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The impact of eco-innovation on CO2 emission reductions: Evidence from selected petroleum companies
Structural Change and Economic Dynamics 53 (2020) 108–115
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Structural Change and Economic Dynamics journal homepage: www.elsevier.com/locate/strueco
The impact of eco-innovation on CO2 emission reductions: Evidence from selected petroleum companies Sami Fethi a,∗, Abdulhamid Rahuma b a b
Department of Business Administration, Eastern Mediterranean University, P.O. Box 99628, Famagusta, North Cyprus Department of Banking and Finance, Eastern Mediterranean University, P.O. Box 99628, Famagusta, North Cyprus
a r t i c l e
i n f o
Article history: Received 8 December 2018 Revised 18 November 2019 Accepted 19 January 2020
Keywords: Eco-innovation CO2 emissions Porter hypothesis Environmental strategy ARDL model
a b s t r a c t 80% of the world’s energy demand is supplied by petroleum companies, whose operations are responsible for 37% of the greenhouse gas emissions. This paper uses the Porter hypothesis to examine the dynamic impact of eco-innovation on CO2 emission reductions in selected petroleum companies. Secondgeneration panel regression econometric techniques are conducted employing quarterly data over the period 2005–2016. Three actual eco-innovation indicators namely, investment (INV), training (TR), and, research and development (R&D), are used to capture the impact of eco-innovation on CO2 emission reductions in both short and long-term periods. The results reveal that INV significantly reduces CO2 emissions in the long-term, whereas R&D and TR make significant reductions in CO2 emissions in the short-term. This paper is a novelty that adds an original contribution to the relevant literature and has valuable implications for petroleum companies’ managers to achieve growth purposes, efficient use of resources, and reducing harm to the environment. © 2020 Elsevier B.V. All rights reserved.
1. Introduction In spite of the growth in alternative sources of energy, 80% of the world’s energy needs are still met by petroleum industry which retains their title as one of the biggest culprits in world pollution, bearing responsibility for 37% of global greenhouse gas emissions (Hughes and Rudolph, 2011; Ismail et al., 2013; Yanez et al., 2018). Increasing awareness of climate change and carbon dioxide (CO2 ) emissions, coupled with tighter regulatory frameworks such as the Kyoto Protocol, are leading petroleum companies to intensify their efforts for integrating eco-innovation into their operational processes and strategic plans (e.g., Porter and Kramer, 2006; Chao and Hong, 2018). Such proactive environmental strategies play a vital role in the reduction of pollution, thereby enhancing corporate environmental performance for both the short and long term (Hart and Ahuja, 1996; Liou, 2015; Iwata and Okada, 2011; Wu et al., 2012). An environmental strategy is defined as the planning of actions to manage a business under consideration of environmental standards to reduce any negative impact on the environment (Rodrigue et al., 2013, P. 303). A major factor that helps to achieve the goals of various environmental strategies is eco-innovation, ∗
Corresponding author. E-mail addresses: [email protected] (S. Fethi), 1760 0 [email protected] (A. Rahuma). https://doi.org/10.1016/j.strueco.2020.01.008 0954-349X/© 2020 Elsevier B.V. All rights reserved.
the development of sustainable products, and processes that minimize a company’s negative impact on the environment (Aragon-Correa, 1998; Rennings, 20 0 0; Porter and Kramer, 2006; Dangelico and Pujari, 2010). It has been observed that eco-innovation is an effective approach to improve operational efficiency as well as environmental performance and future sustainability (Aggeri, 1999; AgulereCaracuel and Ortiz-de-Mandojana, 2013). Porter’s hypothesis states that inspiring eco-innovation has positive effects on business and environmental performance, resulting in a win-win scenario (Porter, 1991; Porter and Van der Linder, 1995; Ramanathan et al., 2017). In this context, implementing eco-innovation at company level needs long-term commitments in form of staff skills training (TR), investments in physical assets (INV), and expenditures for research and development (R&D) (Roome, 1994; Sharma, 20 0 0; Aragon-Correa et al., 2008; Cucchiella et al., 2017; Fernandez et al., 2018). Investment in R&D is considered as an effective tool to enhance environmental strategies, which often involve the development of new environment-friendly technologies such as more sustainable products and services and more efficient, less resource-hungry operational processes (Porter and Van der Linder, 1995; Gottlieb et al., 1995; Nasirtousi, 2017). Staff skills refer to all human resource training and development that encourages employees to be more creative and committed to environmental issues (Chen and Chang, 2103). Physical assets indicate that
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investments in form of new capital assets and software make the operation processes by involving drilling, refining, and being eco-friendlier environment working system (i.e., new technologies) such as developing new infrastructural assets to reduce gas flaring (García-Granero et al., 2018). However, applying an environmental strategy at the company level to simultaneously protect the environment and maximize economic benefits is not without significant challenges (Lee and Kim, 2011). In order for companies to meet these challenges, they must be informed by empirical studies that analyze the performance of environmental strategies. There is some evidence to suggest that companies with proactive environmental strategies are more likely to reduce their CO2 emissions and improve general productivity (Nishitani et al., 2017). Companies with successful environmental strategies are observed to have greater protection against financial risks arising from violating environmental regulations due to a reduction in their overall environmental impact (Anatasia, 2015; Bhupendra and Sangle, 2016). Furthermore, companies with environmental strategies are also more likely to reduce environmental risks by implementing R&D activities, improving employees’ skills, and investing in new technologies related to eco-innovation (Sharms and Vredenburg, 1998; Aragon–Correa and Sharma, 2003). Although existing studies have enriched our understanding of how eco-innovation affects CO2 emission reductions, the task of reducing CO2 emissions by utilizing eco-innovation is an unsolved problem (Ghisetti and Rennings, 2014; Wijethilake et al., 2018; Zhang et al., 2017). More specifically, further research is needed on how companies should invest their resources to ensure that eco-innovation initiatives result in substantial CO2 emission reductions (Youndt et al., 2004; Costa and Freeao, 2010). In the current literature, there are few studies provide strong evidence to guide companies in achieving their environmental performance goals. The majority of these studies are survey studies that observe the policy rather than the actual performance (Garica-Granero et al., 2018). Besides, empirical studies related to this field deal with one or two actual indicators of eco-innovation to explain the impact of eco-innovation on CO2 emission reductions. However, determining the impact of eco-innovation on CO2 emissions should consider all implemented eco-innovation indicators with an environmental benefit. Such considerations are missing in empirical research (Kemp and Pearson, 2007; Garica-Granero et al., 2018). In response to the call of Garica-Granero (2018) and utilizing the Porter hypothesis, we try to fill the gap in research knowledge about the impact of eco-innovation activities on the CO2 emission reductions at petroleum companies. Although there are some studies in the existing literature which did focus on the impact of eco-innovation on CO2 emission reductions, these studies do not focus on the actual activity of eco-innovation that is responsible for the adoption of environmental strategy. Therefore, this paper is the first study that uses three actual implemented eco-innovation indicators to examine the effects of eco-innovation on CO2 emission reductions to offer a more comprehensive and explicit account of cause-effect relationships on the subject. The precise definition of eco-innovation used in this study is adopted from Renining (20 0 0) who defined as the application of new ideas and technologies to improving operational processes to reduce CO2 emissions. By using a sample of seventeen petroleum companies, this paper identifies three implemented eco-innovation indicators for chosen companies namely R&D, TR, and INV. It is noteworthy that the three indicators explain the effect of eco-innovation on CO2 emission reductions as well as the use of panel data for both long and short periods at the company level which are novel contributions made by this study. Thus, it helps to resolve the debate concerning the actual relationship between ecoinnovation and CO2 emission reductions by offering some guiding
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implications for applying and evaluating environmental strategy. Petroleum companies are chosen because of their contribution to CO2 emissions, their ability to invest in eco-innovation, and the availability of their data in terms of both annual and sustainability reports. The quantity of CO2 is used as a dependent variable while R&D, TR, and INV are used as explanatory variables. Although the Kyoto protocol and environmental regulations have made an unequivocal commitment to limit pollution emissions, there is evidence that the target of slowing the growth of CO2 has not yet been achieved and this is becoming an increasingly urgent problem to be solved (Jaio et al., 2018). Within the context of CO2 emission reductions, petroleum companies are increasingly taking measures to reduce CO2 emissions in order to avoid litigation risk (Yáñez et al., 2018). However, previous studies in this field paid little attention to the actual impact of eco-innovation on CO2 emission reductions. The main motivation in writing this paper lies in the fact that this kind of study has not been undertaken before. This study investigates the impact of eco-innovation on CO2 emission reductions as such innovations also improve the efficiency of operational processes at the company level. This makes this study the first of its kind to the best of our knowledge. This paper deals with panel data where relying on the assumptions of cross-sectional independence may lead to inaccurate estimation if the panel data are cross-sectionally dependent.1 Accordingly, second-generation panel models, namely, the CAD unit root test, the CIPS unit root test, Westerlund co-integration test, and the autoregressive distributed lag model (ARDL) are applied which consider cross independence issues in the estimation procedures. The diagnostic test of confidence ellipse is also employed to test the stability of ARDL coefficients so that the study’s findings are obtained through accurate and robust analytic methods. Decisionmakers can deduct from the concluding remarks that future planning of eco-innovation can be formed in terms of the relative sizes and timing of investments in infrastructure, in research & development, and in training. This may guide the companies’ managers to formulate their decisions accordingly to minimize CO2 emissions. The remainder of the paper is organized as follows: the second section includes literature review that focuses on a literature concerning CO2 emission reductions under the Porter hypothesis. The third section includes a data description and a model specification for testing the hypothesis. The fourth section discusses the empirical results and presents a wider discussion of the topic at hand. The final section includes the concluding remarks and some recommendations for future research. 2. Literature review In 1995, the Porter hypothesis left little doubt about the ability of companies to induce eco-innovation to enhance their environmental performance (Porter and Van, 1995; Busch and Hoffmann, 2011). Since then, many studies have focused on the relationship between eco-innovation and CO2 emission reductions, but the debate concerning their relationships are still ongoing. Because, majority of these studies are survey and qualitative studies, and they only reflect the policy intentions rather than the actual corporate behaviors and their impact on the environment (Schultz and Trommer, 2012). Petroleum sector, one of the most pollutive industries on earth, contributes 37% of the global greenhouse gas emissions (Kolk and Levy, 2001; Yáñez et al., 2018; Wang and Li, 2018). With increasing social pressures and tighter environmental regulations, 1 If the number of observations (N) is large and the time-series (T) is small, we need to conduct cross-sectional dependence test to avoid inaccurate estimation results (Hsiao et al, 2012).
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petroleum companies consider CO2 emissions as potential financial risks where failing to meet environmental standards results in higher taxes and penalties. Consequently, the oil companies are proactively formulating strategies to incorporate environmental targets regarding CO2 emissions in both the short and long terms by improving their operational processes and resource use (Kapusuzoglu, 2014; Kalayci and Koksal, 2015). In fact, one of their main strategic aims is to decrease the negative impact on the environment and to minimize the risk of heavier regulation and penalties (Ozcan and Ari, 2017; Çetin and Ecevit, 2017; Gonec and Schholtens, 2017; Wang and Li, 2018). The traditional view on the environment holds that increased social demand to protect the environment has positive effects on the environment but negative effects on business operations and profits (Costa-Campi et al., 2017). However, in 1995, this point of view was challenged by the Porter hypothesis. The Porter and Van indicated that companies could benefit from social demand and regulation by using their resources in an ecologically innovative way to make operational processes more efficient (Porter and Van der Linder, 1995; Cucchiella et al., 2017). Adopting environmental strategies at the company level requires long-term commitments, especially financial commitments in relation to the amount of investment required for eco-innovation (Roome, 1994). Companies’ ability to envision a sustainable environmental strategy can result in improved environmental performance whilst at the same time improving operational efficiency. Thus, environmental strategy plays a vital role in both CO2 emission reductions and business performance. Eco-innovation is a key indicator in implementing a sound environmental strategy that ensures sustainability. Eco-innovation can be defined as the ability of a company to reduce the negative impact on the environment (Kemp and Pearson, 2007, p.5). In the realm of operation processes, eco-innovation can be defined as the ideas that develop new operation processes and new products as well as moves that enhance investment in R&D and new technology (Renning, 20 0 0). Companies can benefit from adopting eco-innovation because their environmental performance will improve as well as their operation processes. Hitherto, the Porter hypothesis suggested that there is an opportunity to benefit from environmental regulation by reallocating resources and investing in eco-innovation to enhance environmental performance and protect the environment. However, the recent arguments suggest that the Porter hypothesis is not precise about the definition of innovation and how eco-innovation affects companies’ operations and reduces their negative impact on the environment (Orlitzky et al., 2003; Lee and Min, 2015). On the other hand, some authors suggest that factors such as resources, managerial obligation, and ability of companies to conduce ecoinnovation are important in improving simultaneously a business performance and environmental performance at the same time (Lopez-Gamero et al., 2010). The studies on eco-innovation can be divided into two categories. The first category of eco-innovation studies consists of survey research studies that have focused on the impact of eco-innovation on a company’s environmental performance. Doran and Ryan (2012) used data from an Irish company and found that higher spending levels on knowledge and R&D, as an eco-innovation indicator, have positive effects on company performance. Similarly, Eiadat et al. (2008) observed positive relationships between eco-innovation and business performance for twenty-two sectors in Jordan. Ramanathan et al. (2017) examined the relationship between environmental regulations, eco-innovation, and sustainability benefits in terms of pollution reduction and environmental impact among British and Chinese companies. They observed that companies that rely on their resources and capabilities actually improve their contributions to-
wards sustainability through reductions in pollution and improved performance. The second category of eco-innovation studies consists of empirical research that analyzes the impacts of both ecoinnovation and Carbon emissions on corporate performance. Lee and Min (2015) used green R&D as a proxy of an ecoinnovation variable and examined its effect on environmental and financial performance. In their study, they used Japanese manufacturing as a sample in the period 2001–2010 and found a negative relationship between R&D and Carbon emissions. On the contrary, Zhang et al. (2017), who measured the effect of eco-innovation on Carbon emissions in China for the period 20 0 0–2013, found that in most cases eco-innovation have positive effects on the reduction of Carbon emissions. M.S. Alam et al. (2019) investigated the impact of R&D investment on the company’s environmental performance in G-6 countries and found that the investment in R&D has a positive and significant impact on energy consumption and CO2 emission reductions. Furthermore, they indicated that the R&D and knowledge of innovation play a vital role in the reduction of Carbon emissions. In summary, eco-innovation can help companies to reduce CO2 emissions by improving the efficiency of the operational processes. Even though the previous studies enriched our understanding of how eco-innovation effects CO2 emission reductions, these studies relied on qualitative assessments that took the presence of eco-innovation intentions in strategic documents, rather than looking at when and how much actual eco-innovation investments are made and their outcomes on performance and CO2 emission reductions. Such qualitative studies do not reflect the actual relationship between policy and performance because the assessments they contain typically do not involve any objective, actualized measure of the impact on emissions of the various actions and expenditures undertaken by the company in relation to their strategic statements (Chatterji et al., 2009; Schultze and Trommer, 2012; Bhupendra and Sangle, 2016). Besides, existing studies fail to provide concrete, tangible recommendations for reducing CO2 emissions through eco-innovation. Considering this gap, this paper is undertaken to fill the gap in the relevant literature for investigating the impact of actual eco-innovation indicators on CO2 emission reductions at the company level. Therefore, this paper makes a novel contribution to the literature by revealing the impact of actual eco-innovation investments in the short and long term, thereby providing insights to petroleum company managers in their short and long term plans for achieving efficient resource use and profitability without sacrificing the environment. 3. Data description, theoretical model and methodology 3.1. Data description In order to investigate the impact of eco-innovation on CO2 emission reductions, we follow Gonenc and Scholten (2017) who suggested that future studies should look not at the corporate ecoinnovation intentions but also at the extent and nature of actions taken by corporations in a line with such intentions. As summarized in Fig. 1, we first searched the official corporate web sites of petroleum companies to gather sustainability reports. Second, we identified the various intentions and planned activities that are contained in environmental strategies. Third, we selected key indicators that reflect how much each firm actually carried out actions towards implementation of its environmental strategy. Fourth, we collected data from both sustainability and annual reports. The first step, visiting the web sites, resulted in the identification of seventeen oil and gas companies, who are all using the same eco-innovation strategy and about whom data are available through their sustainability and financial reports (Morad et al.,
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Table 1 Variable definitions. Notation
Variable names
Measures
LCO LRD LTR LINV
Carbon dioxide emissions Research and development Employee training Environmental investment
Measured Measured Measured Measured
as as as as
the the the the
logarithm logarithm logarithm logarithm
of of of of
total total total total
quantity of CO2 emissions in million tonnes. investment in research and development in million dollars. spending on employee training in million dollars. environmental investment in million dollars.
in related studies such as (Tello and Yoon, 2008; Thoumy and Vachon, 2012; Antonioli et al., 2013; Ramanathan et al., 2017; You et al., 2019) that suggest eco-innovation improves business performance. Based on the discussions, the expected relationship between CO2 emission reductions and eco-innovation can be illustrated in the following model:
C O2 = f (Eco−innovation )
(1)
Eq. (1) indicates that CO2 emissions are a function of stated variables of eco-innovation. Hence, the model in Eq. (1) can be transformed into a regression model as follows:
LC O2,τ = β0 + β1 LRDτ + β2 LT Rτ + β3 LINVτ + ετ
Fig. 1. Methodological steps of data collection.
2013). Oil and gas industry are a good choice with regards to the availability of sustainability data because oil firms are legally obliged to make such environmental information available (LopezGamero et al., 2010). At the same time, looking at oil and gas companies, who are major polluters, to analyze how eco-innovations help reduce pollution, is especially interesting because if it is possible to reduce pollution even in this sector, then it should be possible to do so in all others (Gonenc and Scholten, 2017). These companies include Eni, ExxonMobil, Petrobras, OMV, MOL, BP, Hess Corporation, Shell, Total, Rosneft, Ecopetrol, Repsol, Gazprom, Chevron, Imperial, ConocoPhillips, and Hellenic Petroleum. We employed the quantity of CO2 emissions as the dependent variable in relation to measuring impact of corporations on the environment (Fernandez et al., 2018). We also used three explanatory variables as proxies to explain the impact of eco-innovation on the reduction in CO2 emissions. These three variables were chosen because they are used as key indicators of eco-innovation in the sustainability reports of the sampled corporations. The first variable, R&D, shows company expenditure in the development of new technologies that may improve operational processes and contribute to CO2 emissions reduction. The second variable, TR, illustrates employees’ development and skills, which also improves internal operations and reduces errors. The third and last eco-innovation variable, INV, indicates the investment in physical assets that are responsible for reducing CO2 emissions. Financial data regarding R&D, TR, and INV are measured in millions of dollars, and CO2 emissions are measured in millions of equivalent tonnes. All variables are expressed in the logarithmic form to avoid any possible size effects. Data collected manually from both sustainability and annual reports for each company. Because of this, matching errors were lower than in the case of multi-data sources (Table 1).
(2)
Here, LCO2 represents the logarithmic quantity of CO2 emissions and is used as a measure of CO2 reduction; LNRD is logarithmic of R&D; LNTR represents the logarithmic of training; LNINV is logarithmic of investment in the environment. The nonfinancial data (emissions) are expressed in millions of tonnes while the financial data (LRD, LTR, and INV) are expressed in millions of dollars. 3.3. Methodology First, we need to use a cross-sectional dependence (CSD) test due to the small cross-section in panel-data where the number of the cross-section is small (i.e., seventeen companies) and the number of time series is large. Although our case is in the borderline, we conducted a CSD test to check whether or not cross-sectional dependence exists in our panel-data model. It is well known that the estimated results might suffer from a cross-sectional dependence problem (Dogan and Seker, 2016). Therefore, we apply cross-sectional dependence (CSD) developed by Pesaran (2004). In addition to this process, if the result of the test indicates that the variables are cross-sectionally independent, the Dickey-Fuller (CADF) and (CIPS) unit root tests will be applied to get more accurate results and eliminate inconsistency, cross-section dependency problem (Pesaran, 2007). In the third step, since the benefit of eco-innovation activity happens both in the short and in the long-term (Roome, 1994; Martensson and Westerberg, 2016), we would need to apply a second-generation panel co-integration test to determine whether or not the variables have a long-run relationship. If the variables are not stationary at the same order, the autoregressive distributed lag (ARDL) approach, proposed by Pesaran, Shin and Smith (1998), is more appropriate.2 The ARDL model can be conducted in the following form:
y j, t = øEC j, t + +
p−1 j=1
q−1 j=0
β i, t X i, t − j
λi, j yi, t − j+ ∈ j, t
(3)
3.2. Theoretical model
When the case is mixed-order, its applicability is a more significant approach than the other types of cointegration tests (i.e., Johansen cointegration test, Westerlund and Edgerton cointegration
This paper investigates the impact of actual eco-innovation on CO2 emission reductions. For this, we follow the Porter hypothesis (i.e., eco-innovation improves a company’s operations and reduces negative impacts on the environment), and similar perspectives
2 When the case is mixed-order, its applicability is a more significant approach than the other types of cointegration tests (i.e., Johansen cointegration test, Westerlund and Edgerton cointegration test, etc...) for determining the long- and shortterm relationships among variables in a small sample (Pesaran et al., 1998).
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test, etc.…) for determining the long- and short-term relationships among variables in a small sample (Pesaran et al., 1998). Where: y is the Carbon dioxide emissions (LCO), EC j,t = yi , t-1 – X i , t - θ is the error correction, θ is the long-term coefficient, ø is the adjustment coefficient, X is a vector of independent variables; Research and development (LRD), Training (LTR) and Investment (LINV), β is the short-term coefficient of independent variables, λ is the short-term coefficient of dependent variable, i and t represent company and time respectively, q is the number of lag for independent variables, P is the number of lag for dependent variable, є is the disturbance term.
4. Empirical results and discussion 4.1. Cross-sectional dependence test The result of the CD test, as shown in Table 2, indicates that the associated p values reject the null hypothesis of independent cross-sections for panel data. Henceforth, the second generation of panel unit root tests will be robust and sufficient for cross-sectional dependence issues.
4.2. Panel unit root test result Taking into consideration the result of the CD test, the CADF and CIPS models test the null hypothesis about whether or not the variables contain a unit root. The results of the panel unit root tests are reported in Table 3 and indicate that all variables except LTR are stationary at their first differences, or equivalently, I (1) at a 1% significance level. LTR is stationary at their levels, or equivalently, I (0) at 1%, 5% significance level for the CADF and CIPS tests, respectively. This finding justifies that the ARDL approach can be employed for a co-integration relationship because the variables are in a mixed order of co-integration. This means that the results are consistent with the general characteristics of most macroeconomic and financial variables, thus we are in a position to carry out a co-integration test to check for the presence of a long-term relationship between the variables.
Table 2 Cross-sectional dependence tests. Variables
∗∗∗
LCO LRD LTR LINV Note:
Breush-Pagan LM 1631.29 1977.238∗∗∗ 1070.357∗∗∗ 1214.514∗∗∗
∗∗∗ ∗∗
,
Pesan-scaled LM ∗∗∗
90.665 111.641∗∗∗ 56.653∗∗∗ 65.394∗∗∗
Perasan CD ∗∗
2.395 19.134∗∗∗ 4.282∗∗∗ 2.013∗∗
df(n = 816) 136 136 136 136
denote statistically significant at 1%, 5% respectively.
Table 3 Panel unit root test. Variables
LCO LRD LTR LINV LCO LRD LTR LINV
CADF
CIPS
Constant
Trend
Constant
Trend
−2.026 −1.989 −2.581∗∗∗ −1.692 −4.609∗∗∗ −3.570∗∗∗ −4.094∗∗∗ −3.263∗∗∗
−2.542 −2.433 −3.063∗∗∗ −1.953 −4.705∗∗∗ −3.763∗∗∗ −4.132∗∗∗ −3.495∗∗∗
−1.479 −1.682 −2.687∗∗∗ −1.279 −4.569∗∗∗ −4.531∗∗∗ −4.409∗∗∗ −4.334∗∗∗
−1.903 −2.003 −2.687∗∗ −1.760 −4.578∗∗∗ −4.537∗∗∗ −4.465∗∗∗ −4.526∗∗∗
Note: ∗ ∗ ∗ , ∗ ∗ denote statistically significant at 1%, 5% respectively. indicates first deference.
Table 4 Error-Correction Panel co-integration test. Statistic
Value
Z-value
P-value
Gt Ga Pt Pa
−1.911 −5.708 −7.817 −6.051
−0.811 1.388 −1.821 −1.163
0.209 0.918 0.034 0.122
Notes: All tests are applied constant and with trend. This table indicates the tests where p-values are asymptotic normal distribution values.
4.3. Panel co-integration test Consideration of the results of the cross-section dependence test as well as the unit root tests, lead us to apply the second generation of co-integration developed by Westerlund and Edgerton (2007). The second-generation test has the power for identifying the co-integration among panel time-series data in case of cross-independence issues, whilst assuming that the null hypothesis has no co-integration. The results, shown in table 4, demonstrate that the null hypothesis of no co-integration can be rejected in the model; LCO, LR&D, LTR, and LINV, when the P test shows that the p-value is (0.034). This finding suggests that there is a long-term relationship due to the adoption of eco-innovation activity between the eco-innovation variables and CO2 emissions at the 5% level. This result shows that the eco-innovation variables have long-term and short-term impacts on CO2 emissions in an ARDL model. 4.4. ARDL estimation The ARDL approach is applied to estimate the variables in Eq. (2). The Akaike Information Criteria (AIC) was used to select the appropriate model with the smallest lag length and minimize the loss of degrees of freedom. The result of the estimations in Table 5 shows that the long-term estimation of ARDL has a negative and long-term relationship between LINV and LCO, at a 1% significance level. This means that, in the long-run, a 1% increase in investment decreases CO2 emissions by 6.7%. This finding points to the benefits of environmental investment in the long-run whereas the short-term estimation shows that the lagged error correction term is negative and significant, at 1%. The coefficient of −0.104 suggests that the deviation from the long-term equilibrium of LCO in one quarter is corrected by 10.4% over the following quarter. The elasticities of LCO and LRD are negative and statistically significant, at 1%, which indicates that when LRD increases Table 5 ARDL estimation. Variable Long Run Equation LRD LTR LINV Short Run Equation COINTEQ01 LCO (−1) LRD LRD (−1) LRD (−2) LTR LTR (−1) LTR (−2) LINV LINV (−1) LINV (−2) C Note:
∗∗∗ ∗∗
,
Coefficient
t-Statistic
0.151033∗∗∗ 0.166326∗∗∗ −0.067171∗∗∗
3.596328 5.801126 −2.982313
−0.104195∗∗∗ 0.323879∗∗∗ −0.050718 0.051039 −0.075433∗∗∗ 0.488959∗∗ −0.215311∗∗ −0.100182 0.078875 −0.044773 0.005976 0.056947∗∗
−3.764202 7.053221 −0.805849 1.241342 −3.307909 2.525856 −2.361141 −0.841363 1.320383 −0.792994 0.188813 2.072273
denote statistically significant at 1%, 5% respectively.
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Fig. 2. Coefficient diagnostic with confidence interval.
by 1%, LCO emissions decrease by 7.5%. The elasticities of LCO and LTR are negative and significant at 1% and this result indicates that a 1% increase in LTR decreases LCO by 21.5%. Practically, these results show that the three eco-innovation indicators, utilized in this study, do possess significant and critical explanatory powers for accounting for reductions in CO2 emissions resulting from operational process improvements. Accordingly, it can be suggested that companies pursuing eco-innovative strategies are more likely to improve their environmental performance. It should be noted that the contribution of each of the eco-innovation factors to CO2 emissions differ considerably, with contributions ranging from 21.5% for LTR, to 7.5% for LRD and 6.7% for LINV respectively. The much larger contribution of LTR may be due to the fact that these companies rely much more on human resources (see Bevilacqua and Braglia, 2002), and that such knowledge-intensive workforces have higher levels of awareness about the environment and hence are more willing to participate in activities aimed at reducing emissions (see Lee et al., 2015). Our results suggest that eco-innovation has a positive and significant impact on CO2 emission reductions at the company level (see Ekins 2010). This supports the Porter hypothesis and may be attributed mainly to improvements in operational processes that result from the adoption of eco-innovation activities at the company level. The paper’s findings reveal the significance of eco-innovation activities for simultaneously improving internal operating processes and CO2 emission reductions, thereby making ecoinnovation a central tenet for environment-friendly corporate strategies. Our study points out that integrating environmental considerations into corporate operations and processes does improve environmental performance (CO2 reduction) and corporate reputation as well as internal operations. Having estimated the ARDL output as can be illustrated in Table 4, It is noteworthy to mention that the results of the diagnostic test of confidence ellipse in Fig. 2 reveal the stability of the ARDL coefficients, which are captured within the center of the ellipse (see Alola and Alola, 2018). Hence, this implies that the three coefficients (LRD, LTR, and LINV) are suitable for explaining the future change of CO2 emissions. 5. Conclusion and policy implication Although there are some studies in the relevant literature that have focused on the impact of eco-innovation and CO2 emissions,
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there are none that analyze the impact of actual eco-innovation on CO2 emission reductions (Garica-Granero et al., 2018). This paper breaks new ground by investigating the impact of actual eco-innovation on CO2 emission reductions based on panel data for seventeen international petroleum companies over the period 2005–2016. The ARDL model was employed by using the quantity of CO2 emissions as a dependent variable, and three eco-innovation indicators (R&D, TR, and INV) as independent variables. The results obtained show that LINV has a positive and significant impact on LCO in the long-term, whereas LRD and LTR have a positive and significant impact on LCO in the short-term. However, our finding illustrates that spending on LRD, LTR, and LINV leads to significant reductions in CO2 emissions at the company level. These findings are consistent with the fundamental arguments of the hypothesis of Porter, and fundamental theoretical arguments of Sharma and Vredenburg (1998), Aragon– Correa and Sharma (2003), Bhupendra and Sangle (2016) and Nishitani et al. (2017) that pursuing eco-innovative at company level improves their environmental performance where empirical studies such as Lee and Min (2015), Zhang et al. (2017) and Fernandez et al. (2018) point out that eco-innovation activity at company level improves business operation and environmental performance (CO2 reduction). Furthermore, this paper reveals that investments in training, rather than research and development have particularly high rates of return on lowering CO2 emissions. Such a finding has implications for human resource departments planning their training budgets as well as for top management in prioritizing budgetary resources for eco-innovation initiatives. Last but not least, considerations should be made of the limitations of this study when applying its recommendations and when designing future studies on the subject. Initially, due to data limitations, our paper focused only on seventeen companies within the petroleum sector that aim to reduce CO2 emissions from the operation process. This may provide only limited insights into the effects of eco-innovation on CO2 emissions and our result cannot be easily extrapolated to other industries that aim to reduce pollution emissions. So, further studies are more likely to replicate this study when they employ different strategy’s key indicators in the different sectors or industries. This kind of differences in industries or sectors should be considered otherwise the policymakers may be misguided about the implications. Second, our study does not create a link between the reduction of CO2 emissions and the strategic target. Thus, it’s not clear whether the strategy is successful or not and future research may establish a link to target a successful strategy. However, it is interesting to note that our findings highlight the significance of implemented eco-innovation activities which have positive impacts on CO2 emission reductions. In an ultimate conclusion, these findings have significant policy implications for environmental strategy since it provides new empirical evidence for the importance of spending on eco-innovation at the company level to improve business operations and reduce CO2 emissions. More importantly, the implementation of a CO2 reduction strategy could be applied in the other polluted sectors that emit large amounts of CO2 .
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Structural Change and Economic Dynamics journal homepage: www.elsevier.com/locate/strueco
The impact of eco-innovation on CO2 emission reductions: Evidence from selected petroleum companies Sami Fethi a,∗, Abdulhamid Rahuma b a b
Department of Business Administration, Eastern Mediterranean University, P.O. Box 99628, Famagusta, North Cyprus Department of Banking and Finance, Eastern Mediterranean University, P.O. Box 99628, Famagusta, North Cyprus
a r t i c l e
i n f o
Article history: Received 8 December 2018 Revised 18 November 2019 Accepted 19 January 2020
Keywords: Eco-innovation CO2 emissions Porter hypothesis Environmental strategy ARDL model
a b s t r a c t 80% of the world’s energy demand is supplied by petroleum companies, whose operations are responsible for 37% of the greenhouse gas emissions. This paper uses the Porter hypothesis to examine the dynamic impact of eco-innovation on CO2 emission reductions in selected petroleum companies. Secondgeneration panel regression econometric techniques are conducted employing quarterly data over the period 2005–2016. Three actual eco-innovation indicators namely, investment (INV), training (TR), and, research and development (R&D), are used to capture the impact of eco-innovation on CO2 emission reductions in both short and long-term periods. The results reveal that INV significantly reduces CO2 emissions in the long-term, whereas R&D and TR make significant reductions in CO2 emissions in the short-term. This paper is a novelty that adds an original contribution to the relevant literature and has valuable implications for petroleum companies’ managers to achieve growth purposes, efficient use of resources, and reducing harm to the environment. © 2020 Elsevier B.V. All rights reserved.
1. Introduction In spite of the growth in alternative sources of energy, 80% of the world’s energy needs are still met by petroleum industry which retains their title as one of the biggest culprits in world pollution, bearing responsibility for 37% of global greenhouse gas emissions (Hughes and Rudolph, 2011; Ismail et al., 2013; Yanez et al., 2018). Increasing awareness of climate change and carbon dioxide (CO2 ) emissions, coupled with tighter regulatory frameworks such as the Kyoto Protocol, are leading petroleum companies to intensify their efforts for integrating eco-innovation into their operational processes and strategic plans (e.g., Porter and Kramer, 2006; Chao and Hong, 2018). Such proactive environmental strategies play a vital role in the reduction of pollution, thereby enhancing corporate environmental performance for both the short and long term (Hart and Ahuja, 1996; Liou, 2015; Iwata and Okada, 2011; Wu et al., 2012). An environmental strategy is defined as the planning of actions to manage a business under consideration of environmental standards to reduce any negative impact on the environment (Rodrigue et al., 2013, P. 303). A major factor that helps to achieve the goals of various environmental strategies is eco-innovation, ∗
Corresponding author. E-mail addresses: [email protected] (S. Fethi), 1760 0 [email protected] (A. Rahuma). https://doi.org/10.1016/j.strueco.2020.01.008 0954-349X/© 2020 Elsevier B.V. All rights reserved.
the development of sustainable products, and processes that minimize a company’s negative impact on the environment (Aragon-Correa, 1998; Rennings, 20 0 0; Porter and Kramer, 2006; Dangelico and Pujari, 2010). It has been observed that eco-innovation is an effective approach to improve operational efficiency as well as environmental performance and future sustainability (Aggeri, 1999; AgulereCaracuel and Ortiz-de-Mandojana, 2013). Porter’s hypothesis states that inspiring eco-innovation has positive effects on business and environmental performance, resulting in a win-win scenario (Porter, 1991; Porter and Van der Linder, 1995; Ramanathan et al., 2017). In this context, implementing eco-innovation at company level needs long-term commitments in form of staff skills training (TR), investments in physical assets (INV), and expenditures for research and development (R&D) (Roome, 1994; Sharma, 20 0 0; Aragon-Correa et al., 2008; Cucchiella et al., 2017; Fernandez et al., 2018). Investment in R&D is considered as an effective tool to enhance environmental strategies, which often involve the development of new environment-friendly technologies such as more sustainable products and services and more efficient, less resource-hungry operational processes (Porter and Van der Linder, 1995; Gottlieb et al., 1995; Nasirtousi, 2017). Staff skills refer to all human resource training and development that encourages employees to be more creative and committed to environmental issues (Chen and Chang, 2103). Physical assets indicate that
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investments in form of new capital assets and software make the operation processes by involving drilling, refining, and being eco-friendlier environment working system (i.e., new technologies) such as developing new infrastructural assets to reduce gas flaring (García-Granero et al., 2018). However, applying an environmental strategy at the company level to simultaneously protect the environment and maximize economic benefits is not without significant challenges (Lee and Kim, 2011). In order for companies to meet these challenges, they must be informed by empirical studies that analyze the performance of environmental strategies. There is some evidence to suggest that companies with proactive environmental strategies are more likely to reduce their CO2 emissions and improve general productivity (Nishitani et al., 2017). Companies with successful environmental strategies are observed to have greater protection against financial risks arising from violating environmental regulations due to a reduction in their overall environmental impact (Anatasia, 2015; Bhupendra and Sangle, 2016). Furthermore, companies with environmental strategies are also more likely to reduce environmental risks by implementing R&D activities, improving employees’ skills, and investing in new technologies related to eco-innovation (Sharms and Vredenburg, 1998; Aragon–Correa and Sharma, 2003). Although existing studies have enriched our understanding of how eco-innovation affects CO2 emission reductions, the task of reducing CO2 emissions by utilizing eco-innovation is an unsolved problem (Ghisetti and Rennings, 2014; Wijethilake et al., 2018; Zhang et al., 2017). More specifically, further research is needed on how companies should invest their resources to ensure that eco-innovation initiatives result in substantial CO2 emission reductions (Youndt et al., 2004; Costa and Freeao, 2010). In the current literature, there are few studies provide strong evidence to guide companies in achieving their environmental performance goals. The majority of these studies are survey studies that observe the policy rather than the actual performance (Garica-Granero et al., 2018). Besides, empirical studies related to this field deal with one or two actual indicators of eco-innovation to explain the impact of eco-innovation on CO2 emission reductions. However, determining the impact of eco-innovation on CO2 emissions should consider all implemented eco-innovation indicators with an environmental benefit. Such considerations are missing in empirical research (Kemp and Pearson, 2007; Garica-Granero et al., 2018). In response to the call of Garica-Granero (2018) and utilizing the Porter hypothesis, we try to fill the gap in research knowledge about the impact of eco-innovation activities on the CO2 emission reductions at petroleum companies. Although there are some studies in the existing literature which did focus on the impact of eco-innovation on CO2 emission reductions, these studies do not focus on the actual activity of eco-innovation that is responsible for the adoption of environmental strategy. Therefore, this paper is the first study that uses three actual implemented eco-innovation indicators to examine the effects of eco-innovation on CO2 emission reductions to offer a more comprehensive and explicit account of cause-effect relationships on the subject. The precise definition of eco-innovation used in this study is adopted from Renining (20 0 0) who defined as the application of new ideas and technologies to improving operational processes to reduce CO2 emissions. By using a sample of seventeen petroleum companies, this paper identifies three implemented eco-innovation indicators for chosen companies namely R&D, TR, and INV. It is noteworthy that the three indicators explain the effect of eco-innovation on CO2 emission reductions as well as the use of panel data for both long and short periods at the company level which are novel contributions made by this study. Thus, it helps to resolve the debate concerning the actual relationship between ecoinnovation and CO2 emission reductions by offering some guiding
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implications for applying and evaluating environmental strategy. Petroleum companies are chosen because of their contribution to CO2 emissions, their ability to invest in eco-innovation, and the availability of their data in terms of both annual and sustainability reports. The quantity of CO2 is used as a dependent variable while R&D, TR, and INV are used as explanatory variables. Although the Kyoto protocol and environmental regulations have made an unequivocal commitment to limit pollution emissions, there is evidence that the target of slowing the growth of CO2 has not yet been achieved and this is becoming an increasingly urgent problem to be solved (Jaio et al., 2018). Within the context of CO2 emission reductions, petroleum companies are increasingly taking measures to reduce CO2 emissions in order to avoid litigation risk (Yáñez et al., 2018). However, previous studies in this field paid little attention to the actual impact of eco-innovation on CO2 emission reductions. The main motivation in writing this paper lies in the fact that this kind of study has not been undertaken before. This study investigates the impact of eco-innovation on CO2 emission reductions as such innovations also improve the efficiency of operational processes at the company level. This makes this study the first of its kind to the best of our knowledge. This paper deals with panel data where relying on the assumptions of cross-sectional independence may lead to inaccurate estimation if the panel data are cross-sectionally dependent.1 Accordingly, second-generation panel models, namely, the CAD unit root test, the CIPS unit root test, Westerlund co-integration test, and the autoregressive distributed lag model (ARDL) are applied which consider cross independence issues in the estimation procedures. The diagnostic test of confidence ellipse is also employed to test the stability of ARDL coefficients so that the study’s findings are obtained through accurate and robust analytic methods. Decisionmakers can deduct from the concluding remarks that future planning of eco-innovation can be formed in terms of the relative sizes and timing of investments in infrastructure, in research & development, and in training. This may guide the companies’ managers to formulate their decisions accordingly to minimize CO2 emissions. The remainder of the paper is organized as follows: the second section includes literature review that focuses on a literature concerning CO2 emission reductions under the Porter hypothesis. The third section includes a data description and a model specification for testing the hypothesis. The fourth section discusses the empirical results and presents a wider discussion of the topic at hand. The final section includes the concluding remarks and some recommendations for future research. 2. Literature review In 1995, the Porter hypothesis left little doubt about the ability of companies to induce eco-innovation to enhance their environmental performance (Porter and Van, 1995; Busch and Hoffmann, 2011). Since then, many studies have focused on the relationship between eco-innovation and CO2 emission reductions, but the debate concerning their relationships are still ongoing. Because, majority of these studies are survey and qualitative studies, and they only reflect the policy intentions rather than the actual corporate behaviors and their impact on the environment (Schultz and Trommer, 2012). Petroleum sector, one of the most pollutive industries on earth, contributes 37% of the global greenhouse gas emissions (Kolk and Levy, 2001; Yáñez et al., 2018; Wang and Li, 2018). With increasing social pressures and tighter environmental regulations, 1 If the number of observations (N) is large and the time-series (T) is small, we need to conduct cross-sectional dependence test to avoid inaccurate estimation results (Hsiao et al, 2012).
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petroleum companies consider CO2 emissions as potential financial risks where failing to meet environmental standards results in higher taxes and penalties. Consequently, the oil companies are proactively formulating strategies to incorporate environmental targets regarding CO2 emissions in both the short and long terms by improving their operational processes and resource use (Kapusuzoglu, 2014; Kalayci and Koksal, 2015). In fact, one of their main strategic aims is to decrease the negative impact on the environment and to minimize the risk of heavier regulation and penalties (Ozcan and Ari, 2017; Çetin and Ecevit, 2017; Gonec and Schholtens, 2017; Wang and Li, 2018). The traditional view on the environment holds that increased social demand to protect the environment has positive effects on the environment but negative effects on business operations and profits (Costa-Campi et al., 2017). However, in 1995, this point of view was challenged by the Porter hypothesis. The Porter and Van indicated that companies could benefit from social demand and regulation by using their resources in an ecologically innovative way to make operational processes more efficient (Porter and Van der Linder, 1995; Cucchiella et al., 2017). Adopting environmental strategies at the company level requires long-term commitments, especially financial commitments in relation to the amount of investment required for eco-innovation (Roome, 1994). Companies’ ability to envision a sustainable environmental strategy can result in improved environmental performance whilst at the same time improving operational efficiency. Thus, environmental strategy plays a vital role in both CO2 emission reductions and business performance. Eco-innovation is a key indicator in implementing a sound environmental strategy that ensures sustainability. Eco-innovation can be defined as the ability of a company to reduce the negative impact on the environment (Kemp and Pearson, 2007, p.5). In the realm of operation processes, eco-innovation can be defined as the ideas that develop new operation processes and new products as well as moves that enhance investment in R&D and new technology (Renning, 20 0 0). Companies can benefit from adopting eco-innovation because their environmental performance will improve as well as their operation processes. Hitherto, the Porter hypothesis suggested that there is an opportunity to benefit from environmental regulation by reallocating resources and investing in eco-innovation to enhance environmental performance and protect the environment. However, the recent arguments suggest that the Porter hypothesis is not precise about the definition of innovation and how eco-innovation affects companies’ operations and reduces their negative impact on the environment (Orlitzky et al., 2003; Lee and Min, 2015). On the other hand, some authors suggest that factors such as resources, managerial obligation, and ability of companies to conduce ecoinnovation are important in improving simultaneously a business performance and environmental performance at the same time (Lopez-Gamero et al., 2010). The studies on eco-innovation can be divided into two categories. The first category of eco-innovation studies consists of survey research studies that have focused on the impact of eco-innovation on a company’s environmental performance. Doran and Ryan (2012) used data from an Irish company and found that higher spending levels on knowledge and R&D, as an eco-innovation indicator, have positive effects on company performance. Similarly, Eiadat et al. (2008) observed positive relationships between eco-innovation and business performance for twenty-two sectors in Jordan. Ramanathan et al. (2017) examined the relationship between environmental regulations, eco-innovation, and sustainability benefits in terms of pollution reduction and environmental impact among British and Chinese companies. They observed that companies that rely on their resources and capabilities actually improve their contributions to-
wards sustainability through reductions in pollution and improved performance. The second category of eco-innovation studies consists of empirical research that analyzes the impacts of both ecoinnovation and Carbon emissions on corporate performance. Lee and Min (2015) used green R&D as a proxy of an ecoinnovation variable and examined its effect on environmental and financial performance. In their study, they used Japanese manufacturing as a sample in the period 2001–2010 and found a negative relationship between R&D and Carbon emissions. On the contrary, Zhang et al. (2017), who measured the effect of eco-innovation on Carbon emissions in China for the period 20 0 0–2013, found that in most cases eco-innovation have positive effects on the reduction of Carbon emissions. M.S. Alam et al. (2019) investigated the impact of R&D investment on the company’s environmental performance in G-6 countries and found that the investment in R&D has a positive and significant impact on energy consumption and CO2 emission reductions. Furthermore, they indicated that the R&D and knowledge of innovation play a vital role in the reduction of Carbon emissions. In summary, eco-innovation can help companies to reduce CO2 emissions by improving the efficiency of the operational processes. Even though the previous studies enriched our understanding of how eco-innovation effects CO2 emission reductions, these studies relied on qualitative assessments that took the presence of eco-innovation intentions in strategic documents, rather than looking at when and how much actual eco-innovation investments are made and their outcomes on performance and CO2 emission reductions. Such qualitative studies do not reflect the actual relationship between policy and performance because the assessments they contain typically do not involve any objective, actualized measure of the impact on emissions of the various actions and expenditures undertaken by the company in relation to their strategic statements (Chatterji et al., 2009; Schultze and Trommer, 2012; Bhupendra and Sangle, 2016). Besides, existing studies fail to provide concrete, tangible recommendations for reducing CO2 emissions through eco-innovation. Considering this gap, this paper is undertaken to fill the gap in the relevant literature for investigating the impact of actual eco-innovation indicators on CO2 emission reductions at the company level. Therefore, this paper makes a novel contribution to the literature by revealing the impact of actual eco-innovation investments in the short and long term, thereby providing insights to petroleum company managers in their short and long term plans for achieving efficient resource use and profitability without sacrificing the environment. 3. Data description, theoretical model and methodology 3.1. Data description In order to investigate the impact of eco-innovation on CO2 emission reductions, we follow Gonenc and Scholten (2017) who suggested that future studies should look not at the corporate ecoinnovation intentions but also at the extent and nature of actions taken by corporations in a line with such intentions. As summarized in Fig. 1, we first searched the official corporate web sites of petroleum companies to gather sustainability reports. Second, we identified the various intentions and planned activities that are contained in environmental strategies. Third, we selected key indicators that reflect how much each firm actually carried out actions towards implementation of its environmental strategy. Fourth, we collected data from both sustainability and annual reports. The first step, visiting the web sites, resulted in the identification of seventeen oil and gas companies, who are all using the same eco-innovation strategy and about whom data are available through their sustainability and financial reports (Morad et al.,
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Table 1 Variable definitions. Notation
Variable names
Measures
LCO LRD LTR LINV
Carbon dioxide emissions Research and development Employee training Environmental investment
Measured Measured Measured Measured
as as as as
the the the the
logarithm logarithm logarithm logarithm
of of of of
total total total total
quantity of CO2 emissions in million tonnes. investment in research and development in million dollars. spending on employee training in million dollars. environmental investment in million dollars.
in related studies such as (Tello and Yoon, 2008; Thoumy and Vachon, 2012; Antonioli et al., 2013; Ramanathan et al., 2017; You et al., 2019) that suggest eco-innovation improves business performance. Based on the discussions, the expected relationship between CO2 emission reductions and eco-innovation can be illustrated in the following model:
C O2 = f (Eco−innovation )
(1)
Eq. (1) indicates that CO2 emissions are a function of stated variables of eco-innovation. Hence, the model in Eq. (1) can be transformed into a regression model as follows:
LC O2,τ = β0 + β1 LRDτ + β2 LT Rτ + β3 LINVτ + ετ
Fig. 1. Methodological steps of data collection.
2013). Oil and gas industry are a good choice with regards to the availability of sustainability data because oil firms are legally obliged to make such environmental information available (LopezGamero et al., 2010). At the same time, looking at oil and gas companies, who are major polluters, to analyze how eco-innovations help reduce pollution, is especially interesting because if it is possible to reduce pollution even in this sector, then it should be possible to do so in all others (Gonenc and Scholten, 2017). These companies include Eni, ExxonMobil, Petrobras, OMV, MOL, BP, Hess Corporation, Shell, Total, Rosneft, Ecopetrol, Repsol, Gazprom, Chevron, Imperial, ConocoPhillips, and Hellenic Petroleum. We employed the quantity of CO2 emissions as the dependent variable in relation to measuring impact of corporations on the environment (Fernandez et al., 2018). We also used three explanatory variables as proxies to explain the impact of eco-innovation on the reduction in CO2 emissions. These three variables were chosen because they are used as key indicators of eco-innovation in the sustainability reports of the sampled corporations. The first variable, R&D, shows company expenditure in the development of new technologies that may improve operational processes and contribute to CO2 emissions reduction. The second variable, TR, illustrates employees’ development and skills, which also improves internal operations and reduces errors. The third and last eco-innovation variable, INV, indicates the investment in physical assets that are responsible for reducing CO2 emissions. Financial data regarding R&D, TR, and INV are measured in millions of dollars, and CO2 emissions are measured in millions of equivalent tonnes. All variables are expressed in the logarithmic form to avoid any possible size effects. Data collected manually from both sustainability and annual reports for each company. Because of this, matching errors were lower than in the case of multi-data sources (Table 1).
(2)
Here, LCO2 represents the logarithmic quantity of CO2 emissions and is used as a measure of CO2 reduction; LNRD is logarithmic of R&D; LNTR represents the logarithmic of training; LNINV is logarithmic of investment in the environment. The nonfinancial data (emissions) are expressed in millions of tonnes while the financial data (LRD, LTR, and INV) are expressed in millions of dollars. 3.3. Methodology First, we need to use a cross-sectional dependence (CSD) test due to the small cross-section in panel-data where the number of the cross-section is small (i.e., seventeen companies) and the number of time series is large. Although our case is in the borderline, we conducted a CSD test to check whether or not cross-sectional dependence exists in our panel-data model. It is well known that the estimated results might suffer from a cross-sectional dependence problem (Dogan and Seker, 2016). Therefore, we apply cross-sectional dependence (CSD) developed by Pesaran (2004). In addition to this process, if the result of the test indicates that the variables are cross-sectionally independent, the Dickey-Fuller (CADF) and (CIPS) unit root tests will be applied to get more accurate results and eliminate inconsistency, cross-section dependency problem (Pesaran, 2007). In the third step, since the benefit of eco-innovation activity happens both in the short and in the long-term (Roome, 1994; Martensson and Westerberg, 2016), we would need to apply a second-generation panel co-integration test to determine whether or not the variables have a long-run relationship. If the variables are not stationary at the same order, the autoregressive distributed lag (ARDL) approach, proposed by Pesaran, Shin and Smith (1998), is more appropriate.2 The ARDL model can be conducted in the following form:
y j, t = øEC j, t + +
p−1 j=1
q−1 j=0
β i, t X i, t − j
λi, j yi, t − j+ ∈ j, t
(3)
3.2. Theoretical model
When the case is mixed-order, its applicability is a more significant approach than the other types of cointegration tests (i.e., Johansen cointegration test, Westerlund and Edgerton cointegration
This paper investigates the impact of actual eco-innovation on CO2 emission reductions. For this, we follow the Porter hypothesis (i.e., eco-innovation improves a company’s operations and reduces negative impacts on the environment), and similar perspectives
2 When the case is mixed-order, its applicability is a more significant approach than the other types of cointegration tests (i.e., Johansen cointegration test, Westerlund and Edgerton cointegration test, etc...) for determining the long- and shortterm relationships among variables in a small sample (Pesaran et al., 1998).
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test, etc.…) for determining the long- and short-term relationships among variables in a small sample (Pesaran et al., 1998). Where: y is the Carbon dioxide emissions (LCO), EC j,t = yi , t-1 – X i , t - θ is the error correction, θ is the long-term coefficient, ø is the adjustment coefficient, X is a vector of independent variables; Research and development (LRD), Training (LTR) and Investment (LINV), β is the short-term coefficient of independent variables, λ is the short-term coefficient of dependent variable, i and t represent company and time respectively, q is the number of lag for independent variables, P is the number of lag for dependent variable, є is the disturbance term.
4. Empirical results and discussion 4.1. Cross-sectional dependence test The result of the CD test, as shown in Table 2, indicates that the associated p values reject the null hypothesis of independent cross-sections for panel data. Henceforth, the second generation of panel unit root tests will be robust and sufficient for cross-sectional dependence issues.
4.2. Panel unit root test result Taking into consideration the result of the CD test, the CADF and CIPS models test the null hypothesis about whether or not the variables contain a unit root. The results of the panel unit root tests are reported in Table 3 and indicate that all variables except LTR are stationary at their first differences, or equivalently, I (1) at a 1% significance level. LTR is stationary at their levels, or equivalently, I (0) at 1%, 5% significance level for the CADF and CIPS tests, respectively. This finding justifies that the ARDL approach can be employed for a co-integration relationship because the variables are in a mixed order of co-integration. This means that the results are consistent with the general characteristics of most macroeconomic and financial variables, thus we are in a position to carry out a co-integration test to check for the presence of a long-term relationship between the variables.
Table 2 Cross-sectional dependence tests. Variables
∗∗∗
LCO LRD LTR LINV Note:
Breush-Pagan LM 1631.29 1977.238∗∗∗ 1070.357∗∗∗ 1214.514∗∗∗
∗∗∗ ∗∗
,
Pesan-scaled LM ∗∗∗
90.665 111.641∗∗∗ 56.653∗∗∗ 65.394∗∗∗
Perasan CD ∗∗
2.395 19.134∗∗∗ 4.282∗∗∗ 2.013∗∗
df(n = 816) 136 136 136 136
denote statistically significant at 1%, 5% respectively.
Table 3 Panel unit root test. Variables
LCO LRD LTR LINV LCO LRD LTR LINV
CADF
CIPS
Constant
Trend
Constant
Trend
−2.026 −1.989 −2.581∗∗∗ −1.692 −4.609∗∗∗ −3.570∗∗∗ −4.094∗∗∗ −3.263∗∗∗
−2.542 −2.433 −3.063∗∗∗ −1.953 −4.705∗∗∗ −3.763∗∗∗ −4.132∗∗∗ −3.495∗∗∗
−1.479 −1.682 −2.687∗∗∗ −1.279 −4.569∗∗∗ −4.531∗∗∗ −4.409∗∗∗ −4.334∗∗∗
−1.903 −2.003 −2.687∗∗ −1.760 −4.578∗∗∗ −4.537∗∗∗ −4.465∗∗∗ −4.526∗∗∗
Note: ∗ ∗ ∗ , ∗ ∗ denote statistically significant at 1%, 5% respectively. indicates first deference.
Table 4 Error-Correction Panel co-integration test. Statistic
Value
Z-value
P-value
Gt Ga Pt Pa
−1.911 −5.708 −7.817 −6.051
−0.811 1.388 −1.821 −1.163
0.209 0.918 0.034 0.122
Notes: All tests are applied constant and with trend. This table indicates the tests where p-values are asymptotic normal distribution values.
4.3. Panel co-integration test Consideration of the results of the cross-section dependence test as well as the unit root tests, lead us to apply the second generation of co-integration developed by Westerlund and Edgerton (2007). The second-generation test has the power for identifying the co-integration among panel time-series data in case of cross-independence issues, whilst assuming that the null hypothesis has no co-integration. The results, shown in table 4, demonstrate that the null hypothesis of no co-integration can be rejected in the model; LCO, LR&D, LTR, and LINV, when the P test shows that the p-value is (0.034). This finding suggests that there is a long-term relationship due to the adoption of eco-innovation activity between the eco-innovation variables and CO2 emissions at the 5% level. This result shows that the eco-innovation variables have long-term and short-term impacts on CO2 emissions in an ARDL model. 4.4. ARDL estimation The ARDL approach is applied to estimate the variables in Eq. (2). The Akaike Information Criteria (AIC) was used to select the appropriate model with the smallest lag length and minimize the loss of degrees of freedom. The result of the estimations in Table 5 shows that the long-term estimation of ARDL has a negative and long-term relationship between LINV and LCO, at a 1% significance level. This means that, in the long-run, a 1% increase in investment decreases CO2 emissions by 6.7%. This finding points to the benefits of environmental investment in the long-run whereas the short-term estimation shows that the lagged error correction term is negative and significant, at 1%. The coefficient of −0.104 suggests that the deviation from the long-term equilibrium of LCO in one quarter is corrected by 10.4% over the following quarter. The elasticities of LCO and LRD are negative and statistically significant, at 1%, which indicates that when LRD increases Table 5 ARDL estimation. Variable Long Run Equation LRD LTR LINV Short Run Equation COINTEQ01 LCO (−1) LRD LRD (−1) LRD (−2) LTR LTR (−1) LTR (−2) LINV LINV (−1) LINV (−2) C Note:
∗∗∗ ∗∗
,
Coefficient
t-Statistic
0.151033∗∗∗ 0.166326∗∗∗ −0.067171∗∗∗
3.596328 5.801126 −2.982313
−0.104195∗∗∗ 0.323879∗∗∗ −0.050718 0.051039 −0.075433∗∗∗ 0.488959∗∗ −0.215311∗∗ −0.100182 0.078875 −0.044773 0.005976 0.056947∗∗
−3.764202 7.053221 −0.805849 1.241342 −3.307909 2.525856 −2.361141 −0.841363 1.320383 −0.792994 0.188813 2.072273
denote statistically significant at 1%, 5% respectively.
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Fig. 2. Coefficient diagnostic with confidence interval.
by 1%, LCO emissions decrease by 7.5%. The elasticities of LCO and LTR are negative and significant at 1% and this result indicates that a 1% increase in LTR decreases LCO by 21.5%. Practically, these results show that the three eco-innovation indicators, utilized in this study, do possess significant and critical explanatory powers for accounting for reductions in CO2 emissions resulting from operational process improvements. Accordingly, it can be suggested that companies pursuing eco-innovative strategies are more likely to improve their environmental performance. It should be noted that the contribution of each of the eco-innovation factors to CO2 emissions differ considerably, with contributions ranging from 21.5% for LTR, to 7.5% for LRD and 6.7% for LINV respectively. The much larger contribution of LTR may be due to the fact that these companies rely much more on human resources (see Bevilacqua and Braglia, 2002), and that such knowledge-intensive workforces have higher levels of awareness about the environment and hence are more willing to participate in activities aimed at reducing emissions (see Lee et al., 2015). Our results suggest that eco-innovation has a positive and significant impact on CO2 emission reductions at the company level (see Ekins 2010). This supports the Porter hypothesis and may be attributed mainly to improvements in operational processes that result from the adoption of eco-innovation activities at the company level. The paper’s findings reveal the significance of eco-innovation activities for simultaneously improving internal operating processes and CO2 emission reductions, thereby making ecoinnovation a central tenet for environment-friendly corporate strategies. Our study points out that integrating environmental considerations into corporate operations and processes does improve environmental performance (CO2 reduction) and corporate reputation as well as internal operations. Having estimated the ARDL output as can be illustrated in Table 4, It is noteworthy to mention that the results of the diagnostic test of confidence ellipse in Fig. 2 reveal the stability of the ARDL coefficients, which are captured within the center of the ellipse (see Alola and Alola, 2018). Hence, this implies that the three coefficients (LRD, LTR, and LINV) are suitable for explaining the future change of CO2 emissions. 5. Conclusion and policy implication Although there are some studies in the relevant literature that have focused on the impact of eco-innovation and CO2 emissions,
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there are none that analyze the impact of actual eco-innovation on CO2 emission reductions (Garica-Granero et al., 2018). This paper breaks new ground by investigating the impact of actual eco-innovation on CO2 emission reductions based on panel data for seventeen international petroleum companies over the period 2005–2016. The ARDL model was employed by using the quantity of CO2 emissions as a dependent variable, and three eco-innovation indicators (R&D, TR, and INV) as independent variables. The results obtained show that LINV has a positive and significant impact on LCO in the long-term, whereas LRD and LTR have a positive and significant impact on LCO in the short-term. However, our finding illustrates that spending on LRD, LTR, and LINV leads to significant reductions in CO2 emissions at the company level. These findings are consistent with the fundamental arguments of the hypothesis of Porter, and fundamental theoretical arguments of Sharma and Vredenburg (1998), Aragon– Correa and Sharma (2003), Bhupendra and Sangle (2016) and Nishitani et al. (2017) that pursuing eco-innovative at company level improves their environmental performance where empirical studies such as Lee and Min (2015), Zhang et al. (2017) and Fernandez et al. (2018) point out that eco-innovation activity at company level improves business operation and environmental performance (CO2 reduction). Furthermore, this paper reveals that investments in training, rather than research and development have particularly high rates of return on lowering CO2 emissions. Such a finding has implications for human resource departments planning their training budgets as well as for top management in prioritizing budgetary resources for eco-innovation initiatives. Last but not least, considerations should be made of the limitations of this study when applying its recommendations and when designing future studies on the subject. Initially, due to data limitations, our paper focused only on seventeen companies within the petroleum sector that aim to reduce CO2 emissions from the operation process. This may provide only limited insights into the effects of eco-innovation on CO2 emissions and our result cannot be easily extrapolated to other industries that aim to reduce pollution emissions. So, further studies are more likely to replicate this study when they employ different strategy’s key indicators in the different sectors or industries. This kind of differences in industries or sectors should be considered otherwise the policymakers may be misguided about the implications. Second, our study does not create a link between the reduction of CO2 emissions and the strategic target. Thus, it’s not clear whether the strategy is successful or not and future research may establish a link to target a successful strategy. However, it is interesting to note that our findings highlight the significance of implemented eco-innovation activities which have positive impacts on CO2 emission reductions. In an ultimate conclusion, these findings have significant policy implications for environmental strategy since it provides new empirical evidence for the importance of spending on eco-innovation at the company level to improve business operations and reduce CO2 emissions. More importantly, the implementation of a CO2 reduction strategy could be applied in the other polluted sectors that emit large amounts of CO2 .
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