Master Thesis International Financial Management
Shareholder’s value, Environment and
Energy enterprises in Europe
Student Number: S2489805 Name: Sophie van Doeveren Study Programme: Msc IFM
Field Key Words: Shareholder’s value, Tobin’s Q, Market capitalization, carbon emissions, environmental R&D expenditures, Europe, (Non-)renewable energy enterprises
Abstract: The environment becomes more important. It has change drastically over the past decades and will continue to change. This led to environmental protection measures by the EU and should result in a reaction of the shareholders. The aim of this thesis is to determine the impact of environment on the shareholder’s value (Tobin’s Q and market capitalization). Carbon emissions and environmental R&D expenditures are proxies for
the environment. Influencers on the main relation are the energy sector and the EU target. The EU set targets in 2020 for all member states to have a certain percentage of renewable energy sourcing. This thesis expands current literature by using these environment proxies and focusing on the EU between 2006 and 2016. To reach the conclusions several panel regressions have been done with different sample compositions. It shows environment has an effect on shareholder’s value. Carbon emissions of enterprises in countries not meeting the EU target in 2016 show a negative relation with shareholder’s value. In addition, carbon emissions in West Europe are negatively related to shareholder’s value. Likewise, carbon emissions of non-renewable energy
sources show a negative relation with shareholder’s value. Environmental R&D expenditures have a negative
relation with shareholder’s value, if the enterprises operate in a non-renewable sector. But environmental R&D
expenditures have a positive relation with shareholder’s value, if the enterprise is located in a country not meeting the EU targets. This shows managers have a strong incentive to reduce carbon emissions and to consider
Introduction
Greenhouse gas emissions in relation to market value have been researched in several published papers. These papers often pertain a single country (Chapple, Clarkson and Gold, 2013, Lee, Min and Yook, 2015) or incorporate different variables, resulting in mixed conclusions (Jacobs, 2014). In this thesis, the focus lies on the European Union (EU), which currently lacks attention in research. Even though the EU was the first to introduce the Emissions Trading System (ETS). The Emission Trading System is a trading market for greenhouse gas emissions, with which enterprises can negate fines for exceeding its greenhouse gas emission cap. The first main paper is by Chapple et al. (2013), which investigated the introduction of the ETS in Australia and its market effects. The Australian ETS was modelled after the EU ETS.
from the sample, because it does not fit into any previous category. This leads to the main research question of this thesis: Do carbon emissions and environmental research and development expenditures influence the shareholder’s value?
This corresponds with the aim of this thesis. The aim is to gain more insight into the dynamic energy sector, which is on the verge of an energy transition due to the tightening of environmental regulations and an increase in public awareness. This thesis investigates whether energy enterprises are punished by the market for their carbon emissions and rewarded for their environmental R&D expenditures. It adds on to the current literature by expanding into the EU and investigating two different independent variables: carbon emissions and environmental R&D expenditures. Furthermore, the paper looks into different compositions of the EU countries.
Literature review
European Trading Scheme
Climate change impacts our environment on earth; glaciers are melting; sea levels are rising and longer and more intense heatwaves and blizzards are occurring. Scientists warned the world this would happen. Moreover, scientists believe that in the future more draughts and more extreme hurricanes will occur and the growing season for agriculture will extend (NASA, 2018). This impacts all life on Earth.
identified as contributors to global warming, including carbon emission. This resulted in the EU deciding to reduce its carbon footprint by implementing an emission cap for every enterprise. In 2003 the Directive 2003/87/EC was adopted, which introduced in 2005 the EU’s Emissions Trading System (“EU ETS”). The EU ETS currently accounts for 75 percent of the international carbon trading. According to the EU ETS the EU puts a cap on the emission of certain greenhouse gases, including carbon emissions. The enterprises receive a certain emission allowance based on historic emissions (grandfathering). Within this cap, enterprise’s allowances can be traded as needed. Over time this cap will be further reduced and the total emissions should drop as a result. Based on the emissions in 2005 as the bench mark the aim for 2020 is to reduce the cap on carbon emissions by 21 percent and in 2030 to 43 percent compared to 2005. Heavy fines are imposed on enterprises exceeding the cap. This provides a financial incentive to observe the cap. In addition, enterprises are encouraged to invest in low emission technologies to ensure compliance with the cap now and in the future (European Commission, n.d.).
Currently, the EU ETS is in its third stage (2013-2020). The third stage uses an EU wide cap on emissions instead of a system of national caps. In addition, auctioning has become the default method of allocating emission allowances. There is still the possibility of free allocation subject to harmonised allocation rules. A new programme has started, called the NER 300 program, to stimulate the deployment of renewable energy technologies and carbon capture/storage of new entrants (European Commission, 2017).
steps in their regulations in order to meet their targets. Based on Appendix A, renewable energy sources need to be increased for countries not meeting the EU target. In this thesis this will be the main division; the division between EU countries which have and have not met their targets in 2016.
As depicted in Appendix B there is a downward trend in the carbon emissions produced from energy. In 1990 the EU, consisting of 28 countries, had an average of 523.6 grams CO2 emission per Kilowatt hour. In 2016 this was reduced to 295.8 grams of CO2 per Kilowatt hour ranging from the lowest (Sweden, 1990, 2016) to the highest (Malta, 1990, Estonia, 2016) polluters.
Environment on shareholder’s value
In finance the environment already plays a role. Eccles, Krzus and Serafeim (2011) see sell-side analysts integrating the financial implications of the carbon emissions in their investment recommendations. Moreover, observers speculate that a split will develop between enterprises able and unable to control their emissions. This split will become larger in the future (GS Sustain, 2009).
Enterprises can economically benefit from enhancing their environmental sustainability reputation, for example by investing in renewable energy sources or reduce their carbon emissions, which allows them to sell their excess ETS allowance. This not only offers an increased revenue advantage, but also a reputation boost (Simnett, Nugent and Huggings, 2009).
further reduced in the future. Moreover, carbon emissions go along with carbon-related costs, such as acquiring or developing low-carbon technologies and processes and reducing the enterprise's carbon footprint (Matsumura, Prakash and Vera-Muñoz, 2014). In addition, carbon emission has become part of the enterprise’s risk profile, because it has a potential to become a liability and incur costs with no benefits (e.g. penalties, fines or lawsuits). For example, the debt of Drax, a U.K. power-generating enterprise, was downgraded by S&P due to new European regulations on carbon emission and the expected carbon costs (Barley, 2009). This could indicate that stock markets will respond to carbon emissions. In this thesis we investigate whether the enterprises in EU countries, which will not meet their target respond significantly differently than their on-target counterparts. Kazemilari, Mardani, Streimikiene and Zavadskas (2017) look into the influence of renewable energy on the stock exchange. The authors conclude that over the past decades the renewable energy sector has a growing importance in the overall growth of the world economy. This leads to the conclusion that the stocks of the renewable industry enterprises would become significantly more valuable over the last decades. And more valuable than the non-renewable energy enterprises that cover fossil fuels. Sanzillo, Hipple and Williams-Derry (2018) concluded that over the past five years indices of enterprises without fossil fuels outperformed otherwise identical stocks on a global scale. In this thesis a dummy variable RE (non-renewable energy sector) is created to see if this trend continues. This RE variable is used on the relationship of shareholder’s value and the carbon emissions variable/ the environmental R&D expenditure variable. As renewable energy becomes more important and more focus is shifted on the enterprise’s carbon emissions, it is expected that the carbon emissions have a negative impact on
H1: Carbon emissions have a more negative effect on shareholder value of enterprises in EU countries which did not meet the 2020 target in 2016.
There have been studies examining the market valuation of environmental disclosures. These can be divided into three categories.
method of allocating emission allowances is an auction. This means enterprises do not receive the emission allowances for free. This would imply that a higher polluting enterprise will pay more to receive an adequate allowance, which lowers the shareholder’s value.
The second category are articles examining the market valuation of environmental capital expenditures (Clarkson, Li and Richardson, 2004). Clarkson et al. link the enterprise’s environmental performance with the market valuation of environmental capital expenditures. They find that investors use disclosures of environmental capital expenditures to evaluate the environmental liabilities of high polluting enterprises. The authors also predict that low polluting enterprises increase their value by environmental expenditures, whilst high polluting enterprises do not gain any value. This could indicate that investors will positively value environmental R&D expenditures of the renewable energy sources.
According to the natural-resource-based theory (Hart,1995) the continuity of an enterprise’s competitive advantage lies in the capabilities of its (key) resources. It indicates that the cost-benefit trade-offs of reducing carbon emissions programs are important (Thaler and Sunstein, 2009). It implies that enterprises who do not account for climate change risk, e.g. by investing in reducing carbon emissions, may have a reduced market value in the eyes of the investors (Hart, 1995). Since enterprises in the EU are obliged to disclose their carbon emission, it makes is easier for investors to compare and to evaluate these. Since investors can compare enterprises, it is expected to see this reflected in the shareholder’s value. The second hypothesis is:
H2: Carbon emissions have a higher negative effect on non-renewable energy shareholder value of enterprises on European stock markets.
higher valuation leading to a cleaner enterprise. Since not all EU countries have met their renewable energy sources targets, the environmental R&D expenditures of enterprises operating in those countries will be more critical. This results in the following hypothesis:
H3a: Environmental R&D expenditures have a higher positive effect on shareholder value of enterprises in EU countries which did not meet the 2020 target in 2016.
Renewable energy sources have less environmental harmful emissions than the non-renewable sources. Clarkson et al. (2004) predict that the market views environmental expenditures as creating value for low polluting enterprises. High polluting enterprises will be valued lower. This would indicate that the environmental R&D expenditures of renewable sources would be positive for the shareholder value and lower for non-renewable sources.
H3b: Environmental R&D expenditures have a more positive effect on renewable energy shareholder value in EU stock markets.
governments influence investor’s decisions and that, albeit still quite volatile East Europe’s electorate is becoming less volatile contrary to the trend in West Europe, where the electorate is becoming more volatile. The authors argue that electoral volatility may increase the accountability of the politicians. However, the political stability is still consistently higher in West Europe. This stability gives investors more certainty, which reduces the risks. Disdier and Mayer also investigate the determinants of location choice in either West or East Europe. They found that there are significant differences between West and East Europe. In addition, these location decisions are influenced by the institutional quality of the host country. Previous literature established there is a difference between East and West. To sees if this uncertainty also influences the Environmental R&D expenditures and the Carbon emissions, we use West Europe as a proxy to see if there is a significant difference. Thus, similarly to Hypothesis 1 and 2, we suggest the following hypotheses:
H4a: Carbon emissions have a more negative effect on the shareholder’s value of West EU enterprises.
H4b: Environmental R&D expenditures have a more positive effect on the shareholder’s value of West EU enterprises.
Methodology
Data
enterprises. The time period will last until 2016, because Appendix A was measured in 2016. There are currently 16,685 enterprises operating in the EU in the Energy sector. Either in renewables, non-renewables or nuclear sources. Nuclear sources will be excluded in the sample size. Table 1 shows the sample composition of enterprises per country. the Faroe Islands are included in Denmark. In total the sample size was reduced to 3,514, which had all the required data available. Furthermore, there were 403 enterprises with one main independent variable data available. In table 2, the descriptive statistics shows the number of observations, mean, standard deviations and quantiles.
Table 1. Sample composition
Country Country Number in STATA Number of observations % of the sample
Table 2. Descriptive Statistics
Quantiles
Variable N Mean S.D. Min .25 Mdn .75 Max
Tobin’s Q 47,003 1.00 0.06 0.01 1.00 1.00 1.00 1.77
Market Cap 47,003 2.3e+12 2.9e+13 0.00 3.9e+07 9.2e+08 1.1e+10 6.3e+14
CO2 47,003 6.8e+07 1.8e+08 0.00 0.00 0.00 2.1e+06 7.7e+08
EN. R&D 9,545 2.6e+07 1.4e+08 20.42 64.26 80.32 88.78 1.0e+09
TargetEU 47,003 0.83 0.37 0.00 1.00 1.00 1.00 1.00 RE 47,003 0.86 0.34 0.00 1.00 1.00 1.00 1.00 West 47,003 0.57 0.49 0.00 0.00 1.00 1.00 1,00 Employees 15,894 9.27 1.87 2.64 8.20 9.39 11.21 11.53 Firm Size 43,714 20.86 3.68 3.91 19.11 20.81 23.08 29.35 Tot. Shares 29,491 19.19 2.42 7.06 17.68 19.08 20.78 23.72 Policy Em. 24,325 0.85 0.36 0.00 1.00 1.00 1.00 1.00
Pillar Score 16,854 3.8e+05 1.8e+06 0.00 56.13 76.05 91.49 1.2e+07
Country 47,003 12.32 5.74 1.00 7.00 13.00 19.00 19.00
In this table, employees, firm size and shares are log functions.
In table 2 the descriptive statistics are presented. The Tobin’s Q has a median and mean
Table 3. Market Capitalizations of EU and Spain.
Country Last Previous Minimum Maximum Frequency Range
EU 77.6% (2017) 63.7% (2016) 7.6% (1981) 84.2% (2000) Yearly 1975-2017 Spain 82.0% (2018) 97.5% (2017) 30.0% (1995) 128.1% (2007) Yearly 1995-2018 (CEICdata, 2019)
The Carbon emissions levels fluctuate between enterprises achieving zero emission and 7.7e+08. Most observations in the sample appear to have zero emissions, because until the median the quantiles are zero. This would indicate that most enterprises have a zero emissions policy. However, this does not mean these enterprises do not trade emissions on the ETS. It is possible the enterprises sell their emissions allowances as an additional cash flow. This may go down in the future as grandfathering will be replaced with auctioning. In 2013 approximately 40% of the allowances were auctioned (European Commission, 2017).
Looking at the environmental R&D expenditures there is no enterprise with zero expenditures as the minimum is 20.42. There is a large difference between the quantile .75 and Max. Looking at graph 2 Luxembourg is an outlier in the graph for Environmental R&D expenditures. To test the extend of Luxembourg’s influence on the results the panel regressions
(24,325) would mean the sample size is large enough to draw conclusions. The other dummy variable West is more evenly spread across the sample. As the median is 1.00 and the mean is 0.57.
There are log functions in the panel regressions, consisting of employees, firm size and tot. shares (total amount of shares). The employees have a minimum of 2.64, a mean of 9.27 a similar median of 9.39 and a max of 11.53. Firm size, which is a log function of the total assets, have a minimum of 3.91, a mean of 20.86, a median of 20.81 and a maximum of 29.35. Lastly, the tot. shares have a minimum of 7.06, a mean of 19.19, a median of 19.08 and a maximum of 23.72. It appears with a log function the mean and median become closer as well, making the distribution more normal.
Looking at the quantiles of the pillar score there is a large difference between .75 and Max.
This implies there is an outlier in the pillar score. Looking at the dataset, these high pillar score
enterprises are located in Greece. This is surprising as the Greek economic position was unhealthy,
needing an international bailout with currently no foresight of repayment (Smith, 2018). Currently,
there is no explanation for this.
As seen in the Country statistics number 19 is more heavily represented in the sample (920
out of 3,514 observations). This has been accounted for by running panel regression with and
without Country 19 (United Kingdom, table 1).
Variables
Table 4. Variables
Variables Definition Source
Tobin’s Q Market value of equity plus book value of liability divided by the book value of assets
EIKON
Market Capitalization
Cost per share times share price EIKON
Employees Log of total amount of employees of an enterprise EIKON
Carbon Emissions Sum of scope 1, 2 and 3 EIKON
Environmental Pillar Score
The weighted average score of an enterprise’s resource use, environmental innovation and emission category.
EIKON
RE Dummy variable between renewable energy sector (0) and
non-renewable energy sector (1)
EIKON
Environmental R&D
Expenditures
The total amount of Environmental R&D expenditures in euros
EIKON
Firm Size Log of enterprise assets EIKON
Shares Log of the total number of common share outstanding EIKON
West Dummy variable whether the enterprise is classified as located in West Europe (1) or East Europe (0)
EIKON/CIA classification Environmental
Investment Policy
Dummy variable whether the enterprises has an environmental investment policy (0) or not (1)
EIKON
Policy Emission Dummy variable whether the enterprise has a policy for emissions reduction (1) or not (0)
EIKON
Model
This thesis uses panel regression analyses for all models with different variables. Tobin’s Q is chosen as the dependent variable, because it captures the investors response to the enterprise’s environmental decisions (Lee et al., 2015). Tobin’s Q is calculated following Demsetz and Lehn (1985) and Lee et al. (2015),
𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄 = 𝑀𝑎𝑟𝑘𝑒𝑡 𝑣𝑎𝑙𝑢𝑒𝑒𝑞𝑢𝑖𝑡𝑦+𝐵𝑜𝑜𝑘 𝑣𝑎𝑙𝑢𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
𝐵𝑜𝑜𝑘 𝑣𝑎𝑙𝑢𝑒𝐴𝑠𝑠𝑒𝑡𝑠
(1)
Market capitalization should have a similar result, this variable is used in Chapple et al. (2013). Market capitalization was retrieved directly from the EIKON database.
In this thesis the environment is the main independent variable for the enterprise’s environmental intentions. In order to reflect this in our empirical hypotheses, we use the proxies Carbon emissions and R&D environmental expenditures. The panel regressions were run with fixed effect, to control for some of the endogeneity, like Lee et al. (2015) have done.
𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 = 𝛽1𝑛𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠 + 𝛽2𝑛𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 + 𝛽3𝑛𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒 + 𝛽4𝑛𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑃𝑖𝑙𝑙𝑎𝑟 𝑆𝑐𝑜𝑟𝑒 + 𝛽5𝑛𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑃𝑜𝑙𝑖𝑐𝑦 (3) 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐶𝑂2 = 𝛽1𝑛𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠 + 𝛽2𝑛𝑆ℎ𝑎𝑟𝑒𝑠 + 𝛽3𝑛𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒 + 𝛽4𝑛𝑃𝑜𝑙𝑖𝑐𝑦 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (4) 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 = 𝛽1𝑛𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠 + 𝛽2𝑛𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒 + 𝛽3𝑛𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑃𝑖𝑙𝑙𝑎𝑟 𝑆𝑐𝑜𝑟𝑒 + 𝛽4𝑛𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑃𝑜𝑙𝑖𝑐𝑦 (5)
The following formula format for Tobin’s Q will be used to reach the conclusions, which is consistent with the literature (Lee et al., 2015):
𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄 = 𝛽 1𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽2𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 + 𝛽3𝑅𝐸 + 𝛽4𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 ∗ 𝑅𝐸 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷(2) + 𝜀 (8) 𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄 = 𝛽1𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽2𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 + 𝛽3𝑇𝑎𝑟𝑔𝑒𝑡𝐸𝑈 + 𝛽4𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 ∗ 𝑇𝑎𝑟𝑔𝑒𝑡𝐸𝑈 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷(2) + 𝜀 (9) 𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄 = 𝛽1𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽2𝐶𝑂2𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 + 𝛽3𝑊𝑒𝑠𝑡 + 𝛽4𝐶𝑂2𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 ∗ 𝑊𝑒𝑠𝑡 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐶𝑂2(1) + 𝜀 (10)
Tobin′s Q = β1Constant + β2Environmental R&D + β3West
+ β4Environmental R&D ∗ West
+ Control VariablesEnvironmental R&D(2) + ε
(11)
𝑀𝑎𝑟𝑘𝑒𝑡 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = 𝛽1𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽2𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 + 𝛽3𝑅𝐸 + 𝛽4𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 ∗ 𝑅𝐸 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷(4) + 𝜀 (14) 𝑀𝑎𝑟𝑘𝑒𝑡 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = 𝛽1𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽2𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 + 𝛽3𝑇𝑎𝑟𝑔𝑒𝑡𝐸𝑈 + 𝛽4𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷 ∗ 𝑇𝑎𝑟𝑔𝑒𝑡𝐸𝑈 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑅&𝐷(4) + 𝜀 (15)
Results
Descriptive
Graphs were also done by STATA for the carbon emissions per country and environmental R&D expenditures. In Graph 1 there are four countries with a higher carbon emissions output than the other fifteen countries. These countries are France (6), Italy (11), The Netherlands (13) and United Kingdom (20). Italy has met the EU target for renewable energy sources, which is assumed to have low carbon emissions as a result. However, this is not the case.
Graph 1. Carbon emissions by Country
Each country has been assigned a number, based on the alphabet. Austria (1), Belgium (2), Cyprus (3), Denmark (4), Finland (5), France (6), Germany (7), Hungary (8), Ireland; Republic of (10), Italy (11), Luxembourg (12), The Netherlands (13), Poland (14), Portugal (15), Romania (16), Spain (17), Sweden (18) and United Kingdom (19).
Graph 2. Environmental R&D Expenditures by Country
Each country has been assigned a number, based on the alphabet. Austria (1), Belgium (2), Cyprus (3), Denmark (4), Finland (5), France (6), Germany (7), Hungary (8), Ireland; Republic of (10), Italy (11), Luxembourg (12), The Netherlands (13), Poland (14), Portugal (15), Romania (16), Spain (17), Sweden (18) and United Kingdom (19).
Results model I-IV
Henceforth, all variable explanations assumed ceterus paribus. Several panel regressions have been done to find answers for the hypotheses. In table 6 the main dependent variable is Tobin’s Q. In this table there are four models, two for carbon emissions and two for environmental R&D expenditures, as the main independent variables and RE and TargetEU as influencers on the relation with Tobin’s Q respectively.
target the effect is significantly negative 8.86e-11), employees are also significantly negative (-0.03).
In model II the Carbon emissions variable is significant at a 1% significance level (7.07e-09). This effect is again reversed when we look at enterprises in countries which have not met the EU target (-7.06e-09). A change in Carbon emissions in a country which hasn’t met the EU target decreases the Tobin’s Q with 7.06e-09, this is significant at a 1% significance. In this model there are other significant variables at 1% level employees (-0.03), total shares (5.39e-09) and Firm size (0.12).
In model III every variable is significant on a 1% level. The environmental R&D expenditures are positive, albeit quite small (8.02e-10). If these R&D expenditures occur in a country not meeting its EU target, then the R&D expenditures have a negative relation (-2.81e-09) with Tobin’s Q. Employees (-0.19), total shares (5.39e-09), firm size (0.12) and environmental pillar score (0.01) are also significant.
M ode l IV -23. 62*** (4. 39) -2. 01e -09*** (3. 24e -10) . 2.81e -09*** (5. 08e -10) -0. 19*** (0. 04) 5. 39e -09*** (1. 06e -09) 0. 12*** (0. 01) 0. 01*** (0. 00) T he R 2 of e a c h M ode l: 0. 0 4 (I ), 0. 0 4 (I I) , 0. 23 (I II ), 0. 20 (I V ). T he s ta nda rd e rr o rs a re i n br a c ke ts . * S ta ti st ic a l si gni fi c a nc e a t a 10% le ve l, ** S ta ti st ic a l si gni fi c a nc e a t a 5% l e ve l, *** S ta ti st ic a l si gni fi c a nc e a t a 1% l e ve l. R E i s a dum m y va ri a bl e for i f the e nt e rpr is e ope ra te s in R e ne w a bl e e ne rgy sour c e s (0) or non -r e ne w a bl e e ne rgy sour c e s (1) . T a rge tE U i s a dum m y va ri a bl e f or w he the r c ount ri e s m e t the E U t a rge t (0 ) or no t (1 ). E R & D i s the a bbr e vi a te d na m e f o r E nv ir onm e n ta l R e se a rc h & D e ve lopm e nt E x pe ndi tur e s. T ot a l R e ve nue s, F ir m S iz e a re bot h log func ti o ns . C a rbon e m is si ons a re t he c om bi ne d C a rbon e m is si ons of s c ope 1 , 2 a n d 3. M ode l II I -22. 72*** (4. 29) 8. 02e -10*** (2. 64e -10) . -2. 81e -09*** (5. 08e -10) -0. 19**** (0. 04) 5. 39e -09*** (1. 06e -09) 0. 12*** (0. 01) 0. 01*** (0. 00) M ode l II 1. 28*** (0. 03) 7. 07e -09*** (4. 10e -10) . -7. 06e -09*** (4. 10e -10) -0. 03*** (0. 00) 7. 70e -13 (8. 30e -13) -0. 00 (0. 00) --0. 00 (0. 00) M ode l I 1. 22*** (0. 03) 8. 14e -11*** (1. 85e -11) . -8. 86e -11*** (2. 06e -11) -0. 03*** (0. 00) 1. 01e -12 8. 42e -13 0. 00 (0. 00) 0. 00 (0. 00) T ab le 6 . P an el R eg re ss io n r es u lt s I T obi n’ s Q C ons ta nt C a rbon e m is si ons E nvi ro nm e nt a l R & D e xpe ndi tur e RE Ta rge tE U C O 2E E m is si ons *R e C O 2E m is soi ns *T a rge tE U E R & D *R E E R & D *T a rge tE U E m pl oye e s T ot a l S ha re s F ir m S iz e P ol ic y E m is si ons E nvi ro nm e nt a l P il la r S c or e Results model V-X
the carbon emissions have a negative relation (-1.07e-10) with Tobin’s Q. Not every control variable is significant, employees (-0.03) is significant on a 1% significance level and firm size (0.00) is significant on a 5% significance level.
Model VI has only one significant variable of interest (E R&D), the environmental R&D expenditures have a significant (1% level) negative (-4.12e-10) relation with Tobin’s Q. The significant control variables are total shares (1.55e-09) on a 5% significance level and firm size (0.09) on a 1% significance level.
In the last four models (VII-X) of table 7 the ARC countries (Austria, Romania and Cyprus) are excluded, because these countries did not have enterprises with renewable energy sources present in the dataset. To see whether significant changes occurred because of these countries they were excluded. This served as a robustness check. In the last two models (VII-IX), the sample was divided into East and West Europe using the dummy variable West. The ARC countries were included in this sample.
Results model XI-XIV
Table 8 has a different dependent variable than the previous tables, which is the market capitalization. Table 8 has four models, where model XI and XII (carbon emission models) do not have significant variables of interest. Thus, the results will not be considered as no conclusions can be drawn from these models.
Model XIII and XIV look at the environmental R&D expenditures on market capitalization. Model XIII the environmental R&D expenditures have a positive (10,951.41) relation with market capitalization on a 1% significance level. If the enterprise is located in a country which has not met the EU target this relation becomes negative (-22,816.45). Similar to what happened in the previous models of environmental R&D expenditures. The control variables are all significant, on a 1% level are employees (-1.80e+12) and firm size (5.95e+11) and on a 5% level is environmental pillar score (-1.02e+10).
Tab le 8 . Pa n el r eg re ss io n R es u lt s III M ode l X IV 1. 50e + 12 (1. 53e + 12) -11, 965. 04*** (2, 729. 54) . 22, 816, 45*** (3, 192. 90) -1. 80e + 12*** (1. 49e + 11) 5. 95e + 11*** (7. 75e + 10) -1. 02e + 10* (5. 38e + 09) he R 2 of e a c h M ode l: 0. 0 1 (IX ), 0. 0 1 (X ), 0. 1 2 (X I) , 0. 0 1 (X II ). T he s ta nda rd e rr or s a re i n br a c ke ts . * S ta ti st ic a l si gn if ic a nc e a t a 10% l e ve l, ** S ta ti st ic a l si gni fi c a nc e a t a 5% l e ve l, *** S ta ti st ic a l si gni fi c a nc e a t a 1% l e ve l. R E i s a dum m y va ri a bl e f or i f the e nt e rpr is e ope ra te s in R e ne w a bl e e ne rgy sour c e s (0) or non -r e ne w a bl e e ne rgy sour c e s (1 ). T a rge tE U i s a dum m y va ri a b le f or w he the r c ou nt ri e s m e t the E U t a rge t (0) or not ( 1) . E R & D i s the a bbr e vi a te d na m e f or E nvi ronm e nt a l R e se a rc h & D e ve lopm e nt E xpe ndi tur e s. T ot a l R e ve nue s, F ir m S iz e a re bot h log func ti ons . C a rbon e m is si ons a re t he c om bi ne d C a rbon e m is si ons of s c ope 1, 2 a nd 3. M ode l X III 6. 49e + 12*** (1. 81e + 12) 10, 951. 41*** (977. 45) . -22, 816. 45*** (3, 194. 90) -1. 80e + 12*** (1. 49e + 11) 5. 95e + 11*** (7. 75e + 10) -1. 02e + 10** (5. 38e + 09) M ode l X II 3. 11e + 14*** (3. 11e + 13) 409, 221. 1 (492 ,266. 5) . -395, 673 (492, 253) -9. 48e + 12*** (1. 60e + 12) -9. 62e + 12*** (1. 33e + 12) 4. 92e + 12*** (2. 03e + 12) M ode l XI 3. 09e + 14*** (3. 12e + 13) 16, 186. 72 (21, 417. 39) . -3, 240. 1 (24, 099. 22) -9. 54e + 12*** (1. 62e + 12) -9. 53e + 12*** (1. 35e + 12) 5. 13e + 12*** (2. 02e + 12) M a rke t C a pi ta li z a ti on C ons ta nt C a rbon e m is si ons E nvi ro nm e nt a l R & D e xpe ndi tur e RE Ta rge tE U C O 2E E m is si ons *R e C O 2E m is soi ns *T a rge tE U E R & D *R E E R & D *T a rge tE U E m pl oye e s F ir m S iz e P ol ic y E m is si ons E nvi ro nm e nt a l P il la r S c or e
been excluded because the countries had no enterprises in the renewable sector. Lastly, for all environmental R&D expenditures models in this thesis there was an additional variable (investment policy), which showed the enterprise’s intent on investing in the environment. This variable did not have significance in any regression model. As a result this variable was excluded from the models.
Discussion and Conclusions
In Graph 1 the countries and their carbon emissions are shown. Italy, France, The Netherlands and United Kingdom have noticeably higher carbon emissions than the other countries. In Appendix A, Italy met the renewable energy sourcing target, but still has a high amount of non-renewable energy sources (83%) and a high level of carbon emissions. The other countries (France, The Netherlands and United Kingdom) did not meet the EU targets of renewable energy sources and have a high carbon emission. The Netherlands has the second to last place in renewable energy sourcing ranking, which could tie in to its high carbon emissions amount. It would be expected that these countries would find more uses for using renewable energy. In addition, the high carbon emission countries are in the top 10 of the highest GDP countries in the EU (see Appendix C). United Kingdom is in second place, France in third, Italy in fourth and the Netherlands is number seven.
explanation for these results could be that Luxembourg invests in more efficiency, such as waste reduction rather than renewable energy sources.
In this thesis the effect of the environment on the enterprise’s shareholder’s value is considered, measured by Tobin’s Q and Market Capitalization. The Carbon emissions and Environmental R&D expenditures are used as a proxy for environment. This is coupled with the increasing importance of climate change, the increasing importance of the energy sector in the world, as well as, the market power in the fossil fuel (non-renewable) sector (Wang & Zhao, 2018).
Looking at the first results of this thesis, in table 6 model I, carbon emissions have a positive relation (8.14e-11) on Tobin’s Q. This is not as expected in the literature, as carbon emissions bring carbon costs, which should reduce the value of the Tobin’s Q. However, higher carbon emissions might also be associated with a larger enterprise and higher profits, which would lead to a higher Tobin’s Q. However, when the carbon emissions occur in a country, which did not meet the renewable energy target set by the EU, it has a negative relation (-8.86e-11), with Tobin’s Q. This would appear that those enterprises are being punished by the market. This confirms the first hypothesis.
the future, because it implies that shareholder value will be reduced by high carbon emissions, even if the carbon emissions in the model do not show this. In the literature Wang and Zhao (2018) argue that the fossil fuel market power is substantial spanning over several decades. For instance, Dunlap and McCright (2011) explained the workings of the climate change denial machine in the United States as an intricate collaboration between enterprises operating in the non-renewable sector (e.g. Exxon and Shell), conservative foundations and other institutions. These players influence the Conservative Think tanks and front groups which in turn influence the media, politicians and blogs. These eventually become campaigns, rallies and ‘common knowledge’. This resulted in renewable energy sources being out on a back burner. In fact, most milestones of renewable energy passed in legislative bills, because the ‘Denial machine’ deemed them unimportant at the time to counter. When the actual consequences of the renewable bills were experienced, the counter arguments of the opposition came too late. However, the non-renewable sector still has some influence today, as the current United States president, Donald J. Trump, still denies climate change (McCarthy, 2019, February 7th). In addition, the non-renewable sector is considerably larger than the renewable sector, which brings influence power and money to lobby for the denial machine. Consequently, there is a positive relation between carbon emissions and shareholder’s value.
In model III the environmental R&D expenditures are positive (8.02e-10) in relation to Tobin’s Q. This is different from the previous papers in the literature review, which had
environmental R&D expenditures has a negative relation with shareholder value. However, these papers did expect a positive (8.02e-10) results as environmental R&D expenditures bring benefit to shareholders on the long-term. Boer (1994) argues that an increase in shareholder value can be attributed to real growth rates of an enterprise. This in turn is the rationale for continuous R&D expenditures, which is to sustain the growth of an enterprise, and hence to increase shareholder’s value. The environmental R&D expenditures in countries, which do not meet the renewable energy source targets, the relation with Tobin’s Q is negative (-2.81e-09). This would not confirm hypothesis 3a, as it was expected to be more positive for these countries. Boer (1994) also finds that R&D expenditures are taken from the distributable profits of an enterprise and thus taken from possible dividends for the shareholders. The Environmental investment policy communicates this ahead of time to shareholders, which could cause a negative sign for both variables. Another possibility for the sign is that the shareholders may not directly or immediately benefit from these expenditures and will not value them as such (rather see them as sunk costs).
In the second results table (7) the fourth hypothesis is depicted. This includes a new dummy variable called West. This variable splits the sample in two, depending on whether the country is listed by the CIA as West Europe (1) or East Europe (0). The CIA made this distinction by looking at the countries which are NATO members. Limiting to Europe, this thesis considers eight countries as West Europe; Belgium, France, Ireland, United Kingdom, The Netherlands, Luxembourg, Portugal and Spain. In the models V and VI. In Model V the carbon emission again has a positive (9.61e-11) relationship with Tobin’s Q. However, if the enterprise is located in West Europe the carbon emissions are negatively (-1.07e-10) related to Tobin’s Q. This indicates that there is a difference in West and East Europe. It is considered that West Europe is more developed than East Europe, as West Europe is comprised of only first world countries. In addition, the top ten GDP per country the majority consist of West European countries (6 out of 10). With Portugal being the lowest on the list at number 14 out of 28. This could mean if the country has a higher GDP the market might be more interested in punishing carbon emissions. However, this was not the aim of this thesis, so this claim cannot be supported. Hypothesis 4a is supported, where it states that carbon emissions of West European enterprises have a more negative relationship with shareholder value (Tobin’s Q). In addition, there has been evidence that overall East Europe suffers higher pollution rates than West Europe. This is present in air pollution for example (Banovic, 2019). This might translate for investors having a higher tolerance for Carbon emissions in West Europe rather than East Europe.
In this table the models for the ARC countries, Austria, Romania and Cyprus, are also shown. These countries have been excluded from the sample as they do not have any renewable energy sector enterprises in the sample. Thus, for the robustness check these have been excluded. However, looking at the results they are the same as the previous models in table 6. Therefore, the ARC countries did not have any influence on the results. Moreover, United Kingdom, which was well represented in the sample (920 out of 3,514 observations) did not have a significant effect on the results as well. A panel regression has also been made with and without the dummy variable, environmental investment policy, which showed no significant difference in the results.
Lastly the other proxy for shareholder value is market capitalization as listed in table 8. This proxy’s main function is to serve as a robustness check. For hypothesis 1 and 2 with carbon emissions no conclusions can be drawn because of insignificant results. For environmental expenditures it showed a similar trend as Tobin’s Q. Looking at models III and IV comparing it to models XIII and IX. For the environmental expenditures in countries which did not meet the EU target, it has a negative (-22,816.45) relation with market capitalization. Environmental R&D expenditures by itself has a positive (10,951.41) relation with market capitalization. In model IX, the environmental R&D expenditures has a negative (-11,965.04) relation with market capitalization. Looking at the non-renewable sector the environmental R&D expenditures have a positive (22,816.45) relation with market capitalization. The same reasoning from model III and IV can apply here.
still have a negative effect on shareholder’s value. Hence, managers would benefit from reducing the carbon emissions if the enterprise operates in a non-renewable sector or is located in a country not meeting the EU target. This will increase the shareholder’s value.
The environmental R&D expenditures in countries not meeting the EU target are reducing the shareholder’s value for enterprises. This shows that shareholders will punish enterprises for sunk costs, even when it will help the enterprise grow in the future. It would imply that environmental R&D expenditures need to be considered carefully. Interestingly, the environmental R&D expenditures add value to the shareholder’s value if the enterprise operates in the non-renewable energy sector, more so than the non-renewable sector. Furthermore, depending on the location of the enterprise in Europe (East or West) this effect is again negative if the enterprise is located in West Europe. Thus, managers need to be aware of their location and sector before investing in the environmental R&D.
Recommendations and Limitations
In this thesis there are more enterprises in the non-renewable sector than the renewable sector. This is as expected, because most EU countries have a higher percentage of non-renewable energy sources than renewable energy sources (see Appendix A). However, the renewabl e energy enterprises have enough observations to lessen the risk of unrepresentativeness.
has been shown (Miyajima & Kuroki, 2008, Demsetz & Lehn, 1985). There are four major ownership categories to control: foreign enterprises, individual investors, financial institutions dominated by banking and controlling shareholder’s ownership. This thesis does not consider ownership, because the required data was unavailable in the dataset. In addition, during the time frame of the dataset the Kyoto protocol set binding greenhouse gases targets, including carbon, for 37 industrialized countries, including European countries.
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Appendices
Appendix B: Electricity generation – CO2 emission intensity (European Environmental Agency, 2018).
Appendix C: GDP (PPP) per EU country (2018).
Country GDP (Billions $) Share in 2018 (%) Rank
