Essays at the Intersection of Public

James A. Gordon

2019

THE JOURNEY TO GREEN ECONOMIES: ESSAYS AT THE INTERSECTION OF PUBLIC

POLICY, THE MARKET, AND ENVIRONMENTAL SUSTAINABILITY

JAMES A. GORDON

ABSTRACT

As problems like rising temperatures, declines in global biodiversity, and the great pacific garbage patch can attest, environmental sustainability is arguably emerging as the issue of the 21st century. It is also an issue that holds the ignominy of being highly complex, and likely to influence every aspect of government and modern society. For many of the most difficult problems in environmental sustainability, like climate change or sustainable development, the pursuit of long-term sustainability is as much about the journey as it is the outcome. Across three essays connected by public policy, markets, and the environment, I detail some of the ways demand for greater environmental sustainability will change governments, firms, and society. I also offer a pathway for future research in environmental governance, environmental policy, and education.

In chapter one, I explore the potential impact of unseasonably warm and cold temperatures, as expressed as variations from monthly averages, and particulate matter pollution (PM2.5), on three measures of input productivity for firms engaged in manufacturing: labor, capital, and total factor productivity (TFP). Results suggest that value-added labor, capital, and TFP are negatively affected by large variations in daily average temperatures.

Both value-added labor and TFP are adversely affected by unseasonable warmth, while value-added capital is affected by unseasonable cold. Labor productivity is also negatively affected by increases in PM2.5. If climate change produces a warmer climate and less stable daily temperatures, this

will have important implications for how firms allocate resources between labor, capital, and technology, and have ramifications for both labor markets and climate change adaptation policy.

In chapter two, I exploit the unique longitudinal characteristics of the Thomson Reuters ESG database to investigate whether investors support efforts by firms to become more environmentally sustainable. Across three regions and three categories of environmental corporate social responsibility, I find evidence that investors often penalize firms for their environmental efforts. In North America, investors penalize firms for emissions efficiency and overall environmentalism; in Asia Pacific, they penalize resource efficiency; and in Europe, investors penalize environmental innovation and overall environmentalism. Globally, results show that a one unit increase in firm-level environmentalism on a 0-100 scale reduces market capitalization by about 75 cents per $100 of total capitalization. Likewise, building a passive portfolio of the top 10 percent of environmentally friendly firms underperforms the broader market. On average globally, investing $10,000 in the top 10 percent of firms for average environmentalism reduces the hypothetical investor’s annual returns by about $129.

In chapter three, I investigate environmental sustainability within the context of Public Administration (PA). While greater preemptive research in environmental sustainability is needed, I show that PA research generally lags political and social changes to administrative organizations. I review the philosophical and practical difficulties of environmental sustainability as it relates to public administration, and I develop a framework for future research. Finally, I review MPA programs to see how environmental sustainability is treated by the field, and how PA is engaging with future researchers. Of the top 18 PA departments with MPA programs, four offer a specialized masters with a focus on sustainability, with nine offering at

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least one course using sustainability as the primary pedagogical tool.

Teaching is primarily conducted by non-PA scholars, suggesting PA lacks proprietary knowledge of environmental sustainability.

ACKNOWLEDGMENTS

First and foremost I would like to thank both my advisor, Erdal Tekin, and Daniel Fiorino, for their advice and feedback throughout the entire PhD process. Without them the process would have been a far less enjoyable and more muddled experience. I would also like to thank my committee members,

Claire Brunel for her amazing feedback, David Marcotte for challenging me to produce great research, and Todd Eisenstadt for his insightful commentary. I would also like to recognize Neil and Ann Kerwin for their generous financial support during the dissertation stage of my PhD. I am also indebted to the many people at American University who have suffered through discussions of my research. This includes Karen Baehler, Al Hyde, Jocelyn Johnston, Robert

Briggs, and Lilli Shaffer.

Finally, I would like to express my sincere thanks to Grace Baker for all her editorial guidance and support throughout the PhD process, my daughter Emma for her legendary patience, and Gus the cat for staying up late while we worked on the dissertation together.

TABLE OF CONTENTS

ABSTRACT...... ii

ACKNOWLEDGMENTS...... v

LIST OF TABLES...... vii

LIST OF ILLUSTRATIONS...... ix

LIST OF ABBREVIATIONS...... x

CHAPTER ONE PRODUCTIVITY AND THE ENVIRONMENT – A FIRM LEVEL APPROACH...... 11

Introduction...... 12 Background...... 14 Data...... 19 Data Matching Principles...... 24 Conceptual Framework and Empirical Methodology...... 25 Results...... 29 Conclusion...... 35 Tables and Figures...... 37

CHAPTER TWO GREEN CAPITAL AND PROSOCIAL INVESTING – EVIDENCE FROM INVESTOR RETURNS IN THE STOCK MARKET...... 64

Introduction...... 65 Literature and Conceptual Framing...... 68 Data...... 81 Empirical Methodology...... 86 Results...... 90 Policy Discussion...... 95 Conclusion...... 103 Tables and Figures...... 105

CHAPTER THREE ENVIRONMENTAL SUSTAINABILITY AND PUBLIC ADMINISTRATION...... 122

Introduction...... 123 Literature Review and Conceptual Framing...... 124 Environmental Sustainability as a Growing Concept...... 128 What is “Good” Environment and “Good” Sustainability...... 133 Designing Policy for Environmental Sustainability...... 140 The Risks and Opportunities of Environmental Sustainability for Public Administration...... 148 Towards a Sustainable Public Administration...... 152 Teaching for Sustainability and the Environment...... 169 Conclusion...... 176 Tables and Figures...... 179

REFERENCES...... 182

LIST OF TABLES

Table 1 Summary of Key Results...... 29

Table 2 World Bank Productivity Descriptive Statistics...... 37

Table 3 Berkeley Earth Descriptive Statistics...... 38

Table 4 Annual Average PM2.5 Concentrations by Region and Year...... 39

Table 5 Temperature variation from monthly mean on Value-added Labor productivity...... 40

Table 6 Temperature variation from monthly mean on Value-added Capital Productivity...... 41

Table 7 Temperature variation from the monthly mean on Value-added TFP..... 42

Table 8 Temperature variation from the monthly mean on sales-based Labor productivity...... 44

Table 9 Temperature variation from the monthly mean on sales-based Capital productivity...... 45

Table 10 Temperature variation from the monthly mean on sales-based TFP.... 46

Table 11 Extreme temperature variation from monthly mean on value-added Labor...... 48

Table 12 Extreme temperature variation from monthly mean on value-added Capital...... 49

Table 13 Extreme temperature variation from monthly mean on value-added TFP...... 50

Table 14 Hi/Low Extremes on Labor Sales Productivity...... 51

Table 15 Hi/Low Extremes on Capital Sales Productivity...... 52

Table 16 Hi/Low Extremes on TFP Sales...... 53

Table 17 Extreme Daily Maximum Temperatures on value-added Labor Productivity...... 54

Table 18 Extreme Daily Maximum Temperatures on value-added Capital Productivity...... 55

Table 19 Extreme Daily Maximum Temperatures on value-added TFP...... 56

Table 20 Extreme Daily Maximum Temperatures on Sales-Based Labor Productivity...... 58

Table 21 Extreme Daily Maximum Temperatures on Sales-Based Capital Productivity...... 59

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Table 22 Extreme Daily Maximum Temperatures on Sales-Based TFP...... 60

Table 23 Particulate Matter (PM2.5) and Employee Value-Added Productivity.. 62

Table 24 Particulate Matter (PM2.5) and Employee Output Productivity...... 63

Table 25 Different Types of CSR/SRI Investors...... 79

Table 26 Policy Responses to Investor Interest in E-CSR...... 80

Table 27 Summary of Key Empirical Results...... 91

Table 28 Indexes, Size and Location...... 105

Table 29 Breakdown of Observations by Industry...... 106

Table 30 ESG Summary Statistics...... 107

Table 32 Total Stock Return Summary Statistics (by whole sample and regions)...... 111

Table 33 Environmental Score Approach - Global - Total Stock Return...... 112

Table 34 Environmental Score Approach - Global - Total Stock Return with Interaction...... 113

Table 35 Environmental Score Approach - Total Stock Return Marginal Effects...... 114

Table 36 Environmental Score Approach - Global - Total Stock Return with Dividends...... 115

Table 37 Environmental Score Approach – Global TSR with Dividends - with Interaction...... 116

Table 38 Environmental Score Approach TSR with Dividends - Marginal Effects...... 117

Table 39 Portfolio Approach - Global - Total Stock Return...... 118

Table 40 Portfolio Approach – TSR Marginal Effects...... 119

Table 41 Portfolio Approach - Global - Total Stock Return with Dividends.. 120

Table 42 Portfolio Approach - Total Stock Return plus Dividends - Marginal Effects...... 121

Table 43 - Basic Characteristics of MPA Granting Departments...... 172

Table 44 - Teaching and Availability of Env. and Sustainable Courses...... 179

Table 45 Example of UN SDGs and EPI Goals and Indicators...... 180

Table 46 Summary of Key Variables and Concepts...... 181

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LIST OF ILLUSTRATIONS

Figure 1 Investors, CSR, and Sustainable Development...... 68

Figure 2 Examples of Thomson Reuters ESG Indicators...... 74

Figure 3 ESG Time Trends...... 108

Figure 4 Planetary Boundaries...... 136

Figure 5 Reduced-Form Logic of Governance...... 154

LIST OF ABBREVIATIONS

ACES The American Clean Energy and Security Act of 2009 AOD Aerosol Optical Depth AQA Air Quality Act of 1967 CAA Clean Air Act CEQ US Council on Environmental Quality CO2 Carbon Dioxide COGS Cost of Goods Sold CPI Climate Policy Integration CPP Clean Power Plan CSR Corporate Social Responsibility E-CSR Environmental Corporate Social Responsibility EPA Environmental Protection Authority EPI Environmental Policy Integration ESA Endangered Species Act ESG Environmental-Social-Governance ETS Emissions Trading System FE Fixed Effects GHG Greenhouse Gas GPI Genuine Progress Indicators ISIC International Standard Industrial Classification MA Master of Arts MISR Multi-angle Imaging Spectroradiometer MODIS NASA Moderate Resolution Imaging Spectroradiometer MPA Master of Public Administration NEPA National Environmental Policy Act of 1969 NPM New Public Management OLS Ordinary Least-Squares Regression PA Public Administration PM Particulate Matter SeaWiFS Sea-Viewing Wide Field-of-View Sensor SEDAC Socioeconomic Data and Applications Center SEEA System of Environmental-Economic Accounts SRI Socially Responsible Investing SURE Seemingly Unrelated Regression TFP Total Factor Productivity TR Thomson Reuters TSR Total Stock Return UIB The University of Indiana - Bloomington US SDGs United Nations Sustainable Development Goals

CHAPTER ONE

PRODUCTIVITY AND THE ENVIRONMENT – A FIRM LEVEL APPROACH

ABSTRACT

This paper explores the potential impact of unseasonably warm and cold temperatures as expressed as variations from monthly averages, and particulate matter pollution (PM2.5), on three measures of input productivity for firms engaged in manufacturing: labor, capital, and total factor productivity (TFP). Results suggest that value-added labor, capital and TFP are all negatively affected by large variations in daily average temperatures. Both value-added labor and TFP are adversely affected by unseasonable warmth, while value-added capital is affected by unseasonable cold. Labor productivity is also negatively affected by increases in PM2.5.

If climate change produces a warmer climate and less stable daily temperatures, this will have important implications for how firms allocate resources between labor, capital, and technology, and ramifications for labor markets and climate change adaptation policy.

Introduction

Extreme weather events and air pollution are rising problems in the world and understanding what effect they have on human and economic wellbeing is an urgent concern. Research shows climate change and increased pollution has a negative impact on a number of important economic outcomes, ranging from input productivity in China (Zhang, Deschenes, Meng, & Zhang, 2018), to economic growth in developing countries (Dell, Jones, & Olken, 2012), to country-wide birth rates (Barreca, Deschênes, & Guldi, 2015), and to agricultural productivity (Fisher, Hanemann, Roberts, & Schlenker, 2012). In a world of slowing growth rates, one important piece of this puzzle and an important consideration for climate change policy is understanding how a warming climate and less stable weather affect firm productivity. Research to date shows that climate and/or pollution has a negative effect on productivity, including on call center staff (Cheng et al, 2016), in physically strenuous jobs (Graff Zivin & Neidell, 2012), and in financial markets (Heyes, Neidell, & Saberian, 2016; Meyer & Pagel, 2017). A vast majority of this research, however, represents micro-level studies of specific regions (Sudarshan, Somanathan, Somanathan, & Tewari, 2015; Zhang et al., 2018), facilities (Chang, Zivin, Gross, & Neidell, 2016) or markets

(Anenberg et al., 2017), with few studies considering the effect of climate change on aggregate productivity and no studies considering the effect of

PM2.5 and temperature together. But climate change is likely to produce a range of economic and human effects throughout the global economy. Since

PM2.5 is also known to be correlated with hotter temperatures (Koken et al.,

2003), understanding how a warming climate and pollution will affect global productivity generally is an important issue.

This paper is the first to examine the effect of particulate matter 2.5 pollution (PM2.5) and variation from average daily temperatures on three

types of firm-level input productivity: labor, capital and total factor productivity at this level of aggregation. In order to accomplish this task, the paper assembles data sets from multiple longitudinal sources on air quality, temperature and firm level productivity to create a pooled cross- sectional dataset of about 29,000 firms from 114 countries. In order to account for possible correlation in the error terms between productivity measures, the paper estimates a system of equations using Seemingly Unrelated

Regression (Zellner, 1962). To address potential endogeneity bias in the data, the empirical strategy includes year and industry fixed effects by ISIC code, a series of firm level controls, and a set of regional dummies based on

World Bank regions and longitudinal data to account for geographic heterogeneity. Therefore the identification comes from within industry and regional variation instead of between different regions and industries.

Finally, as the World Bank survey uses a stratified random sample in their survey design, this paper uses related strata information in constructing standard errors.

Results suggest that large variations in daily average temperatures adversely affects value-added labor, capital and TFP. The empirical model predicts that each additional day at least 4°C above or below the average monthly temperature reduces value-added labor by 0.1 percent (p<0.05), value- added capital by 0.21 percent (p<0.05) and TFP by .15 percent (p<0.05) on an annual basis. Unseasonable warmth adversely affects both value-added labor (-

0.2 percent, p<0.01) and TFP (-0.15 percent, p<0.10), and unseasonable cold adversely affects value-added capital (-0.7 percent, p<0.01). Labor productivity is also negatively affected by increases in PM2.5 (-0.48, p<0.01).

This has several important implications for climate change. First, if climate change primarily manifests as global warming, then value-added labor

and TFP will become less productive, providing incentives for firms to prefer capital inputs. Given PM2.5 is also positively correlated with temperature and negatively correlated with value-added labor productivity, PM2.5 is likely to produce further negative effects on labor. In this scenario, climate adaptation policy must consider risks to labor’s share of production, and any likely affect this reduction in labor efficiency will have on a range of market outcomes including unemployment, returns to education, among others. Conversely, climate change is also likely to lead to more extreme events, including heat waves and winter storms (Melillo, Richmond, & Yohe,

2014). In this instance all forms of productivity will suffer, but capital most of all. In this scenario, climate adaptation will require firms to invest in more capital for the same level of output, or switch to more labor or technical efficiency. Regardless, manufacturing efficiency will be adversely affected by climate change due to greater variation in average temperatures. The result will be lower economic output for a given level of inputs.

Background

Particulate Matter

Although the effect of total suspended particulates on health has been well established, narrowing down this effect to very small particulate matter, especially PM2.5 and to a lesser degree PM10, is gaining prevalence.

Indeed, recent evidence indicates that toxicity increases as particulate matter becomes smaller, and that such toxicity varies by the chemical composition of the particles, with industrial chemicals posing greater risks

(Harrison & Yin, 2000). This is especially relevant for developing countries with higher air pollution resulting from industrial activity, especially since industrial air pollution always includes PM2.5. However, there is also

evidence that particulate matter need not be composed of toxic chemicals to be a health hazard. Very small particles, so called fine particulate matter or PM2.5, range in diameter up to 2.5휇푚, and are increasingly being found toxic to human health simply due to their size (Donaldson & MacNee, 1998).

This makes them relevant to all countries emitting PM2.5, even at low levels.

In the United States in 2013, the EPA set new standards for PM2.5 air quality, basing their decision to lower particulate matter levels on evidence that there is a “clear causal and likely causal relationship between long term and short term exposure to fine particulate matter and health” respectively (EPA, 2013). Such health effects include cardiovascular disease, liver damage, and increased risk of heart attack and stroke (Donaldson &

MacNee, 1998). Meanwhile, short term mechanisms that might affect workers include pollution induced changes to mood or cognitive impairment (Heyes et al., 2016). Moreover, PM2.5 can readily penetrate buildings, affecting indoor as well as outdoor workers (Thatcher & Layton, 1995). Since such particles are absorbed through the lungs, are the result of urban and industrial pollution, and can cause short as well as medium term health impairment, they have the potential to affect labor productivity. This could be through reducing work-effort by employees or through impairment to human capital. In the first instance, He et al. (2016) found that a 10% increase in PM2.5 over

25 days decreased daily output at two manufacturing sites in China by between

0.5% and 3%, while Chang et al. (2016) found a 6% decrease in worker productivity for a 10-unit change in PM2.5 at a pear packing plant in

California. This was despite air pollution at the plant being below the current EPA NAAQS standard for particulate matter. Likewise, evidence indicates that PM2.5 levels affect test scores during high stakes exams

(Ebenstein, Lavy, & Roth, 2015), and investor returns at the NYSE (Heyes et al., 2016). Both are clear examples that PM2.5 could impact short term human capital productivity. Separate from the effect on labor, the effect (if any)

of PM2.5 on capital productivity or technical efficiency is unknown, although one possible mechanism may be through damage to machinery as can occur with extreme temperature (Mortier, Orszulik, & Fox, 1992). Suffice to say, if adverse effects from PM2.5 exist, it would provide incentives for firms to shift from pollution impaired labor, including human-capital augmented labor, to capital inputs or investment in technical efficiency. Over time this leads firms to overemphasize capital investment, potentially incentivizing a public policy response if policy makers are concerned about the loss of jobs due to environmental induced automation (Bovenberg & Smulders, 1995; Cole, Elliott,

& Shimamoto, 2005). Conversely, PM2.5 could impair human capital in a way that reduces returns from technology, potentially reducing the incentive for firms to modernize and invest in improved technology.

Temperature

Research into the effect of temperature on workplaces has only recently gained attention, potentially due to increasing concerns around climate change and long-term economic growth. Early research in this field tended to concentrate on agricultural settings, generally finding an economically significant negative effect between increased temperature and farm productivity (Deschênes & Greenstone, 2012; Fisher et al., 2012; Schlenker &

Roberts, 2009). However, considerable recent literature is beginning to show that extreme temperature and severe weather patterns may also impact productivity in a gamut of work environments. For example, using municipal- level household income data from across 12 countries, Dell et al (2009) found that both cross-country and within-country variation in income can be partially explained by variation in temperature, with about half the short- term negative effects offset with long-term adaptation (Dell, Jones, & Olken,

2009). Likewise, research on manufacturing productivity in China indicates that extreme weather days (set as days above 90°F) reduce total factor

productivity among Chinese firms. Here, the authors suggest that unfettered climate change could reduce Chinese manufacturing by 12 percent by the middle of this century (Zhang et al., 2018). Finally, data from the auto industry in the United States suggests that severe weather, again the count of days above

90°F, reduced auto output by about 1.5 percent (Cachon, Gallino, & Olivares,

2012). For direct effects on labor productivity, the effect of temperature is complicated. On the one hand, Niemala et al. (2002) found that increasing temperature by 2.5°C reduced call center productivity by 5-7 percent, while

Lan et al. (2011) showed an increase of 8°C reduced worker efficacy on neurological tests designed to proxy worker productivity. Relatedly, Cai et al. (2016) documented that worker productivity displayed an inverted U-shape with temperature. Piece-rate productivity among workers at the factory they investigated was highest when outside temperatures were around 76-79°F, reducing by 11 percent below 60°F and by 6.7 percent above 95°F. This effect was limited to non-local laborers with long term residents near the factory unaffected by temperature, and accords with similar conclusions by Zhang et al. (2018) who found an inverted U-shape in TFP. Overall, their key pathways argument is that absenteeism did not explain drops in output, but rather it was thermal stress inducing production losses. Meanwhile, from a human capital perspective, Graff Zivin et al. (2018), using NLSY79 data, found that high ambient temperature (above 78.8°F) affected results on math test scores in a statistically significant manner. This suggests that the mechanism for temperature affecting labor productivity is likely through reductions in cognitive performance. However, they argue this effect largely dissipates over time due to long run compensatory factors.

Finally, the effect on capital productivity is less well known, although there is reason to believe machinery could be affected by thermal stress through increased breakdowns and maintenance (Mortier et al., 1992),

or through exogenous mechanisms such as increased energy and cooling costs, or greater instances of power outages due to extra demands during heat waves.

Within a conceptual framework of productivity and output, Cole et al. (2005) suggested that if pollution, and in this instance temperature, affect labor productivity, then firms would switch to greater automation. This is also likely to vary by industrial sector as each sector is impacted by climate differently. This is also true for exchanges between capital and human capital, where the investment in one may increase if the other is impaired by pollution (or persistent, extreme temperature) (Bovenberg & Smulders, 1995).

Particulate Matter, Temperature and Effort

While the discussion above focuses on particulate matter and air temperature separately, there is some evidence that they function together.

PM2.5’s primary mechanism for affecting workers is due to its small size and respirable nature (Donaldson & MacNee, 1998), but extreme or higher than normal ambient temperatures place greater respiratory burdens on workers.

Indeed, high temperatures are positively associated with greater hospitalizations for heart related conditions (Koken et al., 2003) and cardiovascular disease (Michelozzi et al., 2009). Since the general mechanism for temperature and PM2.5 affecting human health is similar, the effect may be cumulative. According to Koken et al. (2003), there is a high degree of correlation between daily maximum temperatures and particulate matter. Since industrial activity primarily occurs during the day, most air pollution is being generated as temperatures peak during daylight. At the same time, it has been well established that countries with hotter climates also tend to be poorer (Nordhaus, 2006).

Data

To investigate the effect of PM2.5 and temperature on firm-level productivity, the paper brings together data from several sources. Enterprise data is collected from the World Bank Enterprise Survey which includes harmonized estimates of capital, labor and total factor productivity for firms across 128 different countries, primarily in non-advanced economies.

The second dataset is of annualized PM2.5 data from NASA’s SEDAC program and has been used for a variety of research including for investigating the effect of diesel fuel consumption on PM2.5 emissions (Anenberg et al., 2017) and the role of PM2.5 in infant mortality (Heft-Neal et al., 2018). Finally, data on temperature is collected from the Berkeley Earth gridded daily temperature anomaly dataset (Berkeley Earth, 2019). The following sections discuss each data source in detail.

World Bank Enterprise Survey

This paper uses firm level productivity measures from the World Bank

Enterprise survey for the years 2006-2017, representing the earliest period from when the World Bank’s Enterprise Analysis Unit first employed their

“Global Methodology” to the survey, making it directly comparable over time, until 2017 to match the environmental datasets discussed below (The World

Bank). To conduct its survey, the World Bank selects a representative sample of firms based on a stratified random sampling methodology that groups firms by size, geographic location, and industry. The survey has a different set of questions depending on whether a firm is a manufacturer or service provider.

As a complete set of questions on productivity are only asked in the manufacturing module, I limit my analysis to firms who are in the manufacturing sector as defined by ISIC codes 15-37.

The most important economic indicator derived from the World Bank survey, and one of the three key indicators of input productivity used in this paper, is an estimate of total factor productivity. While a full methodological description is available from the World Bank website1(2017), for this paper, it is important to recognize that the World Bank uses two methods for calculating TFP: the first is a value-added model (VAKL), and the second is an output-based model (VKLM). For both labor and capital, output based productivity is also derived from World Bank estimates and represents output either per total cost of labor or per replacement cost of machinery, vehicles and equipment. For labor (L), this can be expressed as:

(푆푎푙푒푠푖) 퐿(표푢푡)푖 = (1) 퐿푎푏표푟퐶표푠푡푠푖

while for capital (K) the equivalent is:

(푆푎푙푒푠푖) 퐾(표푢푡)푖 = (2) 퐶푎푝푖푡푎푙푖 where all values are in 2009 USD terms. The key difference between this model and the value-added model is the use of cost-of-goods sold2 (COGS) acting as a negative on output. Here, value-added labor productivity for firm 푖 is:

(푆푎푙푒푠푖 − 퐶푂퐺푆푖) 퐿(푉퐴)푖 = (3) 퐿푎푏표푟퐶표푠푡푖

1 See https://login.enterprisesurveys.org/content/sites/financeandprivatesector/en/ library/combineddata.html (last accessed 11/11/2019). Note: requires (free) login credentials

2 In the World Bank survey, terminology used is the accounting cost-of-goods sold. This is synonymous with cost of inputs.

and for capital (K):

(푆푎푙푒푠푖 − 퐶푂퐺푆푖) 퐾(푉퐴)푖 = (4) 퐶푎푝푖푡푎푙푖 where all values are in 2009 USD terms. As part of the seemingly unrelated regression, 퐿(푉퐴)푖, 퐾(푉퐴)푖 and the VAKL model for TFP are calculated together when analyzing the effect of weather and pollution on value-added productivity, while 퐿(표푢푡)푖, 퐾(표푢푡)푖 and the YKLM model is used when examining output-based productivity. During the calculation of TFP, the World Bank removes outliers three standard deviations from the mean on each key economic indicator. For consistency, these observations are also set to missing in this paper. Summary statistics are shown in Table 2. On average, capital productivity per firm was $16.12 (standard deviation of $56.28) of sales output and $9.46 (S.D. of $33.00) of value-added output per unit of capital employed. Europe and Central Asia had the lowest value at $7.00 (SD of $27.1) of value added compared to East Asia and the Pacific region with $13.30 (SD

$41.8) worth of output per unit of capital employed. Average output per dollar cost of labor is $11.31 (SD $20.68) and $6.37 (SD $12.18) for value- added labor.

PM 2.5 Data

The particulate matter data used in this paper comes from NASA’s

Socioeconomic Data and Applications Center’s (SEDAC)The Global Annual PM2.5

Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR,

1998-20163 dataset. SEDAC developed this dataset primarily by collecting so- called Aerosol Optical Depth (AOD) readings from satellite instruments, including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS),

Multi-angle Imaging Spectroradiometer (MISR) and the Sea-Viewing Wide Field-

3 https://beta.sedac.ciesin.columbia.edu/

of-View Sensor (SeaWiFS), before combining them and calculating the near- surface PM2.5 concentration levels looked at here (Aaron Van Donkelaar et al., 2016). The dataset designers then supplemented the satellite data with surface monitors for the period 2008-2013 (see Brauer et al.(2016) for details on these monitors) and other instruments to provide global annual

PM2.5 concentrations. A more detailed description of the methods used is available in Van Donkelaar et al. (2016) and van Donkelaar et al. (2018).

Data is collected for the years 2005 until 2017 to reconcile with firm-level fiscal year data from the World Bank Enterprise Survey. Each year in the

SEDAC database is presented as a separate GeoTiff raster file and was matched to the WBES using the gdalUtils module developed for R4. This was done by generating grid co-ordinates at the 0.01° level for the center of each city or region for each firm in the WBES, then matching by co-ordinate to each yearly iteration of the SEDAC dataset. In total, 738 locational points were matched over the twelve years of interest with 443 having at least one firm with a full set of productivity measures. This represents about 72.3 percent of firms from the original World Bank dataset where productivity measures are included. One clear limitation of this method is that since enterprise location is either at the city or regional level, and PM2.5 data is taken from what would be between about 0.46 and 1.1km5 from the city or regional center, the PM2.5 and enterprise data do not reconcile perfectly.

Summary statistics are shown in Table 4. Average PM2.5 readings across the data set was 19.4 with considerable variation between years. For example, the lowest PM2.5 reading by year in the dataset is 2007 at 13.0, with the highest average occurring in 2013 at 33.5. Note that PM2.5 averages are for

4 https://cran.r-project.org/package=gdalUtils 5 In metric terms, 0.01° longitude is about 1.1km in length at the equator and about 0.46km at 67° north and south. In this dataset, cities range in distance from the equator to about 39°S and 67°N.

those locations surveyed by the World Bank as part of the Enterprise Survey for that year. For example, Albania was surveyed in 2007 and again in 2013, with PM readings for Albania taken for those time periods. Level of PM2.5 also vary greatly by region. Locations in the Middle East and North Africa recorded the lowest levels in this dataset, at 9.4 while South Asia was the highest at 39.9. Across the entire dataset, the lowest annual PM2.5 level is

0.2 while the highest is 87.3.

Berkeley Earth Temperature Data

Data on temperature variation is taken from the Berkeley Earth project’s gridded daily dataset (Berkeley Earth, 2019) for the years 2005 through 2017. Each data point is recorded at the 1° latitude by 1° longitude level, centered around 0.5°, and represents the daily surface air temperature anomaly from the mean monthly temperature for that grid reference. Berkeley

Earth uses a specific program, known as Berkeley Average, to calculate mean temperatures from 14 databases and 14.4 million temperature observations across over 44,000 sites (Rohde, A. et al., 2013). One benefit of using this dataset is that it provides very little error in the calculation of average temperature compared to its peers, especially during the timeframe used in this paper where the average annual error is less than 0.1° Celsius (Rohde,

2013). To match firm location to temperature data, the central co-ordinate for each city or region is selected, then rounded to the nearest 0.5° on both latitude and longitude, then matched to the Berkeley Earth dataset. In total,

4,758 data points are matched for each location. Finally, this data is sorted into bins as described in 0 – Data Matching Principles.

Summary statistics for the Berkeley Earth dataset are shown in Table 3.

On average across the entire dataset, locations experienced 287.9 days within

2°C of the locational monthly mean, with Europe and Central Asia having the

highest level of variation with an average of 157.6 days of normal weather, and Africa having the least amount of variation with 319.4 days per year being within 2°C of the monthly mean. Across all regions, locations had 19.1 days of extreme temperature per annum on average. South Asia was the lowest with just 5.5 days, while Europe and Central Asia were highest with 93 days per annum.

Data Matching Principles

One important consideration with weather data is that there is often a non-linear relationship between temperature and the outcome of interest, especially when you start looking at extreme temperature values (Auffhammer,

Hsiangy, Schlenker, & Sobelz, 2013). One common method in the literature to account for this non-linearity, and to estimate the effect of daily temperature readings on annual economic statistics, is to discretize the distribution of daily data into bins representing the total number of days with temperature readings relevant for that bin (Deschênes & Greenstone,

2011; Zhang et al., 2018). For this paper, five bins are chosen: < -4°C representing a much colder than normal day; -4°C to -2° representing a mild day; -2°C to 2°C representing an average day; 2°C to 4°C representing a slightly warm day, and greater than 4°C to represent a very warm day compared to the mean for the city. To populate the bins, I take the fiscal year end for the firm’s country as defined in the CIA World Factbook (2016) and count back exactly one year. One reason to use five bins, including two with large positive and negative variance from the mean is because although climate change is predicted to increase global temperatures, it is also likely to lead to more extreme events, including heat waves and winter storms(Melillo et al., 2014). To investigate this latter effect in aggregate, I include complimentary analysis where the key dependent variable is a bin representing days of extreme temperature, whether positive or negative, examined against a

control bin of -2°C to 2°C days. I also investigate daily maximum temperature variation from the monthly mean using a similar methodological approach described above.

For particulate matter, the readings from SEDAC are already annualized averages. However, to match this data where fiscal year is not the same as calendar year, I assign a ratio of PM over the number of days for the two years of the fiscal year for that firm. For example, a firm with a June 30 fiscal year end would have 50.5% of its PM attributed from year t-1 and 59.5% from year t6.

Conceptual Framework and Empirical Methodology

To conceptualize the role of inputs in producing firm-level output, U use a simple Cobb-Douglas production function that includes output (Y), labor

(L), capital (K), and total factor productivity (A). This is based on work by

Francis & Karalashvili (2017), Zhang et al (2018), Sorenson & Whitta-Jacobsen

(2010), and Cobb & Douglas (1928). As output is a function of inputs, we have:

푌 = 퐴(퐿)휎퐿(퐾)휎퐾 (5)

Output elasticities are given for labor and capital as 휎퐿 and 휎퐾 respectively. Taking the log of both sides yields:

퐿푛(푌) = ln(A) + 휎퐿퐿푛(퐿) + 휎퐾퐿푛(퐾) (6) In effect, as PM2.5 and weather conditions have the potential to affect each component of log output, firms may reallocate factor inputs in response to unusual temperatures or pollution due to a change in the relative efficiency of each input. The goal of the empirical analysis is to test how

6 For a leap year in year t, the ratio would be 49.7% from year t and 50.3% from year t-1.

each log input is affected, and whether there is any compensatory effect between inputs.

Given the multiple outcomes considered in this paper, the empirical analysis is performed using a Seemingly Unrelated Regression (SURE) (Zellner,

1962) to investigate the effect of environmental conditions on input productivity. In choosing a SURE model, the intention is to account for any possible cross correlation in error-terms between the different types of input productivity. This maximizes the efficiency of my estimators even if

OLS estimates are otherwise consistent (S. Martin & Smith, 2005). Note that using the SURE method only produces slightly different results compared to

OLS estimators, resulting in no change in interpretation between OLS and SURE estimates, and for this reason, only SURE estimates are presented.

The empirical aim is to estimate a system of equations, whether in OLS or SURE of the form:

푚 푚 푚 푚 푚 푚 푙푛(푝푟표푑푢푐푡푖푣푖푡푦)푖푡 = 훽0 + β1 푇푒푚푝푖푡 + β2 푃푀푖푡 + β3−푛푐표푛푡푟표푙푠푖 + 훾푡 + 훿푖 + ϵ푖푡 (7) where m represents the productivity measure of interest: labor, capital or total factor productivity; 푖 represents each firm; temp is a vector of bins representing count of days in the bin for the relevant year; t represents

푚 fiscal year for firm 푖, controls is a vector of controls and 휖푖푡 represents the idiosyncratic error term for firm 푖 in year t for productivity measure m. The model includes both industry (훿푖) and year (훾푡) fixed effects. Industry is at the two-digit ISIC level. These control for possible endogeneity bias at the industry and year levels that could affect productivity and the environmental variables of interest. Firms across different industries will likely face different levels of productivity and environmental regulations, and they will likely have different preferences to location. This might result in different rates of exposure to climate change, and access to similar levels of economic

development. The industry fixed effect controls for these issues. Meanwhile, the year fixed effect controls for global time trends in warming, productivity, and pollution control. The full empirical model includes a set of control variables, namely: a categorical variable depending on whether the firm is small, medium or large to account for differences in productivity between firms of varying size and opportunity; a dummy for whether a firm is in a low income country; five regional controls reconciling to the World Bank regions; and finally, a set of five latitudinal control groups. These last two groups aim to account for regional differences in productivity and environmental conditions and follows guidance from Fisher et al. (2012) on the optimal level of environmental controls. The authors of Fisher at al.

(2012) argue that using a local fixed effects model, such as at the city/local region level would in effect capture almost all the variation in temperature, complicating interpretations of the key independent variables of interest. Using a cross section of larger regions and latitudes balances the risk of over specifying the model with discerning important relationships between temperature and productivity.

Since many of the variables used in this analysis are categorical or dummy variables, base categories are selected for each. For the temperature variable, the key base category is the bin representing the counts of days in the previous fiscal year where the daily temperature was within 2°C of the average for that region for that month. This bin is excluded from the model, so the interpretation of the remaining β1 estimators is in comparison to this reference group. This has two benefits. First, we are interested in extreme variation from the mean, with days being within 2°C probably representing an average monthly day for most people. Second, climate change is likely to produce increases in average temperatures over time. According to Andronova &

Schlesinger (2001), a doubling of CO2 emissions translates to a 50/50 chance

of a 2°C rise in average temperatures by the year 2100. Thus, using a control variable around 2°C offers a useful guide for examining extreme variations in average temperature, while also giving us a base to study plausible increases in average daily temperatures due to climate change.

Finally, since the World Bank uses a stratified random sample in their survey design, following Abadie et al.(2017), I cluster standard errors at the strata level. The strata include firm size, geographic location and industry. Depending on the model, this produces about 6,500 clusters.

Results

Empirical results proceed along six dimensions representing the three key independent variables, L, K, and TFP, with each measured in output and value-added form. All productivity measures are expressed in natural logs and have been multiplied by 100 to provide easier to read coefficients. Table 1 provides a summary of the key relationships labor, capital and TFP for the value-added models.

Table 1 Summary of Key Results

Seemingly Unrelated Regression - Value Added Daily Average Temperature Labor Capital TFP -0.103** -0.207** -0.149*** Per day > 4°C below & above mean -0.041 -0.097 -0.05

-0.203*** 0.242 -0.15* Per day > 4°C above mean -0.063 -0.172 -0.079

0.071 -0.70*** -0.148 Per day > 4°C below mean -0.093 -0.231 -0.114

Daily Maximum Temperatures Labor Capital TFP Per day of max temp > 4°C above -0.146** -0.168 -0.202*** mean (0.063) (0.149) (0.075)

Full Set of Controls Yes Yes Yes

Labor Productivity and the Environment

For labor productivity, across value-added labor there is a clear negative and statistically significant association between labor and each additional day of highly above average daily mean temperatures compared to long-term monthly averages (Table 5). This relationship is also obtained for daily maximums at least 4°C above the average maximum (Table 11). The point estimates and direction are robust to model specification, with the preferred

model suggesting that each additional day with an average temperature 4°C or more above the mean reduces annual labor productivity by 0.203 percent

(p<0.01) compared to an extra day of normal temperatures. This represents a reduction of about 1.3 cents of value-added labor per dollar of labor cost.

Point estimates for each additional day of unseasonable highs is also statistically significant (p<0.05) with an estimated reduction in value-added labor productivity of .15 percent (.9 cents per dollar of labor) for each additional day compared to a day in the reference group. Empirical analysis of extreme variation in means is shown in Table 17. In the preferred model, the point estimate on the key independent variable, the number of days in the preceding fiscal year with an average temperature either 4°C above or below the long term mean, results in a reduction in annual labor productivity by

0.103 percent (p<0.05) compared to the reference group (Table 11).

However, this direction of effect does not stand up when looking at sales-based labor productivity. Here, no statistically significant result is found across any of the three models: the average daily model (Table 8), total average model (Table 14), and the daily high model (Table 17). However, both the daily average temperature model and the daily maximum temperature models do show negative and statistically significant coefficient for mildly warmer days where the daily temperature was between 2°C and 4°C above normal.

As mentioned in the data matching principles section, the key difference in the design of both models is the use of cost-of-goods sold

(COGS) acting as a negative on value-add. This suggests that although sales are not themselves clearly impacted by higher temperatures, input costs are affecting labor productivity with temperature as a mechanism. Simply, labor may become less efficient in the face of higher positive variation in daily temperatures, resulting in inefficiency in using inputs rather than creating outputs (Cai et al., 2016). Conversely, since value-add is a better measure

of local productivity and local climatic conditions affecting productivity, it is more likely to show the true effect of unseasonable temperature on workplace productivity. The sales method includes production of inputs made elsewhere and subject to a different climate. As such, climatic effects may already be built into the cost of the input but not the level of value-add that contributes to the final sales price.

Finally, for the effect of particulate matter on labor productivity, a different method for expressing labor productivity is used. Since PM2.5 is likely to affect each employee, not cost of employment, the denominator is a count of full-time employees such that:

(푆푎푙푒푠푖 − 퐶푂퐺푆푖) 퐿푖 = (8) 퐿푎푏표푟퐶표푢푛푡푖

The reason for using employee count rather than labor cost is to account for PM2.5 being part of the manufacturing process. Thus, a $1 increase in labor cost would be expected to increase output by at least $1 for a firm to rationally choose to spend the extra dollar, and since PM2.5 is not exogenous to manufacturing, atmospheric PM2.5 would be expected to rise.

For temperature however, capital and TFP are in dollar/elasticity terms, thus labor is also expressed in dollar/elasticity terms. First, analysis using labor costs instead of employee counts show either statistically insignificant or weakly significant and positive effects of PM2.5 on labor productivity (Table 23 & Table 24). One interpretation of this relationship is that by holding labor costs constant, if a firm increases output in some way, PM2.5 pollution might also increase, creating a positive relationship between PM and output. If this were not the case, increases in output would in-effect reduce labor productivity. Since labor is often relatively cheap in the countries surveyed by the World Bank, any extra PM2.5 emissions from increased industrial activity is likely to dominate the net increase in labor

costs, resulting in a positive sign on the PM co-efficient as sometimes occurs in this analysis. On the other hand, on a per employee basis, there is a negative and statistically significant effect of PM2.5 on labor productivity, both when using the value-added and the sales-based method. The point estimate for value-add suggests that each additional unit of PM2.5 reduces annual value-added labor productivity per employee by 0.48 percent, statistically significant at the p<0.01 level (Table 23). Likewise, the point estimate on sales per employee suggests a .294 percent reduction in sales per employee (p<0.05) (Table 24).

Capital Productivity and the Environment

Capital productivity offers an interesting contrast to labor. First, whereas the relationship between each additional day of high average temperatures showed a negative effect on labor productivity, for capital there is no clear effect, whether in the value-added model (Table 6) or sales model (Table 9). However, there is a persistent negative and statistically significant effect of each additional day of unseasonably cold average temperature and capital productivity. In the value-added model, the point estimate predicts that for each additional day of average temperature at least 4°C below the mean, capital productivity is reduced by 0.7 percent

(about 6.6 cents per dollar of investment), significant at the p<0.01 level.

This negative effect is persistent across different model specification.

Likewise, for the sales model, the point estimate was -.561 percent, also statistically significant at the p<0.01 level. Models investigating total count of very high or very low average daily temperatures follows a similar trend. In the value-added model (Table 12), the point estimate is -.207

(p<0.05) while in the sales model, no statistically significant relationship between highly unseasonal temperature days and capital productivity is seen once regional and longitudinal controls are added (Table 18). Finally, when

looking at extreme daily highs in the value-added model, the co-efficient on the >4°C bin is negative and statistically significant across all, bar the final model. A key finding is that while labor productivity is negatively affected by unseasonable warmth, capital is affected by unseasonable cold.

One plausible explanation is that more capital is needed to achieve the same level of output during cold weather, which could be due to expensive investments in insulation, heating, or new equipment that can withstand lower temperatures.

TFP and the Environment

Finally, the data presents little evidence that TFP is affected by either very warm or very cold days when these environmental conditions are considered separately (Table 7 & Table 10), but there is a negative and statistically significant effect on total factor productivity for the bin of days that includes both unseasonably high and low average temperatures added together. As in the labor and capital model, this relationship is robust to model specification. Under the value-added model (Table 13), the point estimate suggests that each additional day of unseasonal average temperature will reduce TFP by 0.149 percent (p<0.01), while the sales model point estimate is also negative (-.121 percent) and statistically significant

(p<0.01) (Table 16). There is also a negative association with days of extreme maximum temperatures and TFP. In the value-added model the point estimate implies a .202 percent (p<0.01) reduction in TFP for each day of extreme temperature at least 4°C above the norm (Table 19), while the sales model suggests a .109 percent decline in TFP (p<0.05) (Table 22).

Discussion of Temperature and Productivity

The analysis presented above depicts a complex relationship between temperature and input productivity. First, there is a clear relationship in the data between unseasonably warm days and reductions in value-added labor productivity. This is true when looking at each additional day of very above average temperatures and very above average maximum daily temperatures. These same conclusions hold for total factor productivity. But whereas there is no clear relationship between sales-based labor productivity and weather, there is a clear negative relationship between weather and sales-based TFP as calculated using the YKLM method. This accords with recent literature looking at TFP and weather in China (Zhang et al., 2018). Conversely, value-added capital productivity appears to be negatively affected for each day of unseasonably cold weather, but with no consistent linkage between warm weather and capital seen across the analysis.

There is however a strong relationship between the bin representing the cumulative count of extreme temperature days, whether very cold or very warm, and all types of value-added input productivity. In the full model, value-added capital has the highest negative co-efficient (-0.207, p<0.05) followed by TFP (-0.149, p<0.01) then labor (-0.103, p<0.05). This implies that each additional day of relatively warm or cold temperatures, as may occur with climate change (Melillo et al., 2014), reduces the effectiveness of all inputs, but capital most of all. This could be due to reduced effectiveness of machinery, or the need for more machinery to compensate for weather effects on manufacturing, among other possible causes. It is plausible that firms may be able to reduce labor-costs on days affected by extreme weather, but they would have fewer options to reduce the stock of property, plant and equipment during the same short-term periods. If firms were engaging in this sort of compensatory effort, it would at least

partially explain why value-add per unit cost of labor is less affected by weather than value-added capital. Finally, compared to sales-based productivity which includes inputs affected by environmental conditions occurring elsewhere, value-add is a better predictor of how environmental conditions affect firm-level productivity because value-add occurs locally.

Thus, although results from the sales-based model is mixed, results from the value-added model provides compelling evidence that firm productivity is being affected by adverse weather and PM2.5 pollution.

Conclusion

This paper provides first evidence on the causal relationship between environmental factors and productivity at the firm level. The results indicate that manufacturing inputs are affected by temperature variation from long-term monthly averages, not just very warm days as previously investigated in the literature. Evidence obtained in this paper suggests that labor and TFP are both adversely affected by warm days, capital by cold days, but all three by large variations in daily temperature means and maximums.

Labor on an employee basis is also adversely affected by particulate matter pollution which could produce a cumulative effect on labor productivity given

PM2.5 concentrations are often higher during heatwaves. This has obvious implications if climate change produces a less stable climate that leads to greater variation in daily temperatures, both up and down. For example, if climate change primarily manifests as global warming, then value-added labor will become less productive, providing incentives for firms to switch from labor to capital inputs and making the negative effect of pollution on productivity even greater. In this scenario, climate adaptation policy must consider risks to labor’s share of production, and any likely affect this reduction in labor efficiency will have on a range of market outcomes including unemployment, returns to education, among others. Second, if

climate change primarily manifests as a less stable climate, then all forms of productivity will suffer, but capital most of all. In this scenario, climate adaptation would require firms to invest in more capital for the same level of output, or switch to more labor or technical efficiency. Regardless, manufacturing efficiency will be adversely affected by climate change due to greater variation in average temperatures, resulting in lower economic growth for a given level of inputs.

Ultimately, the results present a complex relationship between weather, pollution, and input productivity, as evidenced by differential results between value-based and output-based productivity. As such, more research to improve our understanding of how climate change will affect input productivity, and what climate change implies for firms and their efficient allocation of inputs is warranted.

Tables and Figures

Table 2 World Bank Productivity Descriptive Statistics

Std. Productivity Measure/Region Obs. Mean Dev. Min. Max.

Sales - Capital 28,861 16.12 56.28 Africa 7,104 15.9 55.9 0.0 1,200.0 East Asia - Pacific 3,543 21.2 61.8 0.0 900.0 Europe and Central Asia 1,852 12.2 39.0 0.0 660.0 Latin America 9,871 15.1 60.4 0.0 2,500.0 Middle East and North Africa 2,336 11.9 40.5 0.0 866.7 South Asia 4,155 18.8 55.7 0.0 1,051.1 Sales - Labor 28,861 11.31 20.68 Africa 7,104 10.1 17.4 0.3 275.0 East Asia - Pacific 3,543 12.2 24.0 0.3 511.0 Europe and Central Asia 1,852 10.7 22.7 0.4 500.0 Latin America 9,871 8.8 14.3 0.2 323.5 Middle East and North Africa 2,336 13.9 25.3 0.3 520.8 South Asia 4,155 17.3 28.8 0.9 600.0 Sales - ln(TFP) 28,861 255.8 138.6 Africa 7,104 251.8 109.0 (720.2) 999.1 East Asia - Pacific 3,543 274.5 157.7 (375.7) 973.1 Europe and Central Asia 1,852 267.1 173.5 (355.7) 914.7 Latin America 9,871 252.0 133.1 (479.2) 951.3 Middle East and North Africa 2,336 267.4 142.7 (499.1) 806.9 South Asia 4,155 244.1 156.1 (372.0) 948.2 VA - Capital 28,861 9.46 33.0 Africa 7,104 10.0 35.0 0.0 892.9 East Asia - Pacific 3,543 13.3 41.8 0.0 707.2 Europe and Central Asia 1,852 7.0 27.1 0.0 630.0 Latin America 9,871 8.8 30.9 0.0 744.1 Middle East and North Africa 2,336 7.9 30.0 0.0 811.3 South Asia 4,155 8.8 29.7 0.0 859.1 VA - Labor 28,861 6.37 12.18 Africa 7,104 6.4 13.3 0.1 255.0 East Asia - Pacific 3,543 7.1 14.3 0.1 266.3 Europe and Central Asia 1,852 5.8 11.5 0.1 160.0 Latin America 9,871 5.2 9.7 0.1 191.8 Middle East and North Africa 2,336 8.2 13.5 0.1 133.3 South Asia 4,155 7.6 12.7 0.2 210.8 VA - ln(TFP) 28,861 278.5 141.5 Africa 7,104 278.8 134.0 (238.4) 1,040.1 East Asia - Pacific 3,543 281.8 137.8 (161.3) 912.3 Europe and Central Asia 1,852 271.1 167.5 (205.7) 931.7 Latin America 9,871 277.3 142.9 (284.6) 949.7 Middle East and North Africa 2,336 297.5 145.1 (187.9) 803.6 South Asia 4,155 270.5 137.9 (177.2) 939.1

Table 3 Berkeley Earth Descriptive Statistics

Count of days by region Obs. Mean Std. Dev. Min. Max. Neutral 28,660 287.9 71.2 Africa 6,944 319.4 43.4 186 366 East Asia - Pacific 3,543 258.9 74.3 114 366 Europe and Central Asia 1,852 157.6 25.4 107 230 Latin America 9,830 299.6 72.4 144 366 Middle East and North Africa 2,336 256.4 55.2 137 366 South Asia 4,155 307.9 38.9 106 366 Extreme Temperatures 28,660 19.1 31 Africa 6,944 6.1 10.0 0 44 East Asia - Pacific 3,543 19.2 30.6 0 134 Europe and Central Asia 1,852 93.0 28.5 34 187 Latin America 9,830 17.8 29.0 0 119 Middle East and North Africa 2,336 27.6 21.0 0 98 South Asia 4,155 5.5 9.9 0 114 Large Negative 28,660 7.29 12.7 Africa 6,944 1.7 3.5 0 22 East Asia - Pacific 3,543 9.5 14.3 0 83 Europe and Central Asia 1,852 32.3 12.6 9 127 Latin America 9,830 8.4 13.4 0 60 Middle East and North Africa 2,336 5.8 6.4 0 43 South Asia 4,155 1.9 5.0 0 58 2C to 4C Lower 28,660 23.94 30.9 Africa 6,944 11.6 15.6 0 144 East Asia - Pacific 3,543 56.5 60.8 0 181 Europe and Central Asia 1,852 44.2 9.0 14 62 Latin America 9,830 18.8 21.8 0 72 Middle East and North Africa 2,336 23.8 12.2 0 79 South Asia 4,155 20.0 19.3 0 121 2C to 4C Higher 28,660 35.1 28.2 Africa 6,944 28.8 24.6 0 94 East Asia - Pacific 3,543 31.4 26.6 0 91 Europe and Central Asia 1,852 71.1 11.3 41 103 Latin America 9,830 29.7 28.5 0 120 Middle East and North Africa 2,336 58.2 30.2 0 121 South Asia 4,155 32.5 18.4 0 104 Large Positive 28,660 11.8 20.3 Africa 6,944 4.4 7.7 0 35 East Asia - Pacific 3,543 9.8 20.2 0 115 Europe and Central Asia 1,852 60.8 24.8 11 113 Latin America 9,830 9.5 16.4 0 73 Middle East and North Africa 2,336 21.8 16.1 0 65 South Asia 4,155 3.6 5.5 0 56

Table 4 Annual Average PM2.5 Concentrations by Region and Year

Year/Region Obs. Mean Std. Dev. Min. Max. Total 28,623 19.4 15.3 2005 5,521 15.4 8.4 1.8 32.1 2006 3,833 15.3 10.5 2.2 34.6 2007 2,018 13.0 5.9 0.3 32.0 2008 1,010 22.9 15.0 3.0 47.0 2009 4,759 12.5 8.4 0.5 27.7 2010 839 13.7 4.9 2.6 26.8 2011 1,336 37.5 21.3 3.1 87.3 2012 3,492 25.6 20.7 1.3 60.3 2013 2,920 33.5 18.0 1.2 76.7 2014 967 15.2 12.0 0.2 46.1 2015 1,409 21.4 13.8 2.2 48.1 2016 519 17.3 10.7 4.3 36.1 Africa 7,063 13.6 9.2 0.3 34.6 East Asia - Pacific 3,543 30.0 18.5 0.2 87.3 Europe and Central Asia 1,847 17.5 5.7 2.6 34.5 Latin America 9,871 13.6 8.2 0.5 29.7 Middle East and North Africa 2,144 9.4 3.6 4.4 21.3 South Asia 4,155 39.9 16.9 5.4 76.7

Table 5 Temperature variation from monthly mean on Value-added Labor productivity

SURE 1/3 (Labor Productivity) OLS (1) OLS (3) OLS (4) OLS (5) OLS (6) Each day > 4 degrees C below than -0.327*** -0.089 -0.096 -0.055 0.071 mean (0.083) (0.085) (0.084) (0.089) (0.093)

0.083*** -0.005 -0.007 0.042 0.146** Per day 2 to 4 degrees C below mean (0.031) (0.051) (0.051) (0.054) (0.059)

0.016 0.058 0.087** 0.054 0.064 Per day 2 to 4 degrees C above mean (0.043) (0.040) (0.039) (0.043) (0.046)

Per day > 4 degrees C above mean -0.098* -0.189*** -0.195*** -0.171*** -0.203*** (0.054) (0.052) (0.052) (0.061) (0.063)

Particulate Matter (PM2.5) 0.342*** -0.025 -0.053 0.136** 0.066 (0.057) (0.067) (0.066) (0.068) (0.07)

Medium Company (20-100 Employees) 9.589*** 10.073*** 10.550*** (1.391) (1.381) (1.361)

Large Company (over 100 Employees) 17.782*** 18.258*** 18.554*** (1.781) (1.767) (1.747)

Low Income Country 5.090** 5.138** 2.27 (2.160) (2.054) (2.095)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 125.573*** 101.973*** 92.531*** 94.289*** 99.075*** (1.966) (2.868) (3.346) (5.537) (5.68)

Table 6 Temperature variation from monthly mean on Value-added Capital Productivity

SURE 2/3 (Capital Productivity) OLS (1) OLS (3) OLS (4) OLS (5) OLS (6) Each day > 4 degrees C below than mean -0.355** -0.856*** -1.047*** -0.907*** -0.70*** (0.173) (0.203) (0.207) (0.231) (0.231)

Per day 2 to 4 degrees C below mean 0.054 0.376*** 0.246* 0.08 0.242 (0.047) (0.140) (0.143) (0.166) (0.172)

-0.041 0.003 -0.061 0.053 0.13 Per day 2 to 4 degrees C above mean (0.066) (0.081) (0.083) (0.090) (0.091)

Per day > 4 degrees C above mean -0.132 -0.075 -0.076 0.031 0.074 (0.116) (0.111) (0.105) (0.162) (0.142)

Particulate Matter (PM2.5) 0.285** 0.086 0.003 -0.247 -0.214 (0.112) (0.111) (0.111) (0.150) (0.154)

Medium Company (20-100 Employees) 3.195 3.438 3.663 (2.532) (2.512) (2.498)

Large Company (over 100 Employees) 13.442*** 13.526*** 13.642*** (3.364) (3.357) (3.354)

Low Income Country -31.762*** -28.156*** -31.317*** (3.875) (4.191) (4.173)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes Constant 88.317*** 124.008*** 147.405*** 143.236*** 144.161*** (3.139) (5.033) (6.011) (10.303) (10.25)

Table 7 Temperature variation from the monthly mean on Value-added TFP

SURE 3/3 (Total-Factor Productivity) OLS (1) OLS (3) OLS (4) OLS (5) OLS (6) Each day > 4 degrees C below than mean -0.305 -0.068 -0.319*** -0.228** -0.148 (0.208) (0.113) (0.102) (0.108) (0.114)

Per day 2 to 4 degrees C below mean 0.001 0.157** -0.006 -0.039 0.030 (0.045) (0.065) (0.061) (0.064) (0.071)

Per day 2 to 4 degrees C above mean -0.091 0.140*** 0.083* 0.076 0.079 (0.074) (0.048) (0.044) (0.048) (0.051)

Per day > 4 degrees C above mean 0.256** -0.161** -0.162*** -0.115 -0.150* (0.120) (0.071) (0.063) (0.076) (0.079)

Particulate Matter (PM2.5) -0.016 0.075 -0.048 -0.025 -0.071 (0.106) (0.084) (0.072) (0.083) (0.086)

Medium Company (20-100 Employees) 16.317*** 16.444*** 16.761*** (1.524) (1.509) (1.5)

Large Company (over 100 Employees) 25.909*** 25.955*** 26.159*** (2.055) (2.045) (2.037)

Low Income Country -36.054*** -35.599*** -37.315*** (2.274) (2.313) (2.339)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes Constant 281.052*** 296.735*** 318.253*** 318.638*** 321.56*** (2.813) (3.475) (3.518) (5.145) (5.271)

Observations 35970 35874 35874 35874 35874

Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent the count of days in the previous financial year above or below the mean average temperature for that region/city for that month compared to the count of days that were within 2° of the mean for that month, and particulate matter, PM2.5. Labor productivity is expressed as value add per cost of labor. Base company is a small company of less than 20 employees, base region is Sub- Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 8 Temperature variation from the monthly mean on sales-based Labor productivity

SURE (Labor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day > 4 degrees C below than mean -0.325*** -0.047 -0.042 0.011 0.09 (0.077) (0.092) (0.090) (0.094) (0.097)

Per day 2 to 4 degrees C below mean 0.161*** 0.108* 0.118** 0.179*** 0.241*** (0.033) (0.055) (0.054) (0.055) (0.060)

Per day 2 to 4 degrees C above mean -0.130*** -0.102** -0.048 -0.111** -0.126*** (0.041) (0.041) (0.040) (0.044) (0.046)

Per day > 4 degrees C above mean 0.140*** 0.007 0.001 -0.014 -0.048 (0.048) (0.057) (0.056) (0.065) (0.064)

Particulate Matter (PM2.5) 0.370*** 0.152** 0.1 0.233*** 0.138* (0.064) (0.072) (0.068) (0.074) (0.074)

Medium Company (20-100 Employees) 14.915*** 15.230*** 15.684*** -1.433 -1.425 -1.41

Large Company (over 100 Employees) 28.058*** 28.467*** 28.731*** -1.844 -1.808 -1.783

Low Income Country 11.406*** 8.794*** 6.741*** (2.058) (2.039) (2.060) Regional Controls Yes Yes Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Latitudinal Controls Yes

Constant 195.872*** 146.840*** 128.979*** 137.313*** 142.245*** (1.795) (2.991) (3.388) (5.569) (5.665)

Table 9 Temperature variation from the monthly mean on sales-based Capital productivity

SURE (Capital Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day > 4 degrees C below than mean -0.390** -0.684*** -0.858*** -0.714*** -0.561*** (0.168) (0.198) (0.203) (0.205) (0.209)

Per day 2 to 4 degrees C below mean 0.06 0.486*** 0.368*** 0.202 0.336** (0.048) (0.131) (0.135) (0.134) (0.144)

Per day 2 to 4 degrees C above mean -0.191*** -0.165** -0.202** -0.115 -0.053 (0.065) (0.077) (0.079) (0.084) (0.088)

Per day > 4 degrees C above mean 0.073 0.077 0.066 0.129 0.108 (0.113) (0.107) (0.103) (0.145) (0.144)

Particulate Matter (PM2.5) 0.657*** 0.375*** 0.273** -0.144 -0.115 (0.110) (0.108) (0.107) (0.144) (0.148)

Medium Company (20-100 Employees) 10.249*** 10.072*** 10.273*** -2.482 -2.458 -2.452

Large Company (over 100 Employees) 23.633*** 23.184*** 23.402*** -3.179 -3.185 -3.188

Low Income Country -25.971*** -25.990*** -28.527*** (3.713) (3.974) (3.989)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes Constant 142.978*** 164.556*** 179.722*** 183.855*** 185.177*** (2.948) (4.868) (5.808) (9.699) (9.688)

Table 10 Temperature variation from the monthly mean on sales-based TFP

SURE (Total-Factor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day > 4 degrees C below than -0.3 0.029 -0.161** -0.133* -0.067 mean (0.219) (0.080) (0.073) (0.080) (0.084)

0.072 0.080* -0.042 -0.043 0.016 Per day 2 to 4 degrees C below mean (0.056) (0.045) (0.044) (0.051) (0.058)

Per day 2 to 4 degrees C above mean 0.024 0.180*** 0.124*** 0.124*** 0.139*** (0.081) (0.033) (0.028) (0.031) (0.034)

Per day > 4 degrees C above mean 0.277** -0.163*** -0.160*** -0.130** -0.149*** (0.132) (0.051) (0.045) (0.053) (0.055)

Particulate Matter (PM2.5) 0.032 -0.041 -0.121*** -0.036 -0.048 (0.133) (0.054) (0.045) (0.056) (0.059)

Medium Company (20-100 Employees) 8.327*** 8.533*** 8.700*** -1.036 -1.027 -1.024

Large Company (over 100 Employees) 11.379*** 11.547*** 11.664*** -1.291 -1.284 -1.277

Low Income Country -29.461*** -28.280*** -29.545*** (1.722) (1.689) (1.670)

Regional Controls Yes Yes Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Latitudinal Controls Yes Constant 251.519*** 266.270*** 286.634*** 284.657*** 286.157*** (2.770) (2.441) (2.433) (3.470) (3.601)

Observations 74753 41871 41871 41871 41871

Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent the count of days in the previous financial year above or below the mean average temperature for that region/city for that month compared to the count of days that were within 2° of the mean for that month, and particulate matter, PM2.5. Labor productivity is expressed as output per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 11 Extreme temperature variation from monthly mean on value-added Labor

SURE (Labor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below and -0.184*** -0.154*** -0.160*** -0.128*** -0.103** > 4 degrees C above mean (0.030) (0.032) (0.032) (0.038) (0.041)

Particulate Matter (PM2.5) 0.334*** -0.022 -0.051 0.139** 0.071 (0.057) (0.067) (0.066) (0.069) (0.070)

Medium Company (20-100 Employees) 9.618*** 10.100*** 10.599*** -1.392 -1.383 -1.364

Large Company (over 100 Employees) 17.787*** 18.258*** 18.545*** -1.781 -1.767 -1.748

Low Income Country 5.015** 5.156** 2.484 -2.161 -2.059 -2.102

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 126.185*** 102.348*** 92.632*** 94.145*** 98.606*** (1.947) (2.849) (3.332) (5.490) (5.621)

Table 12 Extreme temperature variation from monthly mean on value-added Capital

SURE (Capital Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below -0.215*** -0.345*** -0.410*** -0.310*** -0.207** and > 4 degrees C above mean (0.057) (0.066) (0.065) (0.097) (0.097)

Particulate Matter (PM2.5) 0.277** 0.069 -0.017 -0.261* -0.225 (0.110) (0.111) (0.111) (0.149) (0.152)

Medium Company (20-100 Employees) 2.884 3.222 3.542 -2.541 -2.516 -2.5

Large Company (over 100 Employees) 13.425*** 13.571*** 13.720*** -3.371 -3.362 -3.361

Low Income Country -30.728*** -27.991*** -30.944*** (3.841) (4.146) (4.086)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 88.430*** 122.939*** 145.320*** 144.372*** 145.569*** (3.131) (4.993) (5.950) (10.252) (10.170)

Table 13 Extreme temperature variation from monthly mean on value-added TFP

SURE (Total-Factor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below 0.047 -0.129*** -0.216*** -0.156*** -0.149*** and > 4 degrees C above mean (0.071) (0.041) (0.038) (0.047) (0.050)

Particulate Matter (PM2.5) -0.037 0.077 -0.052 -0.027 -0.071 (0.104) (0.084) (0.072) (0.083) (0.086)

Medium Company (20-100 Employees) 16.266*** 16.417*** 16.761*** -1.527 -1.51 -1.502

Large Company (over 100 Employees) 25.906*** 25.959*** 26.159*** -2.055 -2.045 -2.037

Low Income Country -35.890*** -35.582*** -37.314*** (2.266) (2.309) (2.336)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 281.333*** 296.861*** 317.922*** 318.777*** 321.556*** (2.803) (3.477) (3.512) (5.125) (5.232)

Observations 35837 35741 35741 35741 35741 Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent the count of days in the previous financial year that were 4 degrees Celsius above or below the mean average temperature for that region/city for that month compared to the count of days that were within 2° of the mean for that month, and particulate matter, PM2.5. Labor productivity is expressed as value add per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 14 Hi/Low Extremes on Labor Sales Productivity

Seemingly Unrelated Regression (Labor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below -0.03 -0.012 -0.014 -0.004 0.005 and > 4 degrees C above mean (0.027) (0.033) (0.032) (0.038) (0.040)

Particulate Matter (PM2.5) 0.354*** 0.151** 0.099 0.234*** 0.140* (0.064) (0.072) (0.069) (0.074) (0.075)

Medium Company (20-100 Employees) 14.903*** 15.235*** 15.691*** (1.433) (1.425) (1.411)

Large Company (over 100 Employees) 28.056*** 28.467*** 28.692*** (1.844) (1.808) (1.783)

Low Income Country 11.434*** 8.799*** 6.995*** (2.058) (2.038) (2.058)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 196.060*** 146.779*** 128.912*** 137.262*** 141.796*** (1.795) (2.988) (3.388) (5.518) (5.593)

Table 15 Hi/Low Extremes on Capital Sales Productivity

Seemingly Unrelated Regression (Capital Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below -0.101* -0.191*** -0.257*** -0.181** -0.103 and > 4 degrees C above mean (0.057) (0.067) (0.066) (0.084) (0.088)

Particulate Matter (PM2.5) 0.639*** 0.357*** 0.254** -0.156 -0.138 (0.108) (0.109) (0.107) (0.144) (0.147)

Medium Company (20-100 Employees) 9.956*** 9.880*** 10.191*** (2.494) (2.465) (2.455)

Large Company (over 100 Employees) 23.592*** 23.197*** 23.405*** (3.188) (3.193) (3.195)

Low Income Country -25.046*** -25.889*** -28.375*** (3.674) (3.932) (3.940)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 143.218*** 163.443*** 177.701*** 184.877*** 186.204*** (2.941) (4.855) (5.765) (9.659) (9.607)

Table 16 Hi/Low Extremes on TFP Sales

Seemingly Unrelated Regression (TFP) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Each day with > 4 degrees C below and > 4 0.063 -0.096*** -0.160*** -0.131*** -0.121*** degrees C above mean (0.077) (0.030) (0.028) (0.035) (0.037)

Particulate Matter (PM2.5) 0.01 -0.037 -0.121*** -0.036 -0.047 (0.131) (0.054) (0.045) (0.056) (0.060)

Medium Company (20-100 Employees) 8.327*** 8.532*** 8.736*** (1.039) (1.029) (1.026)

Large Company (over 100 Employees) 11.379*** 11.547*** 11.682*** (1.291) (1.284) (1.277)

Low Income Country -29.459*** -28.279*** -29.627*** (1.712) (1.688) -1.682

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 251.808*** 266.529*** 286.631*** 284.661*** 286.114*** (2.767) (2.446) (2.422) (3.460) -3.571

Observations 70500 39891 39891 39891 39891 Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent the count of days in the previous financial year that were 4 degrees Celsius above or below the mean average temperature for that region/city for that month compared to the count of days that were within 2° of the mean for that month, and particulate matter, PM2.5. Labor productivity is expressed as output per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 17 Extreme Daily Maximum Temperatures on value-added Labor Productivity

Seemingly Unrelated Regression (Labor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean -0.175*** -0.221*** -0.207*** -0.175*** -0.146** (0.054) (0.054) (0.053) (0.059) (0.063)

Per day with max temp 2 to 4° C above mean -0.054 0.063* 0.081** 0.026 0.032 (0.036) (0.034) (0.034) (0.034) (0.035)

Particulate Matter (PM2.5) 0.370*** -0.021 -0.042 0.143** 0.076 (0.057) (0.067) (0.066) (0.068) (0.07)

Medium Company (20-100 Employees) 9.377*** 9.865*** 10.419*** (1.382) (1.373) (1.357)

Large Company (over 100 Employees) 17.489*** 17.981*** 18.348*** (1.777) (1.757) (1.74)

Low Income Country 5.746*** 5.069** 2.257 (2.203) (2.018) (2.082)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 126.731*** 100.764*** 90.613*** 95.114*** 98.859*** (2.105) (2.958) (3.450) (5.475) (5.530)

Table 18 Extreme Daily Maximum Temperatures on value-added Capital Productivity

Seemingly Unrelated Regression (Capital Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean -0.510*** -0.271** -0.314*** -0.272* -0.168 (0.111) (0.114) (0.109) (0.148) (0.149)

Per day with max temp 2 to 4° C above mean 0.351*** 0.383*** 0.341*** 0.361*** 0.391*** (0.065) (0.068) (0.070) (0.072) (0.074)

Particulate Matter (PM2.5) 0.256** 0.081 -0.023 -0.310** -0.274* (0.110) (0.106) (0.105) (0.136) (0.143)

Medium Company (20-100 Employees) 3.851 3.861 4.168* (2.515) (2.494) (2.483)

Large Company (over 100 Employees) 14.151*** 13.966*** 14.15*** (3.35) (3.34) (3.342) ` Low Income Country -32.284*** -29.544*** -32.477*** (3.805) (4.113) (4.122)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 77.292*** 113.760*** 138.209*** 137.798*** 137.459*** (3.525) (5.023) (6.007) (10.070) (10.072)

Table 19 Extreme Daily Maximum Temperatures on value-added TFP

Seemingly Unrelated Regression (Total-Factor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean -0.072 -0.189*** -0.235*** -0.206*** -0.202*** (0.102) (0.073) (0.062) (0.071) (0.075)

Per day with max temp 2 to 4° C above mean -0.009 0.163*** 0.127*** 0.099** 0.099** (0.070) (0.041) (0.037) (0.039) (0.040)

Particulate Matter (PM2.5) -0.063 0.096 -0.046 -0.03 -0.078 (0.106) (0.084) (0.073) (0.081) (0.085)

Medium Company (20-100 Employees) 16.238*** 16.363*** 16.754*** (1.517) (1.501) (1.495)

Large Company (over 100 Employees) 25.765*** 25.816*** 26.076*** (2.039) (2.025) (2.019)

Low Income Country -36.056*** -36.269*** -38.198*** (2.234) (2.244) (2.315)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 281.580*** 293.715*** 316.110*** 319.567*** 322.119*** (3.796) (3.654) (3.655) (5.285) (5.359)

productivity is expressed as value add per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 20 Extreme Daily Maximum Temperatures on Sales-Based Labor Productivity

Seemingly Unrelated Regression (Labor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean 0.064 -0.09 -0.065 -0.097 -0.097 (0.049) (0.059) (0.056) (0.064) (0.063)

Per day with max temp 2 to 4° C above mean -0.178*** -0.122*** -0.086** -0.121*** -0.132*** (0.037) (0.036) (0.035) (0.034) (0.035)

Particulate Matter (PM2.5) 0.368*** 0.147** 0.105 0.226*** 0.123** (0.063) (0.072) (0.068) (0.073) (0.074)

Medium Company (20-100 Employees) 14.769*** 15.184*** 15.623*** (1.422) (1.415) (1.402)

Large Company (over 100 Employees) 27.937*** 28.451*** 28.695*** (1.831) (1.798) (1.778)

Low Income Country 11.106*** 8.170*** 6.725*** (2.079) (2.015) (2.055)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 197.505*** 148.574*** 130.420*** 140.078*** 144.264*** (2.013) (3.064) (3.404) (5.451) (5.467)

Table 21 Extreme Daily Maximum Temperatures on Sales-Based Capital Productivity

Seemingly Unrelated Regression (Capital Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean -0.289*** -0.153 -0.193* -0.205 -0.136 (0.106) (0.106) (0.102) (0.128) (0.127)

Per day with max temp 2 to 4° C above 0.136** 0.167** 0.142** 0.191*** 0.215*** mean (0.063) (0.067) (0.068) (0.071) (0.073)

Particulate Matter (PM2.5) 0.620*** 0.374*** 0.256** -0.214 -0.194 (0.110) (0.105) (0.103) (0.134) (0.141)

Medium Company (20-100 Employees) 10.846*** 10.545*** 10.816*** (2.482) (2.453) (2.448)

Large Company (over 100 Employees) 24.394*** 23.783*** 23.969*** (3.191) (3.191) (3.193)

Low Income Country -26.965*** -27.361*** -29.777*** (3.606) (3.875) (3.972)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 137.234*** 157.410*** 173.673*** 179.961*** 179.963*** (3.330) (4.992) (5.875) (9.681) (9.688)

Table 22 Extreme Daily Maximum Temperatures on Sales-Based TFP

Seemingly Unrelated Regression (Total-Factor Productivity) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Per day of max temp > 4° C above mean -0.027 -0.113** -0.150*** -0.117** -0.109** (0.106) (0.053) (0.043) (0.049) (0.050)

Per day with max temp 2 to 4° C above 0.074 0.153*** 0.117*** 0.088*** 0.093*** mean (0.071) (0.027) (0.024) (0.025) (0.026)

Particulate Matter (PM2.5) -0.004 -0.013 -0.109** -0.016 -0.032 (0.131) (0.054) (0.046) (0.055) (0.059)

Medium Company (20-100 Employees) 8.224*** 8.390*** 8.628*** (1.038) (1.028) (1.025)

Large Company (over 100 Employees) 11.159*** 11.298*** 11.47*** (1.29) (1.283) (1.278)

Low Income Country -29.532*** -28.897*** -30.282*** (1.689) (1.632) (1.661)

Regional Controls Yes Yes Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 250.443*** 263.934*** 285.188*** 285.333*** 286.488*** (3.707) (2.586) (2.553) (3.669) (3.77)

productivity is expressed as output per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 23 Particulate Matter (PM2.5) and Employee Value-Added Productivity

Employee Productivity (Value-added Output) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Particulate Matter (PM2.5) -0.458*** -0.14 -0.494*** -0.330*** -0.480*** (0.125) (0.138) (0.108) (0.111) (0.114)

Medium Company (20-100 Employees) 30.945*** 29.756*** 29.226*** -2.332 -2.274 -2.268

Large Company (over 100 Employees) 63.257*** 61.768*** 61.172*** -2.99 -2.944 -2.888

Low Income Country -91.144*** -76.061*** -59.256*** (3.766) (3.844) (4.042)

Region: East-Asia and Pacific 32.797*** 40.452*** (9.573) (9.546)

Region: Europe and Central Asia 19.305*** 21.237*** (6.608) (7.300)

Region: Latin American & Caribbean 62.848*** 63.670*** (7.377) (6.848)

Region: Middle East & North Africa 82.064*** 71.277*** (6.875) (7.657)

Region: South Asia Region 21.534*** 12.231 -8.235 -8.425

Industry Controls (Not Shown) Yes Yes Yes Yes Year Controls (Not Shown) Yes Yes Yes Yes Latitudinal Controls (Not Shown) Yes

Constant 925.497*** 873.168*** 913.841*** 855.996*** 835.950*** (3.277) (7.147) (5.313) (9.102) (8.487)

Observations 36201 36102 36102 36102 36102 Adjusted R2 0.003 0.125 0.218 0.237 0.246 Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent the count of days in the previous financial year above or below the mean average temperature for that region/city for that month compared to the count of days that were within 2° of the mean for that month, and particulate matter, PM2.5. Labor productivity is expressed as output per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms.

Table 24 Particulate Matter (PM2.5) and Employee Output Productivity

Employee Productivity (Sales Output) OLS (1) OLS (2) OLS (3) OLS (4) OLS (5) Particulate Matter (PM2.5) -0.271** 0.522*** 0.126 -0.171 -0.294** (0.109) (0.137) (0.111) (0.120) (0.126)

Medium Company (20-100 Employees) 39.157*** 35.501*** 34.931*** -2.346 -2.264 -2.248

Large Company (over 100 Employees) 76.104*** 72.042*** 71.450*** -2.941 -2.942 -2.886

Low Income Country -86.082*** -71.969*** -55.182*** (3.524) (3.677) (3.899)

Region: East-Asia and Pacific 49.376*** 56.975*** (9.786) (9.759)

Region: Europe and Central Asia 41.704*** 45.496*** (6.266) (7.396)

Region: Latin American & Caribbean 69.317*** 69.443*** (7.031) (6.567)

Region: Middle East & North Africa 116.345*** 108.015*** (6.482) (7.745)

Region: South Asia Region 83.067*** 74.253*** -8.444 -8.785

Industry Controls Yes Yes Yes Yes Year Controls Yes Yes Yes Yes Latitudinal Controls Yes

Constant 986.688*** 908.873*** 941.873*** 886.757*** 865.710*** (2.631) (6.917) (5.421) (8.946) (8.317)

Observations 75622 42206 42206 42206 42206 Adjusted R2 0.001 0.129 0.218 0.25 0.259 Standard errors are clustered by survey strata and are in parentheses * p<0.10; ** p<0.05; *** p<0.01. Key dependent variables represent satellite derived particulate matter, PM2.5, concentrations. Labor productivity is expressed as output per cost of labor. Base company is a small company of less than 20 employees, base region is Sub-Saharan Africa, base income is a high-income country. Regions and income levels are defined by the World Bank. Industry controls are based on two-number ISIC codes for manufacturers. Years are from fiscal year 2005 until 2017. All values are in 2009 USD terms. 63

CHAPTER TWO

GREEN CAPITAL AND PROSOCIAL INVESTING – EVIDENCE FROM INVESTOR RETURNS IN THE STOCK MARKET

ABSTRACT

As society turns its attention to climate change and sustainable development, corporations face ever greater pressure to reduce their environmental footprint. In this paper, I exploit the unique longitudinal characteristics of the Thomson Reuters ESG database to investigate whether investors support efforts by firms to become more environmentally sustainable. Across three regions and three categories of environmental corporate social responsibility, I find evidence that investors often penalize firms for their environmental efforts. In North America, investors penalize firms for emissions efficiency and overall environmentalism; in Asia Pacific, resource efficiency is penalized; and in Europe, evidence supports an investor base which penalizes environmental innovation and overall environmentalism.

Globally, a one unit increase in environmentalism on a 0-100 scale is predicted to reduce market capitalization by about 75 cents per $100 of total capitalization. Likewise, building a passive portfolio of the top 10 percent of environmentally friendly firms underperforms the broader market. Globally,

$10,000 invested in the top 10 percent of firms for average environmentalism is predicted to reduce annual returns by about $129.

Introduction

As environmental concerns become more important to how investors invest, and how firms choose to structure their activities, understanding whether investors reward firms for their efforts to become more environmentally sustainable is an important question (Auer & Schuhmacher,

2016; Blasi, Caporin, & Fontini, 2018; El Ghoul, Guedhami, Kwok, & Mishra,

2011). Corporate social responsibility (CSR), socially responsible investing

(SRI), and ESG (Environmental-Social-Governance) all broadly refer to the relationship between investors and firms when it comes to doing well by doing good. Since investors ultimately own a firm, they can limit or expand the amount of resources a firm devotes to CSR (Benabou & Tirole, 2010). This ultimately affects how much private enterprises contribute to sustainable development and other objectives for environmental policy.

In this paper, I exploit the unique longitudinal characteristics of the

Thomson Reuters ESG database (Thomson Reuters, 2018) to investigate whether investors support efforts by firms to become more environmentally sustainable. I empirically evaluate the reaction of investors to three categories of Environmental CSR (E-CSR) as measured by Thomson Reuters:

Resource Use (representing efficient use of resources), Innovation

(representing innovation in the environmental sphere), and Emissions

(representing relative emission efficiency compared to peers) for investors in the leading stock indices in Europe, North America and Asia Pacific regions. This paper offers several unique contributions. First, I utilize a rich longitudinal dataset of about 2,100 firms for the years 2003 until 2017 to provide compelling evidence of whether firms who engage in environmental

CSR are being rewarded through stock outperformance. To accomplish this, I use two different models, one representing marginal changes in environmental scores over time, and the other representing a passive investment strategy of

high environmentally performing stocks. Second, I recognize the potential endogeneity of environmental CSR. This is important because it is likely that the propensity to engage in CSR is non-random with respect to firm characteristics that are correlated to performance in the stock market. To guard against bias from this endogeneity, the empirical analysis relies on a fixed effects model that accounts for time-invariant unobserved characteristics at the firm level. Therefore, the identification strategy of the impact of CSR on investor behavior is achieved using variation within firms over time rather than a comparison between firms.

Given the relationship between environmental CSR and total stock return

(TSR) is theoretically ambiguous, this makes investigating the link between firm CSR activity and investor responses an empirical question. For example, as Derwall et al. (2011) suggest, different investors see CSR in either positive or negative terms, and this is likely to affect their propensity to invest in CSR. Likewise, investors may undervalue CSR, or have a prerogative to maximize profits (Friedman, 1970). Even if there is a growing trend towards prosocial investing, investors may only choose high CSR firms to the extent they gain some sort of social reputation or other extrinsic reward

(Bénabou & Tirole, 2006). To provide clues in how investors are reacting to environmental CSR, I develop a theoretical framework for how investors respond over the short and longer term. Based on Derwall et al. (2011), I list three types of investors: value-based, values-based, and profit-based each with heterogenous short- and long-term beliefs in how environmental CSR will affect stock outcomes. Thus, short term empirical results may provide clues to long term outcomes from CSR activity.

Fourth, by relating results of the empirical strategy back to policy, I provide a vital link between corporations and policy makers by better understanding the role of investors in directing corporate attitudes towards

environmental sustainability. I do this by conceptualizing the relationship between policy and firms as truly being between policy and investors (see

Figure 1) and by developing policy options depending on how investors respond to firm-level environmental CSR. This is important for various areas of environmental policy, including climate change. As environmental concerns gain traction in the public discourse, it is reasonable to expect firms to become more responsive to investors and societal demands for CSR. Knowing whether it will benefit the firm in the long run, or whether they will need to absorb the costs provides important clues for policy makers on how to design environmental policy that has the support of investors and firms.

Finally, this paper defines a more modern definition of CSR-type activity that reconciles with how CSR is measured and used in the environmental space. Although historically CSR is seen as a philanthropic undertaking (Benabou & Tirole, 2010; McWilliams & Siegel, 2001; McWilllians,

Siegel, & Wright, 2006), when relating ESG scores to firm activity that contributes to environmental sustainability, ESG measures are not simply philanthropic in nature. Instead, they include quality improvements in environmental outcomes from firm responses to regulations, consumer behavior, and for promotional purposes. In effect, while CSR is defined as voluntary,

ESG scores may include a non-voluntary component whether it is forced on the firm for regulatory or reputational reasons. This defines a modern CSR-type of activity.

By focusing on just the environmental element of CSR, I offer an in- depth analysis of the sub-components of corporate environmental action, including investor responses to those actions. Results show that high E-CSR produces a mixed response in the stock market. Single period changes in CSR scores, and passive investment portfolios built on the top 10 percent of firms by E-CSR routinely show a negative relationship with TSR, or have low

statistical precision. The effect however varies by region and by the environmental score of interest. In emissions and innovation in Asia Pacific, the effect between CSR and TSR is positive, but statistically weak. This effect and the heterogenous results between the two models for the same environmental score points to some potentially important opportunities for policy. With respects to economic significance, the empirical evidence also supports an argument that current levels of firm investment in E-CSR is not producing substantial loses in market value. For example, for each $10,000 of investment in a passive portfolio of the top ten percent of firms by average

CSR across all regions, an investor would earn about $129 less per annum than the broader market after accounting for dividends and changes in stock prices. This is in comparison to average annual returns of about $1,420 per

$10,000 invested across all markets in this paper. Conversely, firms can expect a reduction in market capitalization of about 75 cents per $100 of total capitalization for each unit increase in average CSR score.

Figure 1 Investors, CSR, and Sustainable Development

Investors direct firm activity Investors Firms

firms allocate resources to CSR

CSR

CSR produces an outcome

Environmental Sustainability

Literature and Conceptual Framing

Modern Corporate Social Responsibility (CSR+)

CSR is a familiar concept to stock markets. Although abstractly it represents a firm’s commitment to socially responsible action produced for 68

the benefit of a broad range of stakeholders (Bowen, 1953; Simon, Drucker, &

Waldo, 1952), it is also ultimately a metric used to measure actions that a firm or stakeholder may deem socially desirable. However, methods of calculating CSR have grown more complicated. This is due not only to the recent desires of investors to gain more out of the CSR metric, but underlying generational shifts in what investors deem to be socially responsible. While in the 1950’s a firm’s purpose was regarded as providing for its employees and contributing to welfare as a whole (Simon et al.,

1952), in the 1970’s, investors reassessed the role of firms in society, deemphasizing corporate social action in favor of profitability (Frederick,

1994; Friedman, 1970). Profit has continued to retain primacy since this shift; however, throughout the 1990’s and 2000’s, investors continued to consider and reconsider the role of firms within society. Corporate social responsibility as a concept came to be re-defined as a philanthropic ideal presenting as the “obligation to work for social benefit” (Frederick, 1994), or the undertaking of “actions that appear to further some social good, beyond the interests of the firm and that which is required by law”

(McWilliams & Siegel, 2001; McWilllians et al., 2006).

Beyond the concept of philosophical good, modern CSR, primarily through

ESG (Environmental, Social, Governance) measuring systems, has evolved to include measurements of actions that not only benefit society, but also benefit the firm in some way. For example, actions that improve outcomes along commonly used ESG metrics are not undertaken simply for philanthropic reasons, but they are instead done because a business case exists for firms to engage in this sort of activity. This might include: environmental and social activity designed to manage risk (Jo & Na, 2012; Kytle & Ruggie,

2005), for example by improving supply chains or reducing the risk of firm induced environmental disasters; to directly appeal to investors who demand

improvement in ESG measured activity (Derwall et al., 2011; Feloni, 2019); to improve relations with consumers interested in modern sustainability and equality concepts, including ecological footprints, the working conditions of workers and contractors, organic or free trade products, among others; and, last but not least, to either stave off potential regulation (Frynas, 2012) or in response to regulation. This type of CSR activity flows through to improved environmental, social, and governance scores, and presumably better outcomes in sustainability, social welfare and governance, whether the firm meant it to be philanthropic or not. This is the hallmark of what can be termed CSR-plus (CSR+): activity that improves a firm’s relationship to all stakeholders along environmental, social, governance and profit metrics, and which balances short-term profits with the long-term health of the firm by managing ESG risks with reference to broader investor and other stakeholder expectations. This modern iteration of CSR lends itself well to being measured with ESG-style systems, including the one developed by Thomson

Reuters and used in this paper, with evidence of a nascent movement in support of CSR+ activity already occurring, especially in light of comments by the Business Roundtable in August of 20197, and the broader growth of SRI investing generally.

The (SRI) Market for Corporate Social Responsibility Plus (CSR+)

Like CSR, SRI is not new. In use well before the 1960s, SRI was originally a tool for investors to avoid certain firms for ethical reasons.

These so-called “negative screens” (Deutsche Bank, 2012) were primarily used by unions and governments to avoid investing in firms with questionable labor practices (J. Martin, 1986). Recently, however, SRI has grown in complexity,

7 https://www.businessroundtable.org/business-roundtable-redefines-the- purpose-of-a-corporation-to-promote-an-economy-that-serves-all-americans

with enough information available to permit the creation of positive-screen portfolios -ones where stocks are not just excluded, but actively chosen by matching SRI principles to tangible CSR activity (Deutsche Bank, 2012). SRI is also becoming more popular. According to the Forum of Sustainable and

Responsible Investment, in the United States in 2016 about 20 percent of professionally managed funds (or about $8.72 trillion) included some sort of

SRI calculus. About $4.73 trillion of institutional investing also rely on

SRI concepts, up from $1.49 trillion in 2005 (US SIF, 2018). Likewise, in

2016 approximately 26.3 percent of all global assets under management employed SRI when making investment decisions, or about $22.9 trillion (GSIA,

2016).

This growth in SRI and CSR raises three important questions. First, is

CSR a valid strategy for improving firm profitability? Second, do investors reward high CSR performance with better outcomes in the stock market? And third, could policy makers use investor engagement in SRI and related reactions to CSR disclosure to further broad social goals via policy changes?

Research regarding the first two questions is mixed. With regard to profits,

Reinhardt (1998) argues that firms that engage in a CSR strategy earn abnormally positive returns in the short-term, but do so in the longer term only to the extent that they prevent their competitors from imitating this high CSR strategy. This, he argues, is not possible for an extended period.

Likewise, Blasi et al. (2018) find mixed evidence of the effect of CSR on profit, with variation across regions. Nonetheless, there is considerable evidence that firms which engage in high levels of CSR face lower costs of capital due to a perception that they present lower risk (Deutsche Bank,

2012; El Ghoul et al., 2011). This theoretically could provide lasting benefits to profitability through lower financing costs.

A rich literature on the effect of SRI strategies on mutual funds also exists. This includes reviews of the velocity of cash transfer rates within

SRI managed funds (Bollen, 2007), SRI mutual fund returns (Hill, Ainscough,

Shank, & Manullang, 2007) and mutual fund adherence to SRI portfolios over time (Wimmer, 2012). Likewise, research on individual investors is extensive.

Examples include Benlemih and Bitah (2016), who found that even very high and very low CSR does not improve investment efficiency, and Auer and Schumacher

(2016), who conclude there is no difference between SRI focused investments and market-based passive investment returns in the USA or Asia-Pacific, and negative returns for high SRI investing in Europe. Overall finding from the literature appears mixed, with both positive and negative results, as well as some assessments yielding no difference between SRI strategies and market based out performance (Blasi et al., 2018; Deutsche Bank, 2012).

Environmental Corporate Social Responsibility

This paper concentrates on the environmental aspect of CSR for its empirical strategy and policy discussion, focusing on the three key pillars of environmental CSR (E-CSR) as defined within the Thomson Reuters Eikon platform: Resource Use (representing efficient use of resources), Innovation

(representing innovation in the environmental sphere), and Emissions

(representing relative emission intensity efficiency compared to peers)

(Thomson Reuters, 2018). Focusing on just the environmental element of CSR offers several benefits. First, the environmental issues defined by E-CSR require defined, explicit action to address. Resource use and emissions are directly quantifiable; this stands in contrast to other CSR goals, such as good governance, which are more subjective in nature. Examples for each type of ESG is shown in Figure 2. Thus, in some ways E-CSR is a more direct measure of actual firm activity. Second, investors are likely to have differential interest in environmental, social, and governance issues, and 72

are likely to respond to changes that improve governance or reduce social risks much differently than changes to environmental CSR. Good governance or the reduction of social risk in some ways directly benefit firms. Actions to benefit the environment have a less-direct benefit to investors, making E-CSR a better measure of investors’ altruistic interest. Finally, investors are likely to care about resource efficiency, emissions, and innovation in different ways. For example, investing in innovation is a classic application of the Porter hypothesis (Porter & van der Linde, 1995) where regulation increase efficiency and unlocks innovation, and, together with resource use, is likely to appeal to value-based investors. Likewise, investment in emissions efficiency is likely to appeal to values-based investors (Climate

Action 100+, 2019; Feloni, 2019). In all cases, improving resource use, innovation, or reducing emissions all require specific policy responses, not one-size-fits-all approaches. By adopting an environmentally-centric approach to this paper, it directly links specific investor activity to firm level action and public policy in the environmental sphere, something that would not be plausible if all elements of CSR were considered together.

Figure 2 Examples of Thomson Reuters ESG Indicators

Recreated from the Thomson Reuters ESG Data Factsheet8

Prosocial Policy

As suggested by the United Nations Sustainable Development Goals, it is widely accepted that although “open markets and private enterprise are critical for sustainable development”, governments must also be willing to establish “good governance and a conducive operating environment” to allow private enterprise to be “integral partners in… environmental sustainability”

(UNDG, 2014). But at the same time, most public companies operate in highly competitive environments that are reactive to investor and consumer demand.

8 Retrieved (11/17/2019) from: https://www.esade.edu/itemsweb/biblioteca/bbdd/inbbdd/archivos/ESG_Research.p df 74

Likewise, investors can reward or penalize corporate behavior depending on whether they believe the firm is acting in their best interests or not. For example, if investors are only interested in profit maximization as suggested by Friedman (1970) and if environmental sustainability carries a net cost, firms that invest in sustainability are likely to underperform in the stock market. Conversely, if environmental sustainability produces net corporate benefits through long-term efficiency, as suggested by Porter and van der

Linde (1995), investors that trade for value will reward firms who invest in sustainability with long-term outperformance (Derwall et al., 2011).

Although the role of investors is key to this paper, the role of consumers and public values should not be underestimated (B. Bozeman & Johnson, 2014).

First, if environmental sustainability were to increase the prices firms charge for their goods and services and the public values lower prices above all else, firms that independently engage in environmental protection against the public will, can expect to underperform in a competitive market, and ultimately underperform in the stock market. Likely, the opposite is also true. If consumers penalize poor environmental performance because it is contradictory to their values, then these firms would also underperform relative to their peers. It is reasonable to assume that investors also trade on their values and that these values are more complex than simply equating short-term profit-maximization to long-term stock performance (Kamstra,

Kramer, & Levi, 2003; US SIF, 2018). Indeed, the growing trend in SRI investing is to use active screening techniques that match investor values to a firm’s values, using ESG scoring systems (Deutsche Bank, 2012).

In contrast to profit maximizing investors and value-investors discussed above, these so-called values-based investors gain non-monetary utility by supporting firms that accord to their value-system, and so they should be willing to forgo some profit in order to invest in companies they believe in

(Bénabou & Tirole, 2006). This type of values-based investor, and to a lesser 75

extent the value-based investor, represents the type of investor most interested in SRI investing and ESG scoring systems. A summary of the three different types of investors is shown in Table 25 together with their likely short- and long-term effect on share prices.

This interplay between corporate, investor and consumer activity represent an important relationship that policy makers must understand if they ultimately want to use markets to achieve policy goals related to environmental sustainability. For policy makers interested in promoting outcomes such as those detailed in the UN SDGs, getting firms to positively engage in environmental CSR varies based on how investors reward firms for their efforts. Two scenarios and several key policy options are evident. In the first scenario, investors reward prosocial investing by firms with market outperformance (or at least, do not materially penalize such efforts). Here, policies to promote independent activity by firms to increase their investment in CSR would be effective in leveraging markets to achieve sustainability goals. Likewise, efforts to improve investor perception of the value of CSR as a non-monetary measure of investing success would also be effective. In both instances, policy is used to either “nudge” firms towards contributing to greater sustainability (Thaler & Sunstein, 2009), or investors to demand more of these investments as part of a values-based investing strategy.

Another policy option is working with interested firms in partnership to promote sustainability goals on the expectation that creating CSR leaders will pressure other firms to follow. Here, if we accept the argument that CSR only generates short-term profit driven outperformance (Reinhardt, 1998), it puts pressure on other firms to follow along lest their competitors gain a competitive advantage in CSR. Conversely, if investors are gaining in non- monetary utility, competitors will materially underperform in the market since as they are at a competitive disadvantage for these types of investors. 76

In either situation, an environment can be created where CSR investment causes increasing investor interest, thus enticing further investment by both the firms themselves, and competitors concerned about their own reputation and market performance.

The second scenario is where investors penalize CSR investment with material market underperformance. This is likely to occur if, in aggregate across the market, investors vastly prefer indicators of profit when making investment decisions and regard CSR as an unproductive use of capital, especially in areas that are not directly linked to short-term profit or risk management such as in governance. Here, a valid policy response would be to uniformly regulate market participants to achieve sustainability goals. By doing so, any negative market effect is also uniformly distributed across related firms, leaving the base effect on all firms constant. This is particularly important if firms are responsive to investors who are negatively predisposed to large investments in CSR and could be a necessary pre-condition to get market buy-in for long-term sustainability planning.

Since investors materially penalize CSR activity in scenario two, the policy option of selectively sponsoring firms interested in CSR as discussed in scenario one is less likely to be effective. To make this policy work, policy makers would need to provide just enough incentive to offset the costs firms incur when engaging in sufficient CSR effort, essentially leaving government to fund the entire cost of market-based sustainability reform. This is not necessarily a bad result if the market adds some efficiency to the policy process, but this would still largely require government to fund the activity so that sponsored firms do not face investor backlash. Overall, even if policy makers have to rely on more uniform regulation, such regulation does not need to be command-and-control in nature; well-designed, flexible and market-based reforms would still be effective (Ambec et al., 2013; Burtraw,

2000). But, to receive sufficient buy-in, any negative or positive effects of 77

sustainable policy needs to be similar between firms and industries. Policy options are summarized in table 2 below. A discussion of how they apply to different types of environmental CSR is detailed in the policy discussion section.

Table 25 Different Types of CSR/SRI Investors

Table 1 - Different Types of CSR/SRI Investors Time period of effect Short-term effects Medium to long-term effects

Profit maximizers do not value CSR Profit maximizers do not value Profit and will penalize high CSR firms CSR and push down prices when maximization with material underperformance in ↓ firms engage in CSR ↓ the longer term

Values-based investors gain non- monetary utility from investing in firms who share their values. This Ethical Values based investors choose allows such firms to underperform values high CSR firms, bidding up their the market over time, compared to investing ↑ prices in the short term ↓ if they merely profit-maximized, and still appeal to values investors

Value-based investors regard CSR as undervalued in the short Value based investors expect high term. They should be willing to Value CSR firms to outperform in the bid up the price until the investing longer term, resulting in market - marginal benefit of further ↑ outperformance.

investment equals the expected Predominant type of investing strategy investing of type Predominant future benefit of the firm's CSR

Table 26 Policy Responses to Investor Interest in E-CSR

Table 2 - Policy Responses to Investor Interest in Environmental CSR Investor Response to Independent CSR efforts Negative Positive

Uniform regulation could stifle Related firms are equally affected by innovation. Penalizing all the Uniform regulation. Well-designed regulation same reduces the benefit of Regulation ✓ × could offer opportunities to innovate individual firms to innovate and differentiate along CSR grounds

Creates an environment of

Unknown direction and power of innovation and competition to Nudging and effect. Potentially increases help the market pursue Promotion investor favor to CSR; potentially sustainable development. plus × helps ward off the effect of more ✓ Regulated industries may benefit

Options targeted uniform regulation (See for example from well-designed regulation regulation Welch et al, 2000) that improves stakeholder

relationships Policy Policy Since investors are unwilling to Potential to create a positive subsidize sufficient CSR, policy feedback loop whereby firms makers would need to provide engage in CSR, improving offsetting funding to interested Individual relations with investors, firms -essentially passing the cost Sponsorship ? ✓ reducing costs of capital (see El of market based sustainable Ghoul et al, 2011) and prompting development to government, less any more CSR by the firm and efficiency gains from using the competitors. market.

Data

The data for this study comes from Thomson Reuters Eikon, a large investment platform for professional traders. Within Eikon, access to premium information varies by subscriber level, but generally all users see comprehensive historical price and financial performance information, news, consensus stock opinions and forecasts, and have access to integrated equity trading. ESG data is also available to all users. Thomson Reuters has been collecting and reporting ESG data since 2002, initially focusing on leading stock indexes including the S&P500 and CAC40 which have been reported in full since 2003 (Thomson Reuters, 2018)9. ESG scores for smaller firms in broader indexes such as the Russell 2000, and in emerging markets were still being developed at the time this paper was written.

Between stock markets, firms listed in fully developed markets are more likely to be comparable against each other than firms from smaller and less regulated markets, and more likely to represent markets most interesting to the broadest number of investors. According to FTSE Russell, a leading organization in grading markets, developed markets are classified as being investment grade with strong and fair regulation, fast, reliable and consistent settlement, both a competitive and complete dealing landscape, plus high levels of efficiency and transparency in market information availability (FTSE Russell, 2018). In part due to variation in the long-term availability of ESG data in Eikon, firms selected for this paper come from the leading indexes in their appropriate country or region as chosen from those rated as developed by FTSE Russell. A list of indexes and their size/location is shown in Table 28. In total, this paper includes analysis on

753 firms in North America, 555 in Asia Pacific, and 799 in Europe where a

9 Roughly 150 analysts at Thomson Reuters engage in collecting and inputting ESG data for use in Eikon(Thomson Reuters, 2018). 81

full range of CSR scores were present in at least one year for each firm.

Table 29 shows the distribution of observations by industry with firms widely dispersed across industry groups. Industrials represent the most common type of firm in the dataset, making up about 17 percent of the total. This is followed by financial stocks and consumer discretionary. Energy firms and utilities comprise about 6 percent and 5 percent of the data respectively.

Eikon ESG and Data Collection

Data used to create the harmonized ESG scores used in this paper, except for those used to calculate carbon emissions, is collected solely from self-reported or public sources including company CSR reports, stock exchange filings and news sources. Where information about a dimension of ESG is missing, Thomson Reuters leaves it as missing. The exception is carbon emissions. Here, using a multi-step model, Thomson Reuters calculates CO2 emissions for the roughly 50 percent of firms who do not report such emissions(Thomson Reuters, 2017). Overall, this data collection program aims to measure ESG across 400 indicators with 61 of these measures being used to calculate the three headline environmental scores (Thomson Reuters, 2018).

Indicators that are not relevant to the industry of the firm are ignored in generating overall scores. For all intents and purposes, firms ESG scores act as a rank versus other firms within the same industry. The specific calculation for each firm in each relevant metric is:

n n ∑j=1 1 if Xj = Xi ∑j=1 1 푖푓 푋푗 < 푋푖 + score = 2 for all j ≠ i i n

where i represents firm i, j represents a comparable firm in the same industry, n is the total number of firms, and X represents the relevant score for the relevant firm in the industry. The scores are then aggregated to get an overall rank along the 10 key dimensions, including resource use,

environmental innovation and emissions as used in this paper. Note that higher scores are always better -a high score in emissions for example implies a higher level of emissions efficiency compared to a firm’s peers.

Summary statistics of ESG data is given in Table 30. Within resource use and emissions, means scores for European firms in the sample are about

0.5 standard deviations above the firms in North America and Asia Pacific.

However, although European firms are also higher in environmental innovation, the difference is only about one third of a standard deviation over North

America and 0.2 standard deviations above Asia Pacific. Across the three measures, European firms are highest, followed by Asia Pacific then North

America. One plausible reason for this systematic variation between regions could be data related -if E-CSR scores have been increasing in time, and more of North America was collected early, then scoring bias may occur due to a time bias. Time trends for firms in the sample are thus shown in Figure 3.

Across all years in the sample, European firms have outpaced both North

American and Asia Pacific firms across all three measures. European and North

American firms have generally increased their scores over time, while Asia

Pacific firms were static from 2003 until the early 2010’s when their scores also began to increase. This suggests that higher average scores among

European firms is not due to data collection discrepancies or time trends caused from differential approaches to climate change or resource efficiency, but rather that European firms in the EUROSTOXX800 tend to be better performers on environmental indicators compared to S&P500 and Asia-Pacific index participants.

One benefit of using the Thomson Reuters Eikon ESG scores is that they are built using publicly available sources such as corporate sustainability reports, annual reports and news articles - in effect, revealed CSR activity- to generate ESG scores across ten key categories, including the three

representing environmental CSR used in this paper (Thomson Reuters, 2018).

The only exception to this rule is that Thomson Reuters will estimate firms’ carbon emissions if firms do not voluntarily disclose them. According to the

Global Sustainable Investment Alliance, as of 2016, ESG integration was the most commonly used screening system after negative screening, and the most popular tool in the United States, Canada, Australia, New Zealand and Asia

(excluding Japan) for SRI directed investment (GSIA, 2016). Also, although any measuring system for ESG is likely to have some bias based on how the individual or firm chooses to actually collect and display that information, it is generally accepted that investors do rely on third party tools like the

TR ESG scores in making SRI decisions (Blasi et al., 2018). Indeed, it is argued that investors often only have a vague idea of what they consider socially responsible, and thus, often make use of third part ESG metrics

(Hill et al., 2007). Importantly, Thomson Reuters is a major player in financial data with related revenues of about $6.1 billion in 2017 in their

Finance & Risk division10, with Eikon representing about 33% of the market for financial data, second only to the Bloomberg Terminal (Burton-Taylor

International Consulting LLC, 2018). It is also generally accepted that investors should seek a diversified portfolio (Goetzmann & Kumar, 2008;

Markowitz, 1952) with a contemporaneous diversified SRI portfolio built on both values-driven negative screens and risk/return positive screens

(Deutsche Bank, 2012). In both instances, by ranking firms globally by industry, the TR ESG scores allows investors building a diversified portfolio to compare firms on a like-for-like basis, providing tools to help them invest across and within industries without loss of diversification or SRI ideals. It also allows investors to select or remove firms based on observable behaviors associated with increased risk, such as through lower

10 As of December 2018, The Thomson Reuters Finance and Risk division, including Eikon had been spun-off into a joint venture called Refinitiv. 84

rankings on headline ESG scores or specific metrics of interest to the investor, and through sub-scores in controversies and activities that may imply riskier behaviors.

Intertemporal variation in firm ESG scores

One concern in conducting a firm-level fixed effect analysis is whether there is enough intertemporal variation in key data points (Greene, 2012). This is particularly true for ESG scores if a firm’s commitment to CSR varies only marginally over time. Summary statistics of year-to-year variation in the key environmental CSR/ESG scores is shown in Table 31. For the resource use score, average year-to-year variation was 2.52 points per firm in North

America, 1.78 in Asia Pacific and 1.84 in Europe. Standard deviations across the three means were 13.13, 13.18 and 14.16 respectively. This shows reasonably large changes each year with plenty of variation in the size of these changes. Overall, only 65 year-to-year changes in resource scores were zero for the 19,227 observations. The story is similar for the emissions scores. For Innovation, mean variation was lower (.887, 1.28, 1.42 points per year respectively), and there were more instances of zero variation (717 data points). This still represents a small number of data points across the entire dataset and is probably occurring since most environmental innovation measures are qualitative in nature. Overall however, there is considerable year-to-year variation in the key independent ESG variables.

Financial Performance Information

Firm level financial performance information is also collected from the TR

Eikon platform for the period 2003-2017 for each firm in the sample.

Following Blasi et al. (2018), this paper adopts a geometric mean of stock market performance calculated as:

TSRi = Ln(푃푖(푡)) − 퐿푛(푃푖(푡−1)) where TSR represents total stock return for firm i, and P represents firm i′s price level in local currency terms as at December 31st of year t. Since individual firm total stock returns may include dividends as a component, this paper also examines total return including dividend defined as:

TSR_Divi = Ln(푃푖(푡) + 퐷푖푣푖푡) − 퐿푛(푃푖(푡−1) + 퐷푖푣푖(푡−1)) where Divit represents dividends paid in year t by firm i. Financial summary statistics are given in appendix 5. Average returns for firms in the sample were very similar across all regions, with an average return of 14.2% per annum overall, 15.2 percent per annum in North America, 13.1 percent per annum in Asia-Pacific and 14 percent per annum in Europe. Standard errors across all regions were also similar with slightly larger variation in Asia

Pacific, and less in Europe. Mean total stock return measured per TSRi above was also similar across all regions. Dividend adjusted TSR, measured as

TSR_Divi, was between 0.4(Europe) and 0.7(North America) points below the comparable mean TSRi score. Overall, these statistics suggest that between regions, stocks in the sample generally grow at similar rates over time, but with large variations in individual stock returns from year to year. When dividends are added into annual TSR there is a slight decrease in TSR suggesting that year to year changes to dividend rates are less important than stock prices acting alone.

Empirical Methodology

Two empirical methods are used to examine the relationship between TSR and environmental CSR activity: a firm fixed effects model relating corporate ESG scores to total stock return, and a passive investment portfolio using SRI objectives.

Environmental Score Approach

The objective here is to investigate the effect of a marginal increase in environmental ESG scores on total stock return from a firm perspective. For an active investor, such a change could be useful when developing an SRI portfolio aimed at investing in firms that are actively improving their environmental CSR scores, as measured in ESG terms, relative to their peers.

The full model includes an interaction term between the three regions presented in the data and each ESG score. This method controls for any global effects on firms and industries over time while also considering differential effects of ESG on stock returns at the more localized level.

To estimate the effect of a marginal increase in environmental ESG scores on total stock returns, I estimate a firm fixed effects OLS of the following form:

푇푆푅푖푡 = 퐿푛(푃푖푡) − ln(푃푖푡−1)

= β0 + β1퐸푛푣푖푡−1 + β2푅푖 + β3퐸푛푣푖푡−1 ∗ 푅푖 + β4−푛푋̅̅̅푖푡̅ + γ푡 + δ푖 + ϵ푖푡 where TSR represents the geometric mean of total stock return for firm 푖 in year t; Env represents a vector of the environmental scores of interest: emissions, innovation, and resource efficiency, or the average of all three;

푅푖 is a categorical variable representing one of the three regions presented in the data, North America, Europe, and Asia Pacific; 푋̅푖푡 represents a vector of time-variant business and controls; and 휖푖푡 represents the idiosyncratic error term. The model includes both firm (훿푖) and year (훾푡) fixed effects. These control for possible endogeneity bias at the firm and year levels, that could affect total stock return and the environmental score of interest. The firm- level fixed effect controls for time invariant characteristics like the industry and location of the firm, reputation, size, and other factors that do not vary over time. Meanwhile, the year fixed effect controls for time

trends in economic factors like productivity and GDP growth, changes in public sympathy for the environment, and trend changes in demand for corporate social responsibility. TSR is split into non-dividend and dividend inclusive measures to account for the possibility that firms that engage in high levels of CSR have a systematically different philosophy to dividend payments.

Note that environmental scores are for firm 푖 as at December 31 of the previous year. This is for two reasons: first, Thomson Reuters releases current year scores only when data becomes available. This can vary considerably by firm and may be missing for a large part of the year. Second, current year scores are updated throughout the year as new data is released.

Thus, the final end-of-year score for the previous year is the most recent, stable score available to investors. In the full model, the 푋̅푖푡 vector represents several controls. First, I control for two types of time-variant firm characteristics: a profit dummy variable equal to one if the firm recorded a profit during the current financial year, and zero otherwise, and debt leverage expressed as the ratio of the book value of debt to assets. It is well established that profitable firms have higher stock returns relative to when they are unprofitable (cf. Novy-Marx, 2010), while they may also be more willing to invest more into CSR during profitable periods. Likewise, a high level of leverage may reduce a firm’s ability to support investors through share buy backs and dividends, and so they are likely to have less cash available for CSR activity. In the full model, I also include a year-by- industry control to account for industry time trends that affect both profitability and CSR. Finally, following Abadie et al. (2017), standard errors are clustered at the firm level to match the fixed effect model.

Portfolio Approach

Although the relationship between individual firm performance and corporate social environmental responsibility is the focus of this paper, it is also generally accepted that efficient investing involves creating a diversified portfolio of stocks between different industries and potentially, countries or regions (cf. Markowitz, 1952; Samuelson, 1967). One plausible use of corporate ESG scores would be to create a passive investment portfolio with firms chosen from the top ranked firms for ESG, either for risk management purposes (Deutsche Bank, 2012), on the belief that such a portfolio will outperform comparable benchmarks (Derwall et al., 2011), or to outsource prosocial beliefs (Benabou & Tirole, 2010). In order to simulate this effect,

I adopt a portfolio approach thematically similar to Auer & Schumacher

(2016). The premise of this approach is that each year, the passive investor selects those firms representing the top 10 percent of all firms by specific

E-CSR metric in each industry and region, and holds this “maximal SRI” portfolio for one calendar year. On the last day of the year, they update this portfolio using current end-of-year scores as defined previously. This results in a lagged analysis, where firms are selected in year t-1 and compared to market performance in year t. Where the SRI portfolio lags broader market returns provides some insight into the level of non-monetary utility values-based investors place on stocks and insight into the potential costs for promoting values via SRI investing. Under this approach, the analysis aims to estimate:

TSRit = 훽0 + 훽1푃푂푅푇퐹푂퐿퐼푂푖푡 + 훽2푅푖 + 훽3푃푂푅푇퐹푂퐿퐼푂푖푡 ∗ 푅푖 + 훽4−푛푋̅̅̅푖푡̅ + 훾푡 + 훿푗 + 휖푖푡 where PORTFOLIO which represents a dummy variable equal to one if the firm is in the top ten percent for the relevant environmental score by industry, year and region; and 훿푗 which represents an industry fixed effect. All other variables are equivalent to those discussed in the environmental score

section above. Industry and year fixed effects are included to control for possible endogeneity bias at the industry and year levels which might affect total stock returns, and to account for systematic differences between industries on how they approach environmental issues. This includes common trends in demand for products, similar operating environments, and similar relationships with environmental regulations.

When selecting firms for this passive portfolio, rankings were made at five aggregate industry levels. Four are based on industry groupings from

Hyde and Sherif (2005), and represent consumption, capital, finance, and other, while the fifth, information technology, is broken out from other industry groups to account for the growing size and influence of tech shares in the stock market11. For this analysis, firms in the top 10 percent of each environmental metric were chosen as representing the “maximal SRI” portfolio.

Since the number of data points increased over time as Thomson Reuters expanded their data collection to more firms, the number of firms in the portfolio also increased: from 48 firms in 2004 and 94 firms in 2005 to 193 firms in 2017. The time fixed effects control for the general growth in the number of firms providing SRI information. Standard errors are clustered at the industry level.

Results

As the results document some notable heterogeneity across regions and types of corporate environmental measure, discussion is split between the results of the global model and between each region. Key results are summarized in

Table 27. Note that where results between TSR and TSR plus dividends are similar, only the TSR results are discussed for brevity.

11 IT shares represent 17.8% of the 28,064 observations. 90

Table 27 Summary of Key Empirical Results

Marginal Effects of CSR on TSR & Total Stock Return TSR Dividends Portfolio

Global Resource Efficiency (t-1) -0.040** -0.045*** -0.802 (0.016) (0.017) (0.587) Innovation (t-1) - - -2.058*** (0.667) CSR Average (t-1) -0.072*** -0.075*** -1.354** (0.021) (0.021) (0.558) North America Resource Efficiency (t-1) - - -2.058** (.9193) Emissions (t-1) -0.06** -0.05** - (.0255) (.0252) Innovation (t-1) - - -1.914* (1.081) CSR Average (t-1) -0.096*** -0.074*** -1.478* (.0278) (.0277) (.8697) Asia-Pacific Resource Efficiency (t-1) -0.070** -0.083** - (.0351) (.0346) Emissions (t-1) - 0.047 - (.0324) Innovation (t-1) 0.030 - - (.0251) Europe Resource Efficiency (t-1) - -0.052* - (.028) Innovation (t-1) -0.035 -0.038* -3.036*** (.0219) (.0228) (1.12) CSR Average (t-1) -0.091*** -0.119*** -1.514 (.0348) (.0366) (.9247) Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01; Cells with a "-" and results not shown are most compatible with no important effect and are listed in the relevant appendix.

Global

Globally, the environmental score model implies that a one unit increase in resource efficiency scores reduces TSR by about 0.4 percentage points (p<0.05) (Table 33), while TSR with dividends is reduced by about 0.45

percentage points (p<0.01)(Table 36). This result is robust to model specification across all except the most basic model, which excludes all controls and the year fixed effect. In economic terms, this represents a loss of investment efficiency of about 1/25th of one percentage point for a firm increasing their resource score by one unit. For the other indicators of environmentalism, a one unit increase in average E-CSR is predicted to reduce

TSR by about 0.72 percentage points (p<0.01 level), while no effect is seen for emissions or innovation.

From the portfolio approach, although there is no discernable effect for investing in top performing firms by resource use or emissions efficiency, a passive portfolio of the top 10 percent of firms by environmental innovation is predicted to reduce TSR by about 2.06 percentage points per annum, significant at the p<0.01 level (Table 39). A negative co- efficient is also seen on passive portfolios built around average CSR scores

(about -1.35 percentage points, p<0.05).

North America

For North America, a one unit increase in within-firm emissions scores in year t-1 is predicted to reduce TSR by about .06 percentage points, or about 1/17th of a percent in year t (Table 35). This is statistically significant at the 95 percent level. Results show no clear marginal effect for environmental innovation and resource efficiency on TSR in North America.

However, a one unit increase in the average score across all three environmental dimensions is predicted to reduce TSR by about 0.96 percentage points (p<0.01). Regionally, a portfolio of high environmentally performing firms in North American will underperform the broader market in all measures of environmentalism bar emissions. Point estimates were highest for resource efficiency (-2.058, p<0.05), then innovation (-1.914, p<0.1), and finally CSR

average (-1.478, p<0.1). The results suggest that in North America, investors penalize firms for increasing overall average E-CSR scores and for being in the top 10 percent of all firms by average scores. However, they only penalize firms for marginally increasing their emissions scores, not for being a market leader by being in the top 10 percent for emissions efficiency. This suggests that North American investors are somewhat supportive of firms with strong reputations for reducing their emissions, but they will penalize firms that become only marginally more efficient. This contrasts with passive investment strategies for resource efficiency and innovation only, where firms are penalized for being high performers.

Asia Pacific

For Asia-Pacific, results from the full environmental score approach show a negative relationship between increases in the resource use metric and

TSR (-.07 percentage points, p<0.05), and no clear effect from the overall average score. However, there appears to be a complicated relationship between emissions, innovation, and TSR. Here, the co-efficient for emissions on TSR is almost classically statistically significant12 (p<0.147) in the dividend model, but less precise in the TSR model (p<0.211). For innovation the opposite is true, with the TSR model being more precise than the dividend model (p<0.233 vs p<0.356). For emissions, the point estimate of a one unit increase in within-firm emissions scores translates to an increase in TSR of about 0.47 percentage points, including dividends. Meanwhile, a one unit increase in innovation scores translates to about a 0.3 percentage point increase in TSR. This shows that a positive relationship between emissions and innovation to TSR might exist, even if the point estimates are currently weak predictors of positive TSR. Finally, the passive portfolio models for

12 Classically statistically significant being given as p<0.10 93

Asia Pacific show no loss of investment efficiency on any environmental portfolio. This implies that investors in this region could choose to passively invest in the top environmental performers in the region without risking their return on investment.

Europe

Finally, in Europe, point estimates across both TSR and TSR plus dividend are similar, although statistical precision varies. For example, the point estimate for a one unit increase in emissions efficiency in time t-1 on TSR is about -.033 (p<0.153) while on TSR with dividends it is -0.31 (p<0.236).

For environmental innovation the point estimate on TSR is about -.035

(p<.017) but the precision of the estimate improves when dividends is included, with this model predicting a reduction in total returns by about

.038 percentage points (p<0.1) for a one unit increase in innovation score.

Between the two TSR models, the estimates from an increase in resource efficiency is the least precise. Here, point estimates are 0.024 (p<0.347) and 0.052 (p<0.07) respectively. Finally, as in the overall model and in

North America, a one unit increase in average environmental scores is predicted to reduce TSR in Europe -this time by about 0.91 percentage points

(p<0.01).

In the passive portfolio approach, while no clear relationship was seen for portfolios built on resource or emissions efficiency, investing in firms high in environmental innovation is predicted to reduce total stock return by about 3.04 percentage points, significant at the p<0.01 level. Again, as in the overall model and in North America, investing in a passive portfolio of firms based on average scores would also underperform the broader market in

Europe, this time by about 1.54 percentage points (p<0.102). Finally, investors in Europe follow a similar trend to investors in other markets in generally penalizing environmental CSR activity, with little to no evidence 94

that European traders reward firms for engaging in incremental increases in

ESG score, or for being a leader in environmental action.

Overall, European investors are uniformly negative on the overall average CSR score across all three models, and, uniquely to the dataset, uniformly negative on environmental innovation. The average score for innovation for a firm in Europe is also much lower than the average scores for resource use and emissions efficiency (see Table 30). This suggests that there is something about highly innovative firms in Europe that differentiates them from the broader market, and that leads to short-term market underperformance. This could include the way European regulations approach environmental innovation. Indeed, while the political environment appears to favor centralized economic incentives and regulation to improve resource efficiency (see for example EEA, 2016) and strong uniform regulation to tackle carbon emissions (see for example the EU Emissions Trading System13), regulation to improve environmental innovation has historically been developed in partnership with industry, and has largely been unsuccessful in driving environmental innovation (Ashford, 2005).

Policy Discussion

This section of the paper relates the potential policy responses developed in the literature review, and summarized in Table 27, to the empirical results. Policy options are split along three dimensions: uniform regulation which represents a form of ‘hard’ regulation with quantifiable, legally binding targets; nudging, promotion, and targeted regulation which represent softer options; and individual sponsorship which represents a more collaborative approach between regulators and industry. As shown in the

13 EU Emissions Trading System (ETS) https://ec.europa.eu/clima/policies/ets_en (last accessed October 2nd, 2019)

empirical discussion, there is considerable variation within and between regions in investor responses to different measures of environmental CSR. As such, environmental policy aiming to capitalize on efficiencies driven by market participation needs to vary by region and by the different types of environmental activity. Indeed, an important limitation in resolving international environmental issues is creating a workable uniform environmental policy that is likely to be accepted by all parties (cf

Nordhaus, 1994; Barrett, 2005). Thus, the stock return response to environmental CSR could affect the choice of environmental policy.

Uniform Regulation

Using uniform regulation such as a technology or performance standard to engage the market in environmental CSR-type activity provides an equitable approach to policy that distributes the potential effect of the policy across all firms equally, or at a minimum, affects firms equally by industry or market sector. This could be done to resolve a specific environmental issue, as occurred with international agreements to ban ozone depleting substances, but uniform regulation may also be used for broader environmental goals such as developing a carbon tax or cap-and-trade program to tackle climate change.

If uniform regulation is best used where investors are likely to penalize firms for voluntary environmental CSR type activity, uniform regulation offer an alternative. From the empirical results, the implication is that uniform regulation might be appropriate in several policy areas including: broad based environmental reform in North America except, potentially, emissions;

Europe based on a negative sentiment to average CSR scores and to support environmental innovation; and in the Asia Pacific region to improve resource efficiency. Certainly in Europe, uniform regulation built around performance standards has emerged as a popular tool to combat climate change (C Adelle,

Russel, & Pallemaerts, 2012), while broad based cap-and-trade legislation to 96

address climate change has previously been close to becoming law in the

United States (cf. Krugman, 2009).

When used effectively, uniform regulation has the benefit of reducing market backlash to environmental regulation by equitably distributing the costs to all firms as far as is practically possible, while potentially giving firms an opportunity to innovate and gain a competitive advantage. One example where this has occurred is in catalytic converter technology in automobiles (Hascic, De Vries, Johnstone, & Medhi, 2009). On major issues like climate change, negative investor sentiment to individual firms investing to reduce their carbon emissions provides opportunities for their competitors to chase profit-maximizing investors by not doing anything.

Uniform regulation would effectively remove this incentive.

Individual Sponsorship

Individual sponsorship, or voluntary programs, involves policy makers working directly with firms to address environmental issues. This could include programs where policy makers work with industry partners in a collaborative effort, like the US EPA’s High Global Warming partnerships program,14 or direct sponsorship programs where government subsidize industry participants to undertake environmental reform such as the Biomass Crop Assistance Program operated by the USDA15. Other voluntary programs where firms have reduced their environmental impact include the 33/50 program which acted as a voluntary extension of the EPA’s Toxic Release Inventory program (Khanna &

Damon, 1999), and the New England Lead-free Electronics Consortium program where industry participants, government and academia successfully

14 See High Global Warming Protentional (Gases). https://www3.epa.gov/climatechange/highgwp/voluntary.html (last accessed October 1st, 2019) 15 Biomass Crop Assistance Program. https://www.fsa.usda.gov/programs-and- services/energy-programs/BCAP/index (last accessed October 1st, 2019) 97

collaborated to produce lead-free electronics (Morose, Shina, & Farrell,

2011).

Sponsorship programs can be effective regardless of whether markets and industry are supportive of environmental reform, but costs to government are likely to vary depending on how much firms can invest using their own capital. For large scale environmental interventions, such as satisfying the

UN SDGs or tackling climate change, the scale of public investment needed to make a significant difference is likely to be prohibitively expensive unless markets also participate in some way. For example, from the empirical analysis of overall CSR scores on TSR for the model without interactions

(Table 33), a one unit increase in average scores is predicted to reduce firm-level TSR is reduced by about 0.72 percentage points (±0.42 percentage points at two standard deviations). For each $100 of market capitalization, this roughly translates into a loss of 72 cents for each additional point of environmental score. Assume there is an environmental reform which requires a firm to increase its average score by one unit. Achieving this would, ceteris paribus, cost investors 72 cents per $100 of market capitalization. To offset this loss to investors, some of the 72 cents will come from the subsidy and some from any profits or efficiencies generated within the firm. This would be just enough to leave the firm on equal footing with its competitors who did not participate in the program. Conceptually, if large increases in scores were needed to combat a large-scale environmental issue, then large subsidies or efficiencies would also need to be realized.

One important use of individual sponsorship in lieu of uniform regulation could be in North America, where there is little evidence that investors penalize very high performing firms (defined as the top 10 percent of firms in their industry based on emissions efficiency) for their emissions reduction work (portfolio coefficient is 0.765 (p<0.4)), but where they are

penalizing marginal increases in emissions score (point estimate is -0.06, p<0.05 in the TSR model). Here, individual sponsorship of firms with very high reputations in emissions efficiency provides opportunities for government to work with willing partners to develop a culture of innovation in emissions efficiency. The objective of this type of program is to do more than just subsidize firms who reduce their emissions, but to unleash the market to develop technology that creates new norms for what is possible in emissions efficiency. Such technology is likely to be commercially viable and likely to be attractive to value-based investors. An example of this type of program is the DARPA Transition and Commercialization Support Program16 which works with firms to conduct research and convert promising technology into commercial products. Although this program is not designed to develop environmental technology, programs designed to help firms improve emissions efficiency are likely to be similar and involve high levels of collaboration over an extended period. Another opportunity could be sponsoring firms with preferable loans like those offered for clean energy investment by the US

Department of Energy.17 In each situation, as the firm is not penalized with market underperformance, and eventually could generate longer-term returns that outperform the broader market, sponsored programs would represent a lower cost to government.

One final use of individual sponsorship is where investors reward firms with market outperformance for investing in Environmental CSR, as is potentially occurring in Asia Pacific for emissions efficiency and environmental innovation. This sort of sponsorship program has the potential to create a

16 DARPA Transition & Commercialization https://www.darpa.mil/work-with- us/for-small-businesses/commercialization-continued (last accessed October 2, 2019)

17 Loan Programs Office https://www.energy.gov/lpo/loan-programs-office (last accessed October 2, 2019)

positive feedback loop whereby firms engage in state supported E-CSR type activity which improves relations with investors who drive up stock prices, and improves relations with capital markets which translates into lower costs of capital (El Ghoul et al., 2011), and thus promotes more CSR by both the firm itself, and its competitors who would likely face market pressure to keep up. This type of program is also likely to be cheap to implement since firms and investors benefit directly by being involved in the program.

Soft Touch Policy and Targeted Regulation

This section includes three types of policy options, all of which are best used when there is evidence investors will reward firms for engaging in positive environmental activity. The first is to use nudging and other behavioral economic techniques to entice firms to engage in more CSR, including educating firms about the benefits of CSR activity; the second is targeted regulation which aims to regulate a sub-section of the broader market such as a certain industry, a subset of a particular industry, or an individual type of product; and the third is promotion which represents work by policy makers to promote and advertise firm achievement towards an environmental goal of interest.

An example of promotion policy is the joint US EPA and Department of

Energy’s EnergyStar18 program which aims to recognize firms for improving their energy efficiency and for using more efficient products in their everyday business. This program has been effective at reducing energy usage

(Sanchez, Brown, Webber, & Homan, 2008). Where consumers and investors value the EnergyStar logo on products, the market is likely to respond favorably to companies investing to gain EnergyStar certification. Likewise, a program

18 EnergyStar. https://www.energystar.gov/ (last accessed October 1st, 2019)

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like the US EPA’s Design for the Environment19 program also represents a form of promotion. Here, the EPA certifies detergents, cleaners and other chemicals as being environmentally friendly, and provide firms with a logo to help promote their products as such.

For nudging, if investors reward firms for engaging in environmental

CSR-type activity, policy makers could work to educate firms about this effect and provide “nudges” to get firms to engage in more of this activity.

One example is convincing firms to participate in industry environmental standards in lieu of regulation. Here, since investors reward firms for engaging in environmental CSR, well designed industry standards provide a way for firms to show they are environmentally sustainable without requiring formal regulatory standards. If these voluntary standards are strong enough to achieve a sustainability or environmental goal, firms will have every incentive to participate lest they get penalized by their investors and potentially, consumers. An example of such an industry standard is the

Sustainable Forest Initiative, although many others exist.

Finally, targeted regulation differs from uniform regulation by limiting regulation to certain industries, firms within industries, or product lines. If investors are favorable to firms engaging in environmental

CSR, high quality regulation makes firms subject to the regulation more desirable investments, potentially allowing them to outperform the broader market, and driving further innovation. Conversely, if investors penalize firms for engaging in CSR-type activity, firms targeted by this type of regulation will likely face investor backlash as profit-seeking investors look elsewhere, giving the firm every incentive to push to limit or remove

19 Design for the Environment Alternatives Assessments https://www.epa.gov/saferchoice/design-environment-alternatives-assessments accessed October 1st, 2019 101

the regulation. One example of this occurring is the Clean Energy Act 2011, a so-called carbon tax, which was introduced in Australia in 2011. As the tax only applied to a small number of industry participants, industry backlash was significant, and the tax was dropped three years later. Had investors been willing to support firms in this instance, or if the tax had been uniformly applied, the results may have been different.

Based on the empirical results, there is little statistical evidence that investors reward firms for engaging in environmental CSR-type activity, limiting the ability for policy makers to use these three approaches. Two examples where this might not be the case, and where the empirical results were positive with some uncertainty, is in environmental innovation and emissions efficiency in Asia Pacific. Educating firms about this positive relationship between environmental action and TSR, and by providing incentives to further innovate or reduce emissions will help firms build a reputation as environmental leaders. This would pave the way for attracting values-based investors (especially for emissions efficiency) and value-based investors (especially for innovation), rewarding the firm with stock market outperformance.

Investor Attitudes to CSR

One final method for improving private sector involvement in sustainability is to entice investors to become more values-based in their outlook, as evidenced by the Climate Action 100+ group20 or investor interest groups like The Forum for Sustainable and Responsible Investment. Such a change in behaviors will provide firms with more opportunities to increase their Environmental CSR activity, and since investors, ultimately direct firm behavior, having more investors interested in environmental CSR, whether for

20 https://climateaction100.wordpress.com/ accessed August 27th, 2019 102

purely pro-social, risk management, or other purposes, provides incentives for firms to positively engage in environmental protection. Although this may require investors to accept slightly lower returns on their investments over the longer term, relative increases in the demand for values-based investment helps high CSR firms outperform the market in the short-term. By continually increasing SRI investing through policy intervention or nudging (Thaler &

Sunstein, 2009), decision makers can create a virtuous cycle whereby high-CSR firms outperform the market in the short-term, creating more demand for higher CSR and even greater outperformance.

Conclusion

This paper provides a thorough review of the role of investors in directing firm environmental CSR-type activity, including updating the definition of CSR to represent its implementation in the modern era. Based on work by Derwall, Koedijk, and Ter Horst (2011) this paper characterizes investors as being either values-, value-, or profit-based, and investigates how each type of investor responds to firm-level CSR activity in the short and long term. Across several dimensions, short term empirical results show profit motivated investors penalizing firms for engaging in environmental CSR more than other investors reward these efforts. The implication for policy makers is to make use of uniform regulation to achieve sustainable development goals. This form of regulation could be limited to certain industries or to pursue certain environmental outcomes to lessen the impact across the entire market.

However, the empirical evidence also supports an argument that current levels of firm investment in E-CSR is not producing substantial loses in market value. For example, for each $10,000 of investment in a passive portfolio of the top ten percent of firms by average CSR across all regions,

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an investor would earn about $129 less per annum than the broader market after accounting for dividends and changes in stock prices. This is in comparison to average annual returns of about $1,420 per $10,000 invested across all markets in this paper. Conversely, firms can expect a reduction in market capitalization of about 75 cents per $100 of total capitalization for each unit increase in average CSR score. This provides some support for policy makers to engage firms interested in environmental protection through subsidies and individual firm sponsorship, although it is unclear how expensive these programs might get if policy makers choose to engage a market hostile to environmental CSR to resolve large scale environmental issues such as climate change, or to achieve the UN’s sustainable development goals.

Overall, this paper presents broad policy prescriptions informed by an empirical strategy which unearths some of the complex relationships between regions and different measures of environmental CSR. Since different investors reward firms for engaging in environmental CSR differently over different time periods, more research is needed to better understand the long-term impacts of environmental CSR on market-based stock performance, on the role of private institutions in contributing to sustainable development, and why variations between regions and types of environmental activity exists in the first place. Research into regulatory environments, investor behavior, and cultural norms that affect a firm’s ability to engage in meaningful environmental action, and research into what bespoke policy response to use to promote market participation in sustainable development, or other environmental activity, all present opportunities for future endeavors.

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Tables and Figures

Table 28 Indexes, Size and Location

Asia-Pacific Region (555 firms)

ASX200 – Australia – 200 firms

HangSeng 50 – Hong Kong – 50 firms

Nikkei225 – Japan – 225 firms

NZX50 – New Zealand – 50 firms

STI30 – Singapore – 30 firms

Europe (799 firms)

STOXX800 – Austria – 6 firms

- Belgium – 13 firms

- Czech Republic – 2 firms

- Denmark – 24 firms

- Finland – 16 firms

- France – 88 firms

- Germany – 79 firms

- Republic of Ireland – 9 firms

- Italy – 31 firms

- The Netherlands – 31 firms

- Norway – 13 firms

- Portugal – 4 firms

- Spain – 26 firms

- Sweden – 44 firms

- Switzerland – 51 firms

- United Kingdom – 163 firms

North America (753 firms)

S&P500 – United States – 504 firms

S&P/TSX250 – Canada – 249 firms

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Table 29 Breakdown of Observations by Industry

Industry Count Proportion Avg. CSR Score Industrials 5,760 0.17 58.21 Financials 4,965 0.15 60.00 Consumer Discretionary 4,470 0.14 56.18 Materials 3,690 0.11 55.64 Information Technology 2,955 0.09 58.97 Health Care 2,640 0.08 56.68 Real Estate 2,280 0.07 57.61 Consumer Staples 2,130 0.06 61.10 Energy 1,980 0.06 58.32 Utilities 1,545 0.05 56.61 Telecommunication Services 660 0.02 60.07

58.14 Total 33,090

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Table 30 ESG Summary Statistics

Full Sample North America APAC Europe Resource Use 59.5 53.7 55.7 70.0 (27.9) (28.6) (27.4) (24.5) Emissions 59.6 53.8 57.4 68.6 (28.0) (28.4) (28.3) (24.6) Env. Innovation 55.3 51.5 54.9 60.2 (25.0) (23.7) (25.5) (25.3) E-CSR Avg. Score 58.1 53.0 56.0 66.3 (22.2) (22.3) (22.2) (19.5) Observations 21140 8187 6179 6774 Standard Deviation in parenthesis.

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Figure 3 ESG Time Trends

Figure 3.a Resource Use

Resource Use

)

(

0 4 2003 2005 2007 2009 2011 2013 2015 2017 Year North America Asia Pacific Europe

Figure 3.b Emissions

Emissions

)

i 0

(

0 4 2003 2005 2007 2009 2011 2013 2015 2017 Year

North America Asia Pacific Europe

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Figure 3.c Environmental Innovation

Environmental Innovation

)

(

0 4 2003 2005 2007 2009 2011 2013 2015 2017 Year

North America Asia Pacific Europe

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Table 31 Year to Year Changes in Firm ESG Scores by Region

No Year to Measure Region Mean Std. Dev Observations Year Change

Resources Nth America 2.52 13.13 7,449 17 Asia Pacific 1.78 13.18 5,581 29 Europe 1.84 14.16 6,197 19 Emissions Nth America 2.23 13.55 7,449 17 Asia Pacific 1.96 13.56 5,581 36 Europe 1.93 15.07 6,197 16 Innovation Nth America 0.89 14.07 7,449 318 Asia Pacific 1.28 16.14 5,581 170 Europe 1.42 15.58 6,197 229

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Table 32 Total Stock Return Summary Statistics (by whole sample and regions)

Full Sample North America Asia Pacific Europe

Change in ln(prices) 6.8 7.9 5.4 6.8 (35.8) (34.5) (37.1) (36.) change in ln(prices + Div.) 6.2 7.2 4.8 6.4 14.2 15.2 13.1 14 Actual Return (%) (43.0) (42.6) (47.4) (38.9)

Observations 20,720 8,074 6,041 6,605 Note: Standard errors in parenthesis. Observations are limited to firm/year with full E- CSR scores

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Table 33 Environmental Score Approach - Global - Total Stock Return

(1) (2) (3) (4) (5) TSR- Total Stock Return FE FE FE FE FE

Resource Efficiency (t-1) 0.006 -0.041** -0.048*** -0.040** (0.019) (0.016) (0.016) (0.016)

Emissions (t-1) 0.027 -0.025 -0.024 -0.026 (0.019) (0.016) (0.016) (0.016)

Innovation (t-1) 0.018 -0.008 -0.003 -0.005 (0.0 16) (0.013) (0.013) (0.013)

CSR Average (t-1) -0.072*** (0.021)

Profit 13.493*** 12.440*** 12.419*** (1.249) (1.217) (1.218)

Leverage 0.225 0.854 0.687 (3.133) (3.091) (3.094)

Env Score * region interaction No No No No No Firm Fixed Effect Yes Yes Yes Yes Yes Year Control (Base year is 2004) Yes Yes Yes Yes Industry* Year effects Yes Yes

Constant 3.541*** 18.802*** 4.186 6.157** 6.493** (1 .209) (2.326) (2.569) (3.102) (3.106)

Observations 19852 19852 19836 19836 19836 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01 112

Table 34 Environmental Score Approach - Global - Total Stock Return with Interaction

(1) (2) (3) (4) (5) TSR- Total Stock Return FE FE FE FE FE

Resource Efficiency (t-1) 0.019 -0.041 -0.036 -0.028 (0.029) (0.025) (0.025) (0.023)

Emissions (t-1) -0.009 -0.054** -0.053** -0.060** (0.030) (0.027) (0.026) (0.026)

Innovation (t-1) 0.017 0 0 -0.002 (0.023) (0.021) (0.020) (0.020)

CSR Average (t-1) -0.096*** (0.028)

Profit 13.529*** 12.473*** 12.455*** (1.255) (1.221) (1.223)

Leverage 0.483 1.057 0.812 (3.159) (3.111) (3.118)

Env. Score * Region interaction Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Year Control (Base year is 2004) Yes Yes Yes Yes Industry* Year effects Yes Yes

Constant 3.433*** 17.139*** 3.751 5.365* 5.846* (1.264) (1.322) (2.622) (3.152) (3.145)

Observations 19397 19397 19381 19381 19381 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

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Table 35 Environmental Score Approach - Total Stock Return Marginal Effects

Marginal Effects of CSR on TSR dy/dx Std.Err z P>|z|

North America Resource Efficiency (t-1) -0.028 (.0235) -1.17 0.241

Emissions (t-1) -0.06** (.0255) -2.34 0.019

Innovation (t-1) 0.002 (.0196) -0.11 0.914

CSR Average (t-1) -0.096*** (.0278) -3.47 0.001

Asia-Pacific Resource Efficiency (t-1) -0.070** (.0351) -2.01 0.045

Emissions (t-1) 0.042 (.0335) 1.25 0.211

Innovation (t-1) 0.030 (.0251) 1.19 0.233

CSR Average (t-1) -0.001 (.0379) -0.02 0.986

Europe Resource Efficiency (t-1) 0.024 (.0257) -0.94 0.347

Emissions (t-1) -0.033 (.0233) -1.43 0.153

Innovation (t-1) -0.035 (.0219) -1.61 0.107

CSR Average (t-1) -0.091*** (.0348) -2.63 0.008

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Table 36 Environmental Score Approach - Global - Total Stock Return with Dividends

(1) (2) (3) (4) (5) TSR- Total Stock Return FE FE FE FE FE

Resource Efficiency (t-1) 0.008 -0.047*** -0.051*** -0.045*** (0.020) (0.017) (0.017) (0.017)

Emissions (t-1) 0.042** -0.018 -0.016 -0.018 (0.019) (0.017) (0.017) (0.016)

Innovation (t-1) 0.013 -0.015 -0.009 -0.011 (0.016) (0.013) (0.013) (0.013)

CSR Average (t-1) -0.075*** (0.021)

Profit 16.455*** 15.528*** 15.521*** (1.331) (1.308) (1.309)

Leverage 2.379 3.618 3.490 (3.114) (3.091) (3.087)

Env Score * Region Interaction No No No No No Firm Fixed Effect Yes Yes Yes Yes Yes Year Control (Base year is 2004) Yes Yes Yes Yes Industry* Year effects Yes Yes

Constant 2.454* 20.671*** 8.751*** 3.457 3.623 (1.254) (2.242) (2.628) (3.090) (3.084)

Observations 18884 18884 18870 18870 18870 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

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Table 37 Environmental Score Approach – Global TSR with Dividends - with Interaction

(1) (2) (3) (4) (5) TSR+D- Total Stock Return with Dividends FE FE FE FE FE

Resource Efficiency (t-1) 0.038 -0.026 -0.019 -0.011 (0.029) (0.027) (0.025) (0.024)

Emissions (t-1) 0.019 -0.045* -0.044* -0.051** (0.030) (0.027) (0.026) (0.025)

Innovation (t-1) 0.014 -0.007 -0.008 -0.009 (0.023) (0.021) (0.020) (0.020)

CSR Average (t-1) -0.074*** (0.028)

Profit 16.331*** 15.379*** 15.370*** (1.339) (1.314) (1.315)

Leverage 2.394 3.645 3.493 (3.135) (3.112) (3.111)

Env. Score * Region Interaction Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Year Control (Base year is 2004) Yes Yes Yes Yes Industry* Year effects Yes Yes

Constant 2.453* 26.430*** 9.008*** 3.404 3.514 (1.329) (1.401) (2.662) (3.113) (3.104)

Observations 18447 18447 18433 18433 18433 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

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Table 38 Environmental Score Approach TSR with Dividends - Marginal Effects

Marginal Effects of CSR on TSR+D dy/dx Std.Err. z P>|z|

North America Resource Efficiency (t-1) -0.011 (.0244) -0.45 0.652

Emissions (t-1) -0.05** (.0252) -2.04 0.042

Innovation (t-1) -0.009 (.0199) -0.44 0.658

CSR Average (t-1) -0.074*** (.0277) -2.68 0.007

Asia-Pacific Resource Efficiency (t-1) -0.083** (.0346) -2.42 0.015

Emissions (t-1) 0.047 (.0324) 1.45 0.147

Innovation (t-1) 0.022 (.0241) 0.92 0.356

CSR Average (t-1) -0.017 (.0362) -0.46 0.645

Europe Resource Efficiency (t-1) -0.052* (.028) -1.84 0.065

Emissions (t-1) -0.031 (.0262) -1.19 0.236

Innovation (t-1) -0.038* (.0228) -1.66 0.097

CSR Average (t-1) -0.119*** (.0366) -3.26 0.001

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Table 39 Portfolio Approach - Global - Total Stock Return

(1) (2) (3) (4) TSR- Total Stock Return OLS OLS OLS OLS

Resource Efficiency (t-1) -0.802 -2.058** (0.587) (0.919)

Emissions (t-1) -0.299 0.293 (0.606) (0.938)

Innovation (t-1) -2.058*** -1.914* (0.667) (1.081)

CSR Average (t-1) -1.354** -1.478* (0.558) (0.870)

Region: Asia Pacific -3.200*** -3.453*** -3.219*** -3.272*** (0.517) (0.585) (0.517) (0.553)

Region: Europe -0.756* -0.714 -0.753* -0.75 (0.456) (0.520) (0.456) (0.490)

Profit Dummy 13.798*** 13.807*** 13.792*** 13.795*** (1.108) (1.109) (1.107) (1.108)

Leverage Ratio -3.782*** -3.760*** -3.896*** -3.896*** (1.068) (1.068) (1.066) (1.066)

Env Score * Region Interaction Yes Yes Year & Industry Controls Yes Yes Yes Yes Industry* Year effects Yes Yes Yes Yes

Constant 2.498 2.531 2.514 2.528 (2.192) (2.194) (2.185) (2.185)

Observations 19381 19381 19381 19381 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01. Base region: North America; Base year: 2005; Base industry: consumption

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Table 40 Portfolio Approach – TSR Marginal Effects

Marginal Effects of CSR on Total Stock Return dy/dx Std.Err. z P>|z|

North America Resource Efficiency (t-1) -2.058** (.9193) -2.24 0.025

Emissions (t-1) 0.293 (.9383) 0.31 0.755

Innovation (t-1) -1.914* (1.081) -1.77 0.077

CSR Average (t-1) -1.478* (.8697) -1.7 0.089

Asia-Pacific Resource Efficiency (t-1) 0.744 (1.2353) 0.6 0.547

Emissions (t-1) -0.692 (1.254) -0.55 0.581

Innovation (t-1) 1.202 (1.274) -0.94 0.345

CSR Average (t-1) 0.955 (1.1541) -0.83 0.408

Europe Resource Efficiency (t-1) -0.601 (.95) -0.63 0.527

Emissions (t-1) -0.616 (1.0111) -0.61 0.542

Innovation (t-1) -3.036*** (1.1231) -2.7 0.007

CSR Average (t-1) -1.514 (.9247) -1.64 0.102

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Table 41 Portfolio Approach - Global - Total Stock Return with Dividends

(1) (2) (3) (4) Total Stock Return with Dividends OLS OLS OLS OLS

Resource Efficiency (t-1) -0.852 -1.927** (0.594) (0.904)

Emissions (t-1) 0.065 0.765 (0.621) (0.918)

Innovation (t-1) -2.345*** -2.166** (0.660) (0.992)

CSR Average (t-1) -1.298** -1.117 (0.564) (0.846)

Region: Asia Pacific -2.999*** -3.161*** -3.020*** -3.019*** (0.519) (0.587) (0.519) (0.556)

Region: Europe -0.578 -0.506 -0.571 -0.511 (0.477) (0.550) (0.478) (0.516)

Profit Dummy 16.741*** 16.744*** 16.744*** 16.741*** (1.218) (1.219) (1.218) (1.219)

Leverage Ratio -3.446*** -3.423*** -3.556*** -3.559*** (1.106) (1.106) (1.106) (1.105)

Env Score * region interaction Yes Yes Year Control (Base year is 2004) Yes Yes Yes Yes Industry* Year effects Yes Yes Yes Yes

Constant 0.517 0.509 0.49 0.474 (2.097) (2.098) (2.098) (2.099)

Observations 18433 18433 18433 18433 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01. Base region: North America; Base year: 2005; Base industry: consumption

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Table 42 Portfolio Approach - Total Stock Return plus Dividends - Marginal Effects

Marginal Effects of CSR on TSR+D dy/dx Std.Err. z P>|z|

North America Resource Efficiency (t-1) -1.927** (.9036) -2.13 0.033

Emissions (t-1) 0.765 (.9177) 0.83 0.405

Innovation (t-1) -2.167** (.9919) -2.18 0.029

CSR Average (t-1) -1.117 (.8457) -1.32 0.187

Asia-Pacific Resource Efficiency (t-1) 0.392 (1.2547) 0.31 0.755

Emissions (t-1) -0.172 (1.2564) -0.14 0.891

Innovation (t-1) -1.991 (1.3772) -1.45 0.148

CSR Average (t-1) -1.110 (1.1613) -0.96 0.339

Europe Resource Efficiency (t-1) -0.656 (.9884) -0.66 0.507

Emissions (t-1) -0.525 (1.1041) -0.48 0.634

Innovation (t-1) -2.968*** (1.1064) -2.68 0.007

CSR Average (t-1) -1.680* (.9782) -1.72 0.086

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CHAPTER THREE

ENVIRONMENTAL SUSTAINABILITY AND PUBLIC ADMINISTRATION

ABSTRACT

Arguably, environmental sustainability is the issue of the 21st century.

It is also an issue that holds the ignominy of being highly complex and likely to touch every aspect of government and modern society. Despite this, environmental sustainability remains under addressed within the field of

Public Administration (PA), treated as a specialty rather than a core research issue.

In this paper I present evidence that PA has historically lagged behind political and social changes to administrative organizations. But greater preemptive research into environmental sustainability is needed. I review the philosophical and practical difficulties of environmental sustainability as it relates to public administration, and I develop a framework for future research. Finally, I review MPA programs to see how environmental sustainability is treated by the field, and how PA is engaging with future researchers. Of the top 18 PA departments with MPA programs, treatment of environmental issues is lackluster: four offer a specialized masters with a focus on sustainability, with nine offering at least one course using sustainability as the primary pedagogical tool. Teaching is primarily conducted by non-PA scholars, suggesting PA lacks proprietary knowledge of environmental sustainability.

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Introduction

Environmental sustainability is a growing issue for the world at large.

Within the field of public administration (PA), however, it has largely only been considered through the lens of specific environmental policy initiatives, with little to no integration of sustainability theory into core

PA theory. This is despite steps taken by the scientific community and other academic fields to promote environmental sustainability as an important issue for public governance (cf. Clapp & Dauvergne, 2011; Fiorino, 2010; Lafferty &

Hovden, 2003).

By challenging the field of public administration to integrate environmental sustainability into its core intellectual framework, I offer three unique contributions to public administration. First, I show that the concept of environmental sustainability in the context of public administration comprises a complex set of ideas. These include the science of climate change, the economics of ecological limits to growth, the theory of planetary boundaries, changing patterns in public values and societal goals for sustainability, and transformational goal setting for both sustainable development and green growth. Second, update and use Caldwell’s (1963) call for making the environment a focus for policy as a framework to show that societal goals for environmental sustainability are likely to result in a transformation of public governance.

Third, I discuss how widespread consideration for environmental sustainability could affect the practice and the field of public administration, and thus why it is important to preemptively consider its influence. I use evidence from the civil rights era and the rise of new

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public management (NPM) to show that the field of public administration has traditionally been more reactive than proactive to changes in societal values or public governance (Bertels, Bouckaert, & Werner, 2016; Lynn Jr., 1994,

2001), something that might occur with changes driven by a demand for greater sustainability. I then adapt a framework for governance research developed by

Lynn, Heinrich & Hill (2000) to help understand some of these practical implications for public administration in creating good environmentally sustainable governance, and to suggest some areas for future research.

Finally, as public administration is tasked with teaching future public servants through the MPA and equivalent programs, and if sustainability is affecting the field and will continue to affect the field in the future, developing capability to teach sustainability as part of the MPA program is key. As such, I look at the current trends in teaching environmental and sustainability policy in MPA programs in the United States.

Literature Review and Conceptual Framing

Demand for greater environmental sustainability is becoming more entrenched in global society. Governments are working to understand and address climate change through global21, national (Baehler, Liu, & Rosenbloom,

2014; Biesbroek et al., 2010; Dubash, Hagemann, Höhne, & Upadhyaya, 2013) and regional policy interventions (Betsill & Bulkeley, 2007; Kousky & Schneider,

21 See for example the United Nations COP-XX meetings on Climate Change, including the Kyoto Protocol and Paris Agreement. https://unfccc.int/process- and-meetings/the-convention/what-is-the-united-nations-framework-convention- on-climate-change (last accessed 10/10/2019)

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2003; Tang, Brody, Quinn, Chang, & Wei, 2010). Investors22 and firms23 are calling for greater environmental sustainability initiatives and improved corporate social responsibility, and individuals are becoming more vocal in demanding action on climate change and other environmental issues24.

Sustainable development is also becoming more institutionalized. First popularized by the Brundtland Commission (Solow, 1993; The Brundtland

Commission, 1987), it is also now a key focus of the United Nations, who currently have 17 goals for achieving sustainable development. These goals are known as the UN SDGs, and are supported by other international organizations, including the World Bank and International Monetary Fund (IMF,

2015; The World Bank Group, 2019).

Clearly if global demand for sustainable thinking flows through to all aspects of government, academic fields also need to consider how one designs, implements, and governs effective environmentally sustainable public policy.

Although this is discussed more later, in economics - a field intimately linked to public policy - debate includes economic trade-offs, ecological limits to growth, and how to measure environmental services. But what debate exists in public administration as a field of study? There are a number of questions PA could, and should, explore. How might sustainability affect

22 See for example, statements by the Climate Action 100 group of investors: https://climateaction100.wordpress.com/ accessed August 27th, 2019

23 See for example a statement by corporate CEOs : https://www.businessroundtable.org/business-roundtable-redefines-the-purpose- of-a-corporation-to-promote-an-economy-that-serves-all-americans

24 See for example: https://www.latimes.com/world-nation/story/2019-09- 19/climate-change-strike-walkout and https://www.cnn.com/2019/09/15/europe/frankfurt-iaa-motor-show-climate- protests-grm-intl-scli/index.html

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collaborative governance? How will it impact street-level bureaucrats in their everyday work (Lipsky, 1980)? What happens if public administrators and government disagree on sustainable approaches to policy (O’Leary, 2005)? What about the decision-making processes that underpin policy design and its effective implementation? This is merely a selection of available questions.

Public Administration as a field of study is ultimately about understanding what makes for effective policy “in-action”, and as an academic department it is about teaching future government leaders the right skills to be effective managers. If sustainable thinking is likely to affect our research and our students, then it is paramount that public administration not just work to better understand how sustainability will change government, ex-poste, but to ex-ante develop the skills and capability to lead this change.

When thinking about environmental sustainability in public administration, a useful starting point is one of the leading articles introducing the importance of environmental issues to public administration:

Environment: A New Focus for Public Policy?, by Lynton K. Caldwell (1963). At its core, Caldwell’s article is a call to consider the comprehensive environment worthy of discussion for public policy. He argues that it is not enough to consider environmental issues in isolation. Research in areas like human ecology, public health, natural resource management, and development already see the environment as a “policy framework within which many specific problems can best be solved” (1963, p. 138). Thus, as research into specific environmental problems is already common place, developing a more generalized concept of the comprehensive environment is warranted. In this paper,

Caldwell offers many important historical insights into why environmental issues have not garnered much attention from public policy scholars, and why 126

sustainability could become a central part of the business of public administration. For this paper, I use many of the concepts developed by

Caldwell to frame sustainability as an issue for public administration. As it is from the 1960’s, Caldwell’s work is obviously outdated. Sustainable development and, indeed, the US EPA had not even been created yet. However, his discussion is prescient to the current debate on sustainability, as it establishes many of the conceptual issues government will need to tackle to properly incorporate environmental issues into policy and governance.

The first of these ideas is that the public must legitimize environmentally sustainable (nee environmental) policy as something important that represents “a necessary field for public action”. I will show evidence that this is occurring in the next section, drawing upon not just the science behind climate change or the economics of sustainable development, but also pointing to the public values which are creating demand for sustainable development. The second idea developed by Caldwell is the normative question of what exactly does a “good” environment, and equivalently, “good” sustainability look like. Defining what we want our environment to look like is critical because how we choose to define sustainability frames all other questions and determines goals for policy. The third idea implied by

Caldwell’s paper is that public policy and administration was not well prepared to investigate environmental policy during its emergence as an important issue in the 1960’s. Indeed, there is evidence that public administration was completely out of touch with a changing society in the

1960’s (Frederickson, 1971), and there is evidence Public Administration as a field of study generally reports on change after the fact, rather than informs change (Bertels et al., 2016; Lynn Jr., 1994, 2001). It is important 127

to recognize that a lack of historical preparation to investigate environmental policy may persist as a lack of preparation to investigate environmentally sustainable policy today. Each of these concepts developed by

Caldwell is discussed below.

Environmental Sustainability as a Growing Concept

Caldwell (1963) argues that for environmental sustainability (nee environment) to be an important concept for public administration, a first step is for the public to “have begun to see the comprehensive environment as a legitimate and necessary field for public action”. According to Kingdon

(1984), ideas become agenda items and agenda items become public policies for two main reasons: through the gradual accumulation of knowledge and perspectives among specialists; and through a specific crisis or prominent event focusing the public attention. For environmental sustainability, scientific inquiry may find evidence of an environmental issue that requires a policy response, or a shift in public values may occur that leads to a demand for action on the environment regardless of scientific backing. This will often happen through a focusing event that captures the public’s attention.

Science Induced Environmental Reform

When we look at the environmental reforms of the modern era, we see science has often led the way in creating demand for reform. For example, in the United States research by the Stanford Research Institute on smog in Los

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Angeles led to the creation of the Air Pollution Control Act of 195525. This act provided federal funding into air pollution research, which was used when designing the Clean Air Act of 1963 (CAA of 1963). The CAA of 1963 itself has gone through several revisions based on advances in the science, including the Air Quality Act of 1967 which required states to develop pollution abatement programs using the “best available science” (R. Martin & Symington,

1968), and was amended in 1990 to reduce sulfur dioxide emissions after they were found to be damaging forests and aquatic ecosystems (Schmalensee &

Stavins, 2015). The hole in the ozone layer was first documented in Nature in

1985 (Farman, Gardiner, & Shanklin, 1985). This resulted in the Montreal

Protocols in 1987 and the banning of oxygen depleting substances.

Globally, the science of climate change shows a clear risk to climate stability from greenhouse gas emissions (Cook et al., 2016; IPCC, 2013;

Nordhaus, 1994). This has led to attempts to address the issue at all levels of government, including locally (Lundqvist & Biel, 2007), domestically (cf.

Victor, 2004) and internationally, of which the most prominent example is the

United Nations Climate Change Conferences26. These conferences have led to a number of important international accords to address climate change which include the Kyoto Protocols and the 2015 Paris Agreement to limit climate change to less than 2ºC of pre-industrial levels.

25SRI International, 1947, Smog Research: https://www.sri.com/sites/default/timeline/timeline.php?timeline=physical- world#!&innovation=smog-research (last accessed 11/04/2019)

26 United Nations Climate Change: https://unfccc.int/ (last accessed 11/04/2019)

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Public Values and Environmental Reform

Just as science informs and directs reform, changes in public opinion and public values can prove to be an even greater persuader for government action. In the words of Bozeman (2007):

Public values are those providing normative consensus about (1) the

rights, benefits, and prerogatives to which citizens should (and should

not) be entitled; (2) the obligations of citizens to society, the

state, and one another; and (3) the principles on which governments and

policies should be based.

In areas like environmental and climate change policy, public values are clearly important in informing national discourses. This is especially true when concern for the environment intersects with economics and politics.

This can manifest itself in two clear ways: whether environmental policy reform will happen at all, and if it does, what direction it takes.

In the United States, several key pieces of environmental legislation were created due to public demand for change, often as a result of key focusing events like a bestselling book or environmental disaster. The most famous of these is the rash of environmental legislation developed at the end of the 1960’s and beginning of the 1970’s. Here key focusing events, including the general social upheaval of the civil rights movement, the

Chuyaoga River fire, and the publishing of Silent Spring by Rachel Carlson, galvanized public support for environmental issues. This ultimately put pressure on the Nixon administration to act. Although the environment was a non-event during the 1968 election campaign, by the end of 1970 significant changes to environmental regulation were occurring. Examples include

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revisions of the Clean Air Act of 1970 to centralize regulation of air pollutants in the federal government (Rogers, 1990), the creation of the US

EPA, and the signing of the National Environmental Policy Act of 1969 (NEPA) on the first Earth Day in 1970 (Andrews, 1999). Importantly, many of these new environmental legislations were federally funded and a model for delegating implementation to the states (Andrews, 1999). More recently, the dangerous European heat wave in 2003 increased public sympathy in Europe for addressing climate change (Poumadere, Mays, Le Mer, & Blong, 2005), while events like flooding in Venice and the dangerous bush fires in Australia, both in 2019, have been anecdotally attributed to climate change by public figures (BBC, 2019; Doyle, 2019). In the United States, growing demand for action by local residents and members led to several states, cities, and environmental advocate organizations successfully suing the EPA in

Massachusetts vs EPA, 549 U.S. 497 (2007) and thus forcing the federal government to regulate greenhouse emissions as air pollutants27(Fiorino,

2018).

Even with public buy-in, variations in public values can produce starkly different ideas for resolving an environmental issue between different groups, even if the groups share the same basic environmental objective (Corley, 2004). For example, when Congress developed the Air

Quality Act of 1967 (AQA), the decision to devolve responsibility to the states reflected decentralization as a public value among elected officials

27 https://www.law.cornell.edu/supct/html/05-1120.ZO.html (retrieved 11/04/2019)

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at the time. This was despite designers of the act doubting that such an approach would work (Rogers, 1990). But by 1969, a community desire for strong federal action on environmental issues created a demand for centralized action. This resulted in the passing of the Clean Air Act of

1970, which superseded the AQA with a much more forceful centralized program

(Rogers, 1990). According to Mazmanian and Kraft (2009), environmental reforms in the 1970’s represent the first of three different modern approaches, or epochs, to environmental protection, with emphasis in this period placed on technology-based rules, and legal compliance. Starting in the 1980’s, the second epoch of environmental policy built on political conservatism, a refocus on local government, lower taxes, and private markets gained momentum (Fiorino, 2018, p. 127; Layzer, 2012). In response, environmental policy makers began to emphasize efficiency, cost- effectiveness, and market incentives (Mazmanian & Kraft, 2009). One example of this change in public values is the 1990 revisions to the Clean Air Act.

As part of these revisions, a provision to create a market for tradeable permits to reduce sulfur dioxide emissions was included. Finally, Mazmanian and Kraft argue that a slow transition to a third epoch built on sustainability began to appear in the 1990’s, bringing with it a holistic and global approach to environmental protection that sought to balance human and natural systems, and to consider future generations. One example of this new approach is the effort by the EPA to develop the Clean Power Plan. This plan reflected a desire by public policy makers for a more collaborative and bespoke approach to environmental regulation, thus doing away with one size fits all approaches that had been common since the late 1960’s (Fiorino,

2018).

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As Caldwell suggested, comprehensive environmental reform requires the public to see the environment as a necessary field for action. Scientific enquiry has played a major role in the past in this respect, and it continues to play a role in emerging issues in environmental sustainability, especially for action on climate change. Public values and focusing events also represent important ways for the public to demand environmental reform.

Finding a common approach to environmental protection relies heavily on the predominant public values expressed at the time. Evidence suggests that environmental sustainability is a growing concept for public policy. But, for environmental sustainability to become an important issue for public administration, specific issues in environmental sustainability will require changes to the business of government, including changes to public governance, the regulatory environment, and the role of the public service in resolving environmental issues. Understanding the different concepts in sustainability likely to affect public administration is important.

Perspectives on some the major issues are in the next section.

What is “Good” Environment and “Good” Sustainability

Undoubtably one of the most important questions a public administrator faces when designing and managing environmentally sustainable public policy is to decide what exactly good environmentally sustainable policy looks like.

For Caldwell, this represents a basic values question. He argues that although the science may one day be able to “tell us what kinds of environment are good for our mental and physical health,” there is always likely to be some values-based and subjective reasoning when an issue relating to environmental [sustainability] emerges as a question for policy.

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This diversity in what environmental sustainability should look like, and what it should achieve, like will need to be carefully considered as part of any sustainability reform.

Take the contrasting ideology of weak versus strong sustainability as an example (Neumayer, 2003). In simple terms, weak sustainability argues that natural capital and human capital are fungible assets, and reallocation between the two is perfectly reasonable and potentially desirable so long as the total stock of intergenerational capital does not decrease (Solow, 1993).

Conversely, strong sustainability argues that natural capital and human capital are non-substitutable (Neumayer, 2003) and are essentially complements. Both are needed in certain amounts for prosperity, with too little natural capital likely to constrain productivity (Daly, 1995). Which idea makes for “good” sustainability is a question of perspective. If natural and human capital are substitutes, converting a biologically important wetland into a highly productive transportation hub is reasonable under weak sustainability if the value of the hub is worth more than the value of the loss to biodiversity. However, it does not make for good policy under strong sustainability if the wetland’s biological assets are valuable and cannot be replaced. Indeed, even defining value in this instance can be very difficult

(P. Victor, Hanna, & Kubursi, 1998) and likely to be left to the policy maker.

A second important concept in environmentally sustainable policy is whether there are ecological limits to growth (Meadows, Randers, & Behrens,

1972), a concept generally defined in terms of planetary boundaries

(Rockström et al., 2009). Under the planetary boundaries approach there are

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nine ecological boundaries. Each of these represents different earth systems, including climate change, biodiversity, and global freshwater use, and each boundary has a measurable upper limit for how far humans can push the earth without undue risk of catastrophic natural disaster. Figure 4 recreates and lists these boundaries. Not surprisingly, not everyone agrees that planetary boundaries are likely to ever be an issue for humanity, instead arguing in favor of human ingenuity and geoengineering projects overcoming any environmental issue (Lomborg, 2007). John Dryzek (2013) labels this type of belief system as the promethean discourse. He states that: “Prometheans believe that humans left to their own devices will automatically generate solutions to problems - and that an invisible hand guarantees good collective outcomes”. Indeed, using economic modelling, economists have argued that countries including the United States, China, India, and Brazil are already on sustainable paths (Arrow, Dasgupta, Goulder, Mumford, & Oleson, 2012).

Skeptics believe Prometheans are in denial (J. Dryzek, 2013).

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Figure 4 Planetary Boundaries

From “A safe operating space for humanity” by Rockstrom et al. (2009).

For policy makers, these differing ideologies are key to determining how sustainable policy should be developed and implemented. If one were to ascribe to the Promethean discourse, it would be enough to continue business as usual for most policy, albeit with a focus on supporting engineering feats like climate geoengineering projects to extend our ecological limits. While

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this is often used to justify doing nothing in the short-term, the long-term impacts of this approach are not well understood. There is a very real risk that just because humanity has solved environmental issues in the past, it will not be able to solve the all of the emerging environmental issues in the future. Of course, working to adhere to planetary constraints has its own issues. Since most policies are only likely to have very incremental effects on only a few planetary systems, designing local, regional and even national policy to adhere to global limits would be difficult, especially if a policy is only diametrically connected to ecological systems.

A third issue for sustainable policy makers, and one that is related to both planetary boundaries and the weak versus strong sustainability argument, is understanding what goals society has for economic growth. Here, there are many different definitions and arguments for any number of sustainable growth models but for this paper I will limit the discussion to no-growth, green growth, and sustainable development, as these are likely to be most relevant to policy makers. Alternative ideas that may be of interest however include: de-growth, which aims to reduce economic output (Schneider, Kallis, &

Martinez-Alier, 2010); agnostic growth, which argues that good environmental- economic theory should steer clear of normative decisions on whether growth is good or not (Van den Bergh, 2011); and business as usual.

Sustainable development is the best known modern system for integrating environmental thinking into economic growth (Fiorino, 2018). First popularized by the Brundtland commission, the most commonly used definition of sustainable development is “development that meets the needs of the present without compromising the ability of future generations to meet their

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own needs” (The Brundtland Commission, 1987). Themes common to sustainable development generally follow along ecological, economic and societal dimensions (Fiorino, 2018, p. 75), with these dimensions forming the basis for the 17 goals and 169 targets of the best known program for international sustainable development: the UN Sustainable Development Goals, or SDGs28. By tackling key themes in development, environmental protection, and inequality, the UN SDG’s can best be described as a nuanced view of development, where we pursue development to improve or maintain quality of life, equity and fulfillment (Fiorino, 2018).

Development by this definition is different to how we approach green growth. For green growth, economic growth is seen as something desirable and likely to continue to occur. The difference between green growth and how economies have grown in the past is that improvements to environmental sustainability are considered an aspect of good growth. Thus, policies to further green growth might include switching to renewable energy, transitioning work to less resource intensive fields, improving recyclability of everyday items, and changing how we measure good growth to have a green bias. This still represents quantitative increases in output as the mechanism for achieving qualitative improvements in quality of life (Daly, 1991), but aims to do so while respecting ecological limits. It is also arguably separate from sustainable development in two ways: first, green growth may be designed with already industrialized nations in mind, while sustainable

28 United Nations Sustainable Development Goals https://sustainabledevelopment.un.org/sdgs

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development focuses more on less developed regions (Fiorino, 2018, p. 11); and second, while implementation strategies for green growth use well known market-based and compliance tools like cap-and-trade systems and performance standards (Fiorino, 2018), sustainable development is intertwined with more expansive governance reforms (Camilla Adelle & Russel, 2013), including the concept of environmental policy integration (Lafferty & Hovden, 2003), which is discussed in more depth in the following section.

The third example of growth theory is a no-growth paradigm. Perhaps best popularized by Tim Jackson (2011), the key objective here is to develop an economy that grows only through improvements from technical efficiency without adding further stress to planetary boundaries, but is otherwise

‘steady’ in so far as resources are consumed at the lowest feasible level

(Daly & Townsend, 1996, p. 325). To achieve this, Jackson suggests a number of reforms to the modern economic system including: reevaluating prosperity in terms of happiness and fulfillment instead of income and GDP; guiding private enterprise away from material output towards delivering human services; recasting work as about participation rather than wealth generation; and refocusing capital investment to protecting assets for future generations.

Each idea for environmental sustainability requires different approaches to policy design and implementation, and the process of understanding whether policies adhere to sustainability goals under each paradigm is likely to differ. Just as economists are best positioned to create meaningful measures of economic indicators like unemployment or growth, and ecologists or natural scientists are best positioned to provide

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meaningful measures for ecological sustainability or planetary boundaries,

PA scholars are best positioned to create a measurable framework for integrating environmental sustainability into public management, and to determine how public administrators can measure success under this new framework. Understanding perspectives on what makes for “good” environmental sustainability is just one part of that puzzle.

Designing Policy for Environmental Sustainability

If the field of public administration wants to be effective in advocating for best-practice governance, an important consideration is how to satisfy societal demands for environmentally sustainable government. From the outside, this may appear a purely political question. As we saw with the civil rights era and NPM, however, changes in societal values can have huge implications on the conduct of public governance (Marini, 1973), and PA theory can quickly become out of touch with the practical reality (La Porte,

1973; Lynn Jr., 1998). More, given the modern public service has broad powers to “make rules, implement those rules, and adjudicate questions concerning their application and execution” either through delegation of these power by

Congress (Rosenbloom, 1983), or by working closely with ministers in a

Westminster style system, public administrators are likely to be on the front line for implementing environmentally sustainable reform. In application, three different options present themselves:

1. Using the current regulatory framework and existing environmental policy;

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2. Developing new regulation to fit specific public policy problems in climate change and sustainability; and

3. Integrating climate and environmental policy into non-environmental policy areas.

Working with Existing Regulations

With respect to using the current regulatory and governance framework, the question is to determine how appropriate the current regulatory and governance setup is for addressing emerging issues in environmental sustainability and climate change. One potentially useful piece of legislation developed in this period is the Clean Air Act of 1970 (CAA).

Although for many years the EPA argued that the CAA did not provide authority to tackle carbon emissions, the landmark Massachusetts vs EPA (2007)29 gave them authority to use the CAA to regulate carbon emissions from engines insofar as they endanger human health. In practice, however, not much has been achieved in adapting the CAA to reduce greenhouse gas (GHG) emissions.

One attempt was the creation of the Clean Power Plan (CPP) in 2014 to regulate GHG emissions from power plants. But, given the EPA’s reliance on certain ambiguities stemming from the 1990 revisions to the CAA for its authority for this type of regulation (Harvard Law Review, 2016), the CPP was challenged in court almost immediately after it was released, with political considerations leading to its demise in 2017.

29 https://www.law.cornell.edu/supct/html/05-1120.ZO.html

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Another example of current regulation important for environmentally sustainable policy is the Endangered Species Act of 1973 (ESA). Since biodiversity is an important element of the UN SDG’s30,31, and an important concept for strong sustainability (Neumayer, 2003, p. 116), having regulation to protect biodiversity is important, for which the ESA is the closet example in the United States. But, while this act has been effective in protecting endangered species, it requires species to be listed as endangered to benefit from the protections of the act, with prompt listing needed for effective protection(Taylor, Suckling, & Rachlinski, 2005). This severely limits the utility of the act as a tool for environmental sustainability if broad based protection of biodiversity is demanded. The ESA also creates perverse incentives for landowners to destroy habitat, lest it become occupied by endangered species and protected by the ESA (Byl, 2019; Lueck & Michael,

2003). Although the CAA and ESA are just two examples, tailoring the existing regulatory regime to environmental sustainability clearly presents difficulties for policy makers, even where the policies are seemingly compatible with emerging concepts in sustainability.

New Legislation, New Regulation, Current Governance Regime

The second option is to develop new legislation and regulation to address specific issues in sustainability. Historical examples of this include the CAA being created to reduce air pollution, or the Superfunds

30 SDG 14 on protecting marine ecosystems: https://sustainabledevelopment.un.org/sdg14

31 SDG 15 on protecting terrestrial ecosystems: https://sustainabledevelopment.un.org/sdg15

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program being created to clean up hazardous waste sites. While this strategy helps create expertise among public administrators working on a particular issue, for complex environmental problems like climate change the question is whether regulations designed and administered in isolation are capable of effectively addressing all aspects of the environmental issue over an extended period (Christensen, Fimreite, & Lægreid, 2014; Tosun & Lang, 2017).

For example, just as the CAA was designed to regulate specific air pollutants affecting health, could a similar act be designed to regulate air pollutants affecting climate change? After all, the US EPA lists only four main pollutants affecting climate change: Carbon Dioxide, Methane, Nitrous Oxide, and fluorinated gases (US EPA, 2019), while six pollutants are regulated under the CAA. However, while the CAA regulates just a few areas like large stationary sources like power plants, and mobile sources like vehicles, controlling greenhouse gas emissions would require implementing regulation across all areas affecting climate change. This includes energy, transportation, agriculture, land clearing and urban development (Camilla

Adelle & Russel, 2013). The closest the United States has come to achieving this was the American Clean Energy and Security Act of 2009, which sought to establish an emissions trading scheme to reduce greenhouse gas emissions.

Although this act did pass the House of Representatives, disagreement on the terms of the Act was intense (cf. Krugman, 2009), and it ultimately failed to become law. For more complicated initiatives like sustainable development where the outcome is not straightforward, creating specific policies that do not interfere with existing programs become even more problematic. For example, creating new policies to reduce greenhouse gas emissions makes sense if the goal is simply to reduce GHG emissions, but creating a policy to make

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a country strongly sustainable or to satisfy all 17 UN sustainable development goals is much more transformative. In this sense, achieving environmental sustainability will be less about specific legislation and new regulations, and more about developing a system of public governance that promotes environmental sustainability generally.

Environmental Policy Integration (EPI)

The third option for public administrators is integrating environmental and climate policy into non-environmental policy areas (Camilla

Adelle & Russel, 2013; Lafferty & Hovden, 2003; Urwin & Jordan, 2008).

Commonly known as environmental policy integration, or EPI, Lafferty and

Hovden (2003) defines the concept of EPI as:

- the incorporation of environmental objectives into all stages of

policy making in non-environmental policy sectors, with a specific

recognition of this goal as a guiding principle for the planning and

execution of policy; and

- accompanied by an attempt to aggregate presumed environmental

consequences into an overall evaluation of policy, and a commitment to

minimize contradictions between environmental and sectoral policies by

giving principled priority to the former over the latter. (p. 9)

The EPI process would have significant ramifications for the public sector if it were ever adopted in the United States in any meaningful way.

For starters, as in other western democracies US policies have historically

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given principled priority to economic policy integration (Lafferty & Hovden,

2003, p. 10). However, EPI specifies that policies should give principled priority to environmental sustainability, an already significant difference.

EPI also favors the precautionary principle in policy design, which shifts the burden of proof away from those promoting environmental protection.

Measuring progress towards achieving EPI standards in a meaningful way is also difficult. According to Adelle & Russel (2013), EPI is successful when the relevant administrative structures are in place to support environmental concerns receiving principled priority. This is distinct from specific environmental policy, as measures of success extend beyond achieving specific policy goals. For public administration as a field, political attempts to integrate EPI into public sector governance would have significant impacts on how government is run, much as the rise of NPM produced significant change.

Part of the reason for this is because for EPI to be successful, it needs new organizational structures and procedures that connect every part of government to effectively prioritize environmental concerns (A Jordan &

Lenschow, 2008). In Europe, early political support for EPI arose through its close association with sustainable development. Political support in Europe has been inconsistent however (Pallemaerts, 2006), and very little discussion on the future role of EPI in the United States appears to be taking place. If

EPI were to ever become a centerpiece of policy – which is not implausible –

PA would have a significant role to play in preparing administrators on how to integrate EPI into their everyday work.

While EPI would result in transformative changes to public administration, the size of those changes involves considerable political and administrative risk. One solution is to apply key themes from EPI to a 145

narrower and more manageable set of policy objectives. An important area where this is occurring is climate change policy integration (CPI) (Camilla

Adelle & Russel, 2013; Urwin & Jordan, 2008), but it is also becoming more popular in other areas, including in biodiversity conservation integration

(Primmer, 2011). This method differs from EPI in several important ways.

First, CPI32 is situational and sectoral, and affects governance only in areas specifically targeted for climate change purposes. Thus, rather than giving the environment principled priority across all policy areas, CPI gives climate principled priority in areas crucial for climate change. This results in CPI being less vague and abstract next to EPI (Camilla Adelle & Russel,

2013). Second, a narrow focus for integration may make it easier to get political buy-in. This is certainly true for CPI in Europe, where climate change has become a focus at the whole of government level, rather than resting with a single minister or department (Mickwitz et al., 2009). Third, by having a narrow focus, CPI is much easier to measure as an outcome compared to EPI. Understanding if EPI is being successful requires measuring how well environmental concerns are integrating into administrative processes, but CPI, to the extent it represents an outcome not a process, can be measured in terms of reductions in greenhouse gas emissions or similar

(Camilla Adelle & Russel, 2013).

The key risk to using this sectoral approach to environmental policy is that it may miss the point that environmental issues are integrated. Adelle &

Russel (2013) argue that CPI is limited to a narrow set of fields,

32 This analysis could refer to other versions of narrowly focused EPI, such as biodiversity management integration. For the sake of simplicity, I refer to this type of policy integration as CPI.

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representing transportation, development, energy, and water. These policy areas directly impact climate change so traditional climate regulation may be as relatively as effective as CPI. To the extent other areas indirectly contribute to climate change, CPI will fail to include them in the integrative process.

Public Administration and Environmentally Sustainable Policy

For environmentally sustainable policy to be effective, efficient and equitable, choosing the right set of tools for the right environmental issue is important. This requires more than simply understanding the different ways of approaching environmental policy, or in understanding that many concepts and issues in environmental sustainability are not easily put into a policy silo. It also requires more than being able to develop a policy framework that can resolve complex environmental issues. For environmentally sustainable policy to be effective, efficient, and equitable, regardless of the complexity of the issue, there needs to be effective implementation of the right set of policies matched to the complexity of the environmental issue. In some instances like sustainable development which may require an

EPI approach, or climate change which may require a holistic approach like

CPI, resolving issues in environmental sustainability will require substantial changes in public governance and the work of public administrators. This makes environmental sustainability an important issue for the field of public administration.

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The Risks and Opportunities of Environmental Sustainability for Public Administration

Historically, the field of public administration has lived through several important changes. These include the advent of the civil rights era and the rise of New Public Management. However, while Waldo (1972) once suggested that “public administration will be centrally involved in change and transformation”, evidence tends to support a field that is more reactive than proactive to changes in societal values or public governance (Bertels et al., 2016; Lynn Jr., 1994, 2001). This section will show that public administration has both generally failed to predict previous paradigmatic shifts in governance, while also having limited input in influencing these shifts. As Caldwell suggests, understanding the effect of policy on the environment to the fullest practicable sense requires considering ‘all the major elements relevant to an environment-affecting decision’, a high watermark for many concepts in the sustainability literature. This could result in significant changes to public governance. But, as occurred with

NPM, without becoming more proactive, any shifts to integrating sustainability into public governance will likely go un-predicted.

Importantly, how the field responded to previous changes in governance and public values offers insights into some of the risks, challenges, and opportunities for the field in the course of making environmental sustainability a conceptual focus (Fiorino, 2010).

The experience of public administration during the civil rights era and the subsequent rise of new public administration in the early 1970’s, then the rise of new public management in the 1980’s and 1990’s is a good lesson for how the field has dealt with societal change in the past. Public

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administration before the civil rights era had a primary focus on building a

“science of public administration” with theory building focused on developing better, more efficient public management devoid of political considerations, the so-called politics-administration dichotomy (Frederickson, 1971).

However, as confidence in government collapsed during the civil rights era, public administration had a crisis of confidence in how appropriate its theories were at actually describing the administrative state in action

(Marini, 1973). Demands for greater social equity, and a desire to adapt public administration theory to match the changing social and political realities of the day led to substantial changes in the field, including a new focus on normative thinking (cf. Frederickson, 1971; La Porte, 1973) and a realization that administration had become intimately involved in politics through the devolution of the design and implementation of policy from

Congress.

Comparatively, when New Public Management (Hood, 1995) became the dominant governance paradigm in the United States in the 1980’s and early

1990’s, public administration was either weak in its criticism of the movement (Lynn Jr., 2001), or when it did mount a serious challenge

(Riccucci, 2001) it was unable to have a meaningful impact on directing government with respect to these reforms. Ultimately it took decades for public administration to reposition its intellectual traditions

(Frederickson, 1999) and to develop a systematic critique of new public management ideology (Meier & O’Toole, 2009; O’Toole & Meier, 2015), by which time Public Administration was seemingly left to measure the impact of NPM on government rather than informing government on best practice governance. And yet, Public Administration’s experience with NPM is likely a good learning

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tool for how PA will experience the rise of an environmentally sustainable public sector. As Torgerson (1998) suggests, conventional administration conveys many inadequacies when it comes to grappling with questions of the environment, particularly with a need to cut the environment down to size to make it administratively manageable. Overcoming this for effective environmental sustainability reform is likely require a transformation of administrative thinking with much improved creative problem-solving capability, and an integrated decision-making process that can quickly define and redefine complex environmental issues (Torgerson, 1998). Thus, just as integrating NPM into the public sector created fundamental changes to public governance and how government policy is administered, integrating environmental sustainability may have an even greater transformative effect.

If public administration is interested in environmental sustainability, or indeed, other governance reforms, it is also important to understand why it might be slow to respond to major revisions to public sector governance.

Several reasons present themselves. First, it has been argued that public administration research follows three separate fields of study roughly synonymous with the separation of powers: public management, administrative politics, and legal approaches (Rosenbloom, 1983). Second and relatedly,

Public Administration may place too little emphasis on theory building and on literature reviews or meta-analyses, studies useful for unifying theory and developing areas for future research (Meier, 2015). Third, the field also relies heavily on other fields, including sociology, business management, and economics for its underlying theories of organizations and management

(Frederickson & Stazyk, 2011). Fourth, it has been suggested that the public sector, especially in the United States, focuses on incrementalism in its

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decision making processes (Rosenbloom, Kravchuk, & Clerkin, 2009, p. 318).

Under incrementalism, agencies define their end objectives with respects to the resources, or means, they have available. As their access to resources change, agencies redefine the goal for policy with a decision process aimed at getting to a satisfactory outcome. If government is mostly incremental and rarely transformative, it stands to reason that PA scholarship will tend towards incremental research. Indeed, evidence based research in public management often starts with the presumption that what organizations do today is closely connected to what they did yesterday (Meier & O’Toole, 2009). This is clearly an issue during transformative periods in administrative processes.

Clearly, the challenge for integrating environmental sustainability into PA is overcoming a lack of a common core to build upon, a lack of interest in theory building, a system of adaptation that requires other fields to have already built a solid theoretical framework, and a general approach that expects incrementalism in public sector decision making.

However, since sustainability theory is very multi-disciplinary, and public administration is often open to and proactive in integrating research from other authoritative sources, it will develop well rounded expertise in the science of climate, economic transitions, and sustainability management suitable for understanding the actual doing of effective environmental sustainability. What it needs however is the will to do so, and a framework to direct research and teaching.

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Towards a Sustainable Public Administration

As sustainability is neither simple nor perfectly concrete, it is not clear how and to what extent it will affect public administration. Given differing ideas of sustainability and different approaches to environmental regulation, developing a framework for future research in public administration is an important step to engaging in the sustainability discussion. One important determiner of successful environmental policy is quality governance (Fiorino, 2018, p. 81). Not only has good governance been shown to have a strong positive effect on environmental policy generally

(Dasgupta, Hamilton, Pandey, & Wheeler, 2006; Dasgupta, Mody, Roy, & Wheeler,

2001), it has also been shown to improve local environmental policy implementation (Bhattarai & Hammig, 2001; Tang et al., 2010). For sustainability, quality governance is important for achieving desired policy outcomes in a cost effective and efficient manner. Governance is also an important element of public management (Heinrich, Lynn, & Milward, 2010), so conceptualizing a research agenda for sustainability using a governance framework could be instructive for future research. In pursuit of this goal,

I adapt a framework for governance research developed by Lynn, Heinrich and

Hill (2000) to the topic of environmental sustainability. At its core, their approach frames governance in heuristic rather than comprehensive terms to provide a broad platform to help develop a governance based research agenda

(Frederickson & Smith, 2003, p. 212). In general terms, they define governance as:

“regimes of laws, administrative rules, judicial rulings, and practices

that constrain, prescribe, and enable government activity, where such

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activity is broadly defined as the production and delivery of publicly

supported goods and services” (Lynn et al., 2000, p. 3).

According to Frederickson and Smith (2003, p.210), Lynn et al. (2000, p. 1) further argue that at the heart of all governance related research is the question of “how can public-sector regimes, agencies, programs and activities be organized and managed to achieve public purposes”. Setting environmental sustainability as a public purpose, then designing an effective system of governance to deliver the appropriate level of publicly supported environmental services then becomes an important area for research. In order to frame their governance research agenda, Lynn et al. (2000) develop a reduced form model of the logic of governance:

푂 = 푓{퐹, 퐶, 푇, 푆, 푀} (1) where O, representing outputs and outcomes, is a function of environmental factors, client characteristics, treatments, structures, and managerial roles and actions. Examples of variables relevant to each reduced form component of the model is recreated from Lynn et al. (2000) in Figure 5. This model allows for a flexible approach to public governance research by assuming that interrelationships between the different factors affecting outputs and outcomes exist. This is an important feature of any framework that considers any of the more complex ideologies in environmental sustainability.

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Figure 5 Reduced-Form Logic of Governance

From “Governance and Performance: New Perspectives” by (Lynn et al., 2000)

Outputs and Outcomes

In defining a research agenda for public sector sustainability governance, understanding how to create measurable outcomes for each type of sustainability ideology is an important first step. After all, as Jordan and

Lenschow (2010) suggest: ‘principles are only principles, and process is only process; policy outcomes are what really matter’ (Camilla Adelle & Russel,

2013). First, environmental policies might be developed to address a specific environmental concern, and they will often include quantifiable goals to measure their effectiveness. For example, regional efforts to combat climate change might include specific goals, such as reducing the federal government’s carbon footprint by 40 percent as occurred with the Obama era EO

13693 (Richerson, 2015), or country-wide GHG emissions by 17 percent by 2020, as was proposed in the American Clean Energy and Security Act of 2009.

Conversely, global objectives for climate change might include specific efforts like limiting carbon dioxide concentrations in the atmosphere to 350 parts per million, as suggested in the planetary boundaries framework

(Rockström et al., 2009), or abstract objectives, such as limiting climate change to below 2°C as part of the UN SDGs (UN, 2017). Selecting an outcome target is an important first step in designing environmentally sustainable governance systems, as having a measurable goal is the first step in making sure an agenda is achievable.

For large scale environmental reform programs like sustainable development and strong sustainability, understanding what the outcome will look like and then developing measurable benchmarks is difficult. This is due to both the means and the ends of the policy being administrative reform, rather than a quantifiable policy objective. This is certainly true for 155

sustainable development implemented using EPI (Lafferty & Hovden, 2003), and is one reason why there is a paucity of research measuring the effectiveness of EPI initiatives (Camilla Adelle & Russel, 2013). For the UN sustainable development goals, many indicators for compliance are vague or qualitative in nature. Table 45 lists several examples from both the UN SDGs and the goals for EPI. Where clear and quantifiable outcomes do not exist, developing the right set of tools for effective governance become problematic. Policies in this situation are more likely to represent a process or output, than a quantifiable outcome. This is one area where theory becomes an important building block for policy design.

For areas like no-growth or green growth, developing governance structures is also likely to be complex. There may be some hope, however, for measuring key outcomes. For example, Fiorino (2018) defines the concept of green growth as ‘the idea that the right mix of policies, investments, and technologies will lead to beneficial growth within ecological limits”.

Measuring economic growth within limits, or to protect natural capital, could be achieved by integrating ecological stocks and flows into GDP or similar measure of economic output/throughput. One example is the Genuine Progress

Indicators (Kubiszewski et al., 2013). In simple terms, GPI aims to measure improvements in human wellbeing by factoring environmental degradation, pollution, and 22 other “quality-of-life” components into personal consumption expenditure, a major component of GDP. Policies designed to increase GPIs are then likely to be contributing to green GDP growth, even if economic growth is muted. An alternative to genuine progress indicators is the System of Environmental-Economic Accounts (SEEA) developed by the United

Nations. According to the United Nations, the SEEA represents: 156

a framework that integrates economic and environmental data to provide

a more comprehensive and multipurpose view of the interrelationship

between the economy and the environment and the stocks and changes in

stocks of environmental assets, as they bring benefits to humanity.

(United Nations, 2014)

By accounting for environmental stocks and flows together with economic activity, the UN SEEA represents a measurable outcome for policy looking to protect natural capital. To the extent it complies with green growth, sustainable development generally, or strong sustainability is open for debate (cf. Obst, 2015), but it does provide information to support nine of the UN SDGs (United Nations, 2014).

As Lynn et al. (2000) suggest, outputs and outcomes can be either precisely or broadly defined. With sustainability theory, the level of complexity in policy design is likely to vary with the complexity of the environmental issue and our ability to create measurable outcomes. Developing the right governance approach to tackle these issues involves defining the issue and conceptualizing what the end goal will look like. Not surprisingly, just as economic growth, unemployment rates, and average lifespans are continuous goals, many of the goals for environmental sustainability are likely to represent long term processes and outputs rather than fixed term outcomes.

The Right-Hand Side of Environmental Governance: 풇{푬, 푪, 푻, 푺, 푴}

While the broad direction of environmentally sustainable policy may be decided by politics and informed by science or the public, best practice

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environmental governance is clearly a question for public administration. In this section, I will detail some of the variables that are likely to interact with each other in determining how effective environmental policy is. A summary is in Table 46.

Environmental Factors

Key environmental factors likely to play a role in environmentally sustainable policy design include the political environment, economic conditions, and the rate of technological dynamism in the economy. While politics sympathetic to environmental reform are important for sustainability, the public sector is surprisingly resilient to weak political governance (Meier, 1997). On the one hand, political buy-in for climate change adaptation has clearly helped to elevate climate to the very highest level of government in Europe (Camilla Adelle & Russel, 2013). But on the other hand, the lack of political will for environmental reform, including the occasional call for the EPA to be shut down during presidential campaigns33, has not resulted in any significant winding back of environmental protections in the United States. For public administration, the key challenge is to find the right type of governance structure to fit current political realities and to find the right set of governance tools to advocate for high quality sustainability reform when the political environment favors it.

33 https://www.theguardian.com/environment/2016/feb/26/republican-candidates- donald-trump-eliminate-epa-law-experts (retrieved November 15, 2019)

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The economic environment is a strange beast for sustainability governance. On the one hand, economic downturns may reduce public support for environmental reform (Burns & Tobin, 2016). On the other, economic downturns may actually reduce the negative effects of pollution on human health

(Peeples, 2019). Changes in how we define the economic environment are also a core part of some sustainability ideologies (cf. Daly & Townsend, 1996;

Jackson, 2011). For the public administrator this represents two important questions: One, how do we make public sector environmental governance robust to the economic cycle? And two, in pursuing sustainability reform, what set of tools can public administrators use to get buy in by the public to reassess the importance of traditional economic indicators of prosperity?

Almost all ideologies in sustainability include a technology component, and technology standards have been a common tool for environmental protection since the 1970s. Prometheanism requires it (J. Dryzek, 2013), green-growth and no-growth scenarios list technical efficiency as an important route to economic growth without affecting ecological boundaries (Fiorino, 2018;

Jackson, 2011), while common market based environmental reforms like cap-and- trade systems promote technological innovation as part of the “cap” part of the system (Fischer & Morgenstern, 2009, p. 3). This makes maintaining an environment of technological innovation and dynamism vitally important in any sustainable governance model.

Client Characteristics and Behavior

Understanding the differences in how people are affected by change, and then understanding how people are likely to respond to sustainability initiatives is a vital question for policy for several reasons, particularly 159

with respect to environmental justice. For some sustainability goals like sustainable development or no-growth, environmental justice is a central component of their ideologies. It is likely to be an important issue in implementing environmental sustainability. To give it a definition, environmental justice “focuses on the distributional implications of the way in which our society seeks to manage environmental threats and to improve and protect environmental quality” (Been, 1995). But, it could just as easily be called environmental racism or environmental equity (O’Leary, Durant,

Fiorino, & Weiland, 1999, p. 11). According to Dryzek and Schlosberg (1998, p. 467), in the North American context, the environmental justice movement is built on a guiding principle that “the poor, people of color, and indigenous peoples are disproportionally at risk from environmental hazards.” Even the best laid sustainability ideas which feature a reduction in inequality as part of their ideology, such as sustainable development and no-growth, are likely to have some distributional issues. Proper procedures and implementation strategies, from the top through to the street level, where frontline discretion can slow or create uneven opportunities during their implementation (cf. Lipsky, 1980) will need to consider the best way to equitably apply sustainability initiatives.

Related to environmental justice is the so-called NIMBY movement34, where individuals show strong social support for environmental reform as long as they are not being adversely affected by the reform process (Devine-

Wright, 2014). According to Bell et al. (2005)’s look at renewable energy projects, tailoring policy response to NIMBY situations by engaging in public

34 “Not-in-my-backyard”

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participation and local engagement can reduce hostility to sustainability projects. Thus, engaging in participative governance and finding an effective approach to deliberative governance is important to increase buy-in for sustainability reform (Karl et al., 2012), and how to do should be consistently considered, not just when “not in my backyard” issues crop up

(Kettl, 2018, p. 276).

Finally, an important issue for effective policy is to recognize that certain groups are going to be able to participate in sustainability reform much easier than others. For example, the Chesapeake Bay catchment area for years struggled with excess nutrient pollution throughout the watershed. The pollution itself comes from both point-source polluters, including town water and sewer services, and non-point sources, which are often farmers

(Stephenson et al., 2009). While point-source pollution is easy to track, it is comparatively expensive to reduce. Using a tradeable permit system where point-source polluters have an incentive to pay non-point source polluters to reduce their effluent runoff provides a cost-effective solution to a complex problem. In sustainability policy more generally, getting to a preferred outcome in a cost-effective, efficient, and equitable way requires tailoring policy to fit those affected.

Treatments

For sustainability reform, creating an effective implementation plan relies on several key points: understanding how government organizations will view sustainability reforms as part of their everyday work, deciding on target populations for the reform, and deciding which policy tools are most applicable to the goal at hand. Here, I will discuss some of the ways 161

government organizations might approach sustainability implementation, as well as some of the governance issues in the choice of environmentally sustainable policy.

When government organizations approach environmental issues, they do so as either a core part of their everyday mission or as a compliance-driven mission-extrinsic goal. First, consider the mission statements for the US EPA and the US Department of Commerce: “The mission of EPA is to protect human health and the environment”35 versus “[t]he mission of the Department [of

Commerce] is to create the conditions for economic growth and opportunity”36.

Under traditional policy making theory, sustainability represents a mission extrinsic public value for departments like Commerce (Baehler et al., 2014).

Finding the optimal balance between a broader goal for environmental sustainability and the traditional mission of the department could be achieved in at least two different ways. First, unless a department is specifically tasked with sustainability policy, it could continue to see sustainability as a mission extrinsic goal. Rather than viewing the environment with any principled priority, this would resemble a compliance activity, a common method of seeing sustainability within the US federal government today (Baehler et al., 2014)37. One way of integrating broad-based

35 US EPA, Our mission and what we do: https://www.epa.gov/aboutepa/our- mission-and-what-we-do (retrieved November 16, 2019)

36 US Dept. of Commerce, About Commerce https://www.commerce.gov/about (retrieved November 16, 2019)

37 See for example, the implementation instructions for EO 13834, Efficient Federal Operations https://www.sustainability.gov/pdfs/eo13834_instructions.pdf (retrieved November 16, 2019)

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sustainability reforms as a mission extrinsic goal might be through a sustainability cost-benefit framework (such as an enforceable environmental impact statement), or by regarding sustainability as its own form of budget.

For example, while policies generate both economic costs and economic benefits, they almost always include at least indirect and possibly direct environmental costs and benefits as well. Using a framework like the UN SEEA or equivalent, department budgets could conceivably include a sustainability budget. Sustainability cost overruns would then be theoretically identical to cost overruns and, just as overspending may reduce the ability of government to fund other projects, overusing environmental sustainability resources provides a measurable tool to inform government for the need to find extra sustainability resources elsewhere.

Second, sustainability could be integrated into the core mission of the department. For the Department of Commerce, its mission statement might become: “the mission of the Department is to create the conditions for environmentally sustainable economic growth and opportunity”. This model would be especially compatible with large-scale sustainability reforms, where principled priority is given to environmental policy issues. An example is the UN SDGs, where goal 13.2 of the SDGs is to: “integrate climate change measures into national policies, strategies and planning” (United Nations,

2019). Similarly, the UN Department of Economic Affairs leads and considers the UN sustainable development program as one of its core missions. However, this approach might be less appropriate to, say, the UN Refugee Agency, which may have a difficult time treating environmental issues as anything other

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than a mission-extrinsic goal. The key question in this circumstance is thus whether environmental policy integration is possible, or even appropriate.

The second consideration for treatment is what tools are best applied to a given sustainability goal. Sometimes the best choice might be simple, such as installing a ‘scrubber’ to reduce sulfur dioxide emissions from smokestacks. Other times the best choice might be an environmental performance standard (Hafstead & Williams III, 2018), or a market based reform like tradeable permits (Stephenson et al., 2009). Sometimes the solution may be less clear cut and complicated. Take a decision to reduce the threat of catastrophic climate change as a potential outcome for sustainability policy. Policy ideas like cap-and-trade systems and other market based solutions have been suggested, as have engineering approaches

(Lomborg, 2007), and climate policy integration (Urwin & Jordan, 2008).

Indeed, even CPI might be insufficient with effective policy representing something more like EPI (Lafferty & Hovden, 2003) After all, as Adelle &

Russel (2013)suggest, partially integrating environmental issues into non- environmental policy misses the point that environments are integrated. For a genuinely wicked problem like this, getting to the right policy solution is likely to require a very high level of managerial competence and on-going work.

One thing shared among the many theorems for environmental sustainability is that they usually include goals for policy, even if they rarely provide guidance on how to get there. For example, in Prosperity without Growth, Tim Jackson (2011) advocates a no-growth model as a way of creating long term prosperity within ecological limits. He suggests several

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treatments, including: guiding private enterprise away from material output towards delivering human services; recasting work as about participation rather than wealth generation; and refocusing capital investment to protecting assets for future generations. Designing a public governance system to promote and achieve these goals is not explicitly discussed, but it would be required. If this sort of sustainability reform were ever to be demanded or required, public administrators would face a monumental governance task to decide what tools to use. In broad based sustainability reform, knowing the best treatment is only half the battle, designing a governance structure for effective treatment is a whole other public management issue.

Structures

Structuring government for effective sustainability management involves several considerations. Here I will concentrate on two issues particularly relevant to the United States: centralization of control and centralization of implementation.

Centralization of control is likely to be a potentially contentious issue for any type of broad-based sustainability reform. In the EU, it is common for climate change policy to be a focus for a prime minister’s office, whole cabinets, or all of government (Mickwitz et al., 2009), while for many governments around the globe, a climate change or sustainability minister, such as the French Ministre de l’Écologie (Minister for Ecology), leading a department with climate change, sustainability, or other ecological word in its title is increasingly common. Again, in France, the Ministère de la

Transition écologique et solidaire (Ministry for the Ecological and Solidary 165

Transition). This sort of centralization of control should, in theory, make implementing sustainability reforms easier. There is evidence this has been true for European climate change policy (Camilla Adelle & Russel, 2013).

In the United States, however, the key agency38 tasked with environmental protection at the department level is the EPA. It is relatively unchanged since its inception in 1970 (Fiorino, 1995), and remains focused on administering regulation around water, air, chemicals and waste (Fiorino,

2012). Except for authority granted under Massachusetts vs EPA, the EPA has little formal authority to implement climate change and other environmentally sustainable initiatives. The US does have an executive level environmental advocacy unit, the US Council on Environmental Quality (CEQ). Created as part of the NEPA act in 1970, CEQ is tasked with administering NEPA, and to

“develop and recommend national policies to the President that promote the improvement of environmental quality” (CEQ, 2019). As Andrews (1999, p. 390) suggests, while NEPA and the CEQ came with a vision for integrated national environmental policy, the CEQ model has a critical weakness in relying on the personal political support of the president of the day. When developing environmental sustainability reform, an effective organization is one that will have the centralized authority to make sustainability regulations, implement those regulations, and adjudicate disputes relating to those regulations. But if those reforms were to cross multiple levels of government, like sustainable development or green growth, a decision would need to be made of where to centralize governance. Such a choice could

38 Note that the US EPA is an agency, not a ‘full’ department within the US Federal Government.

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include at the executive level like the CEQ, within a specialized department that already does environmental policy like the EPA, or with a different organization focused solely on sustainability implementation. The answer is likely to depend on the type of reform being proposed but building administrative capability would be extensive in almost every reform process.

Even though it was singularly focused on climate change mitigation, the

American Clean Energy and Security Act of 2009 (ACES) offers some insight into some of this complexity. The act included amendments to legislation across many areas of government, and it expanded the powers of many organizations that include the EPA, the Department of Energy, the Federal

Trade Commission, the Small Business Administration, and others. Where to centralize power to achieve effective implementation of a policy as complex as ACES, and policies of increasing complexity for environmental sustainability, is as much a political question as it is a question for public governance.

The second structural question that is vitally important for sustainability programs is finding a level of centralization for effective policy implementation. In the United States, a vast majority of government business is decentralized to the states and local authorities (Krane, Ebdon,

& Bartle, 2004). This is also true for environmental policy (Cimitile,

Kennedy, Lambright, O’Leary, & Weiland, 1997). According to Andrews (1999, p.

249) the EPA has never had the resources in staff, budget or state-by-state expertise to implement its statutes by itself, and instead works closely with states to implement its mandate. How to effectively engage state and local government for the purposes of implementing broad based sustainability reforms is a very important question. Sustainable development, for example, 167

is likely to have important local effects, but it requires a high level of integration of environmental policy into other areas of government (Lafferty

& Hovden, 2003). Additional questions include the impact of government decentralization on policy effectiveness by type, measuring the quality of governmental integration on a policy effort, and how to develop networks of federal, state and local administrators to compel and measure compliance

(O’Toole Jr, 1997). Engaging local governments also creates questions specific to the local governance level, including how local governments should structure themselves to maximize the benefits of this type of federal sustainability mandate (Krause, Feiock, & Hawkins, 2014), and how do local governments develop the capability to implement sustainability reforms effectively (cf. Teodoro & Switzer, 2016). A second issue is whether sustainability mandates should be funded or unfunded. Unfunded environmental mandates are already commonplace, often with federal and state governments moving the burden of paying for regulatory compliance to local governments

(Cimitile et al., 1997). Perhaps ironically, sustainability reforms calling for de-emphasizing economic growth rates will likely shift the financial burden for their implementation to local governments. Research into understanding the right level of decentralization for compliance, and the best way to allocate costs is an important job that public administration is well placed to fill. For the latter, drawing on well-accepted theories in public administration including fiscal equivalence (Olson Jr, 1969) and fiscal federalism (Krane et al., 2004; Oates, 1999) could represent an important first step.

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Managerial Roles

The role of the public manager in implementing environmentally sustainable policy is the core thesis of this paper. To do so effectively, they require knowledge, skills and perspective to engage in sustainability reform as and when it is demanded. This requires having academic departments of public administration who can research, teach, and advocate for effective sustainability governance. As I have shown, sustainability ideology represents a complex set of ideas with numerous outputs and outcomes, and many variables interacting to determine success. To be effective, sustainability ideologies require implementation and ongoing maintenance. The role of the public sector in achieving environmental sustainability, however it is defined, should not be underestimated. The next section discusses the role of public administration education in engaging with future public managers.

Teaching for Sustainability and the Environment

If environmental sustainability is to be a conceptual focus for public administration (Fiorino, 2010), and if future government leaders are to understand and apply sustainable concepts, building capability in academia is vitally important. This requires developing MPA39 programs to teach students sustainability theory, and then offering those courses to a similar extent as economics and political science. In this section I review current trends in teaching sustainability and environmental policy. Two key themes are of

39 Interchangeably used to refer to the Master of Public Administration and the Master of Public Affairs

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interest: one, to what extent are MPA programs beginning to integrate sustainability into their core curriculum or through course concentrations; and two, to what extent are public administration scholars leading the way in teaching sustainability to MPA students as opposed to relying on professionals from other fields of study.

Integrating sustainability into the work of MPA-granting departments could take several directions, including:

1. integrating environmental sustainability into the core MPA curriculum;

2. offering majors/concentrations in the environment and sustainability;

3. developing new masters programs focused on sustainability management.

As noted by Frederickson and Stazyk (2011), public administration relies on different fields for its theory, and public administration departments usually comprise academic staff from PA and related social sciences like public policy, economics, and political science. For courses in environmental policy and sustainability, this raises three interconnected questions:

1. to what extent are PA scholars developing the expertise necessary to teach students in MPA programs;

2. to what extent are these programs relying on traditional scholars from the social sciences, and

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3. to what extent are these courses being taught by professors with expertise in non-traditional PA fields like engineering, the natural sciences, and law.

I will investigate these questions by looking at current offerings in environmental theory by PA programs.

Data Collection and Discussion

Data for this section comes from the top 20 public affairs schools according to the U.S. News rankings current as of October 2019. I use publicly available information from each program’s core curriculum and university websites to investigate the extent to which environmental and sustainable policy courses are currently being offered to students. All programs are either a Master of Public Administration or Master of Public

Affairs. Two schools in the list do not offer any type of MPA program and are excluded, although one of these schools offered a Master of Environmental

Science and Policy which stopped accepting applications as of the 2019/20 school year.

Relevant courses are separated into four different categories. The first category is for courses focused on environmental policy and/or law.

This includes all master’s level courses teaching environmental public policy, including in specialized areas such as water management, but for which neither sustainability nor energy are a focal point. The second category is for courses in energy policy, including energy economics. Note that some environmental courses include sections on energy, and these are included in the environmental policy category as they more closely represent

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broader environmental programs. The third category is sustainability. To be counted as a sustainability course, the program must be focused on using the sustainability literature to solve issues in public policy. This includes courses designed to address specific topics, for example public transportation, but where sustainability is used as the key pedagogical instrument. Finally, I list courses in environmental economics if they are taught within the department40 offering the MPA. Trends in the availability of environmental, energy, and sustainability concentrations are shown in Table

43, while trends in the teaching and availability of individual classes is shown in Table 44. Note that course information is for the 2019/20 academic year, unless information was missing from the relevant course catalog. In these instances, courses were taken from the most recent semesters.

Table 43 - Basic Characteristics of MPA Granting Departments

Basic Characteristics Schools Ratio Total Schools 20 MA Sustainability or similar program? 5 0.25

Schools with MPA 18 Environmental Policy Concentration 7 0.39 Energy Policy Concentration 1 0.06 Sustainability Concentration 1 0.06

40 If the University does not have departments, I use the school level instead. Courses must still be available to MPA students to be counted. For the University of Indiana – Bloomington (UIB), I have excluded courses that are in the natural or physical science concentrations and where technical knowledge, not PA/PP, is the primary pedagogical objective. I also only counted courses focused on policy and management from the core MPA specializations. In total, the environmental concentration in the UIB MPA has 39 course offerings in environmental policy and law, and in environmental science. https://bulletins.iu.edu/iu/spea/2018- 2019/programs/bloomington/mpa/concentration/environmental.shtml

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At the school level, five of the 20 departments offer an MA in

Sustainability or similar degree41. Harmonization of naming conventions was low, suggesting a lack of standardization in curriculum between schools. For example, one school offers an MA in Sustainable Solutions, another has two separate degrees in Environmental Science and Environmental Sustainability, and a third offers an MA in Environmental Resource Policy. Of the five schools offering a specialized master’s program, four also offer an MPA42, with three of the four including an environmental policy concentration as part of the MPA degree. However, only one department across all 18 MPA granting schools offers sustainability as a concentration for MPA students.

For schools without specialized environmental programs, three offer environmental policy as an elective concentration. This implies that some

MPA-granting departments are developing expertise in the sustainability field, but they do so through specialized degrees instead of through the MPA program. For students looking for an MPA program, access to sustainability concentrations is very low. In fact, six of the 18 MPA granting departments do not appear to offer any courses in environmental policy at all, and nine of the 18 offer no courses in sustainability. It is worth noting that for two programs which offer no environmental courses of their own, they do offer students the option to take those courses at other departments in the university. For one of these programs, the other departments appear to have

41 One was not accepting applications for the 2019/20 school year.

42 The other is listed as a Public Affairs school and is ranked in the top 21 schools according to U.S. News, but does not offer an MPA. It only offers a Master of Public Policy.

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considerable expertise in sustainability, especially their Public Health school.

Across the 18 MPA granting departments, there are 33 courses in environmental policy and/or law, 11 in energy policy, 15 in sustainability, and seven in environmental economics being offered to students43. No school requires students to take a course in sustainability as a core requirement of their degree. Comparatively, each MPA student is required to take 2.33 courses in economics or statistics on average44. Looking at the highest earned degree for each course convener, PA scholars are not the primary teachers of environmental policy or sustainability in MPA-granting departments. For environmental policy, six of the 33 courses list teachers with public administration as their highest degree, while only one of the 12 teachers of sustainability is a PA scholar. Who teaches each course also matters, as they are likely to bring different perspectives and approaches to teaching based on their own academic traditions. For example, we can expect a course designed by economists and public policy scholars to emphasize very different ideas compared to a physical scientist, law professor, or political scientist. For environmental policy, while 13 of the teachers come from fields traditionally associated with PA -political science, public policy, and economics - an equal number of teachers are from non-traditional MPA fields. These are primarily engineering, the natural sciences, and law. This

43 As noted earlier, the school offering an MA in Environmental Science had significantly more courses on offer. The count of courses is limited here to those available to MPA students which had a clear policy or social science flavor, and which were listed in the course catalog as being offered at some point in the 2019/20 school year

44 The split was roughly 50/50 economics/statistics on the core curricula.

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is suggestive of just how multi-disciplinary environmental policy has become.

It also poses the question of whether schools offering only single courses in environmental policy offer students enough perspectives on environmental management. For sustainability classes, only one of the 12 courses with named teachers is taught by a PA scholar45. This suggests that either there is a lack of capability within PA to teach courses on sustainability, or PA regards academics from other fields as being better prepared.

Finally, six MPA-granting departments teach environmental economics within the department, with all but one course being taught by an economist.

Two of the six offer no courses in sustainability, while another two offer the highest number of environmental, energy and sustainability courses of any school. One of the other departments also has the highest number of sustainability courses in the 2019/20 academic year of any MPA-granting institution. This suggests that MPA-granting departments who offer environmental economics often have highly developed environmental programs, but there is little evidence that PA scholars are teaching environmental economics to MPA students.

It is suggested that PA uses theories developed in other fields as a basis for its own theories of public management and public organizations

(Frederickson & Stazyk, 2011). But it is also clear that MPA-granting universities are using academics from many different fields to teach environmental policy and related fields in energy and sustainability. Of the

45 A teacher is defined as being a PA scholar if their highest degree was in Public Administration. However, as PA is a newer field of study, many other scholars, particularly political scientists, may consider themselves PA scholars.

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58 courses in energy, environmental policy, and environmental sustainability examined for this paper, for courses where the highest degree of the teaching professor is known, only 25 percent have a qualification in public administration. More than half have qualifications outside of the social sciences in energy courses, while 50 percent and 41 percent are outside the social sciences for sustainability and environmental policy respectively.

This has two key take-aways. First, it shows that environmental issues are genuinely multi-disciplinary, with public administration students being taught by a wide range of different professionals. But teaching is also diffuse, with a high level of variation between the number of courses between universities and among who is teaching at each institution. Second, if PA is to participate in the sustainability debate and develop governance tools for environmentally sustainable public managers, it needs to develop capability within the field in both teaching and research. As it currently stands, the field is clearly lacking in PA scholars who teach sustainability, and PA scholars represent a minority of teachers in MPA programs teaching environmental policy. If PA specialists are not teaching sustainability, then it stands to reason they are also not researching important topics in sustainability governance. This reduces the fields ability to participate in the environmental sustainability debate in a meaningful manner, despite it being ideally positioned to lead research in understanding what environmental sustainability looks like “in-action”.

Conclusion

As suggested by Fiorino (2010), ‘sustainability as a concept and goal has not garnered much influence in public administration’. It is also clear 176

that growing trends towards sustainable policy will require public administrators and policy makers to intimately engage with sustainable ideologies. Like any body of research, the research on environmental sustainability is diverse, and includes many conflicting theorems to describe the relationship between the natural environment, economics, and politics.

This includes weak versus strong sustainability, sustainable development versus green growth and no-growth, ecological limits versus human ingenuity, and many others. All concepts in environmental sustainability do have one thing in common and that is a need to be effectively implemented. Even here, the options are immense, include modifying current regulations, new regulation, and integrative approaches. Given this complexity, future leaders in policy making and the public sector will need to develop an understanding of the risks and rewards of sustainability reform.

When looking at the evolution of research in public administration, the field has had much more success in researching incremental trends in government rather than developing research to inform transformative change.

Examples in the modern era include the apparent disconnect between theory and practice during the civil rights era, and during the rise of new public management. But, as sustainability is likely transformative, and at its core a question of governance, public administration as a field of study is perfectly placed to teach, research, and advocate for best practice in the implementation of environmentally sustainable reforms.

To become more prospective with respects to environmental reform, PA requires two things: the capability to research questions in sustainability governance, and the ability to teach sustainability governance in programs

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like the MPA. For the former, I use a framework for governance research by

Lynn et al. (2000) to relate environmental sustainability outcomes to some of the practical questions public administration could ask in designing and implementing sustainability reforms. This includes ideas along four dimensions: environmental factors; client characteristics; treatments; and structures. Finally, I review the current state of teaching sustainability in

MPA granting institutions. Evidence shows many MPA granting departments offer sustainability courses. Although no MPA students are required to take a sustainability class at any of the top 18 MPA granting institutions, four offer some sort of MA/MS program in sustainability46.

When considering how to approach sustainability, Caldwell (1963) offers a useful suggestion: “dealing with environments comprehensively need not imply endlessly detailed analyses and hopelessly complex syntheses of all environmental factors before policies can be formulated.” Instead, a common- sense approach informed by good science will help the field of public administration integrate sustainability into its teaching and academic traditions, leaving the field well placed to inform and prepare future leaders on how to integrate sustainability into their everyday work.

46 Five if you include one school in the top 20 which does not have an MPA program but does have a sustainability program. This program is not taking new students in 2019/2020 however.

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Tables and Figures

Table 44 - Teaching and Availability of Env. and Sustainable Courses

Course Type Teaching Scholar Total Ratio Mean Mode Median Min Max Topics in Environmental Policy and/or Law 33 1.83 0 1 0 6 Public Administration 6 0.19 Economics 5 0.16 Public Policy 5 0.16 Political Science 3 0.09 Other 13 0.41 Unknown 1 Topics in Energy Policy and/or Law 11 0.69 0 0 0 3 Public Administration 1.5 0.25 Economics 0 Public Policy 0 Political Science 2 0.40 Other 1.5 0.30 Unknown 6 Focused on Sustainability 15 0.83 0 0.5 0 4 Public Administration 1 0.08 Economics 1 0.08 Public Policy 3 0.25 Political Science 1 0.08 Other 6 0.50 Unknown 3 Environmental Economics 7 0.39 0 0 0 2 Public Administration 0 Economics 6 0.75 Public Policy 1 0.14 Political Science 0 Other 0 Unknown 0

Economics & Statistics Courses in core curriculum 42 2.33 2 2 1 4

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Table 45 Example of UN SDGs and EPI Goals and Indicators

Example of UN Sustainable Development Goals Goal Indicator 3.5 Strengthen the 3.5.2: Harmful use of alcohol, defined prevention and treatment of according to the national context as substance abuse, including alcohol per capita consumption (aged 15 narcotic drug abuse and years and older) within a calendar year harmful use of alcohol in liters of pure alcohol

12.2 By 2030, achieve the 12.2.1 Material footprint, material sustainable management and footprint per capita, and material efficient use of natural footprint per GDP resources 13.1 Strengthen resilience 13.1.3 Proportion of local governments and adaptive capacity to that adopt and implement local disaster climate-related hazards and risk reduction strategies in line with natural disasters in all national disaster risk reduction countries strategies

Examples of Goals for Environmental Policy Implementation To provide principled Are the relevant administrative priority to environmental structures in place for principled policy in non-environmental priority. and are the prevailing areas of policy (Lafferty & political and economic conditions Hovden (2003)) favorable. (Adelle & Russel (2013))

The use of the precautionary What percentage of policy decisions make principle in environmental use of the precautionary principle with policy making respects to the environment

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Table 46 Summary of Key Variables and Concepts

Group Variable Key Concepts Environmental Factors Political Factors Political buy-in Economic Environment Economic Cycles Reassessing the important of economic indicators Technology Dynamism Growth through technical efficiency Technological innovation Technology Standards Client Characteristics Environmental Justice NIMBY Deliberative Governance Participative Governance Participation Priors and capability to be sustainable Treatments Organizational Engagement Mission-extrinsic approach to Sustainability Principled priority approach to sustainability Tools for sustainability Technology and performance standards Market-based Reforms Environmental Policy Integration Partial EPI -For example CPI Large scale societal change Structures Centralization of Control Executive versus department level Department of Sustainability Centralization of Implementation Federalism Fiscal Federalism Unpaid Mandates

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