths for the atmospheric CO2 concentration and its impact on

temperature. The temperature changes translate into GDP reductions. IPCC uses Nordhaus

(2017) estimates of SSC. Nordhaus updates the 2013 version of the Dynamic Integrated

Model of Climate Change and Economy (DICE-2016R) and estimates that the global SSC in

2015 is 31.21($/tCO2, 2010$) with the value rising at 3% per year or roughly 40$ in terms of

current US dollars. He estimates the regional SSC for the U.S to be 15.3% of the total

damage, 4.78 ($/tCO2, 2010$) or 6$ in terms of current US dollars.

Pindyck (2019) presents a survey-based approach to estimating an average SCC31

. There is

considerable uncertainty regarding the probability of alternative (or extreme) economic

outcomes of climate change. Also, the required emission reduction to prevent these events is

uncertain. He relies on a survey of economists and climate science experts to produce the

probabilities and the required emission reduction. Based on economists survey the average

SSC is 80$ tCO2. The average SSC considering all experts is 200$ tCO2 (Table 12).

Table 13, summarizes the welfare impact of the emission in the US that could have been

avoided by increasing the PCCA level. The social cost of the PCCA gap lies between 10 to 50

billion dollars a year for US citizens. Meanwhile, we estimate that the global cost of the

current PCCA gap exceeds 70 up to 350 billion dollars annually, depending on the choice of

the SSC.

8. Conclusion

The PCCA refers to the extent of public agreement with the fundamental scientific

knowledge of climate change. We examine the role of PCCA as a measure of public

awareness of climate change. Surveys report that Americans are highly aware of climate

change; however, their understanding of the causes and effects of climate change is not quite

aligned with the strong scientific consensus of the issue. PCCA level is heterogeneous across

31 The average SCC is the ratio of the present value of lost GDP from an extreme outcome

to the total emission reduction needed to avert that outcome (Pindyck, 2019).

19

the US and varies over time. We use the variations in the US to provide evidence that PCCA

can predict public support for mitigation policies as well as the stringency (i.e., quantity and

quality) of the local environmental regulations. Also, we examine the causal relationship

between PCCA and the amount of GHG emissions generated in the US economy. Using

temperature anomalies as instrumental variables for PCCA, we find that changes in the public

agreement have a dramatic causal impact on the industrial facilities clean-up: a 10% increase

in PCCA reduces GHG emissions of extensive direct emitting facilities the following year by

nearly 6%. The impact of PCCA changes is even more substantial for the sample of the

electricity generation industry. The existing PCCA gap has an economically significant effect

globally and across the US. We estimate that the social cost of the PCCA gap to exceed 10

billion dollars annually for US citizens. The global cost of the additional CO2 emission is

even more substantial, exceeding 70 billion dollars a year. This damage could have been

avoided by rectifying the information gap regarding climate change among Americans.

20

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Appendix 1.

Studying the causes of US climate change misperception has been a focus of researchers

in the past decade. These strand of studies are dedicated mainly to understanding climate

change denial (i.e., opposing with the statement that climate change is happening) and

climate change skepticism (i.e., accepting the occurrence of the climate change but doubting

about its human-made origins). Among several possible causes of public misperception, the

role of "special interests" and "the media" has gained more attention among the scholars.

The environmental policies in many cases aim to limit the GHG emission as they are the

main human-made factor contributing to global warming. The main ingredients of GHGs are

CO2 and methane. Burning fossil fuels in internal combustion engines and fossil fuel-based

power plants generate CO2. Methane is leaked mainly in oil and gas extraction facilities. As a

result, complying with the environmental policies are costly especially for fossil fuel

suppliers and major GHG emitters including oil and gas extractors and (coal-based) power

plants. These industries may form and finance lobbying entities to influence public policies

directly (e.g., contributions to election campaigns) or indirectly (e.g., by influencing public

opinion through various campaigns). For instance, in 2017, based on the Senate Office of

Public Records, the oil and gas industry spend more than 125 million dollars in campaign

contributions through their affiliated lobbying groups.

Shapiro (2016) uses a model to understand when public policy is likely to reflect the best

scientific information. His paper studies the vital role of special interests and the media in

determining climate change information bias. He mentions the case of the American

Petroleum Institute (API) as an example to illustrate the mechanism of effect. In 1998, API

created an information kit to "present scientific uncertainties in language that the media and

public can understand." The metrics of success included "the percent of media articles that

raise questions about climate science" and "total audience exposed to newspaper, radio and

TV coverage of science uncertainties." The main purpose of lobbying activities is to produce

"doubt" about fundamental knowledge of climate change especially the role of humankind.

How skeptical views find their path to the media? Boykoff and Boykoff (2007) emphasize

that the inadequate translation of IPCC views into the public arena "is not random." "It occurs

not only because of complex macro-political and economic reasons rooted in power relations

but also because of the underlying journalism norms." among which the norm of "balanced

reporting" has gained more popularity. He mentions that with balanced reporting, journalists

"present the views of legitimate spokespersons of the convicting sides in any significant

dispute, and provide both sides with roughly equal attention." As a result, although 97 percent

of related scientific publications agree on the main concepts and consequences of climate

change (Cook et al., 2013), the remaining 3 percent may get an equal chance to oppose the

consensus views.

Bruggemann and Engesser (2016) perform a full-text search of all articles published in the

leading newspapers of five high-income countries during 2011 and 2012 which explicitly

mentioned climate change. They find out that British and US media most heavily quote

contrarian voices (25% and 17% of articles) and almost all of the outlets with a substantial

share of contraries have a "right-leaning" editorial policy. These results fit Shapiro’s (2016)

argument that "press mentioning these skeptics, naturally create an impression of ongoing

scientific controversy" about climate change. Also, Maibach’s et al. (2014) claim that

inducing the perception of human-made climate change as an ongoing scientific debate “was

designed to resonate with the sensibilities of political conservatives who are inherently

suspicious of government intervention in markets and societies.”

23

Appendix 2.

Extreme weather conditions can significantly shift the average temperature variation.

Radical temperature variation often accompanies with the occurrence of unusual heat waves,

extended summer days. These events are more likely to gain media attention followed by

discussions linking these events to global warming. The media coverage increases awareness

and knowledge of climate change. For example, Sampei and Aoyagi-Usui (2008) run a full-

text search of all articles related to climate change in newspapers of Japan from 1998 to 2007.

They find that the number of articles on global warming significantly influenced public

concern for global warming.

Furthermore, "vulnerability" to the consequences of climate change can intensify the

media effect. The people experiencing the consequences of climate change are more likely to

involve in climate change investigations. Schmidt et al. (2013) run a full-text search of all

articles related to climate change in the newspapers of 27 countries published from 1996 to

2010. They find that "media attention is a higher level in those countries with significant

climate impact." In sum, temperature variation may translate into higher public awareness of

global warming directly (by experiencing the situation) or indirectly (through media coverage

of extreme events). Media coverage is critical for public opinion formation. Higher informed

individuals foster collective actions and support legislation to confront climate change.

24

TABLE 1

Public Climate Change Agreement Estimates, United States

Yale climate opinion maps - 2014 to 2018

2014 2016 2018

Explanatory Variable agree disagree agree disagree agree disagree

Global warming is happening 63% 18% 70% 12% 70% 14%

Global warming is caused

mostly by human activities 48% 35% 53% 32% 57% 32%

Most scientists think global

warming is happening 41% 34% 49% 28% 49% 28%

Note. - The national survey error is approximately three percentage points based on 95%

confidence intervals.

25

Figure1. Top: Histogram of Public Climate Change Agreement in the USA for 2014 and

2016, at the county level. Bottom: Cumulative distribution of PCCA. The percentage of the

population agree that “Most scientists think global warming is happening” constitute the

measure of PCCA. (Data from Yale Climate change Communication Program, public opinion

surveys).

26

Figure 2. Geographic dispersion of PCCA across US counties, 2014 and 2016

Figure 2: These maps show in a glance how U.S. people perceive global warming. The first and the second row corresponds to public

opinion in 2016 and 2014, respectively. The left column in the map corresponds to percentage agreement with the statement “global warming

is happening.” The middle column relates to the percentage agreement with the statement “global warming is caused mainly by human

activities,” and the right column reflects the extent of agreement regarding the statement “most scientists think that global warming is

happening.” The maps show “Americans’ PCCA vary widely depending on where they live.” (Maps depicted from Yale Climate Opinions

Map 2014 and 2016 at the county level).

20

14

2

016

27

0

50

100

150

200

250

[35-40] (40-45] (45-50] (50-55] (55-60]

Figure 3: PCCA and the number of programs related to support

Renewable Energy at the state level

Financial Incentive Regulatory Policy Technical Resource

Figure 3 shows the relation between PCCA and the variations observed in the quantity and

type of renewable energy programs at the state level. We sorted the states based on their PCCA level in 2016 in 6 different bins and computed the average amount of each types of

programs for each bins. We analyzed more than 6400 state- and local-wide programs,

starting from 2000 to 2017 provided by The North Carolina Clean Energy Technology

Center (http://www.dsireusa.org/). This center provides a public access to information on

thousands of policies and incentives for renewable energy and energy efficiency.

Explanatory Variable

Changes in PCCA

log % public agreement with

scientific consensus of climate

change

constant

Time fixed effets

County fixed effects

R2

observations

*** Significantly different from zero at 99 percent confidence.

TABLE 2

Public Policy Support and PCCA

Dependent Variable : chanages in log % public agreement with

strict CO2 limit on existing power plants

Note. - Regression disturbance terms are clustered at the State level.

* Significanly different from zero at 90 percent confidence.

** Significantly different from zero at 95 percent confidence.

0.83***

(0.04)

-0.031***

(0.004)

first-difference

yes

yes

0.55

1874

28

Table3: Reports descriptive statistics of temperature anomalies for 1874 counties located in the US

mainland. These counties host at least one large direct emitting facility (>25,000 MT CO2 e per year),

reporting to GHG reporting program. “Temperature anomaly is the difference between the current year

average temperature and the average temperature of the last century (1900-2000)” (Climate at a Glance).

Table 4: Reports correlations between temperature anomaly and PCCA measures in 2014 and 2016. The

term “Human-made” refers to the percentage of people agree that human activities mostly cause global

warming and “Consensus” refers to the percentage people agree that most scientists think that global

warming is happening.

human-made consensus human-made consensus

temp. anomaly2013 0.43 0.47

temp. anomaly2014 0.35 0.39 0.28 0.27

temp. anomaly2015 0.23 0.28

temp. anomaly2016 0.08 0.09

TABLE 4

Correlation between lagged temperature variations and PCCA measures

2014 2016

Mean Std. Deviation Range Min. Max. # of obs.

Temp. anomaly 2013 -0.40 1.16 6.50 -2.50 4.00 1874

Temp. anomaly 2014 0.26 0.75 4.30 -1.70 2.60 1874

Temp. anomaly 2015 1.85 0.88 4.60 -0.10 4.50 1874

Temp. anomaly 2016 1.41 1.12 7.00 -2.60 4.40 1874

TABLE 3

Descriptive statistics: Temperature anomaly

29

Industry Type reported # of plants

Emission

(MMT

CO2 e.)

% of total

Emission # of plants

Emission

(MMT

CO2 e.)

% of total

emission

GHG

reduction

(MMT CO2

e.)

% of total

GHG

reduction

Power Plants 1,450.0 1,961.4 65.1 1,294.0 1,752.0 64.1 209.4 75.7

Minerals 360.0 110.6 3.7 349.0 105.0 3.8 5.6 2.0

Waste 1,273.0 97.6 3.2 1,168.0 92.9 3.4 4.7 1.7

Chemicals 335.0 96.3 3.2 323.0 98.5 3.6 -2.1 -0.8

Petroleum and Natural Gas Systems 1,361.0 78.8 2.6 931.0 59.2 2.2 19.6 7.1

Metals 279.0 77.2 2.6 274.0 67.4 2.5 9.9 3.6

Petroleum Product Suppliers,Refineries 65.0 64.6 2.1 65.0 65.8 2.4 -1.2 -0.4

Chemicals,Refineries 39.0 60.7 2.0 39.0 62.5 2.3 -1.8 -0.6

Power Plants,Waste 9.0 47.6 1.6 9.0 39.1 1.4 8.5 3.1

Other 1,807.0 416.7 13.8 1,714.0 392.6 14.4 24.1 8.7

Grand Total 6978 3011.6 100 6166 2,735.0 100 276.6 100

Top 10 states - direct GHG emitters # of plants

Emission

(Milion

MT CO2

e.)

% of total

Emission # of plants

Emission

(Milion

MT CO2

e.)

% of total

emission

GHG

reduction

(Milion MT

CO2 e.)

% of total

GHG

reduction

Texas 867 406.6 13.5 715 384.0 14.0 22.6 8.2

Indiana 204 159.7 5.3 192 137.9 5.0 21.8 7.9

Louisiana 321 132.7 4.4 306 133.2 4.9 -0.5 -0.2

Florida 179 129.9 4.3 164 129.2 4.7 0.7 0.3

Pennsylvania 330 136.8 4.5 277 116.8 4.3 20.0 7.2

Ohio 266 135.7 4.5 242 116.1 4.2 19.6 7.1

Illinois 275 131.4 4.4 256 106.0 3.9 25.5 9.2

California 480 115.9 3.8 409 97.4 3.6 18.5 6.7

Kentucky 152 110.3 3.7 136 91.0 3.3 19.2 7.0

West Virginia 135 94.4 3.1 106 89.4 3.3 5.0 1.8

other 3769 1457.8 48.4 3363 1334.3 48.8 124.1 44.9

Grand Total 6978 3011.6 100 6166 2,735.0 100 276.6 100

Note.- Emission levels are in Million Metric Ton of CO2 equivalent. Direct emitters does not include emissions from: transportation,

agriculture, food stocks, and frostry, on-shore oil and gas production, transmission pipelines.

TABLE 5

Major Direct GHG Emitters (>25000 MT CO2 e.) - United States

Green House Gas Report Program (GHGRP) - 2015 and 2017

2015 2017

2015 2017

30

Mean* Std. Min Max Mean* Std. Min Max

Global warming is happening

(% agree)59 4.8 45 81 64.6 5.4 48.8 84

Global warming is caused mostly by

human activities

(% agree)

44.7 4.3 35 68 48 4.8 35.9 68.4

Most scientists think global warming is

happening

(% agree)

36.3 5.4 24 61 42.8 6.3 24.9 69.1

Direct GHG emission Mean Std. Min Max Mean Std. Min Max

Emission level**

(MT CO2 e.) 1,607,000 3,653,000 0 49,030,000 1,460,000 3,345,000 0 52,970,000

Emission per capita**

(MT CO2 e. per capita) 60.1 209.6 0 3,659 54.1 197.3 0 3,785

# of direct emitters per county 6 4.9 0 79 5.3 4.4 0.0 75

Lagged-Temprature Anomaly (°F) Mean Std. Min Max Mean Std. Min Max

12-month average temprature anomaly -0.43 1.2 -2.5 4 1.9 0.9 -0.1 4.7

** The amounts are calculated accounting only emitting counties.

2015 2017

2013 2015

Note.- Emission levels are in Million Metric Ton of CO2 equivalent. Direct emitters does not include emissions from: transportation,

agriculture, food stocks, and frostry, on-shore oil and gas production, transmission pipelines.

*The Means are not weigted by counties population.

TABLE 6

DESCRIPTIVE STATISTICS

County level (# obs. 1874)

Public Climate Change Awareness

(Yale Climate Opinion surveys)

2014 2016

31

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Elec

tric

ity

Gen

erat

ion

(100

0 M

Wh

)

US Electricity Generation From Different Sources

Coal Natural Gas and Other gas renewables

US Manufacturing Production growth – 2015 to 2017

Figure 4: The mean growth of the US Manufacturing Production from Jan 2015 to Dec 2017,

is close to zero.

Figure 5: The ten-year trend of US electricity generation from coal, natural gas and

renewables. From 2015 onward, coal is not the prominent source of electricity

generation in US.

32

Figure 4. PCCA on lagged temperature anomaly. Nonparametric Fan regression, conditional on county

fixed effects and state-time fixed effects.

33

Figure 5. GHG emission per capita on lagged temperature anomaly. Nonparametric Fan regression,

conditional on county fixed effects and state-time fixed effects.

Explanatory VariableOLS

(1)

GLS

(2)

Fixed-eff.

(3)

IV2SLS

(4)

IV2SLS

(6)

IV2SLS

(7)

Lagged Temperature

Anomaly (2013 and 15)

0.032***

(0.007)

0.04***

(0.01)

0.03*

(0.013)

0.025***

(0.003)

0.03***

(0.007)

0.024*

(0.012)

Temperature Anomaly

(2014 and 2016)

0.019**

(0.004)

0.002

(0.006)

0.018***

(0.007)

0.021***

(0.002)

0.02***

(0.004)

0.023**

(0.007)

Lead Temperature Anomaly

(2015 and 2017)

0.001

(0.003)

0.01

(0.007)

0.001

(0.008)

Income

(log median income, $)

0.18***

(0.045)

0.13***

(0.04)

0.065*

(0.04)

0.11***

(0.03)

0.35***

(0.08)

0.12***

(0.04)

Poverty

(log % under poverty)

0.12***

(0.027)

0.09***

(0.02)

-0.002

(0.016)

0.02*

(0.01)

-0.017

(0.03)

0.01

(0.02)

Education

(% adult holding college

degree or above)

0.005***

(0.0001)

-0.005***

(0.0005)

0.006***

(0.0007)

Major oil producer

(among top 20 percentile)

-0.028**

(0.011)

-0.02

(0.013)

-0.02

(0.02)

Political Leaning

(percentage Republican)

0.007

(0.007)

0.004

(0.006)

0.004

(0.006)

Political difference

(% difference b/w

Republicans and Democrates)

-0.005

(0.003)

-0.003

(0.003)

-0.003

(0.003)

Urbanization influence index -0.001

(0.001)

-0.001

(0.001)

-0.001

(0.003)

Specialized in Mining -0.044***

(0.012)

-0.038***

(0.012)

-0.042

(0.03)

Specialized in Manufacturing -0.003

(0.006)

(0.15)

-0.004

(0.006)

(0.15)

-0.001

(0.02)

(0.15)Specialized in Farming 0.003

(0.009)

0.018

(0.01)

0.013

(0.028)

Specialized in recreation 0.028***

(0.009)

0.027***

(0.008)

0.022

(0.018)

Specialzed in Government 0.029***

(0.008)

0.027***

(0.008)

0.03

(0.018)

constant 0.89

(0.66)

1.64***

(0.57)

2.89***

(0.42)

1.68***

(0.39)

-0.18

(0.9)

2.28***

(0.47)

State-time fixed effets no yes yes yes no yes

County fixed effects no no yes no yes yes

R2 0.54 0.61 0.34 0.35 0.37

F-test 7.23 7.8

observations 6210 6210 6210 3748 3748 3748

Standard errors type state state state state state state

*** Significantly different from zero at 99 percent confidence.

TABLE 7

Public climate change agreement and Lagged temperature anomaly

Dependent Variable : log % public agreement with scientific consensus on global warming

Note. - Regression disturbance terms are clustered at the State level. The instrumental variables for

public climate agreement in regressions 4-7 is lagged and current temperature anomalies.

* Significanly different from zero at 90 percent confidence.

** Significantly different from zero at 95 percent confidence.

34

Explanatory VariableOLS

(1)

GLS

(2)

Fixed-eff.

(3)

Fixed eff.

(4)

IV2SLS

(5)

IV2SLS

(6)

IV2SLS

(7)

Public Climate Change

Agreement

(log of percentage public

agreement with scientific

consensus on global

warming)

-4.84***

(0.47)

-1.13***

(0.19)

-0.34***

(0.08)

-0.24**

(0.12)

-1.21***

(0.36))

-0.31**

(0.15)

-0.62***

(0.22)

Lead Temperature Anomaly 0.11

(0.1)

0.015

(0.03)

-0.008

(0.01)

-0.01

(0.02)

0.1*

(0.02)

-0.008

(0.015)

0.01

(0.02)

Income

(log median income, $)

-0.83

(0.8)

0.09

(0.28)

-0.14

(0.23)

0.17

(0.28)

0.21

(0.23)

-0.17

(0.25)

0.24

(0.26)

Poverty

(log % under poverty)

-1.11

(0.47)

-0.24*

(0.14)

0.15

(0.1)

0.16

(0.14)

-0.12

(0.12)

0.16

(0.1)

0.15

(0.11)

Education

(% adult holding bachelor

degree or above)

-0.05***

(0.01)

-0.07***

(0.02)

-0.05***

(0.01)

Major oil producer

(among top 20 percentile)

0.42**

(0.24)

0.47**

(0.22)

0.44***

(0.17)

Political Leaning

(percentage Republican)

-0.08*

(0.04)

-0.11***

(0.03)

-0.1**

(0.04)

Political difference

(% difference b/w

Republicans and Democrates)

0.03*

(0.01)

0.06***

(0.02)

0.05**

(0.02)

Urbanization influence index 0.25***

(0.02)

0.28***

(0.04)

0.25***

(0.02)

Specialized in Mining 1.17***

(0.31)

1.63***

(0.44)

1.38***

(0.23)

Specialized in Manufacturing 0.3**

(0.15)

0.3**

(0.15)

0.36***

(0.13)

Specialized in Farming 1.53***

(0.27)

1.33***

(0.27)

1.48***

(0.22)

Specialized in recreation 0.11

(0.18)

0.09

(0.14)

-0.10

(0.17)

Specialzed in Government 0.35**

(0.14)

-0.37**

(0.15)

-0.20

(0.14)

constant 25.7***

(9.64)

2.54

(4.22)

-6.00**

(2.58)

-9.57***

(041)

1.15

(3.37)

-5.8***

(2.56)

-9.08***

(3.05)

State-time fixed effets no yes no yes yes no yes

County fixed effects no no yes yes no yes yes

R2 0.45 0.4 0.13 0.03 0.4 0.13 0.1

observations 3748 3748 3748 3748 3748 3748 3748

Standard errors type state state state state state state state

*** Significantly different from zero at 99 percent confidence.

TABLE 8

Public climate awareness and Green House Gas Emission per capita of the next year

Dependent Variable : log GHG Emission per capita (log MT CO2 eq. /capita)

Note. - Regression disturbance terms are clustered at the State level. The instrumental variables for public

climate agreement in regressions 4-7 are lagged temperature anomaly and temperature anomaly.

* Significanly different from zero at 90 percent confidence.

** Significantly different from zero at 95 percent confidence.

35

Explanatory Variable

IV2SLS

(1)

IV2SLS

(2)

IV2SLS

(3)

IV2SLS

(4)

IV2SLS

(5)

IV2SLS

(6)

IV2SLS

(7)

Lagged Temperature

Anomaly (2013 and 2015)

0.03*

(0.01)

0.01

(0.014)

0.002

(0.006)

0.027**

(0.011)

0.014

(0.013)

0.027**

(0.012)

0.014

(0.013)

Temperature Anomaly

(2014 and 2016)

0.02*

(0.01)

0.02**

(0.009)

0.011**

(0.003)

0.02**

(0.008)

0.022**

(0.008)

0.019**

(0.008)

0.025**

(0.008)

Lead Temperature Anomaly

(2015 and 2017)

0.01

(0.01)

0.003

(0.013)

-0.001

(0.005)

0.001

(0.08)

-0.0007

(0.011)

0.01

(0.008)

-.0007

(0.01)

CPP trend effect

(coal electricity share in

2015)

-0.0003**

(0.0009)

-0.0003**

(0.0001)

CPP trend effect -Accepting

states

(coal electricity share in

2015)

-0.05***

(0.001)

-.035**

(0.01)

CPP trend effect -rejecting

states

(coal electricity share in

2015)

-0.02**

(0.001)

-.037**

(-0.01)

Income

(log median income, $)

0.51**

(0.17)

0.22**

(0.09)

0.18**

(0.07

0.52**

(0.16)

0.23**

(0.09)

0.51**

(0.16)

0.24**

(0.09)

Poverty

(log % under poverty)

-0.07**

(0.03)

-0.05**

(0.02)

-0.028

(0.03)

-0.07*

(0.03)

-0.05*

(0.025)

-0.07*

(0.03)

-0.05*

(0.03)

constant -1.61

(1.7)

1.34

(0.24)

1.74**

(0.67)

-1.87

(1.71)

1.2

(0.96)

-1.79

(1.73)

0.78

(0.98)

County fixed effects yes yes yes yes yes yes yes

State-time fixed effets no yes no no yes no yes

Time fixed effects no no yes _ _ _ _

R2 0.28 0.34 0.3 0.28 0.35 0.28 0.35

F-test 7.1 7.6 8.94 7.34 7.77 7.24 7.76

observations 1550 1550 1550 1550 1550 1550 1550

Standard errors type nerc nerc nerc nerc nerc nerc nerc

TABLE 9

Note. - Regression disturbance terms are clustered at the NERC level. The instrumental variables for PCCA are lagged and

current temperature anomalies.

*** Significantly different from zero at 99 percent confidence.

* Significanly different from zero at 90 percent confidence.

** Significantly different from zero at 95 percent confidence.

Dependent Variable : log % public agreement with scientific consensus on global warming

Public climate change agreement and (Lagged) temperature anomaly

36

Explanatory VariableIV2SLS

(1)

IV2SLS

(2)

IV2SLS

(3)

IV2SLS

(4)

IV2SLS

(5)

IV2SLS

(6)

IV2SLS

(7)

Public Climate Change

Agreement

(log of percentage public

agreement with scientific

consensus on global

warming)

-5.05***

(0.98)

-5.00*

(3.1)

-4.34*

(2.8)

-4.07***

(1.16)

-6.9*

(3.3)

-4.04***

(1.31)

-6.51**

(3.32)

Lead Temperature Anomaly 0.17

(0.12)

0.05

(0.16)

0.17

(0.12)

0.16

(0.12)

0.011

(0.16)

CPP trend effect

(coal electricity share in

2015)

-0.4

(0.2)

-.6**

(0.2)

CPP trend effect -Accepting

states

(coal electricity share in

2015)

-0.55*

(0.32)

-1.14***

(0.3)

CPP trend effect -rejecting

states

(coal electricity share in

2015)

-0.39

(-0.3)

-0.57**

(0.3)

Income

(log median income,$)

-0.51

(2.62)

-0.88

(2.82)

-0.47

(2.7)

-0.64

(2.58)

-0.96

(2.65)

-0.7

(2.54)

-.36

(3.43)

Poverty

(log % under poverty)

-0.77**

(0.37)

-0.85

(0.22)

-0.77**

(0.38)

-0.75

(0.33)

-0.87

(0.71)

-0.75**

(0.32)

-1.07

(0.75)

constant 27.45

(24.20)

31.73

(30.91)

24.56

(22.12)

25.34

(24.36)

26.35

(30.85)

25.86

(24.06)

32.14

(31.03)

State-time fixed effets no yes no no yes no yes

County fixed effects yes yes yes yes yes yes yes

time fixed effects no no yes _ _ _ _

R2 0.13 0.14 0.16 0.1 0.09 0.28 0.11

observations 1550 1550 1550 1550 1550 1550 1550

Standard errors type nerc nerc nerc nerc nerc

*** Significantly different from zero at 99 percent confidence.

TABLE 10

Public climate awareness and Green House Gas Emission per capita

Dependent Variable : GHG Emission per capita (log MT CO2 eq. /capita)

Note. - Regression disturbance terms are clustered at the NERC level. The instrumental variables for public

climate agreement are lagged and current temperature anomalies. For the state-time fixed effects, texas is omitted

to avoid colliniarity.

* Significanly different from zero at 90 percent confidence.

** Significantly different from zero at 95 percent confidence.

37

Public opinion of climate change – US, 2018

Figure 5: The US public opinion of climate change measures the extent to which the beliefs of

Americans are aligned with the scientific consensus on climate change. The figure is depicted

from the Yale Climate Opinion Map 2018 (http://climatecommunication.yale.edu/visualizations-data/ycom-us-2018/).

PCCA 49%

PCCA gap 51%

Awareness gap 16%

Denial gap 14%

Skepticism gap 13%

Knowledge gap 8%

Total 100%

PCCA gap - US, 2016

TABLE 11

SSC

Global ($)

SSC

US ($)

Model-based 40 6

Survey-based

(economists) 80 12

Survey-based

(all experts) 200 30

TABLE 12

Social Cost of Carbon (SSC)

38

model based

survey based

(economists)

survey based

(all experts) model based

survey based

(economists)

survey based

(all experts)

Knowledge gap 8% 0.27 1.63 3.27 8.17 10.8 21.6 54

Skepticism gap 18% 0.61 3.68 7.35 18.39 24.4 48.8 122

Denial gap 14% 0.48 2.86 5.72 14.3 19.2 38.4 96

Awareness gap 11% 0.37 2.25 4.49 11.24 14.8 29.6 74

Total gap 51% 1.74 10.42 20.84 52.09 69.6 139.2 348

Note: Calculations are based on social cost of carbons in the Table 12. The total CO2 emissions generated by direct emitting facilities in 2017 is

2.735 Billion tCO2. The PCCA level in 2017 is 49%. The PCCA elasticity of GHG emission per capita used in the calculation is 0.61. That is if

PCCA increases 1% (i.e. PCCA=49.49%) the GHG emission will reduce for 0.61%.

Welfare effect -US (Billion $) Welfare effect- Global (Billion $)equivalent

CO2

(Billion tCO2)PCCA GAP

TABLE 13

Welfare damage of PCCA gaps - Global and US