Supply chain performance,information and communication technology capital formation,and green growth in top carbon emitter economies

Abstract

The emergence of green growth has proved vital for achieving sustainable development goals, and a supply chain can expedite the nation's journey toward green growth. This analysis aims to investigate the impact of supply chain performance and information and communication technology on green growth in top polluted economies by controlling the impact of education, financial development, renewable energy consumption, and foreign direct investment. For empirical analysis, we have employed the ordinary least squares, two-stage least squares, and generalized methods of moments. The main findings of the study confirm that logistics performance, infrastructure, and services are vital in promoting green growth. The diffusion of information and communication technology, education, the use of renewable energy, and financial development all help to achieve green growth. We suggest that firms should invest in green manufacturing practices, intelligent distribution networks, and better and energy-efficient logistics infrastructure, which can help the economies keep the environmental impact of economic growth at bay. Thus, our results emphasize the priorities of supply chain performance and information and communication technology diffusion in top carbon emitter economies.

Keywords

Supply chain performance information and communication technology capital formation green growth

Introduction

Green growth, often known as environmentally friendly economic expansion, is an essential tactic for ensuring long-term sustainability. Encouraging economic development while safeguarding the environment is a fundamental strategic goal for governments.¹ Reducing carbon emissions is essential to the green economy, and it may be achieved via supply chain optimization, improved manufacturing processes, and innovative clean technology.² There is a chance that advancements in environmentally friendly energy-producing technology may also contribute to green development.³ This might reduce carbon dioxide emissions, which would be good for the environment and for making power plants run more smoothly. Many individuals are now embracing “green growth,” an economic strategy that reduces energy consumption and carbon emissions in an effort to halt or reverse the planet's ecological decline.⁴ The development of carbon-cutting and environmentally friendly technology is mutually supportive.⁵

Over the past few years, we have seen business organizations confronted by challenges to resource consumption and rising ecological threats. Both advanced and emerging economies are dealing with a variety of environmental concerns as a result of resource utilization due to rising economic activities.⁶ In light of the reality that the manufacturing sectors in emerging and advanced economies have long been a key engine of growth, it's indeed crucial to observe the implications of manufacturing sectors in general, and in specific their environmental implications. This is because these commercial activities constitute a threat to the ecosystem owing to factors including transportation bottlenecks, carbon monoxide emissions, single-use wrappings, the recycling of hazardous waste, and pollution from factories.^7,8 As a result, these corporations may benefit from adopting environmental strategies like green supply chain management (GSCM) and green growth in order to accomplish greener activities. It is hypothesized that incorporating GSCM and green growth approaches into daily operations would boost productivity and profitability. Although it may seem counterintuitive, the GSCM, an initiative aimed at green growth, may lead to an expanded customer base and higher profits. As per Rao,⁹ “greening the suppliers” strategies within the framework of GSCM would result in a greener supply and more green products from the manufacturers. The level of green innovation actions taken to create green goods is generally improved by the use of GSCM.^10,11 Adopting these procedures may reduce environmental constraints brought on by regulatory requirements and laws and the general public (consumers, producers, purchasers, and community).^12,13

Another idea that falls under the domain of environmental management is green growth, which has been widely advocated lately to reduce or even eradicate the effect on the ecosystem.¹⁴ Businesses want to expand into new areas, and green technology is seen as a key component of doing so because of the tremendous growth it is expected to have over the next decade.¹⁵ When manufacturers are introduced to the green innovation concept, they are more likely to adopt GSCM and its innovative methods and ideas. In a similar vein, Chen et al.¹⁶ suggested that environmental management, and more especially GSCM, may benefit from green innovation to better meet the needs of modern businesses. Additionally, green innovation provides a unified ground for manufacturers and their suppliers to work together, which may lead to more green innovativeness and better green goods overall.¹⁷ However, to execute GSCM with regard to current environmental objectives, continuous green innovation is required.

A well-coordinated distribution network is growing more important in today's business world as an essential component of any network committed to environmental preservation.¹⁸ Gupta and Vegelin¹⁹ argued that a market-based economic system is an effective tool for allocating limited financial capital and drawing attention to environmental challenges that must be taken into account by businesses seeking to practice sustainable development. Utilization of local materials is only one indicator that a company is succeeding with its GSCM implementation strategy; increased profits and a leg up on the competition are two more. According to Yu et al.,²⁰ adopting GSCM is crucial for businesses to maintain a competitive advantage in the marketplace. Its effective use requires the concerted effort of every member of the organization. In general, GSCM methods focus on creating win-win scenarios from the firm's performance viewpoint.²¹ Firms may begin to improve their environmental performance, operational structure, and cost efficiency by adopting environmentally friendly management of supply chain procedures.²¹ Better performance and lower carbon emissions may be understood via the continuous implementation of green initiatives, which is important for both academics and professionals. In order to be eco-friendly, businesses must adopt green strategies that include aspects of IT, procurement, and supply chain.²²

Incorporating “green” notion into the industrial supply chain includes embracing a distinct viewpoint where both ecological and supply chain operations are taken into account concurrently to create a greener world.²³ Chin et al.⁷ found that a firm with a sustainability point of view creates a better tomorrow by following sustainability objectives as it enhances the connection with stakeholders leading to fewer costs of disputes and far more work performance. Consequently, management should regard green initiatives as the company's business strength for future gains since long-term sustainability profits rely on well-administered social, ecological, and economic structures.²⁴

Information and communication technologies (ICTs) help make the supply chain and distribution networks more environmentally friendly and less resource intensive; thereby, paving the path for green economic growth.²⁵ There is a broad range of consequences that ICTs have on important global processes.²⁶ Fast adoption and proliferation of these technologies have been linked to improved output, faster economic progress, and reduced energy consumption.²⁷ Therefore, since the early 1990s, researchers have paid particular focus to the issue of ICT's ecological effects. Since then, there has been much macro-level study on the energy effects of ICTs. There is currently no agreement on the environmental impact of the rapidly growing use of ICTs, despite the belief that it would increase output and energy efficiency. While some research suggests that ICT may help reduce the carbon footprint, others have shown the opposite to be true²⁶: that increased power usage is a major contributor to CO₂ emissions throughout the world.²⁸ The theoretical framework underpinning the nexus between ICT and green growth presents divergent viewpoints. Within the literature, there is a prevailing argument suggesting an adverse role of ICT in stimulating green growth.²⁹ This contention stems from the idea that ICT proliferation contributes to the increased manufacturing of ICT devices, heightened ICT applications, and the generation of electronic waste.³⁰ Furthermore, the theory of “rebound effects” posits that ICT diffusion results in enhanced production efficiency and reduced production costs, thereby creating a scale effect as the demand for cost-effective and efficient products rises. Consequently, the “rebound effect” is perceived as a hindrance to the prospects of green growth.³¹

Indeed, it is widely recognized in the available literature that supply chain performance is a vital determinant of environmental quality³²; however, the literature lacks studies that shed light on the impact of supply chain performance on green growth. Moreover, there is enough empirical evidence to confirm the role of ICT diffusion in determining environmental quality, economic growth, and energy consumption.^27,33 However, the literature is in its infancy when studying ICT's impact on green growth. Further, the literature is missing from studying the influence of supply chain performance and ICT capital formation on green growth in top polluted economies. Therefore, this study aims to investigate the impact of supply chain performance and ICT diffusion on the green growth in top carbon emitter economies over the period 2007–2020.

First, this analysis estimates the influence of supply chain management on green growth by employing three different indices: logistics performance index, logistics infrastructure quality, and quality of logistics services. Using three different indices can give us a broader picture of how supply chain management could impact green growth. This also allows us to compare the impact of these indices on green growth. Second, the study estimates the impact of ICT capital formation on green growth. Third, this is the first effort that has combined supply chain management and ICT capital formation in the green growth function of top carbon emitter economies. These nations account for a significant portion of the world's carbon emissions, and their actions have a significant bearing on the course of climate change. Therefore, estimating the factors that can contribute to achieving green growth in these economies could be beneficial for achieving sustainable development goals worldwide. Fourth, the study utilized the two-stage least squares (2SLS) and two-step system-generalized methods of moments (GMM) that can help control the endogeneity issue and provide robust estimates. Moreover, our study has used a panel quantile regression approach to address the normality issue. Lastly, the research results provide suggestions that could boost global attempts to mitigate climate change effects.

Literature review

Countries experiencing rapid industrialization and economic growth have often done so at the expense of their natural environment, resulting in a deterioration of their ecological systems. This economic development has, in many cases, intensified carbon emissions, contributing to severe ecological degradation and climate change.³⁴ Consequently, the concept of green growth has emerged as a framework to assess and address these environmental challenges. Green growth embodies the notion of fostering economic growth while concurrently ensuring environmental sustainability. Central to this green paradigm is the belief that a nation's GDP must disentangle itself from the detrimental impacts of carbon emissions and resource exploitation in order to mitigate climate change and other ecological threats.³⁵ Green growth is often described as a shift away from traditional economic growth, emphasizing the need for environmentally adjusted multifactor productivity growth. Organization for Economic Cooperation and Development (OECD)⁶ reported that green growth refers to a sustainable economic development approach that aims to achieve economic growth while reducing negative impacts on the environment. This involves using natural resources efficiently, promoting sustainable practices, and investing in technologies and infrastructure that support environmental sustainability. The idea of green growth acknowledges the significance of economic growth and development in enhancing the quality of life for individuals, while also emphasizing the need for sustainability and the preservation of resources to ensure the well-being of future generations.³⁶ Within the emerging literature, supply chain performance and ICT have been identified as crucial elements influencing the trajectory of green growth.

The relationship between supply chain performance and green growth has been established through various studies and theories. The literature supports the idea that supply chain performance, which includes aspects such as efficiency, effectiveness, and responsiveness, can have a significant impact on environmental sustainability and green growth.³⁷ According to green growth theory,³⁸ green growth is driven not only by capital accumulation and environmental technological progress but also by knowledge creation. In this context, supply chain performance can be seen as a critical factor in facilitating innovation by improving the flow of goods and services, reducing transaction costs, and promoting green trade. This also improves supply chain performance, companies reduce their environmental impact, enhance their reputation as responsible corporate citizens, and promote green growth. Overall, green growth theory represents a shift away from traditional growth models that ignore the environmental costs of economic activity, and toward a more holistic approach that recognizes the importance of sustainable economic growth for long-term prosperity and environmental sustainability.³⁹ Several empirical studies also support the relationship between supply chain performance and green growth. For example, a study by Li et al.⁴⁰ found that supply chain practices such as supplier collaboration, green product design, and waste management had a positive impact on both environmental and financial performance. Another study by Choi and Hwang⁴¹ found that green supply chain practices such as environmental auditing, green purchasing, and green logistics had a positive impact on the triple bottom line.

Literature also highlighted transmission channels through which green growth practices can significantly influence supply chain performance. Firstly, green growth practices often involve optimizing the use of resources, such as raw materials, energy, and water, to reduce waste and improve efficiency. This can lead to cost savings in the supply chain, as well as reduced environmental impacts. The use of renewable energy sources in manufacturing processes can lead to reduced costs for electricity and lower emissions.⁴² Secondly, green growth practices can help reduce risks associated with climate change and other environmental issues. For example, implementing sustainable sourcing practices can help mitigate supply chain disruptions due to natural disasters or resource scarcity.⁴³ Thirdly, companies that adopt green growth practices can improve their reputation and brand image among consumers and other stakeholders. This can lead to increased demand for their products and services, as well as improved relationships with suppliers and other partners in the supply chain.⁴⁴ Lastly, many countries have regulations in place to promote environmental sustainability, such as carbon taxes or emissions standards. Companies that adopt green growth practices can ensure compliance with these regulations, avoiding potential penalties and reputational damage.⁴⁵

Empirical studies investigating the relationship between ICT and economic growth have presented varying results, contingent upon the chosen proxies for ICT measurement and the economic development levels of the countries under scrutiny.^27,46 Notably, Usman et al.²⁷ and Myovella et al.⁴⁷ have observed distinct impacts of ICT on economic prosperity across the Middle East, South Asia, and Sub-Saharan Africa. In a comprehensive analysis of data from 122 developing nations, Dong and Ullah⁴⁸ have discerned a consistent and favorable correlation between ICT and economic growth. Despite these insights into the substantial role of ICT in fostering economic growth, there remains an unaddressed query regarding its influence on green growth, especially in the context of China.

In contrast, there is also a body of research that underscores the significance of ICT in promoting green growth. The increasing adoption of ICT in society enhances environmental awareness and facilitates the adoption of advanced and environmentally friendly technologies.⁴⁹ Moreover, ICT applications aid in the forecasting of environmental threats and support the management of such challenges. For instance, digital simulation tools facilitate “learning by simulation,” thereby aiding decision-making processes by managing the potential consequences of trial and error. Similarly, ICT, in the form of an “internet network,” enables the dissemination of information related to conservation.⁵⁰ Furthermore, ICT adoption within an economy promotes the “dematerialization effect,” wherein socioeconomic interactions shift from physical product delivery to service-oriented delivery. In this context, the internet and mobile phones facilitate the transition from paper-based and physical communication to digital interactions. Consequently, this shift reduces the burden on transportation and lowers greenhouse gas emissions.⁵¹ Additionally, ICT offers intelligent solutions for energy generation, advanced transportation services, and enhanced urban living. This review has provided an overview of the diverse empirical and theoretical perspectives regarding the impact of ICT on green growth, emphasizing the complexity of this relationship and the need for further research to comprehensively assess the environmental implications of ICT adoption and its role in shaping sustainable economic development. However, research hasn’t explored the supply chain impact on green growth. While little is known about ICT's role in green growth. No research on supply chain and ICT's effect on green growth in top-polluted economies.

Theoretical framework, model, and methods

When integrated with supply management efficiency and organizational procedures, environmental ethos forms a green supply chain, which can be defined as a company's coordinated initiatives to minimize its impact on the natural ecosystem throughout a product's lifespan.⁵² The notion of green supply chain performance is significant because it seeks better environmental performance in supply chains to fulfill ecological, cultural, and financial goals in terms of sustainable green supply chain and sustainable operations management.⁵³ There is little doubt that the green supply chain is a vital topic in today's organizational and ecological management literature.²⁰ The implementation of green supply, or the adoption of ecologically balanced and less polluting manufacturing activities, has a substantial, beneficial effect on the employee, societal, and community indicators.⁵⁴ To create a green economy where environmental and economic goals can be achieved simultaneously, a green supply chain should be widely pushed, especially in the industrial sector of developing nations. Embracing and implementing a green supply chain has been recognized as a crucial strategy for boosting the efficiency of sustainability and community-driven services in developing countries.⁵⁵ Not only do green supply chain methods play a specific part in the advancement of economic development, but they are also crucial for achieving desired outcomes in the realms of sustainability and the environment.⁵⁶ In this analysis, we have used green logistics to represent green supply chain management because “green logistics” refers to supply chain approaches and initiatives that lower the ecological and fossil fuels footprint of freight distribution, focusing on procurement, waste disposal, packaging, and transportation.^57,58 The relationship between ICT and green growth is explained by two key mechanisms: scale and technology. ICT is initially integrated into businesses as part of their growth cycle, resulting in a “scale effect” where ICT facilitates business expansion, potentially leading to increased pollution and thus reducing green growth.²⁹ Once the foundational ICT infrastructure is established, incremental ICT usage can contribute to enhancing manufacturing processes, improving energy efficiency, reducing pollution levels, and promoting green growth through the “technology effect.”⁵⁹ Against this backdrop, we have developed the following model that investigated the relationship between green supply chain performance, ICT, and green growth.

G G_{i t} = φ_{0} + φ_{1} G L P_{i t} + φ_{2} I C T_{i t} + φ_{3} E D U_{i t} + φ_{4} F D_{i t} + φ_{5} R E C_{i t} + φ_{6} F D I_{i t} + α_{i} + ε_{i t}

In the above model, green growth (GG) is determined by green logistic performance (GLP), ICT diffusion (ICT), education (EDU), financial development (FD), renewable energy consumption (REC), foreign direct investment (FDI), country-specific effect (

α_{i}

), and random error term (

ε_{i t}

). The model is established for panel data analysis. Panel settings are the source of the data that was utilized in the study. Panel data, which incorporates both cross-sectional and time-series information, provides a number of advantages over other types of data. For instance, it raises the total number of observations, which results in the provision of more information as well as more accurate estimations. The panel data sets may be handled by a variety of methods, the most frequent of which are the fixed effect (FE), the random effect (RE), the two-stage least squares (2SLS), and the GMM. Both the fixed and random effect models both have advantages and disadvantages. For example, FE may be used for the pre-determined sample, while RE can be used for the randomly picked model. In the same vein, FE cannot include the factors that do not change throughout the course of time. On the other hand, the RE makes the assumption that there is no association between the country-specific impacts and the independent variables. If this assumption is incorrect, then the RE will yield estimates that are biased. In addition, these methods are incapable of addressing the many issues that are associated with panel data, including heteroskedasticity, endogeneity, and serial correlation. In light of this, for our research, we have relied on the approaches of both 2SLS and GMM. 2SLS is a method that is an enhanced version of ordinary least squares (OLS) that may function effectively in the scenario of simultaneous equations and yield structural parameters that are consistent. In this method, the endogenous independent variables are substituted by factors of the same kind, which are referred to as independent proxy predictors. However, these variables do not correlate with the residual terms. The 2SLS is an important development in econometrics, yet, in comparison to the GMM, it is more likely to be plagued by the issues of endogeneity and heteroscedasticity.⁶⁰ The model extension includes

G G_{i t} = φ_{0} + λ_{1} G G_{i t - 1} + φ_{1} G L P_{i t} + φ_{2} I C T_{i t} + φ_{3} E D U_{i t} + φ_{4} F D_{i t} + φ_{5} R E C_{i t} + φ_{6} F D I_{i t} + α_{i} + ε_{i t}

In such a scenario, the GMM proposed by Arellano and Bond⁶¹ is more appropriate for panel analysis and can combat the prevalent problems that arise when analyzing panel data. These problems include heteroskedasticity, endogeneity, and serial correlation. GMM is a reasonably excellent approach, primarily in situations where the number of cross-sections (N) is more than the number of time periods (T). We are required to incorporate the lagged dependent variable into the GMM estimation approach since this variable is essential for addressing the problem of endogeneity. The system-GMM, as opposed to difference-GMM, is used in this study because it more successfully manages variables that are near to or represent a random walk by enhancing estimates and minimizing the bias associated with using a small number of samples.⁶² According to Aluko and Ajayi,⁶³ “The system GMM estimator utilizes the level and first difference series to overcome the problem of weak instruments, thus providing more efficient estimates.” In a similar spirit, Hansen⁶⁴ affirms that the best weighting matrix for the estimator has been identified and is asymptotically effective in producing the two-step GMM estimator. This study considers the two-step system-GMM estimator. Since it generates more reliable estimates, a two-step GMM estimator is preferable to utilize when dealing with imbalanced data. On the other hand, when dealing with imbalanced data, the one-step GMM method resulted in the loss of many observations and generated skewed estimates.⁶⁵ In a similar vein, the performance of the two-step GMM is superior when dealing with autocorrelation and heteroscedasticity issues.⁶⁶ Moreover, we employ the Sargan and AR(2) tests to evaluate the validity of instruments and detect potential second-order autocorrelation in our diagnostic statistics.

To deal with the issue of non-normality, we apply the quantile regression model. Variability of the data distribution is not always uniform, and a regressand variable's conditional dispersal might cause the variables’ connection to shift depending on its value.⁶⁷ In order to have a fuller understanding of the connection between the variables, quantile regressions assess the conditional dispersal of the regressand variable at various echelons.⁶⁸ The panel quantile technique was developed as an alternative to the pooled OLS, which simply considers the mean in order to represent the heterogeneous framework of different income brackets and market conditions. The major justification for using panel quantile regression is heavily reliant on the data themselves. The findings of the conditional mean analysis will be inaccurate if the regression's residuals are not regularly dispersed. It is more pertinent to provide the slopes for various quantiles of the dependent variable with greater freedom as a result. The issue of “over- or under-estimation” centered on the classic mean regression technique is prevented by taking into account the heterogeneous distribution of the panel data. Most ecological variables have outliers often, according to Lin and Xu.⁶⁹ Therefore, conditional mean estimates might provide false findings. With quantile regression, the estimator may take outliers into account more effectively than with the provisional mean method. This method validates findings across different data segments, enhancing result credibility. This method effectively manages outliers, ensuring more robust results compared to traditional linear regression. It's ideal for studying non-linear relationships and variations across groups or periods in a dataset. Because of this, quantile regression analysis has grown more common in scholarly research on energy and the environment.

Data and descriptive analysis

This study collected panel data for the top 20 polluted economies from 2007 to 2020. The selection of countries is grounded in two primary rationales: Firstly, these countries have high levels of gross domestic product. Secondly, these countries are leaders in technology and innovation, with significant investments in research and development. We selected the data period based on the availability of data. Table 1 provides a detailed summary of variable definitions and sources of data. In this study, green growth (GG) is used as a dependent variable, which is measured by pollution-adjusted GDP growth. The data series for GG is derived from the OECD. Our focused explanatory variable is supply chain performance. We have measured supply chain performance through three proxy variables. These are the logistics performance index (LPI), logistics infrastructure quality (LIQ), and quality of logistics services (QLS). An index is used to measure logistics performance where a score of 1 represents low logistics performance and a score of 5 represents high logistics performance. Logistics infrastructure quality is measured through the quality of trade and transport-related infrastructure with a score of 1 for low LPI and a score of 5 for high LPI. Quality of logistics services is measured through competence and quality of logistics services, where score 1 represents low QLS and score 5 represents high QLS. Another focused variable in this study is ICT diffusion, which is measured through individuals using the internet as % of the population. Education is one of the fundamental determinants that play a significant role in determining green growth.⁷⁰ An increase in education level leads to enhanced green growth. The education variable is measured in terms of secondary school enrollment. According to Cao et al.,⁷¹ another fundamental determinant of green growth is FD, which is measured in terms of private sector domestic credit as % of GDP. Li et al.⁴⁰ study denoted that REC also plays a significant role in enhancing green growth. An increase in REC is expected to enhance green growth. REC variable is taken in terms of total final consumption of energy in %. Lastly, FDI is expected to play a significant role in enhancing green growth. A high inflow of FDI is expected to improve green growth in the region. FDI in this study is measured in terms of net inflows of FDI as % of GDP. The data series for LPI, LIQ, QLS, ICT, EDU, FD, REC, and FDI is derived from the World Bank. Table 6 of the Appendix contains a list of countries.

Table 1.

Definitions and data sources.

Variables	Symbol	Definitions	Sources
Green growth	GG	Pollution-adjusted GDP growth (%)	OECD^a
Logistics performance index	LPI	Logistics performance index: overall score (1 = low to 5 = high)	World Bank^b
Logistics infrastructure quality	LIQ	Quality of trade- and transport-related infrastructure, score (1 = low to 5 = high)	World Bank^b
Quality of logistics services	QLS	Competence and quality of logistics services, score (1 = low to 5 = high)	World Bank^b
ICT diffusion	ICT	Individuals using the Internet (% of the population)	World Bank^c
Education	EDU	School enrollment, secondary (% gross)	World Bank^c
Financial development	FD	Domestic credit to private sector (% of GDP)	World Bank^c
Renewable energy consumption	REC	Renewable energy consumption (% of total final energy consumption)	World Bank^c
Foreign direct investment	FDI	FDI, net inflows (% of GDP)	World Bank^c

ICT: information and communication technology; OECD: Organization for Economic Cooperation and Development.

https://stats.oecd.org/Index.aspx?DataSetCode=green_growth

https://data.worldbank.org/indicator/LP.LPI.OVRL.XQ

https://databank.worldbank.org/source/world-development-indicators

A summary of descriptive statistics is given in Table 2. Descriptive statistics provides various information regarding variables such as J-B tests, kurtosis, skewness, standard deviation, median, and mean of variables. The mean and standard deviation values are reported as follows: for GG (mean = 2.828, SD = 3.111), for LPI (mean = 3.516, SD = 0.412), for LIQ (mean = 3.520, SD = 0.533), for QLS (mean = 3.518, SD = 0.426), for ICT (mean = 4.016, SD = 0.629), for EDU (mean = 4.619, SD = 0.175), for FD (mean = 4.427, SD = 0.577), for REC (mean = 2.088, SD = 1.664), and for FDI (mean = 2.186, SD = 1.579). The findings of the J-B test confirm that all the data series are not normally distributed. Moreover, the estimates of skewness describe that some series are negatively tailed while others are positively tailed.

Table 2.

Descriptive statistics.

	Mean	Median	Max	Min	SD	Skewness	Kurtosis	Jarque-Bera	Prob.
GG	2.828	2.172	13.13	−5.257	3.111	0.373	2.914	6.575	0.037
LPI	3.516	3.612	4.226	2.368	0.412	−0.491	2.365	15.94	0.000
LIQ	3.520	3.632	4.446	2.230	0.533	−0.326	2.015	16.28	0.000
QLS	3.518	3.628	4.318	2.457	0.426	−0.389	2.255	13.53	0.001
ICT	4.016	4.257	4.584	1.374	0.629	−1.997	6.850	59.10	0.000
EDU	4.619	4.618	5.274	4.048	0.175	0.445	6.042	17.19	0.000
FD	4.427	4.547	5.378	3.029	0.577	−0.402	2.037	18.38	0.000
REC	2.088	2.427	3.890	−4.605	1.664	−2.878	11.81	92.02	0.000
FDI	2.186	1.889	11.92	−1.167	1.579	1.792	9.708	67.68	0.000

GG: green growth; ICT: information and communication technology; EDU: education; FD: financial development; REC: renewable energy consumption; FDI: foreign direct investment; QLS: quality of logistics services; LPI: logistics performance index; LIQ: logistics infrastructure quality.

Empirical results and discussion

Before proceeding with the estimation technique, we have first confirmed the status of multicollinearity by employing the VIF test. Table 3 reports the results for VIF for all three models (OLS, 2SLS, and GMM). The mean VIF is found below 10 in all three models which confirms that there is no evidence of multicollinearity in these models.

Table 3.

Results for VIF.

	(1)		(2)		(3)
Variable	VIF	1/VIF	VIF	1/VIF	VIF	1/VIF
LPI	2.690	0.372
LIQ			3.020	0.331
QLS					2.750	0.364
FD	2.650	0.378	2.910	0.344	2.720	0.367
ICT	2.480	0.404	2.530	0.395	2.490	0.402
EDU	2.140	0.468	2.150	0.466	2.150	0.466
REC	1.150	0.873	1.110	0.901	1.180	0.846
FDI	1.110	0.901	1.110	0.903	1.110	0.899
Mean VIF	2.030		2.140		2.070

ICT: information and communication technology; EDU: education; FD: financial development; REC: renewable energy consumption; FDI: foreign direct investment; QLS: quality of logistics services; LPI: logistics performance index; LIQ: logistics infrastructure quality; VIF: variance inflation factor.

Table 4 reports the OLS, 2SLS, and GMM regression results, respectively. The results indicate that the LPI has a significant and positive impact on GG in all three models. The coefficient estimates show that a 1% increase in LPI increases GG by 2.710% in the OLS model, 2.221% in the 2SLS model, and 2.031% in the GMM model. The results for LIQ indicate a significant and positive impact on GG in all models. It is reported that a 1% upsurge in LIQ enhances GG by 2.128% in the OLS model, 2.050% in the 2SLS model, and 1.764% in the GMM model. QLS is also reporting a positive and significant impact on GG in all the models. The estimates report that a 1% rise in QLS increases GG by 1.840% in the OLS model, 1.690% in the 2SLS model, and 1.463% in the GMM model. The results for LPI, LIQ, and QLS report a positive influence on GG in all three models (OLS, GMM, and 2SLS), it reveals that green supply chain logistics play a significant role in enhancing green growth in top polluted economies. Thus, it is concluded that green supply chain logistics is a significant indicator of green growth in top polluted economies.

Table 4.

Green growth estimates (OLS, 2SLS, and GMM).

	OLS			2SLS			GMM
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
L.GG							0.213**	0.214**	0.234**
							(0.060)	(0.062)	(0.056)
LPI	2.710***			2.221***			2.031***
	(0.599)			(0.994)			(0.491)
LIQ		2.128***			2.050***			1.764*
		(0.495)			(0.534)			(1.007)
QLS			1.840***			1.690***			1.463***
			(0.580)			(0.277)			(0.280)
ICT	1.685***	1.626***	1.629***	1.465**	1.313**	1.110**	1.385**	1.380**	1.295*
	(0.376)	(0.384)	(0.374)	(0.626)	(0.520)	(0.491)	(0.656)	(0.661)	(0.665)
EDU	0.189	0.176	0.124	1.950***	1.967***	1.895***	1.722***	1.409***	1.704***
	(1.261)	(1.275)	(1.252)	(0.341)	(0.483)	(0.415)	(0.103)	(0.183)	(0.219)
FD	1.244***	1.297***	1.407***	0.702	1.204	0.783	0.890***	1.094***	1.003***
	(0.424)	(0.449)	(0.427)	(0.926)	(0.972)	(0.917)	(0.299)	(0.327)	(0.348)
REC	0.386***	0.460***	0.337***	0.783***	0.580**	0.949*	0.776***	0.610***	0.990***
	(0.096)	(0.096)	(0.097)	(0.244)	(0.281)	(0.502)	(0.242)	(0.238)	(0.248)
FDI	0.074	0.053	0.100	0.057	0.027	0.052	0.014	0.023	0.015
	(0.100)	(0.101)	(0.099)	(0.093)	(0.087)	(0.092)	(0.090)	(0.090)	(0.091)
Constant	16.90***	14.76***	17.71***	5.016***	4.959***	4.720***	12.33***	8.961**	6.105**
	(4.833)	(4.846)	(4.802)	(1.691)	(1.502)	(1.842)	(3.511)	(4.412)	(3.105)
Observations	280	280	280	280	280	280	240	240	240
R ²	0.361	0.349	0.372
Number of codes	20	20	20	20	20	20	20	20	20
Sargan test				0.215	0.223	0.245	0.189	0.178	0.198
AR(2)				0.178	0.201	0.221	0.154	0.145	0.112

Note: ICT: information and communication technology; EDU: education; FD: financial development; REC: renewable energy consumption; FDI: foreign direct investment; QLS: quality of logistics services; LPI: logistics performance index; LIQ: logistics infrastructure quality; OLS: ordinary least squares; 2SLS: two-stage least squares; GMM: generalized methods of moments.

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

To study the link between a green supply chain and green growth, we’ve used three indicators (LPI, LIQ, and QLS), all promoting green growth. The finding of the green supply chain is also supported by Zhang et al.,⁷² who highlighted that the performance of a supply chain affects the cost of producing goods, transportation costs, and other related costs. Companies that improve their supply chain performance by reducing waste and increasing efficiency can save money and improve their profitability. This, in turn, can encourage companies to invest in green technologies and practices, which can lead to further environmental benefits. The study by Wang et al.⁷³ claimed that supply chains that are sustainable and well-managed can have a positive impact on local communities. For example, companies that adopt sustainable practices may provide more employment opportunities, improve working conditions, and increase local investment in infrastructure. These benefits can lead to improved quality of life and social welfare, which can further encourage the adoption of sustainable practices and enhance green growth. These findings are backed by Yang et al.,⁷⁴ who noted that companies are increasingly incorporating environmental considerations into their supply chain processes in an effort to lessen their negative effects on the natural world. Green supply chain is a term used to describe the incorporation of environmental considerations into traditional supply chain procedures.⁷⁵ Hence, it is not surprising at all if a green supply chain can lay the foundation of economic activities that are less carbon-intensive and the results are in line with.^23,58,71

ICT is another focused variable that is also producing a significant and positive impact on GG in top polluted economies. It is noticed that expansion in ICT diffusion increases GG in all nine models. Thus, the estimates of ICT diffusion confirm that the ICT sector is an important determinant of green growth that needs to flourish in top polluted economies. This finding is in line with Zheng et al.,²⁹ who noted that ICT also plays a significant role in improving green growth. The ICT revolution helps economies to shift from physical to informational resources and helps the economy to become smarter, weightless, and less capital intensive,²⁷ and pave the way for sustainability.⁷⁶ Our study result is also consistent with,⁷⁷ who described that the adoption of ICT technologies leads to significant energy savings in many sectors of the economy. Such as smart grid technologies can help manage and reduce energy consumption in the power sector, while the use of video conferencing can reduce travel-related emissions. These energy savings can contribute to the achievement of green growth, by reducing pollution emissions and improving energy efficiency. Li et al.⁴⁰ claimed that investment in ICT capital can drive innovation in green technologies, leading to new products and processes that are more environmentally friendly. For example, the development of new software tools for monitoring and controlling energy use can help reduce energy waste and improve energy efficiency. Such innovation can lead to a more sustainable and environmentally friendly economy, contributing to the goal of green growth. Moreover, investment in ICT capital improves the productivity of businesses and industries, leading to increased economic output while reducing environmental impact. The use of automation technologies can help reduce waste in manufacturing, leading to more efficient use of resources and a reduction in environmental impact. This increased productivity can contribute to the achievement of green growth, by promoting sustainable economic growth while reducing environmental degradation.

Regarding control variables, EDU reports a significant and positive impact on GG in all three GMM models and 2SLS models. However, the nexus between EDU and GG is found insignificant for all three OLS models. Similarly, a higher level of education can increase environmental-related consciousness, promoting green consumption and production activities.⁷⁰ Moreover, human capital is the by-product of education that allows firms and businesses to replace labor-intensive techniques of production with human-capital-intensive techniques of production, and thus help to decouple economic growth and emissions. FD is significantly and positively attached to GG in all three OLS models and GMM models. However, the association between FD and GG is found insignificant for all three 2SLS models. Finally, the positive impact of financial development on green growth can be explained by the fact that it can make the availability of funds and financial capital for the promotion of green production and consumption activities a lot easier and thus promote green growth.⁷⁸ REC is another important control variable that is producing a significant and positive impact on GG in top polluted economies. It is observed that the increase in the use of REC increases GG in all nine models such as OLS, 2SLS, and GMM. Conversely, the impact of FDI on GG is found statistically insignificant in all nine models. Further, renewable energy consumption is widely recognized as an essential factor in decoupling economic growth and CO₂ emissions⁷⁹ because they are carbon-free energy sources and can replace the carbon-intensive energy sources in the firm's production function, helping the firms to produce more green products. The positive impact of renewable energy on green growth is supported by Rath et al.⁸⁰ It reveals that FDI has an insignificant impact on GG in top polluted economies. In terms of control variables, the estimates of the OLS model, 2SLS model, and GMM model reveal that EDU, FD, and REC contribute significantly to enhancing green growth, while FDI has no impact on green growth in top polluting economies.

Some diagnostics are also presented in Table 4 to check the validity of instruments such as second-order serial correlation (AR(2)) and Sargan test. Arellano and Bond⁶¹ discourage “second-order serial correlation” in the residual while allowing for a “first-order serial correlation.” Consequently, the null hypothesis is that there is no serial connection in the first and second orders. The AR(2) diagnostic in Table 4 doesn’t reject the null hypothesis of second-order correlation, confirming the absence of it and the validity of our GMM estimates. The Sargan test examines the reliability of instruments as well. The null hypothesis of this test implies that there is an appropriate “model specification” and “overidentified limitations.” Since the test statistic is insignificant, it suggests that the null hypothesis is not rejected and thus confirms our instrument's validity in the GMM.

Table 5 reports the green growth estimates for panel quantile regression. LPI is positively and significantly attached to GG in top polluted economies from 0.20th quantiles to 0.95th quantiles. However, the nexus between LPI and GG is observed as insignificant at lower quantiles, that is, at 0.05th quantile and 0.10th quantiles. ICT indicates a positive and significant connotation with GG from 0.30th quantiles to 0.95th quantiles. In contrast, the nexus between ICT and GG is observed as insignificant at lower quantiles (0.05th quantiles to 0.20th quantiles). EDU is positively and significantly linked with GG in top polluted economies from 0.70th quantiles to 0.95th quantiles. Conversely, the relationship between EDU and GG is observed as insignificant at lower and medium quantiles, that is, from 0.05th quantiles to 0.60th quantiles. FD reports a positive and significant impact on GG from 0.20th quantiles to 0.95th quantiles. The nexus between FD and GG is found statistically insignificant at lower quantiles (i.e. 0.05th quantile and 0.10th quantile). REC reports a significant and positive impact on GG from 0.30th quantiles to 0.95th quantiles. The relationship between REC and GG is found statistically insignificant from 0.05th quantile to 0.20th quantile. The relationship between FDI and GG is reported insignificant at all quantiles, that is, 0.05th quantiles to 0.95th quantiles. Similar to 2SLS, GMM, and OLS estimation results, panel quantile regression reports that LPI, ICT, EDU, REC, and FD contribute significantly to enhancing green growth in selected economies, but FDI has no impact on green growth in selected economies.

Table 5.

Green growth estimates (panel quantile regression).

	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
LPI	2.091	3.668	3.477***	4.024***	3.267***	3.350***	3.198***	2.673***	3.523***	4.437***	3.709**
	(1.271)	(1.547)	(2.868)	(5.189)	(5.300)	(6.175)	(6.879)	(6.563)	(5.800)	(3.180)	(2.310)
ICT	0.395	0.743	1.038	1.227***	1.821***	1.870***	1.967***	2.039***	2.043***	2.207**	2.170**
	(0.330)	(0.843)	(1.546)	(2.895)	(4.997)	(5.377)	(5.429)	(4.350)	(3.603)	(2.414)	(2.237)
EDU	0.905	1.367	1.540	0.859	0.922	1.056	0.239	0.218**	0.189**	0.350**	0.709*
	(0.164)	(0.255)	(1.118)	(0.765)	(1.074)	(1.280)	(1.591)	(2.071)	(2.219)	(2.223)	(1.883)
FD	0.293	0.659	1.107*	1.255**	0.561**	0.475**	0.540*	0.335**	0.442***	1.289*	1.773*
	(0.284)	(0.765)	(1.710)	(2.541)	(2.256)	(2.035)	(1.954)	(2.069)	(2.596)	(1.710)	(1.869)
REC	0.032	0.268	0.356	0.387*	0.447**	0.478***	0.536***	0.581***	0.515***	0.623***	0.579*
	(0.082)	(0.320)	(1.237)	(1.753)	(2.097)	(3.427)	(3.531)	(3.266)	(4.302)	(2.934)	(1.834)
FDI	0.231	0.112	0.076	0.048	0.114	0.109	0.131	0.057	0.009	0.168	0.115
	(1.418)	(1.032)	(0.900)	(0.479)	(1.195)	(1.091)	(1.074)	(0.465)	(0.060)	(0.351)	(0.158)
C	1.192	3.754	6.150	12.21**	15.30***	15.93***	19.72***	21.98***	22.97***	24.97***	21.16*
	(0.055)	(0.670)	(1.034)	(2.421)	(4.537)	(4.902)	(5.909)	(6.643)	(7.261)	(5.863)	(1.784)
R ²	0.070	0.078	0.126	0.188	0.246	0.293	0.325	0.340	0.332	0.248	0.230

Note: ICT: information and communication technology; EDU: education; FD: financial development; REC: renewable energy consumption; FDI: foreign direct investment; LPI: logistics performance index.

T-stat in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Conclusion and implications

Due to the large-scale consumption of natural resources and resulting climate change, environmentalists have called for the sustainable usage of natural resources. Consequently, the issue of sustainability has come into the limelight and become the central focus of all international forums. Green growth is a type of growth that helps to separate economic growth from its harmful environmental impact; therefore, in recent times, the emergence of green growth has proved vital for achieving sustainable development goals. Green growth largely depends on implementing and initiating green practices in different sectors of the economy. A green supply chain or green distribution network can prove vital in achieving green growth. However, there is a dearth of empirical evidence on the relationship between green supply chains and green growth, and this study is an effort to plug this gap into the literature. This analysis aims to investigate the impact of green supply chain performance and ICT on green growth by controlling the impact of education, financial development, renewable energy consumption, and FDI.

For this purpose, we have collected panel data across top carbon emissions countries. First, we have employed the VIF to check the issue of multicollinearity, confirming the non-existence of this issue. As the data used is a panel; hence, to get the estimates of the variables, we have employed the OLS, 2SLS, and GMM. The main findings may be summed up as follows: logistics performance, the quality of logistics infrastructure, and logistics services are all vital in promoting green development. Efficient and sustainable logistics operations lead to reduced emissions, minimized waste, and improved resource utilization, ultimately contributing to green growth. The diffusion of ICT, education, the use of renewable energy, and financial development are all advantageous for green growth. This infers that digitalization enhanced resource efficiency, more informed decision-making, and improved environmental monitoring practices, all of which contribute significantly to fostering sustainable economic development. The results also highlight the substantial contributions of education, the adoption of renewable energy sources, and financial development in facilitating and driving the process of achieving green growth. Finally, foreign direct investment has no noticeable effect on green growth. The panel quantile regression model was used to deal with the problems of non-normality problem, and the results are the same as those obtained from the main methods.

In addition to the theoretical advancement, the study also suggests numerous significant practical and policy consequences. The three main advantages of green growth are social affinity, ecological sustainability, and economic prosperity, and they are helping to increase the popularity of green growth throughout the world. Countries of all levels of development (advanced, emerging, and underdeveloping) have devised an enormous range of strategies for achieving these objectives. With this in mind, the results of this study are crucial for achieving green development via more cost-effective means. In our opinion, the green supply chain and green growth need equal weight in the attention of corporate leaders and authorities. In this regard, green manufacturing practices, smart distribution networks, and better and energy-efficient logistics infrastructure can help the economies to keep the environmental impact of economic growth at bay. Executives should pay close enough attention to implementing a green supply chain, which drives green growth, to improve community-based and ecological efficiency. Moreover, the government should back the firms that rely on green supply chain methods by removing financial and administrative hurdles that come their way. To facilitate green growth, there should be investments in intelligent and efficient distribution networks. This includes the development of smart logistics systems that optimize transportation routes, reduce emissions, and minimize energy consumption. It is essential to prioritize the improvement of logistics infrastructure. This involves upgrading transportation systems, ports, and warehouses to ensure the efficient movement of goods while minimizing the environmental impact. Governments provide financial incentives and funding for research and development efforts focused on green supply chain technologies and practices. This can lead to innovation in environmentally friendly logistics and distribution.

Moreover, the large-scale implementation and adoption of ICT in society can be a giant step towards green growth due to the dematerialization and digitalization of the top polluted economies. Policymakers should prioritize the development of ICT infrastructure within their countries. This includes investments in high-speed internet access, digital connectivity, and ICT education programs. Offer incentives and subsidies to businesses that invest in green ICT solutions, such as energy-efficient data centers, smart grids, and telecommuting technologies. These incentives can reduce the carbon footprint of ICT operations. Expand e-government services to streamline administrative processes and reduce paper-based bureaucracy. This can lead to significant resource savings and environmental benefits. Further, increasing the formal education level can also bring more awareness about the harmful environmental impacts of traditional economic growth and society's shift towards more green production and consumption activities, resulting in green growth. Lastly, through increased financial development, policymakers must induce financial institutions to provide financial capital on a priority basis for more sustainable and environmentally friendly ventures.

While this study provides some noteworthy suggestions for lawmakers and management, it has its limitations. For instance, only a small number of research attempted to compare different industries. Therefore, future studies must consider the sectoral data for different countries and regions. In addition to logistic performance, other aspects of green supply chain management, such as top management support and their background, are crucial in defining the role supply chain in sustainable practices. The study's time span is limited due to data constraints; future research should consider extending the data collection period. In this analysis, we have collected data from top polluting economies, including advanced and emerging economies; however, future studies must perform a comparative analysis between advanced and emerging economies. Lastly, the study ignores the issue of cross-sectional dependence, which is a mandatory feature of the panel data, and future studies must consider cross-sectional dependence during the analysis.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: 1. Key Projects of Teaching Reform Research in Colleges and Universities of Hunan Province in 2021, Innovative Practice of Cultivating New Business Digital Management Engineers (HNJG-2021-0168). 2. Hunan Provincial New Liberal Arts Research and Reform Practice Project in 2021, Digital Intelligence Empowerment and Cross-Chain Integration - Innovative Practice of Collaborative Education Mechanism of New Business, Science, Government, Industry and Research（Xiangjiaotong[2021]NO.94-51).

ORCID iD

Xu Qifeng

Huang Ren received a bachelor’s degree in engineering and a master’s degree in management from the University of Electronic Science and Technology of China. Currently, he serves as the director of the teaching and research section of the School of Management of Hunan Institute of Technology. Current research focuses on transparent logistics management and big data management and analysis. He also continue to conduct in-depth research on rural revitalization, regional economic planning, and logistics park planning.

Xu Qifeng received a bachelor's degree in accounting from Hunan University of Finance and Economics. She continues her master's degree education in the field of Management Science and Engineering at Guanxi University of Science and Technology. Her main research directions include technology innovation management, enterprise innovation, and industrial economy. She have published 3 papers, including 2 core papers.

Guan Yongjun obtained a doctorate in Management from Central South University. He serves as the dean of the School of Economics and management at Guanxi University of Science and Technology. He mainly engaged in research related to financial management and technological innovation management, as well as high-quality development of the manufacturing industry.

Hu Gaole received her MSC in Marketing from the University of Exeter and currently works at the School of Management, Hunan Institute of Engineering. Her research focuses on digital marketing and big data analytics.

Appendix

Table 6.

List of countries.

Code	Country	Code	Country
1	China	11	Brazil
2	United States	12	Mexico
3	United Kingdom	13	Australia
4	India	14	South Africa
5	Russian Federation	15	Turkey
6	Japan	16	Italy
7	Germany	17	France
8	Canada	18	Poland
9	Republic of Korea	19	Spain
10	Saudi Arabia	20	Indonesia

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