Environmental Regulations and Pollution: Measuring Direct and Indirect Impact Using Innovation as Mediation Factor in G-7 Economies

doi:10.21203/rs.3.rs-1753412/v1

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Environmental Regulations and Pollution: Measuring Direct and Indirect Impact Using Innovation as Mediation Factor in G-7 Economies

https://doi.org/10.21203/rs.3.rs-1753412/v1

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published 26 Jan, 2023

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The alarming impact of climate change and environmental pollutants has increased the focus of policymakers and think tanks on the formulation of environmental regulations. The environmental regulations may reduce emissions directly and indirectly as postulated by the famous Porter Hypothesis. It shows that environmental regulation may enhance pollution-reducing innovation, by reducing agency costs, while at the same time increasing firms’ private benefit. The study is designed to investigate the direct and indirect impact of environmental regulations on CO2 and GHG emissions using innovations as mediation factors. The study employs a structural equation method using data on G-7 economies for the period 1990–2020 to test the relationship between regulations, innovations, and pollution. The study findings confirm that environmental regulations help reduce emissions directly. Our findings also confirm the Porter hypothesis whereby regulations encourage innovations and result in reduced emissions through this indirect channel. The study findings have significant implications for controlling pollution through placing environmental regulations and encouraging innovations.

JEL Codes: C33, H23, Q53, Q55

Pollution

CO2 emissions

Environmental regulations

Innovations

Structural Equation Model (SEM)

Global economic history has witnessed that environmental pollution in terms of global warming and carbon cycle is ultimate result of rapid economic growth (F. Wang, Wang, & He, 2021). For instance, countries have enjoyed phases of development at the cost of environment. Likewise, it may not possible to achieve a pollution reduction and sustainable management of resources simultaneously (S.-H. Wang & Song, 2014). Negative externalities of economic development (carbon, chlorofluorocarbons and other emissions) indicate the long-term nonexistence of environmental regulations. Environmental degradation, as notable public hazard has gained the attention not only at national level but has also alert the international bodies, comprising the Kyoto Protocol (KP) and Paris accord. These mentioned organizations are emphasized to embrace the policies that generate and boost economic significance (Porter & Kramer, 2019).

Environmental pollution is the well-known global issue. In order to attain sustainable environmental development, local administrators, governments embraced a set of ecological regulation, including pollution taxes (levies) and industrial-progress management leading to reduced emission within their authorities (Ouyang et al., 2019). Environmental regulations directly improve environmental quality through Pollution control equipment, taxes and subsidies imposed on firms and perform more frequent maintenance. In addition, local officials way to directly blackout or transfer the unclean industry by using their influence over local land use (Massari & Monzini, 2004).

With the passage of time, the importance of environmental regulation in solving the issues caused by economic activities has become a widely accepted notion (Blohmke, Kemp, & Türkeli, 2016; Yu, Ramanathan, & Nath, 2017). It is witnessed that environmental quality is deteriorating, and pollutants keep on increasing at global level. The only solution is government policies in terms of environmental regulations both for developed and developing countries to overcome the environmental pollutants (Chang & Wang, 2010; Korhonen, Patari, Toppinen, & Tuppura, 2015; Ouyang et al., 2019). Can developed countries’ environmental regulation enough strong to effectively solve environmental problems. Ecological regulations are categorized into three terms; administrative ecological regulation, market oriented ecological regulation, and public contribution ecological regulation (Ren, Li, Yuan, Li, & Chen, 2018). Administrative ecological regulation deals with precautions, managerial policies and orders to directly solve ecological problems. Market oriented regulation increase inherent power of enterprises designed by government units through market means (cost and price). Public participation ecological regulation refers to public interest through understanding, knowledge awareness in order to reduce environmental issues. Empirical findings are inconclusive in capturing the impact of environmental regulations on environment quality. Some studies showed that environmental regulations are unable to get expected effect of sustainable development by reducing pollution. For instance, Hao, Deng, Lu, and Chen (2018) did not find the expected impact of Environmental regulation in improving environmental quality (low air pollution) by using panel dataset of 283 cities of China from 2003–2021. Yuan, Ren, and Chen (2017) stated that current environmental regulation level is not enough to improve green efficiency. Moreover, firms’ decision to flow government regulation policy depends primarily on environmental cost (Jaffe & Palmer, 1997). Nevertheless, different regions has not experienced the same eco-friendly ecological impact through implementation of different types of environmental regulations (Coxhead, 2003; Solarin, Nathaniel, Bekun, Okunola, & Alhassan, 2021; Telle & Larsson, 2007; Yan Wang & Shen, 2016; Zhao, 2019).

A vast research has focused the direct impact of regulations on sustainable environment (Chang & Wang, 2010; Y. Lin, Huang, & Yao, 2021). However, environmental regulations may also effect environmental regulations may also effect environment indirectly through the promotion of environmental innovations. Higher production cost caused by environmental regulations encourage innovation as tight environmental regulation will force the companies to invest in environmental protection technologies in order to reduce additional cost (B. Lin & Chen, 2018). These processes will cause the improvement of low pollution technologies to diminish pollution emissions (F. Wang et al., 2021). Innovation caused by environmental regulation has a significant impact on environmental pollution. It is also matter of discussion either green innovation lead to tight environmental standards. Also, need to examine the role of green innovation in environmental issues.

Green innovation in reducing environmental pollution and attaining sustainable environment has gained significant research consideration (Biondi, Iraldo, & Meredith, 2002; Nyiwul, 2021; Pan, Ai, Li, Pan, & Yan, 2019; Su & Moaniba, 2017). Green innovation is comprehensive strategy to attain reasonable development, for example it performs ecological protecting, energy saving, pollution averting and waste recycling procedures (Albort-Morant, Leal-Millán, Cepeda-Carrion, & Henseler, 2018). Green innovation can be classified into green marketing, green management and green products. Innovation is termed as green processes, management and competitiveness of firm and providing pollution free environment (Seman et al., 2019) whereas pollution prevention explain sustainable development of the firms (Y. Lin et al., 2021). Results obtained from these studies highlight the prominence of innovation to attain sustainable environmental (W. Li et al., 2019; Managi, Lindner, & Stevens, 2021; Nill & Kemp, 2009). Furthermore, transformation of economic development through green innovation reduces environmental pollution. Many firms try to achieve better environmental and economic growth instantaneously (Dangelico & Pujari, 2010). From 4th Industrial Revolution, environment and innovative technologies are interconnected megatrends (Gheraia, Abid, Sekrafi, & Abdelli, 2021).

The current study intends the direct and indirect impact of environmental regulations on sustainable environment through innovation channel in G7 countries. Main objective of the current study is to analyze the direct impact of environmental regulations on environment, further we have tested the indirect impact of regulations on environmental quality through innovation channel. G7 countries are used for the analysis by covering the dataset 1990 to 2019.

Co2 emissions are major concern in the G-7 countries. Figure 1 below presents the environmental pollution in terms of CO₂ emissions of G-7 countries from 1990–2020. Figure clearly shows that USA has the highest level of CO₂ emissions throughout the selected years. While, Canada has lowest CO₂ emissions as compared to other countries. It is important to control further deterioration of environment in these countries for environmental sustainability and sustainable economic growth.

Structural Equation modeling is used to explore direct and indirect impact of ecological regulations on environmental issues and takes green innovations as a mediations variable.

The further sections of paper are designed as: Section 2 presents the literature review and the theoretical relationship among environment regulations, technological innovations and sustainable environment. Section 3 consists of methodology and gives the detailed structure of dataset used for the analysis. Section 4 explains the estimated results and Section 5 provides the concluding remarks with limitations and suggestions.

Economies are facing severe environmental issues around the globe instead of implementation of a series of regulations. However, environmental issues are becoming worse (Blohmke et al., 2016; Y. Lin et al., 2021). Extreme environmental issues stimulated the responsiveness of society and put burden on government to implement environmental regulation (Lu et al., 2018) in order to reduce serious pollution behavior (Z. Chen, Kahn, Liu, & Wang, 2018).

In case of higher clean production cost than environmental regulation cost, firms will not change the production techniques in order to get profits. Study found the positive relationship between environmental regulation and environmental pollution. Nevertheless, some studies conclude the desired impact of environmental regulation as He (2006) revealed that environmental regulations has potential to reduce air pollution. Yutao Wang, Liu, Hansson, Zhang, and Wang (2011) concluded the Porter hypothesis that in case of higher environmental cost that clean production cost, firms will search and familiarize new production techniques in Shandong, China and ultimately sustainable environment can be achieved through environmental regulations. Further, some economist concluded the inverted U- shaped relationship between regulation and ecological pollution (R. Li & Ramanathan, 2018; Ouyang et al., 2019). With rise in strict environmental policies, firstly pollution emission increases then decreases as production capital needs time to adjust. Firms’ reaction may be slow with low environmental regulations (Krysiak, 2011) indicating low cost of environmental regulation. On the other hand, with higher environmental regulation cost than clean production cost, firms’ response is quicker.

Literature has witnessed that environmental regulation has both ecological and economic effects, particularly in labor market. Environmental regulations promote biased technological progress which lead to different employment demand (Curtis, 2015). Heterogeneous nature of environmental regulation on employment has been observed, new desires of 1990 Air Act Amendments damaged the employment of thermal power plans (Sheriff, Ferris, & Shadbegian, 2019). Whereas Altman and Hunter (2015) found the positive impact of carbon capturing project on employment.

Another stream of literature postulates that imposition of environmental regulation may promote innovation in cleaning sector (Acemoglu, Aghion, & Hémous, 2014). However, prerequisites are vital for innovation, to be exact, need to define certain environmental regulation level. Environmental regulation is vital cause of development and creation of green technologies as, with tight environmental regulation lead to innovative research to adopt environment friendly policies. Furthermore, under recognized settings of environmental regulations, innovations play positive role in reducing pollution (Acemoglu et al., 2014). As, environmental regulations may enforce high polluting industries to adopt pollution control technologies which leads to investment in research and development (J. Zhu & Ruth, 2015). Strong countries like Japan, Germany and United States devote more consideration to environmental innovation- their innovation expenditures is more than expenditures on pollution treatment (Lanjouw & Mody, 1996).

Different kinds of environmental regulations have different impact on green innovations performance, green innovation behavior and intensions promote with market- incentive ecological regulations (Zhang, Wang, Xue, & Yang, 2018). However, strong authorities can implement strict regulations to avoid “polluting the paradise” (D.-Z. Zeng & Zhao, 2009). Literature has divided the green innovation into two categories. First explains the green innovation as firms' ability (Gluch, Gustafsson, & Thuvander, 2009) and second explains green innovation as an administrative ecological practices (C.-Y. Lin, Ho, & Chiang, 2009). Organization practices are management practices that promotes ecological and organizational performance and provide competitive verge to firms (Rennings, 2000). Other stand of literature take green innovation as unique practices and processes including technology and innovations and that ultimately provide benefit to environment and firms’ sustainability (Ilvitskaya & Prihodko, 2018; Xie, Huo, & Zou, 2019).

Win-win situation can be achieved through environmental regulations (Chan, Shen, & Cai, 2018) with achieving dual tasks, high profit and lessen pollution. Application of green innovation are important for economic and environmental goals through inside and outside implication of firms’ restrictions (Saeed, Jun, Nubuor, Priyankara, & Jayasuriya, 2018). For motivation and practicing green innovation, role of societal expectations, organizational support play vital role (Shahzad et al., 2020). Moreover, Fernando, Jabbour, and Wah (2019) concluded that regulations, technology and green innovations have strong impact on sustainable performance by innovation capabilities. Ecological friendly policies and practices have direct and indirect strong positive impact on environment (Famiyeh, Adaku, Amoako-Gyampah, Asante-Darko, & Amoatey, 2018). (Xie et al., 2019) didn’t find supportive results for green process innovation and organizational performance by using green innovation as mediator.

Green innovation lies in the category of technological efficiency. Hence, in order to explore the relationship between environmental regulation and green innovation, relevant literature has linked environmental regulation with technological efficiency. Neo classical theories suggested that environmental regulation would cause economic burden to firms, which ultimately rise cost of production, unfavorable for firms to improve technological efficiency. Whereas, porter hypothesis suggests that environmental regulations enhance efficiency in technological innovations. Three aspects of literature has been formed; environmental regulation is helpful to promote technological efficiency, environmental regulation is not helpful to promote technological efficiency and uncertainty prevails in attaining the ecological regulation and technological innovation. First aspect follows porter hypothesis as Lanjouw and Mody (1996) found positive impact of ecological regulation on environmental innovation in Japan, Germany and United States. Similar line of work done by Jaffe and Palmer (1997) and Brunnermeier and Cohen (2003) in American manufacturing industry. Second aspect follow neoclassical approach with believe that high regulation put economic pressure on firms in terms of rising cost of production, ultimately firms feel reluctant to spend on innovations (Gollop & Roberts, 1983). Third aspect provided inconsistent relationship between ecological regulations and innovations (Alpay, Kerkvliet, & Buccola, 2002; R. Li & Ramanathan, 2018), as Lanoie, Patry, and Lajeunesse (2008) found inconsistent short run and long run impact of environmental regulation on manufacturing productivity in Canada.

Literature has analyzed the direct impact of environmental regulation towards green innovation through efficiency channel (W. Li et al., 2019). As, neoclassical economists consider that environmental regulations increase cost of production and put burden to initiators, which discourage enterprises to improve efficiency in terms of innovations. Whereas, (Porter, 1991) believes that environmental regulation encourage enterprises to focus on innovations in order to reduce cost of production. Similarly, the importance of technological and green innovations in achieving environmental protection has been widely recognized (Biondi et al., 2002; F. Chen, Wang, & Pu, 2022; Y. Lin et al., 2021; Managi et al., 2021; Su & Moaniba, 2017). In short, a huge literature has found the direct impact of regulations on environment (Chan et al., 2018; Saeed et al., 2018; Shahzad et al., 2020), regulations on green innovations (Alpay et al., 2002; Brunnermeier & Cohen, 2003; Jaffe & Palmer, 1997; Lanjouw & Mody, 1996; Lanoie et al., 2008; R. Li & Ramanathan, 2018) and innovation on environment (Albort-Morant et al., 2018; Biondi et al., 2002; W. Li et al., 2019; Managi et al., 2021; Nyiwul, 2021; Pan et al., 2019; Seman et al., 2019; Su & Moaniba, 2017). Yet the impact of environmental regulations on sustainable development through green innovation channel is still dearth and matter to study. In addition, current study contribute to existing literature my introducing Structural equation modeling. Current study examines the stimulus mechanism of environmental regulation, green innovation and sustainable environmental progress.

2.1 Environmental regulations and Pollution: Firm response to reduce environmental pollution with increment in cost of ecological regulations which is known as Porter hypothesis. Direct impact of environmental regulations on ecological pollution can be capture in three ways. First, ecological regulations disrupts the constraints of reducing the cost of enterprises. Firms with greater cost will have more burden and ultimately go bankrupt. This economic failure of pollution intensive firms will lead to lessening in generally pollution emissions (Dey et al., 2018). Second, environmental regulation will pressurize firms to familiarize and use safe production techniques which lead reduction in pollution and toxic emissions (Arouri, Caporale, Rault, Sova, & Sova, 2012). Third, environmental regulations will also encourage the technological based firm structure (S. Zeng, Jiang, Ma, & Su, 2018) which ultimately improve ecological progress in terms of low emissions. Porter hypothesis suggests the suitable ecological regulations (Krysiak, 2011). However, different countries adopt different policies and environmental regulations (Lo, Fryxell, & Van Rooij, 2009), developing areas and countries may prefer economic growth at the cost of environment which will delay the regulation implementation process (Francesch-Huidobro, Lo, & Tang, 2012). Nevertheless, it is wise to accept that with lower ecological regulations, sustainable ecological development is impossible. With certain level of environmental regulations, desired results will appear.

Hypothesis 1: In presence of other things unchanged, environmental regulations reduce pollution level.

2.2 Environmental regulations to innovations: Environmental regulation is a key factor to implement green innovations in the following facets, Porter hypothesis states the importance of environmental regulations in carrying out of R & D, technical skills, competitions and innovative skills (Porter & Linde, 1995). As, Yang, Tseng, and Chen (2012) consider that ecological regulations may boost innovation by enterprises through reimbursement outcome of innovation. Further, ecological regulations may offer guidance to formulate innovation tactics (Yu et al., 2017). In short term, ecological regulation will have adverse influence on enterprises whereas increase ecological protection capability of firms and encourage enterprises to promote innovations in long run (Dechezleprêtre & Sato, 2020). In short, ecological regulations promotes R & D and promote green innovations.

Hypothesis 2: In presence of other things unchanged, environmental regulations promote green innovation.

2.3 Green innovation to pollution: Environmental performance is assessed as firm’s performance into two types: ecological and organizational betterment. Ecological performance, encompasses less pollution, low carbon emissions, resource utilization and improvement in energy savings (Q. Zhu, Geng, Fujita, & Hashimoto, 2010), indicates the effect of firm’s actions on natural environs (Klassen & Whybark, 1999). Whereas, organizational performance includes reputation, sale capacity and investors satisfaction etc. (Venkatraman & Ramanujam, 1986). Firms supervisory processes and practices with effective resource utilization more powerful impact on long term ecological impacts (Khan, Jianguo, Ali, Saleem, & Usman, 2019; Sarkis & Cordeiro, 2001). Literature is witnessed that efficiency and betterment in production process through research and development innovations may boost environmental performance (Montabon, Sroufe, & Narasimhan, 2007).

Green innovation includes innovation related to green products with energy saving, waste application and toxic waste prevention (Y.-S. Chen, Lai, & Wen, 2006). Stakeholder theory considers that contractors, consumers and participants are main powerful force for enterprise to perform green innovation (Fernando & Wah, 2017). Institutional theory believes that green innovation struggle is result of the environmental regulations pressure of authorities on enterprises (H. Lin, Zeng, Ma, Qi, & Tam, 2014). Moreover, natural resource theory considers the administrative resources and significance of managers are vital to motivate enterprises to accomplish green innovations (Y.-H. Lin & Chen, 2017). In short, green innovation boost R&D investment, green technology which reduce pollution.

Hypothesis 3: In presence of other things unchanged, green innovation reduce pollution level. In the light of mentioned links, current study explores the direct impact of ecological regulation on environmental quality and indirect impact through green innovations as mediator.

Description and Source of Data

The objective of the current study is to find the direct and indirect impact of environmental regulation on environmental pollution through innovation channel in G-7 countries. The group of G-7 countries include Canada, France, Germany, Italy, Japan, United Kingdom and United States. Panel data is taken from World Development Indicators (WDI) and OECD for the time period 1990–2020. Table 1 presents the summary of variables used in the analysis.

Environmental pollution, an exogenous variable which is measured through CO₂ emissions. Whereas, environmental regulation and innovation, as endogenous variables, are measured as environmental tax and patents respectively. Control variables used in the analysis are as: Economic growth: when a country experiencing the developing stage, government ignores pollution in order to gain economic growth. After reaching particular desired level of growth, people quest of a healthier life and improvement in the environment (Strange, 2006). Urban population (UP): high urban population indicates higher economic life in terms of industrial growth, which is associated with environmental pollution (J. Liu & Diamond, 2005). School enrollment (SE) indicates the responsive and positive attitude of individuals towards environmental issues (Erhabor & Don, 2016). Consumption of renewable energy (RE) is helpful in improving environmental quality through reduction in CO₂ emissions (Boudri et al., 2002; Dincer, 2000). Great share of Industrial value addition (IV) causes higher electricity generation, mining of coal, burning fossil fuels that leads to increase CO₂ emissions (A. Wang, Hu, & Lin, 2021). Same mentioned variable influence the level of innovations that’s why same control variables are used for innovations and pollution.

Table 1

Description of variables


Variables	Units of Variables	Source of Data
Exogenous Variables
CO₂ emissions	metric tons per capita	WDI (1990–2020)
Total greenhouse gas emissions	Kilo tons of CO₂ equivalent	WDI (1990–2020)
Endogenous Variables
Environmentally related taxes	% total tax revenue	OECD (1990–2020)
Environmental Tax	Environmental related total tax as percent of GDP	OECD (1990–2020)
PATENT_ENV	Patent data present percentage of attractive properties related to environmental technologies compared to other alternative metrics of innovation.	OECD (1990–2020)
Control Variables
Urban Population	% of total Population	WDI (1990–2020)
Renewable energy consumption	(% of total final energy consumption	WDI (1990–2020)
Industrial value added	Industry (including construction), value added (% of GDP)	WDI (1990–2020)
GDP per Capita	constant 2015 US$	WDI (1990–2020)
School enrollment, tertiary	(% gross)	WDI (1990–2020)

Table 2 indicates the summary statistics of variables with 217 observations are used in the analysis. According to the observations, Table 2 highlights the countries and years of maximum and minimum values of mentioned variables. Table 3 in appendix shows the correlation analysis.

Table 2

Summary Statistics of variables


Variables	Observations	Mean	Std. Dev.	Min	Max
CO₂ Emission	217	10.52629	4.478345	4.592377 France (2014)	20.47193 USA (2000)
Greenhouse gas emission	217	1542591	1983220	399600 Italy (2018)	6861150 USA (2000)
Environmental related tax	217	5.751659	1.725348	2.78 USA (2015)	9.3 Italy (1995)
Environmental Tax	217	1.50549	.6665918	.4480648 USA (2016)	3.006881 Italy (1995)
Patent	217	9.362366	2.765699	4.8 Italy (1992)	15.47 Germany (2011)
Urban Population	217	77.94805	5.5714	66.706 Italy (1990)	91.782 Japan (2020)
Renewable Energy Consumption	217	9.390194	6.559564	.6082641 UK (1991)	22.7699 Canada (2017)
Industrial Value	217	24.4354	4.447301	16.44503 France (2020)	34.55387 Japan (1994)
GDP per capita	217	37475.94	7378.243	27479.58 Italy (1990)	60836.77 USA (2019)
School enrollment	217	60.1008	14.56219	26.48239 UK (1990)	97.61021 Canada (1992)

Table 3

Correlation Analysis of Variables

	1	2	3	4	5	6	7	8	9	10
1	1
2	0.7161	1
3	-0.6972	-0.5713	1
4	-0.7055	-0.6212	0.8400	1
5	-0.1247	-0.1243	-0.3043	-0.2052	1
6	0.2259	0.1848	-0.4478	-0.7056	0.4078	1
7	0.1989	-0.2238	-0.4765	-0.1520	0.4187	0.0165	1
8	0.1463	-0.2327	0.0972	-0.0986	-0.1663	0.0163	-0.0549	1
9	0.5249	0.6846	-0.5859	-0.5797	0.3067	0.4014	0.0730	-0.5140	1
10	0.5588	0.5569	-0.6705	-0.5083	0.3232	0.2545	0.4269	-0.4288	0.6871	1

1 = CO2 Emission, 2 = Greenhouse gas emission, 3 = Environmental related tax, 4 = Environmental Tax, 5 = Patent, 6 = Urban Population, 7 = Renewable Energy Consumption, 8 = Industrial Value, 9 = GDP per capita and 10 = School enrollment

Source: Author calculations

Graphical Analysis:

Figure 2 presents the graph of environmental patent among G-7 countries. Figure indicates the mixed, overlapping and upward trend with passage of time. Highest patent trend is captured in Germany and lowest in USA.

Figure 3 indicated the environmental tax among G-7 economies. Italy has practiced the highest environmental tax. At the same time, lowest environmental tax is observed in UK throughout the mentioned years.

In Fig. 4, Mix trend is observed in case of environment related tax. Comparatively, Italy has experienced highest environment related tax, then UK has higher trend. Whereas UK has relatively lowest level of environmental related tax.

According to Fig. 5, the highest greenhouse gas emission is observed in USA (6000000kilo tonnes) from 1990–2020. While, all other G-7 countries have comparatively lower level of GHG emissions. Moreover, Italy has lowest level of GHG emissions.

Overview of Structural Equation Model

SEM is used to indicate the casual association between latent and explicit variables through generalized linear equation. Explicit variables may be termed as measurable variables which signify the objective and main factor of the analysis. Latent variables are known as unmeasured variables as many variables are unable to quantify in real life. The overview of latent variables is essential to specify the unquantifiable variables and to signify the latent aspects of the analysis object. In this way, precision and comprehensiveness of the concrete analysis improve which is the main objective of economic and social research. The Structural equation model is capable of presenting the concept of latent and explicit variables which may be used to examine the unquantifiable variable model and investigate the endogeneity and probable path dependence.

Following Structural Equation Modelling, environmental regulation (ENRG), innovation (INNOV), and environmental pollution (CO₂) are considered manifest variables. Among them, environmental regulation is an exogenous variable that affects both pollution and innovations. Innovations is a mediation variable that affects pollution directly. However, environmental regulations affects both innovations and pollution. Henceforth, environmental regulations affects pollution directly and indirectly through its impact on innovations.

Environmental Regulation, an exogenous variable which is measured through environmental taxes. Whereas, environmental pollution and innovation, as endogenous variables, are measured as CO₂ emissions and patent respectively. In first part impact of environmental regulation is checked on pollution (CO₂ emissions) and innovation (INNOV). Keeping the View of the literature, environmental regulation has impact on innovation and pollution in mentioned ways. First, regulations encourage companies to invest in innovations in order to reduce cost. Further, Porter analysis suggests that company will respond to environmental regulations to reduce pollution. Second, environmental cost burden through strict environmental regulations discourage companies to improve environmental efficiency through innovations, presented by neoclassical economists. Additional, impact of innovations is checked on pollution level of G7 countries. Green innovation in terms of R& D investment to promote green products with less toxic wastes and energy saving all have beneficial impact on sustainable environmental growth with less CO₂ emissions. Then direct and indirect impact is observed on pollution level of G7 countries.

$${CO}_{2it}={\alpha }_{0}+{\alpha }_{1}{ENRG}_{it}+{\alpha }_{2}{INNOV}_{it}+{\alpha }_{3}{UPOP}_{it}+{\alpha }_{4}{RE}_{it}+{\alpha }_{5}{GDP}_{it}+{\alpha }_{6}{INDS}_{it}+{\alpha }_{7}{ENRL}_{it}+{\epsilon }_{it}$$

$${INNOV}_{it}={\beta }_{0}+{\beta }_{1}{ENRG}_{it}+{\beta }_{2}{ETPT}_{it}+{\beta }_{3}{UPOP}_{it}+{\beta }_{4}{RE}_{it}+{\beta }_{5}{GDP}_{it}+{\beta }_{6}{INDS}_{it}+{\beta }_{7}{IR}_{it}+{\epsilon }_{it}$$

Control variables used in the analysis are as: Economic growth (GDP): when a country experiencing the developing stage, government ignores pollution in order to gain economic growth. After reaching particular desired level of growth, people quest of a healthier life improve the environment (Strange, 2006). Urban population (UPOP): high urban population indicates higher economic life in terms of industrial growth, which is associated with environmental pollution (J. Liu & Diamond, 2005). School enrollment (ENRL) indicates the responsive and positive attitude of individuals towards environmental issues (Erhabor & Don, 2016). Consumption of renewable energy (RE) is helpful in improving environmental quality through reduction in CO2 emissions (Boudri et al., 2002; Dincer, 2000). Great share of Industrial value addition (INDS) causes higher electricity generation, mining of coal, burning fossil fuels that leads to increase CO2 emissions (A. Wang et al., 2021). Same mentioned variable influence the level of innovations that’s why same control variables are used for innovations and pollution. Panel data is used for G7 countries namely Canada, France, Germany, Italy, Japan, United Kingdom and United States are used for the current analysis. Data is taken from World Development Indicators (WDI) and OECD for the time period 1990–2020.

This section presents the results from the structural equation model to test the direct and indirect impact of the environmental regulations on the CO₂ emissions using innovations as mediation variable. Figure 6 shows the standardized path coefficient of innovations affecting CO₂ emissions is -0.042. The impact is significant, and the direction is negative. Higher the innovations, lower is the level of CO₂ emissions. This result confirms our hypothesis that higher innovations lead to lower emissions. The path coefficient of environmental taxes to CO₂ emissions is -0.093. The effect is significantly negative confirming our hypothesis that higher regulations lead to lower emissions. The path coefficient of ET to innovations is 0.75 that is positive and significant. It confirms our hypothesis that more environmental related regulations lead to higher level of innovations. Table 4 presents the parameter estimation of the structural equation of the impact of ET on CO₂ emissions.

Table 4 shows the model fits well and the coefficients confirm our tested hypothesis. Patents and environmental taxes significantly reduce CO₂ emissions. Also, environmental taxes have a positive impact on patents. The coefficient is 0.75 and highly significant.

Table 4

Measuring Impact of Environmental Regulations on CO ₂ Emissions (Environmental Tax per cent of total tax)

Note: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses. Arrow sign represents the path direction. Renewable energy, urban population, GDP per capita, industry value added, and enrollment are used as control variables in both CO₂ and patent equations as control variables. Environmental regulations are proxied by environmental related tax as percent of total tax and innovations are proxied by number of environmental related patents. mc = correlation between dependent variable and its prediction. mc2 = mc^2 is the Bentler-Raykov squared multiple correlation coefficient.

Table 5 presents the direct and indirect effect of the structural equation measured in table 4. The direct and total effect of patents (innovations) on CO₂ emissions is -0.042. The direct and total effect of environmental regulations on CO₂ emissions in 0.75. In both of the cases our hypothesis are confirmed that regulations and innovations have a significant impact on the CO₂ emissions.

Table 5

Direct and Indirect Effect of Environmental Regulation on CO₂

Note: The effects are measured after the estimation of structural equation of the impact of environmental regulations (measured as a ratio of environmental taxes to total taxes) on CO₂ emissions using innovations (measured ad patents) as a mediation factor.

The important coefficient in this table is the indirect effect of taxes on CO₂ emission through the channel of innovations. The indirect impact of environmental taxes on CO₂ emissions is -0.03 leading to a total impact of -0.124. Henceforth, environmental taxes not only have a direct significant impact on CO₂ emissions but also it may influence emissions by encouraging innovations.

Next we estimate the same structural equation by using the greenhouse gas emissions to test the robustness of our estimates. Figure 7 presents the impact of direct and indirect impact of environmental regulations on GHG emissions using patents as mediation variable. The path coefficient of taxes to GHG emissions is -0.31 that confirms our hypothesis that taxes reduce emissions. The path coefficient of innovations to CO₂ is -0.023 implying that innovations lead to lower GHG emissions. The path coefficient of taxes to innovations is again 0.75 that explains a highly significant and positive impact of taxes on innovations.

Table 6 presents the structural equation estimates to test the impact of environmental regulations on GHG emissions using patents as mediation variable. Again, in this specification regulations have a positive impact on patents and patents in turn are reducing GHG emissions in this specification as well.

Table 6

Measuring Impact of Environmental regulations on GHG Emissions (Environmental tax to total tax)

Table 7 presents direct and indirect effects measured after estimation of structural equation model in table 6. The direct impact of environmental taxes on emissions is -0.313 that is highly significant. The indirect impact is -0.017 and the total impact is -0.33. Henceforth, environmental regulations not only have the potential to reduce emission directly but also through its positive impact on innovations.

Table 7

Direct and Indirect Effect of Environmental Regulation on GHGE (Environmental tax to total tax)

Note: The effects are measured after the estimation of structural equation of the impact of environmental regulations (measured as a ratio of environmental taxes to total taxes) on GHG emissions using innovations (measured ad patents) as a mediation factor.

Furthermore, as a sensitivity analysis both of the equations presented in table 4 and 6 are re-estimated using overall environmental taxes as a proxy for environmental regulations.

Table 8

Measuring Impact of Environmental regulations on CO ₂ Emissions (Environmental Tax)

Table 9

Direct and Indirect Effect of Environmental Regulation on CO ₂

Note: The effects are measured after the estimation of structural equation of the impact of environmental regulations (measured as environmental related taxes) on CO₂ emissions using innovations (measured as patents) as a mediation factor.

Table 10

Measuring Impact of Environmental regulations on GHG Emissions (Environmental tax)

Table 11

Direct and Indirect Effect of Environmental Regulation on GHGE

Note: The effects are measured after the estimation of structural equation of the impact of environmental regulations (measured as environmental related taxes) on GHG emissions using innovations (measured as patents) as a mediation factor.

The hypothesis are conformed and remained robust to this measurement change. Figures 8 and 9 in present the path coefficients of these estimations. Structural equation estimates and direct and indirect effects are reported in tables 8 to 11.

The study findings align with three important streams of literature in environmental and energy fields. Firstly, in line with the findings of the current study, it has been widely found in the literature that technological innovation is an essential and useful way to reduce environmental pollution and improve air quality (Grossman & Krueger, 1995; B. Lin & Zhu, 2019; Mol & Sonnenfeld, 2000; Nyiwul, 2021; Pan et al., 2019; Su & Moaniba, 2017; K. Zhu et al., 2020), and these results highlight the importance of innovation for sustainable and green development (Kuhl, Da Cunha, Maçaneiro, & Cunha, 2016; Kuzma, Padilha, Sehnem, Julkovski, & Roman, 2020; W. Li et al., 2019; Managi et al., 2021; Nill & Kemp, 2009; Schiederig, Tietze, & Herstatt, 2012). Technological innovation and managerial innovation are viewed as the two most significant factors in achieving environmental sustainability (Biondi et al., 2002). Extant research has indicated that most pollution reductions in the United States have come from technological changes, rather than any changes in imports or goods produced domestically (Levinson, 2009).

Secondly, the study has found a significant impact of environmental regulations on environmental performance. Environmental regulation is believed to be an effective tool to solve the environmental problems caused by economic activities and energy consumptions (Blohmke et al., 2016; Yu et al., 2017). Environmental regulation can not only affect environmental problems but also affect economic activities, which will eventually change the labor market (X. Liu, Zhang, & Bae, 2017). Stick environmental regulation will increase the cost of polluting enterprises (Dey et al., 2018; Ramos, Chatzopoulou, Freeman, & Markides, 2018). Therefore, these companies will invest in environmental protection technologies to reduce extra costs (B. Lin & Chen, 2018).

Thirdly, we have found a positive impact of regulations on innovations thereby confirming the Porter Hypothesis. Neoclassical economic theorists believed that environmental regulation would bring an economic burden to enterprises, resulting in increased production costs of enterprises, which was not conducive to improving technical efficiency. However, Porter (1991) held the opinion that environmental regulation could promote the technology innovation of enterprises and then improve technical efficiency, which was called the “Porter Hypothesis.” Hence, the study has established a direct as well as indirect link of environmental regulations with environmental performance using innovations as a mediation channel.

This study has examined the direct and indirect impact of environmental regulations on environmental performance using innovations as a mediatory factor. The study employed structural equation method using data on G-7 economies for the period 1990–2020 to test the relationship between regulations, innovations and pollution. Our findings have considerable policy implications for the governments and policy makers. Environmental regulations are found to have a direct and indirect impact on environmental performance through innovation. Therefore, the governments must devise and implement environmental regulations wisely. Regulation has a significant role in promoting technological innovations and renewable energy development, therefore, the governments must consider the important role of environmental regulations. The pollution taxes that push enterprises towards the adoption of cleaner production must be imposed. A set of incentives and punishments be devised whereby financial subsidies and incentives be provided to those with cleaner production and penalties be implement on enterprises that are making sever pollution emissions.

Next, we suggest combining a variety of environmental regulation measures to achieve maximum benefit from them. The regulation that are energy saving and reduce emissions significantly such as carbon taxes must be implemented. Finally, awareness and information must be provided by the governments to residents and enterprises to improve the use of clean energy and development of green economy.

Ethics approval and consent to participate: Not applicable
Consent for publication: Not applicable
Availability of data and materials: All data generated or analyzed during this study will be available at appropriate demand.
Competing interests: The authors declare that they have no competing interests.
Funding: This work did not receive any funding.
Authors' contributions: Rabia Nazir: Conceptualization, writing-original draft, formal analysis, data handling, and methodology. Seemab Gailani: Supervision, write-up . M. Nouman Shafique: Writing-review and editing, variable construction.

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Environmental Regulations and Pollution: Measuring Direct and Indirect Impact Using Innovation as Mediation Factor in G-7 Economies

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