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Journal of Hospitality and Tourism Insights

ISSN : 2514-9792

Article publication date: 28 October 2022

Issue publication date: 1 December 2023

The aim of this research, which is based on a literature review and bibliometric analysis, is to reveal the development of green technologies in hotels, based on the articles published in tourism and hospitality journals between 1999 and 2020.

Design/methodology/approach

Based on five conditions and five databases, 64 journal papers were retrieved and reviewed. Among the surveyed publications pertinent to the eco-friendly/green technology practices at hotels, the majority focus was on the need for eco-friendly/green technology practices at hotels and the schemes implemented to achieve sustainable development.

The research findings especially from the last decade report that today's guests generally prefer green hotels based on their increased awareness of environmental degradation and an ever-growing need for conservation and sustainability.

Practical implications

The environmental responsibility which is inherent in the hospitality and tourism industry due to the environmental burden generated by the combined effect of both industries on Mother Earth, brings forth a substantial sense of commitment on the part of hotel companies. In that regard, a set of corporate initiatives in the form of green technology practices are implemented by hotels, toward the development of new product and service offerings, management of processes and corporate policy formation.

Originality/value

This research focuses on green technologies aimed at sustainability in the field of accommodation and tourism, consisting of a systematic literature search on the subject. It is important in the way that it provides a general overview to researchers in terms of the theoretical implications of green technologies while also offering a road map with respect to green technology applications to the practitioners of the field.

• Green technology
• Sustainability
• Tourism and hospitality
• Systematic literature review

Gunduz Songur, A. , Turktarhan, G. and Cobanoglu, C. (2023), "Progress on green technology research in hotels: a literature review", Journal of Hospitality and Tourism Insights , Vol. 6 No. 5, pp. 2052-2072. https://doi.org/10.1108/JHTI-10-2021-0280

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• Open access
• Published: 24 April 2024

Impact of green technology and energy on green economic growth: role of FDI and globalization in G7 economies

• Mohammad Jibran Gul Wani 1 , 2 ,
• Nanthakumar Loganathan 3 &
• Hanaa Abdelaty Hasan Esmail 4  

Future Business Journal volume  10 , Article number:  43 ( 2024 ) Cite this article

342 Accesses

Metrics details

With the increase in economic growth, the world is facing serious challenges concerning environmental sustainability. Hence, the green economic growth is imperative for sustainable and inclusive development. The objective of this study is to contribute to the existing literature about the factors that influence green economic growth. The study investigates the role of green technology, green energy, foreign direct investment, and globalization on green economic growth in G7 countries. The data of the study is collected from WDI, KOF Swiss Economic Institute, and OECD database and the data period ranges from 1995 to 2020. The existence of cointegration between the variables of the study was tested by Westerlund’s (Oxford Bull Econ Stat 69(6):709–748) cointegration test. Due to the presence of cross-sectional dependency, the study employed the cross-sectional autoregressive distributed lag (CS-ARDL) method to estimate the coefficients in the long and short run. The study also used a common correlated effect—mean group (CCEMG) estimator for robustness check. The findings of the study reveal that green energy and FDI positively contribute to green economic growth in the long and short run. The green technology also contributes positively to enhance green economic growth but only in long run. To accelerate green economic growth, G7 countries should incorporate policies promoting green energy and technology, while acquiring more foreign investments to ensure a sustainable development.

Introduction

A significant concern facing the global economy is the issue of environmental degradation, coupled with climate change. This issue not only poses a threat to individuals’ well-being but also has negative implications for their financial stability and overall productivity. Economic activity directly correlates with rising energy consumption, leading to a surge in greenhouse gas emissions that significantly harm the environment [ 48 , 58 ]. Hence, the global community has prioritized the reduction of CO 2 emissions and the enhancement of environmental quality as crucial measures for achieving sustainable development and mitigating the adverse impacts of climate change [ 49 ]. Many countries have united under the Paris Agreement, pledging to reduce their greenhouse gas emissions to combat global climate change. In order to guarantee the success of the mission and the achievement of the sustainable development goals, it is crucial for nations to demonstrate unwavering commitment toward the established targets. According to Khan et al. [ 33 ], climate change will lead to a significant decline in the global economy, with a loss of more than 18 percent of its gross domestic product. Chen et al. [ 17 ], climate change is wreaking havoc on both urban and natural systems, with an estimated $500 billion in lost worldwide economic output. However, we can significantly mitigate the potential impact by implementing proactive measures to achieve the goals of the Paris Agreement. In 1987, the Brundtland Report, also known as "Our Common Future," came into publication. It presented a proposal for the concept of "sustainable development" as a response to these concerns [ 27 ]. In the realm of economic development, sustainable development is defined as a development that meets the needs of the present generation while safeguarding the ability of future generations to meet their own needs. According to Auty and Brown [ 10 ], sustainable development is a global concept comprising three main aspects: social, economic, and environmental. In a recent study, Ahmed et al. [ 4 ] identified the need for a new paradigm in global economic growth due to recurring failures in international policy to attain sustainable growth.

Hence, there is a necessity to examine a novel growth model due to the recurrent shortcomings of global strategies to contain environmental degradation and attain sustainable growth. The United Nations has emphasized the importance of environmental sustainability for achieving long-term economic stability. There has been a recent shift toward a new model of growth known as "green economic growth." The notion of "green economic growth" is intricately connected to the sustainable development paradigm and signifies economic expansion that considers the prudent utilization of natural resources, mitigates and prevents pollution, and creates avenues for enhancing social welfare by establishing a carbon–neutral economy [ 12 ]. According to the study conducted by Khan et al. [ 34 ], green economic growth is an expansion of the economic growth model that promotes economic progress while also prioritizing environmental protection and social sustainability. By enhancing productivity and ensuring macroeconomic stability, a sustainable economic growth strategy can unlock new avenues for growth. Ali et al. [ 6 ] argued that it also protects the environment, promotes social progress, and mitigates the barriers to development caused by resource scarcity. The United Nations Production Gap Report 2021 warns that the current production plan is on the verge of exceeding the Paris Agreement’s limit (United Nations Environment Program, 2021). Hence, it is crucial for all countries to implement measures to effectively reduce carbon emissions and support the attainment of sustainable development objectives. Although many countries have started adopting renewable and green energy as alternatives, this alone falls short of meeting the global energy demand.

Green technology innovation may offer a potential solution to the environmental problem. This is due to its role in promoting sustainable and balanced economic development, as well as enhancing environmental management [ 61 ]. Brunnermeier and Cohen [ 15 ] discuss the positive spill over that arises from technological innovation activities. These activities contribute to the development of new goods, processes, and methods that can help mitigate environmental harm. According to Alfaro and Chauvin [ 5 ], foreign direct investment (FDI) involves acquiring a lasting stake and controlling ownership in a host nation’s commercial enterprise. This phenomenon arises when a company or individual from one nation engages in foreign direct investment (FDI) in another country. Foreign direct investment plays a crucial role in fostering and upholding green economic growth, facilitating the shift toward a low-carbon economy, and attaining sustainable development [ 40 ]. Foreign direct investment (FDI) has the potential to foster long-term economic growth through investments in technological innovation, research and development, and renewable energy resources. Implementing these measures can lead to a decrease in gas emissions and mitigate the economic burdens linked to expansion [ 9 ].

This research has significantly contributed in numerous ways. With respect to the expansion of green economic growth, the research will provide the policymakers of the G7 countries with a substantial contribution. Due to the rapidity of global change, the G7 nations have a significant impact on the course of world history. This is due to the G7’s capability to execute policies, demonstrate technologies, utilize alternative energy sources, and undertake requisite actions to attain net-zero emissions in a manner that is both secure and economically viable. These nations contributed 26.6% of the total global GDP in 2023, which was a substantial proportion. In addition, the G7 significantly contributed to the 14.4% expansion of the global GDP from 2013 to 2023. The preceding research has predominantly concentrated on specific nations. Additionally, it is noteworthy to mention that although considerable research has been conducted on green economic growth, there has been comparatively little emphasis on the contributions of green energy, green technology, and investment. As foreign investment, green energy, and technology gain prominence as viable strategies to mitigate carbon emissions, it is anticipated that green growth activities will increase proportionally. This research endeavors to assess the consequences of this trend. Additionally, the question of how to attain green economic growth remains unresolved, as prior research has failed to effectively ascertain the precise determinants that foster such expansion. This research makes a substantial contribution to the ongoing discourse surrounding green economic growth. The empirical contributions of green technology, energy, foreign direct investment, and globalization to the expansion of the green economy are exhaustively described in this study. Furthermore, the research offers insights into the cointegration that exists among green technology, green energy, investment, and green economic development, with a particular focus on the G7 countries. Amid the G7’s attempts to address environmental issues and promote sustainable development, the present period assumes considerable significance for the economies involved.

The G7 countries, including Canada, France, Germany, Italy, Japan, the UK, and the USA, collectively represented a significant portion of the global economy. In 2023, they accounted for 26.6% of the world GDP and contributed to 14.4% of the growth in global GDP from 2013 to 2023.The quantity of carbon emissions has experienced a significant increase in comparison with previous years. In 2022, global CO 2 emissions from energy sources hit a record high of over 36.8 Gt, showing a growth of 0.9% or 321 Mt (IEA 2023). In 2020, the G7 members and the European Union accounted for around 30% of global energy demand and 25% of energy-related CO 2 emissions. However, the increasing cost of imported energy has heightened the urgency surrounding energy consumption. During this process, the ongoing use of fossil fuels leads to increased emissions, resulting in climate change, global warming, reduced agricultural production, and a potential risk to human life. In order to achieve sustainable economic growth and energy consumption, it is crucial to adopt innovative approaches to thinking. Our research will not only examine the economic expansion in these countries, but also explore strategies for sustaining long-term economic growth. Given the significance of economic growth and environmental sustainability in informing policy decisions, it is imperative to examine the impact of energy consumption on green growth. This study utilizes panel data analysis to examine the effects of green energy, green technology, foreign direct investment, and globalization on the green economic growth in G7 countries. The purpose of this study was to address the existing gaps in the literature, as previously identified. This study utilizes a second-generation panel unit root test to check the stationarity of the data, cross-sectional autoregressive distributed lag (CS-ARDL) model to estimate the coefficients. Additionally, we evaluate the robustness of the estimates using a dynamic common correlation effect mean group model.

The present study is organized into the following sections: first, we introduce the entire study, followed by a literature review on the relationship between green energy, technology, foreign direct investment (FDI), globalization, and green economic growth. Subsequently, the research design and methodology portion will be presented, followed by the analysis and discussions. The final section of the study discusses the conclusions drawn from the empirical data pertaining to the variables under investigation.

Literature review and hypothesis development

Green energy and green economic growth.

Hypothesis 1: Green energy has significant and positive impact on green economic growth.

The impact of green energy on environmental quality and economic development has been extensively investigated by a number of researchers. The impact of green energy on economic development was studied by Olmo et al. (2020) and findings of the research demonstrated a positive relation between the adoption of green energy consumption and the economic expansion in many European countries. Other studies carried out by Shahbaz et al. [ 51 ], Saidi and Omri [ 50 ] have also demonstrated the positive association between the renewable energy and economic growth. Renewable energy is essential for advancing the green economic development of a nation [ 22 , 52 ]. In other words, a rise in renewable energy consumption is associated with a corresponding average acceleration in economic growth. Similar findings were reported by Mohsin et al. [ 38 ] for West African States, and the results suggested that a 1% increase in energy (renewable) contributes to an increase in green economic growth by approximately 3%. According to Bhattacharya et al. [ 14 ], the correlation between the utilization of renewable energy sources and economic growth is dependent on the level of economic development. A number of researchers have investigated the connection between green energy and economic growth. Nevertheless, the subject of green economic growth in relation to green energy remains a matter of contention.

Additionally, Sohag et al. [ 55 ] conducted an analysis on the relationship between green energy and green economic development in Turkey, and the results demonstrated that green energy has a positive impact on green economic expansion. Fang [ 24 ] examined the environmental and economic benefits and repercussions of the extensive implementation of green energy sources in China, and the findings revealed that there is a substantial positive impact of renewable energy on green economic growth. The study concluded that environmental protection and green energy use tend to stimulate economic expansion. On the basis of the aforementioned researches, it is hypothesized that green energy contributes substantially to green economic expansion. Limited research has been conducted on the relationship between green energy and green economic growth in the developed countries. The present study endeavors to investigate the impact of green initiatives on green economic growth in G7 countries.

Green technology and green economic growth

Hypothesis 2: There is a significant relationship between Green technology and green economic growth.

The connection between technological progress, environmental quality, and economic growth has been the subject of extensive study. In order to improve environmental quality, technical innovation is essential, according to many academic studies. This is because technological innovation typically leads to a decrease in carbon emissions by making factor production more efficient [ 54 ]. Fan et al. [ 23 ] asserted that technological progress serves as the most effective means to ensure the efficient, clean, and eco-friendly use of resources. This will improve the environment, people’s standard of living, and social sustainability. Omri [ 41 ] examined the impact of technological innovation on sustainable economic development, and the results suggest that technological innovation positively affects economic development. Murad et al. [ 39 ] looked into the robust relationship between economic development, environmental quality, and technological innovation. The results of the study concluded that economic growth and technological advancement are positively and significantly correlated. According to the study, the results showed a strong relationship between new technologies and environmentally friendly growth.

However, green economic growth is an aspect that has not been extensively studied in the literature, specifically looking at how green technology innovation affects green economic development. This has led to a dearth of studies investigating how environmentally friendly technology affects green economic expansion. However, there are few studies that look at the effects of renewable energy, nonrenewable energy, and technology connected to the environment and green economic growth. For example, Ulucak [ 56 ] carried out research in the BRICS countries, and the results showed that green economic growth is positively impacted by renewable energy and technology linked to the environment. Our research attempts to enhance the literature on the topic of green economic growth and the relationship between green technologies. Since the majority of studies are focused on how new technologies work. The current study adds to the existing literature by conducting an empirical investigation into the impact of green technology in promoting green economic growth within the framework of G7 economies.

Green economic growth, foreign direct investment and globalization

Hypothesis 3: There is a significant and a positive relationship between FDI and green economic growth. Hypothesis 4: There is a significant and negative relationship between green economic growth and globalization.

The effect of foreign direct investment (FDI) on green economic growth has been the subject of various studies in the last decade. According to Hille et al. [ 28 ], FDI could be a solid basis for achieving sustainable development. Ghorbal et al. [ 25 ] carried out a study in South Korea and found that foreign direct investment (FDI), gross domestic product (GDP), and domestic patents all contribute to the value of foreign patents. Hence, an increase in FDI has a beneficial effect on foreign patents, which in turn leads to more economic growth and less pollution. FDI boosts technological innovation and domestic competitiveness among similar local enterprises, which in turn reduces pollution and improves carbon emission efficiency [ 53 ]. However, on the other side, the expertise and contemporary technology that FDI brings to both the upstream and downstream industries in an economy creates a multiplier impact that increases labor productivity, and with respect to green economic development, a disproportionate effect is observed in many countries [ 57 ]. Paramati et al. [ 43 ] looked at the relationship between foreign direct investment (FDI) and issues like pollution, green growth, carbon emissions, and environmental damage. FDI, trade, and green technology are the main factors that influence the reduction of CO 2 emissions and the advancement of green growth. Zafar et al. [ 63 ] carried out a study on OECD economies to look at how green economic growth is affected by FDI, R&D, and trade openness. There is a strong positive correlation between trade openness, foreign investment, and green growth. In conclusion, after considering the significant beneficial effects of FDI such as economic stability, innovations in environmentally friendly technology, efficiency in the use of resources, and the decrease of pollutant emissions.

According to Ahmad and Wu [63], globalization (GLO) helps boost total factor productivity, which in turn leads to economic growth. However, the counterargument, that GLO slows green economic growth by adding to carbon emissions and greenhouse gases, has only been somewhat investigated [ 13 ]. The literature does, however, make brief reference to the link between GLO and green economic expansion. Conversely, numerous studies have explored the connection between green economic growth and environmental degradation [ 37 ]. Therefore, using the GLO as a focal point, Kirikkaleli et al. [ 35 ] calculated ecological imprints. The empirical results show that environmental footprints are positively and significantly associated with GLO. Ahmad and Wu [ 3 ] also found a correlation between the rise of green economic growth, ecological innovations, and ecological sustainability. Therefore, considering the environmentally friendly economic growth, i.e., green economic growth, is negatively correlated with GLO, because GLO increases ecological degradation due to the increase in ecological footprints [ 7 , 11 ].

Theoretical framework of the study

The link between environmentally friendly economic growth, green energy, and technology is left unexplored to a greater extent by the academics in contrast to traditional economic growth. Green energy, green technology, and green growth are expected to be correlated as reported by the previous researches. This section begins by explaining the theoretical foundations that justify this expectation. Hence, this section elaborates on the theories which concluded that green energy and green technology contribute to green economic growth.

An effective technique for boosting economic growth while resolving environmental concerns is green economic growth. The primary concern for the policymakers is to know the factors promoting green economic growth. Macroeconomic theory concludes that renewable energy and related aspects are critical for long-term economic growth. According to Alper and Oguz [ 8 ], the reduction of dependency on volatile energy resources like coal, crude oil, and fossil fuels is only possible when the adoption of renewable energy sources is boosted while also reducing the negative externalities linked to energy production. Moreover, it also helps countries’ economies grow substantially [ 51 ]. The fundamental macroeconomic theory postulates that green energy has a significant impact on the expansion of green economic growth. Additionally, technological innovation is crucial for promoting long-term economic growth, according to the notion of comparative advantage. Moreover, technological progress has a major bearing on economic growth, as economic theory elaborates. Modern technology is able to accomplish a certain amount of output with far less energy consumption. They are also known as "green technologies." Green technologies are highly valued according to Porter’s theory. According to the theory [ 16 ], green technology helps the environment and the economy at the same time. Chen et al. [ 17 ] stated that technological advancements in carbon reduction and energy conservation improve environmental quality and stimulate green economic growth.

The "Pollution Halo" concept states that foreign direct investment (FDI) has the potential to enhance energy and industrial systems by distributing green technology across the host country and optimizing the allocation of resources, leading to a decrease in carbon dioxide emissions and an improvement in environmental quality [ 42 ]. So, according to this theory, FDI might improve and strengthen countries’ abilities for sustainable long-term economic growth. However, on the contrary, pollution port theory posits that the receiving nation’s emissions actually rise as a result of FDI. According to Cole [ 20 ], this theory is based on the idea that polluting factories are migrating from highly industrialized nations to underdeveloped industrial nations as a result of foreign direct investment (FDI) flows. This, in turn, stimulates economic growth in nations and leads to a significant increase in carbon dioxide emissions from fossil fuel combustion.

Research methodology

This segment provides an overview of the research design employed in the empirical study. It encompasses particulars regarding the sources and methodologies utilized for data collection, as well as the definition and references of both the dependent and independent variables. Furthermore, it encompasses the research’s model specification and the estimation technique utilized in order to accomplish the aims and objectives of the research.

Data collection and research variables

This study analyzes the impact of green energy, green technology, globalization, and foreign direct investment on green economic growth. The study utilizes panel data collected over 26 years (1995–2020) from G7 countries: Canada, France, Germany, Italy, Japan, the UK, and the USA. The variables employed in the study are of secondary nature and are sourced from authentic organizations such as the World Bank’s World Development Indicators (2023), the OECD (2023), and the KOF institute (2023). The variables are converted into natural logarithms in order to get normally distributed data by minimizing the variance, and by doing so, the coefficients can be interpreted in a more meaningful way (Table  1 ).

Model Specification and Research Design

The study analyzes the impact of GT, GE, GLO, and FDI on GG. The model is grounded on the classical production function, a theoretical framework that establishes economic progress by utilizing labor, capital, and technology as inputs to the output function.

where Y , L , K , and A represent economic growth, labor input, capital formation, and technology levels, respectively. In accordance with the endogenous growth theory, this study adjusted the model by replacing economic growth ( Y ) with green economic growth (GG), labor with globalization (GLO) and green energy (GE), capital with foreign direct investment (FDI), and technology with green technology (GT). Based on the theoretical approach, the functional form of the model is specified as:

All the variables mentioned are transformed into natural logarithms in order to get precise results. This work modified the methodologies of Ahmed et al. [ 4 ] and Khan et al. [ 32 ] and developed the subsequent models for the present investigation. Following the functional form of the model, Eq.  2 can be written in the econometric form as follows:

where t is the time period, i indicates the number of cross sections (countries), GG (dependent variable) represents green economic growth, whereas GE, GT, FDI, and GLO (explanatory variables). \({\beta }_{0}\) is the intercept, and \({\beta }_{s}\) are the variable coefficients. The error term is indicated by \(\varepsilon \) .

In order to understand the relationship between the variables of the study. The study employs various preliminary tests, such as descriptive statistics, correlation, and multicollinearity. Following the preliminary test, the econometric techniques of cross-sectional dependency, unit roots, and cointegration are employed. Based on these test results, the appropriate estimation procedures are carried out.

Econometric techniques

Cross-sectional dependency.

Previous studies have stated that panel data usually endures the problem of cross-sectional dependence. The reason being that the cross sections undergo mutual shocks [ 45 ]. Hence, ignoring the problem of cross-sectional dependency might lead to biased and misleading results [ 29 ]. Therefore, in order to check the cross-sectional dependency, the study employs the Pesaran CD test. The CD test is based on pairwise correlation coefficients and is suitable where the number of cross sections is small and the time period is large. The null hypothesis of the CD test proposes that the panel does not endure cross-sectional dependency, while the alternative hypothesis of the test suggests the occurrence of cross-sectional dependence. Following is the equation (Eq.  4 ) used to test the cross-sectional dependence [ 46 ].

where N denotes the number of cross sections (countries), T specifies the time period (1995–2020), and \(\delta \) is the coefficient of correlation which is estimated. Following the cross-sectional dependency test, the study carries a test for slope homogeneity in order to check whether the study variables satisfy the homogeneity condition or the panel is heterogeneous. The study employs the homogeneity test developed by Pesaran and Yamagata [ 47 ].

Panel unit root test

We perform the unit root test to verify the stationarity of the data at either a level or a difference.

To avoid spurious regression, it is important that the data be stationary. As we discussed earlier, the panel data usually encounters the problem of cross-sectional dependence. Therefore, the study employs second-generation unit root tests which account for cross-sectional dependence. The presence of cross-sectional dependence leads the study to employ the CIPS and CADF tests rather than the first-generation unit root test, which have been widely used in the majority of studies. However, the inability of first-generation unit roots [ 31 , 36 ] to deal with the issue of cross-sectional dependency does not make it the appropriate test for our study. Pesaran [ 45 ] proposed a second-generation test CIPS, which is established on cross-sectional augmented Dickey–Fuller (CADF) regression. The CIPS test statistics are based on the individual mean of the CADF. CADF computes the cross-sectional lagged average of individuals in order to monitor the effect of common factors.

Panel cointegration

In order to avoid imposing a common-factor limitation, Westerlund [ 59 ] created four novel panel cointegration tests that rely on structural rather than residual dynamics. In a conditional panel error-correction model, the goal is to determine if the error-correction term is equal to zero in order to test the null hypothesis of no cointegration. All of the novel tests follow normally distributed distributions and are sufficiently generic to account for cross-sectional dependence, unit-specific trend and slope parameters, and unit-specific short-run dynamics. In order to account for cross-sectional dependence, the study will incorporate the Westerlund cointegration test to confirm the association between the variables. Out of four tests, two panel statistics (Pa and Pt) are employed to examine the alternative hypothesis that the panel as a whole is cointegrated. The remaining two cross-sectional statistics (Ga and Gt) examine the alternative hypothesis that at least one unit is cointegrated.

Cross-sectional autoregressive distributed lag (CS-ARDL)

Once the existence of a long-term relationship has been established using Westerlund’s [ 59 ] panel cointegration test, this study will utilize a newly created method known as the cross-sectionally augmented autoregressive distributed lags model (CS-ARDL) developed by Chudik and Pesaran [ 19 ]. This study employs the CS-ARDL assessment to undertake both long-term and short-term estimations. This test is much more efficient compared to the mean group (MG), pooled mean group (PMG), common correlated effect mean group (CCEMG), and augmented mean group (AMG). Since the reason for obtaining inaccurate estimation results is directly linked to the neglect of unobserved common components. The CS-ARDL model effectively addresses previously unnoticed problems of endogeneity, non-stationarity, mixed-order integration, slope of homogeneity, and cross-sectional dependence. The CS-ARDL model incorporates the mean of cross section of both dependent and independent variables to overcome the issue of cross-sectional dependency [ 19 ]. The CS-ARDL equation for the model can be expressed by the following equation (Eq.  5 ).

where \({\overline{M} }_{i,t}\) = (Δ \(\overline{Ln{GG }_{it}}, \overline{{X}_{it}}\) ), the mean of dependent and independent variables.

While nl refers to the number of lagged cross-sectional averages for each variable.

Methodological framework

The graphical presentation of the methodology flowchart of the study is presented in Fig.  1 .

Methodological Flowchart

Findings and discussion

The test results shown in Table  2 show the descriptive statistics of the variables involved in the study. The results specify the mean, median, skewness, dispersion, and distribution of the data curve, along with the standard deviation. The data of green technology (GIT) exhibits the highest volatility with a standard deviation of 1.464, and among the study variables, globalization (GLO) is the least volatile with a standard deviation of 0.131. The data for green growth, green energy, and globalization is negatively skewed, while green technology and foreign direct investment data are positively skewed. All the variables except GIT have a more profound curve with a high peak, and GIT data has a flatter curve.

Table 2 reveals that there is a positive and significant correlation between GE and GG with the coefficient of 0.201 significant at 1%. The correlation between GLO and GG is also positive and significant, with a 0.12 coefficient and statistical significance at 5%. GLO also has a positive correlation with FDI. However, there is a negative correlation between GLO and GIT. The FDI and GIT are also negatively correlated at 1% significance. After the correlation matrix, the study exhibits multicollinearity test results in Table  3 through the VIF test. The results show that no multicollinearity exists between the variables, as the VIF value of each variable is below 5, with the overall mean VIF being 1.34. The VIF value above 5 would have concluded with the presence of multicollinearity.

Table  4 results confirm the presence of cross-sectional dependency. The Pesaran [ 44 ] CD test results reject the null hypothesis of no cross-sectional dependency. All the variables have significant coefficients with a p value 0.000. Hence, accepting the alternate hypothesis. Following the CD test, the study tests the homogeneity condition of the variable data. The Pesaran and Yamagata [ 47 ] test reveals that the null hypothesis of the cointegration coefficients are homogenous is rejected. The test confirms the presence of heterogeneity. Therefore, based on the above two tests, the study would proceed with a second-generation unit root test and heterogeneous panel estimations.

Due to the presence of cross-sectional dependency, the study employed second-generation unit root tests as they account for such a problem. To check the stationarity of the underlying variables, the CIPS and CADF tests are employed. The findings in Table  5 suggest that the variables have mixed stationarity. Some are stationary at level and some are not. However, all the variables are stationary at the first difference, I(1).

The results of the cointegration test in Table  6 suggest that there exists the cointegration between the variables of the study. The Westerlund cointegration test results reveal that out of four statistical tests two are significant (one from the panel, P t , and one from cross-sectional statistics, G t ). The null hypothesis of the test proposes that no cointegration exists between the variables of the study. The findings suggest that the null hypothesis of the Westerlund test is rejected at the level of the 5% probability value of P t and G t . The study accepts the alternative hypothesis, signifying the presence of a long-term relationship between GG, GE, GIT, FDI, and GLO.

In order to estimate the coefficients of the long and short run, the study utilized the advanced autoregressive distribution lag model, CS-ARDL. The main focus of the research is to understand the relationship between green energy and green economic growth. The study also sheds light on the role of green technology, investment, and globalization and investigates its impact on green economic growth. The relationship between the factors is studied for the G7 countries as they account for major economic powers. To achieve the overall sustainability, the G7 powers can be the driving forces. These economies have enormous and sizeable impacts on the overall economic corridors.

The empirical findings of CS-ARDL in Table  7 suggest that the GE has a positive impact on the GG in the long run as well as in the short run. In the long run, GG will increase by 1.195%, following a 1% increase in GE. In the short run, the increase in GE by 1% increased the GG by 0.913%. The results reveal a positive impact of GE on the GG at the level of 5% statistical significance in both the long and short run. The results indicate that enhancing green energy will push the G7 economic growth toward more sustainable growth which is beneficial for social and environmental sustainability. The attempt to rely more on renewable energy and less on nonrenewable energy can stimulate greener economic growth. The study result is in line with Alper and Oguz [ 8 ], Ahmad et al. [ 3 ], and Dong et al. [ 21 ]. The macroeconomic theory concludes that renewable energy and related aspects are critical for long-term economic growth, the results of the study confirm the theory. Hence, hypothesis of the study that green energy has a positive and significant impact on green economic growth is validated. The studies carried out by Shahbaz et al. [ 51 ] and Zafar et al. [ 63 ] indicate that the usage of renewable energy source can enhance the sustainable economic growth by reducing the impact on the environment.

The results in Table  7 reveal that GT has a positive impact on the GG in the long and short run. However, the coefficient is significant only in the long run. The economic benefits of green technology primarily stem from increased investment in the initial years. Over time, more growth gains come from cheaper, cleaner energy and more efficient production processes. The findings suggest that with an increase in GT by 1% in the long run, the green economic growth increases by 0.41%. The study is in line with recent studies carried out by Ahmed et al. [ 4 ] for South Asian countries and Khan et al. [ 32 ] for OECD countries, suggesting that green innovation technology has a favorable and substantial impact on green growth. As suggested by Sohag et al. [ 55 ] that green technology is an effective method for enhancing green economic growth. Greener technology significantly reduces the impact on the environment by utilizing resources efficiently and curtailing carbon emissions. Aghion et al. [ 2 ] also emphasized the importance of technological advancements in order to decrease the overreliance on nonrenewable energy and increase the usage of green energy for emission mitigation. In order to achieve green development, mitigating carbon emissions is of prime importance [ 1 ]. Green technologies are highly valued according to Porter’s theory. According to the theory [ 16 ], green technology helps the environment and the economy at the same time. Chen et al. [ 17 ] stated that technological advancements in carbon reduction and energy conservation improve environmental quality and stimulate green economic growth. The results of the study confirm the theory and hence the hypothesis of the study that green technology has a significant relationship with green economic growth holds true for G7 countries.

The study findings indicate that an increase in FDI in G7 countries results in an increase in green economic growth. An increase of 1% in FDI leads to an increase of GG by 0.219% in the short run and by 0.258% in the long run. The findings are in line with Xiao et al. [ 60 ], who also found that the increase in FDI has a positive and significant influence on the green economic growth. The study carried out by Khan et al. [ 32 ] emphasized that the investments acquired assist in technological advancements, infrastructure progress, and research development in order to enhance growth while emitting less carbon in OECD nations. Sustainable growth is achieved when economic growth expands while limiting the negative effects on the environment. Similar findings were reported by She and Mabrouk [ 53 ] in BRICS countries, wherein FDI positively impacted green economic growth. According to Hille et al. [ 28 ] and Xiao et al. [ 60 ], there is a positive impact of FDI on green economic growth. FDI mainly contributes to increasing the level of green economic growth through capital effect, environmental effect, technology spill over, improved resource utilization, and reduced pollution emissions. The increase in foreign investments tends to advance the environmental standards of the host nation by reducing environmental damage through the implementation of green production and the utilization of green energy with the help of green technology. The "Pollution Halo" theory states that foreign direct investment (FDI) has the potential to enhance energy and industrial systems by distributing green technology across the host country and optimizing the allocation of resources, leading to a decrease in carbon dioxide emissions and an improvement in environmental quality. On the other hand, pollution port theory posits that the receiving nation’s emissions actually rise as a result of FDI. The results of the study confirm the pollution halo theory and contradict the population port theory. Hence, the hypothesis of the study that FDI has a significant relationship with green economic growth is validated for G7 countries. The findings of the study in Table  8 reveal that globalization has a negative impact in the short and long run on green economic growth. However, the coefficients are statistically insignificant. The study done by Ali et al. [ 7 ] found that green economic growth is negatively correlated with globalization. However, globalization severely affects the environmental sustainability, which is an important component of green economic growth. Yang [ 62 ] also concluded in their study that although globalization contributes to the financial growth globally, it also proves to be detrimental to the ecology and environment. Hence, the hypothesis postulating that globalization has significant and negative relationship with green economic growth is partially validated. Although the coefficient suggests a negative relationship between globalization and economic growth, at the same time the result is insignificant.

In order to verify the strength of the model and the econometric approaches used in the study, we used a dynamic common correlated effect mean growth (DCCE) estimator to conduct a robustness check. The DCCE is an extension of the CCEMG developed by Pesaran and Chudik [ 19 ]. This model allows for slope heterogeneity and controls for endogenous regressors. It is also robust against cross-sectional dependence induced by unobserved common factors and shocks that appear at the same time as the result of economic integration between the countries. Table 8 provides the results of DCCE, and the findings are consistent with those of CS-ARDL. The results confirm the positive and significant relationship between GE, GT, FDI, and the GG.

The aim of this paper is to assess the impact of green energy and technology on green economic growth for G7 countries while incorporating FDI and globalization as explanatory variables. A greener economy means less burden on the environment, which leads to a healthy and improved quality of life. The research used various econometric techniques in order to investigate the relationship between the variables. The study involved CIPS and CADF for the unit root test, the CD test for cross-sectional dependency, the Westerlund test for cointegration, CS-ARDL for estimation of the coefficients, and DCCE for the robustness check.

The study concluded with three main findings; first, the usage of green energy leads to increased green economic growth. Our study reveals a positive impact of green energy on green economic growth, thus validating the hypothesis that green energy has significant and positive impact on green economic growth. As the literature suggests, economic growth can be detrimental to the environment due to overreliance on carbon-emitting energy sources. And on the contrary, green energy lessens the burden on the environment which proves to progress green economic growth. Secondly, the findings revealed that green technology can also contribute to green economic growth, thus validating the hypothesis that green technology has significant and positive impact on green economic growth. Green technology is a constructive force for green energy. Innovation in new technologies directly influences the state of energy. The more technological advancements in energy can lead to greener sources of energy. The increased consumption of green energy due to green technology heavily contributes to green economic growth. Third, the increased FDI positively impacts green economic growth. The study validates the pollution halo hypothesis, which states that FDI promotes green growth. FDI has the ability to improve environmental quality by reducing carbon dioxide emissions through optimum resource allocation and the use of efficient energy by introducing green technology.

This study’s findings provide valuable information for policymakers in G7 nations to build frameworks that promote green energy and green technology. The G7 nations are at the forefront of understanding the significance of constructing sustainable economies due to their strong hold on the global economy. Improved energy independence, long-term economic growth, and job creation can result from new policies that the government enacts to promote green technology and remove barriers to the consumption of green energy. The governments of G7 countries should implement green energy systems tailored to local climates, given the growing concerns about climate change and the need to improve energy security. Additionally, governments should consider implementing incentive programs, tax benefits, investment subsidies, incentive programs for technological innovations, and the trading of green energy certificates as ways to encourage the expansion of green energy consumption in order to develop greener economy. By replacing nonrenewable energy with renewable energy, we can lessen the financial strain of importing energy, stabilize energy prices on global markets, and slow the rate of environmental damage caused by carbon emissions. To further enhance environmental quality, lawmakers should invest more in green technology to promote green energy and increase green growth. G7 countries, due to their reputation, can acquire a decent amount of foreign investment and then distribute it to create an ecosystem that is environmentally friendly while at the same time enhancing economic growth. The investment can be utilized in developing infrastructure, technology to displace nonrenewable energy, and replace it with greener energy. Although the cost of replacing nonrenewable and excessive carbon-emitting energy sources is very high and in order to lessen the burden on the local economy, FDI is the most crucial component that can propel this change.

Because this study mostly includes industrialized and developed nations, future studies should look at low-income countries to see how green energy use relates to green economic growth. Furthermore, the present study explored four variables and their contribution to green economic growth. However, future researchers can add in more factors and see their impact on green economic growth. This study contributes to the literature of FDI and its relationship with green growth. Future researchers can incorporate green FDI as an indicator of investment and study its relationship with green economic growth. Another recommendation for future research related to this topic is to include cross-nations, which would provide extensive findings due to their varied characteristics.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

World Development Indicators

Organisation for Economic Co-operation and Development

Cross-sectional autoregressive distributed lag

Common correlated effect mean group

Augmented mean group

Foreign direct investment

Gross domestic product

Cross-sectional Im Pesaran Shim

Cross-sectional augmented Dickey–Fuller

• Green energy

Green technology

Green economic growth

• Globalization

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We are grateful to Editor Marianne W. Lewis and the anonymous reviewers for valuable time and considerations.

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Wani, M.J.G., Loganathan, N. & Esmail, H.A.H. Impact of green technology and energy on green economic growth: role of FDI and globalization in G7 economies. Futur Bus J 10 , 43 (2024). https://doi.org/10.1186/s43093-024-00329-1

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How does the digital economy affect the development of the green economy? Evidence from Chinese cities

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Key Analysis Laboratory of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, China

• Published: August 10, 2023
• https://doi.org/10.1371/journal.pone.0289826
• Reader Comments

The digital economy may accelerate the upgrading of industrial structures and boost regional innovation output, effectively contributing to China’s green economic transformation. The impact of the digital economy on developing the urban green economy is analyzed using data from 280 cities across China from 2010–2019. Using a fixed-effects model and the Spatial Durbin model, the digital economy is found to have a significant impact on urban green economy development. This result is shown to be robust to various factors. There is significant regional variability in the impact of the digital economy on green economic growth, with the strongest impact in the northeast, followed by the central and western regions. Meanwhile, non-resource-based cities and policy pilot cities have a more pronounced role in promoting the digital economy. The intermediate transmission chain of industrial structural upgrading and regional innovation output fosters the growth of the urban green economy via the digital economy. Regional innovation production is responsible for 30.848% of this growth, with the intermediate effect of industrial structural upgrading contributing to 38.155%.

Citation: Liao W (2023) How does the digital economy affect the development of the green economy? Evidence from Chinese cities. PLoS ONE 18(8): e0289826. https://doi.org/10.1371/journal.pone.0289826

Editor: William Mbanyele, Shandong University, CHINA

Received: March 17, 2023; Accepted: July 25, 2023; Published: August 10, 2023

Copyright: © 2023 Wenqi Liao. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Over the past four decades, China has experienced rapid economic growth since its economic reforms and opening to international trade. From 1978 to 2022, China’s GDP surged from 367.87 billion yuan to 121.01 trillion yuan, establishing itself as the world’s second-largest economy, trailing only the United States. China’s global significance has grown alongside its increasing contribution to global economic expansion. However, the prevailing development model, characterized by a focus on “high consumption, high pollution, and low efficiency,” has primarily led to quantitative growth without significant qualitative advancements. As a result, China has faced significant environmental degradation and extensive resource and energy depletion [ 1 ]. According to the 2021 China Ecological and Environmental Status Bulletin, the country has made continuous progress in improving ecological and environmental quality, with substantial reductions in emissions of major pollutants. However, approximately one-third of the 339 cities still fail to meet the national secondary standard for PM2.5, and there are periodic occurrences of severe regional pollution weather patterns. The 2020 Annual Report on Environmental Prevention and Control of Solid Waste Pollution in Large and Medium-Sized Cities highlights that the volume of domestic waste generated in 196 major Chinese cities reached an alarming 235.6 million tons. These statistics underscore the significant strain imposed on the environment due to rapid economic expansion. Recognizing the importance of environmental enhancement and resource conservation, the Chinese government has long prioritized achieving harmonious development of resources, the environment, and the economy. In response to escalating challenges of urban ecological and environmental pollution, the Central Committee of the Communist Party of China and the State Council have emphasized the urgent need for resolute pollution prevention and control measures. They are also accelerating the promotion of green and low-carbon development, enhancing environmental quality, bolstering the stability and quality of ecosystems, and comprehensively improving resource utilization efficiency. Consequently, achieving national green economic development, fostering the coordinated growth of the economy and ecological environmental protection, and exploring the paradigm of “green water, green mountains, and golden and silver mountains” in pursuit of sustainable green development have assumed paramount significance.

The digital economy represents a new economic paradigm centered on digital knowledge and information, driven by digital technology and facilitated by the information network. It enables the integration of digital technology and the real economy, leading to increased digitization, connectivity, and intelligence levels in society. This paves the way for the reconstruction of economic development and governance models [ 2 ]. China’s digital economy has experienced remarkable growth, expanding from 260 million yuan in 2005 to 4.55 billion yuan in 2021, making it the world’s second-largest digital economy. Its contribution to GDP has also risen from 14.2% in 2005 to 39.8% in 2021, playing a vital role as an “economy stabilizer” and “gas pedal” [ 3 ]. Despite this progress, China’s digital economy still has untapped potential compared to major developed nations like the USA, Germany, the UK, and Japan, where the digital economy accounts for around 50% of GDP. Moreover, the digital industry inherently promotes green development by balancing economic growth, resource conservation, and environmental protection. The goal is to achieve optimal green economic development by maximizing output while minimizing resource consumption, establishing a sustainable economic system with minimal environmental impact. The development of the digital economy marks a significant shift in development patterns, economic structure, and growth dynamics in China. It serves as a critical tool for realizing the “double carbon” plan, advancing ecological civilization, and accelerating green and low-carbon development.

This study focuses on the relationship between the digital economy and green economic development in China. Specifically, it examines how the digital economy impacts cities’ green economic development and how the dividends of the digital economy can be used to enhance green economic development. By addressing these questions, this research contributes to the understanding of the digital economy, provides new insights for improving China’s green economy, and holds significance for the country’s green transformation and the exploration of coordinated regional development.

This paper is structured as follows. Section 2 briefly reviews the relevant literature, before Section 3 introduces the theoretical mechanism and research hypotheses. Section 4 presents the relevant models and data. Section 5 lays out the results of empirical analysis, including robustness tests and heterogeneity analysis. Finally, Section 6 presents policy recommendations based on the main findings.

Literature review

Digital economy.

The concept of the digital economy was initially introduced by Don Tapscott in 1996. Over the past two decades, it has experienced significant growth and emerged as a new driving force for global economic recovery [ 4 ]. Particularly in the context of the COVID-19 pandemic, the digital economy has played a crucial role in supporting epidemic control efforts and facilitating the resumption of work, production, and education [ 5 ]. In contrast to the traditional offline physical economy, which relies on physical spaces, the digital economy leverages the advantages of networks and data, showcasing a wide range of applications and substantial development potential [ 6 ]. As a new economic and social form, the digital economy recognizes data as a new factor of production alongside capital, labor, and land [ 7 ]. It offers benefits such as easy access to information, diverse interaction methods, and reduced information and interaction costs [ 8 ]. Regarding the measurement of the digital economy, there is no definitive indicator system. The majority of research is qualitative, with the few quantitative studies mostly focusing on the national and provincial levels. National-level comparisons of digital economy development primarily examine the foundational industries and the impact of digital economy integration. At the provincial level, the measurement index system for the digital economy is primarily based on three dimensions: digital infrastructure, digital industries, and the digital environment [ 9 ]. At the city level, due to data limitations, the current indicator system for the digital economy primarily focuses on internet development and digital financial inclusion [ 8 ]. This study develops, from the perspective of hardware support and service scenarios, a digital economy indicator system for prefectural-level cities. The impacts stemming from the development of the digital economy are multifaceted and intricate. Taking a macro perspective, the advancement of the digital economy fosters effective economic growth [ 10 ] and positively contributes to the promotion of high-quality economic development [ 8 ], heightened total factor productivity [ 11 ], and the optimization of employment structures [ 12 ]. From a meso standpoint, digital economy advancements are intimately linked to the upgrading of industrial structures, which in turn drive regional innovation outputs [ 13 ], urban technological progress [ 14 ], the concentration of human resources, and industrial competitiveness [ 13 ]. At the micro level, breakthroughs in digital technology enhance labor mobility, generate high-quality employment opportunities [ 15 ], improve the alignment between workers and jobs, and expedite structural changes in employment [ 16 ]. Furthermore, the digital economy stimulates corporate innovation, enhances internal controls, and elevates risk levels [ 10 ].

Green economy

Green economic development embodies an approach to economic growth that prioritizes efficiency, harmony, and sustainability. Its fundamental principle lies in achieving the intrinsic unity, mutual reinforcement, and harmonious coexistence of economic development, environmental preservation, and social equity [ 17 ]. Currently, research on green economic development primarily revolves around two key aspects: measurement and influencing factors. Existing measurement methodologies encompass the stochastic frontier approach (SFA), data envelopment analysis (DEA), and principal component analysis (PCA). SFA offers the advantage of comprehensively considering the causes of production within the frontier boundary, accounting for stochastic shocks and technological inefficiencies, and enabling the use of panel data to study temporal trends among distinct entities. DEA, on the other hand, eliminates the need for indicator data standardization, bypasses the construction of a production function and subjective assignment steps, and frames the problem as a linear optimization challenge within the production domain. However, one limitation is that efficiency values are still calculated in the absence of an explicit relationship between input and output indicators, necessitating careful selection of these indicators. In contrast, PCA incorporates environmental-type indicators as a means of dimensionality reduction, treating environmental pollution as an undesirable outcome, while replacing outdated output indicators that fail to consider environmental factors [ 18 ]. Notably, this method aligns with the actual production process and avoids the generation of “infeasible solutions” that may arise from the introduction of undesirable outputs. Thus, PCA is used to develop the indicator system in this paper.

In recent years, numerous studies have been conducted on the achievement and influencing factors of green economic development. Scholars have primarily explored the conditions for realizing green economic development from two perspectives: economic transformation and environmental factors. With regards to economic transformation, fiscal decentralization has been found to facilitate green economic development in regions experiencing growth in green total factor productivity [ 19 ]. Additionally, factors such as R&D investment [ 20 ], foreign direct investment [ 21 ], and a sophisticated labor market [ 22 ] are conducive to promoting the upgrading of industrial structures, enhancing regional economic efficiency, and consequently fostering green economic development [ 23 ]. Concerning environmental factors, infrastructure construction plays a positive role in propelling the development of regional green economies [ 24 ], while the mitigation of pollutant emissions also influences the level of green economic development [ 25 ]. Building upon these findings, this paper establishes an enhanced indicator system to measure the level of urban green economic development and selects appropriate control variables.

Digital economy and green economy

Given the prevailing economic uncertainties, the advancement of both the digital economy and the green economy has become indispensable for achieving a harmonious blend of economic growth and environmental progress [ 26 ]. Existing research on these subjects can be broadly categorized into two groups. The first category centers on the coordinated development of the digital economy and the green economy, which proves advantageous for economic recovery [ 27 ]. Specifically, with the guidance of pertinent green policies, the synergistic effect between these two sectors stimulates the growth of associated industries, enhances labor market flexibility, and facilitates the transformation and upgrading of the industrial structure [ 28 ]. This approach lays the groundwork for post-pandemic economic revival [ 29 ] and nurtures sustainable economic development. The second category of research explores the impact of the digital economy on the development of the green economy. It posits a positive relationship between the two, as the digital economy offers high-quality technological resources that support various aspects of life [ 30 ]. This includes the transformation and modernization of industrial structures, the empowerment of the circular economy [ 31 ], the digitization of business processes [ 32 ], and the promotion of household consumption [ 12 , 33 ]. Consequently, this not only accelerates the emergence of new industries but also enhances the efficiency of the green economy and facilitates the transition toward a more environmentally friendly economic model [ 34 ]. Nevertheless, some scholars have raised concerns about potential adverse effects of the digital economy on the green economy. They argue that the operation of digital infrastructure and the storage of vast amounts of data necessitate substantial electricity consumption, thereby placing pressure on the environment. Additionally, the increased demand for digital products, coupled with their rapid obsolescence, leads to heightened raw material consumption and the need for effective waste recycling, which may pose challenges to the development of a green economy [ 35 ].

After reviewing the existing literature, we can identify four main deficiencies in current research. Firstly, studies on the digital economy are still in their early stages, primarily consisting of theoretical analyses and logical frameworks, with limited quantitative research and city-level exploration. Moreover, the measurement indicators for the digital economy require further refinement. Secondly, when assessing the level of green development, only positive indicators such as economic growth and ecological benefits are considered, and negative indicators such as pollution emissions and environmental pollution are neglected. Therefore, it is necessary to establish a scientific and reasonable index system to accurately measure the green development of cities. Thirdly, although several studies have confirmed the mediating effect of regional innovation, few incorporate industrial structure upgrades into their analytical framework. Therefore, a deeper exploration is needed to understand the underlying mechanisms through which the digital economy influences urban green development. Lastly, there is considerable room for expanding the analysis of spatial and heterogeneity aspects regarding the impact of the digital economy on green development.

The main contributions of this paper are as follows: (1) Supplements the existing indicator systems by constructing evaluation frameworks for the digital economy based on hardware support and service scenarios, as well as evaluation frameworks for green economic development based on resource utilization, environmental governance, growth quality, and environmental quality. (2) Introduces the intermediary channel of industrial structure upgrading, considering regional innovation output and industrial structure upgrading as mediating variables. This approach provides a deeper examination of the pathway mechanisms through which the digital economy promotes urban green development. (3) Further explores the spatial spillover effects of the digital economy on urban green development. The study distinguishes cities based on different geographical locations, city types, and policy pilot cities, and separately discusses the heterogeneity of the impact of the digital economy on urban green development. This enriches the research scope and perspectives.

Theoretical mechanism and hypotheses

Theoretical mechanism.

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https://doi.org/10.1371/journal.pone.0289826.g001

Based on Eqs ( 16 ) and ( 17 ), it can be argued that the advanced industrial structure and regional innovation output are the mediating variables between the digital economy and the development of the green economy.

Theoretical hypotheses

Fig 1 illustrates the direct and indirect impacts of the digital economy on urban economic growth, including economies of scale, economies of scope, and long-tail effects [ 38 ]. The digital economy, with its focus on green development and low pollution emissions, plays a crucial role in shaping the urban green economy. From the supply side, the digital economy transforms the traditional model of economic development by reducing reliance on natural resources and minimizing environmental pollution. Data, along with land, labor, capital, and technology, become key drivers of economic growth, facilitating the creation of supplies with lower ecological damage. Furthermore, the digital economy enables enterprises to enhance the technological complexity of their export products [ 39 ], deepen their integration within the global value chain, and promote green development, resource efficiency, and energy use improvement. From the demand side, the infrastructure of the digital economy enables the exploration of the value and potential of large-scale data, leading to the identification of differentiated consumer needs and the expansion of the demand goods market. This process generates new social and economic values [ 40 ]. Through the utilization of digital technology, a green bridge can be established among the government, enterprises, and the public, fostering the development of green consumer products and promoting green consumption among the public [ 41 ]. This, in turn, promotes the dissemination of green environmental protection concepts, generates greater economic and ecological dividends, and ultimately enhances the development of the urban green economy. Based on these observations, we propose the following hypothesis.

H1 : The digital economy makes a significant contribution to urban green economic development.

According to the theory of New Economic Geography, the proximity and spatial differences between regions significantly influence industrial collaboration, development, and innovation. The digital economy, with its ability to compress spatial and temporal distances through efficient information transfer, enhances inter-regional economic activity linkages, knowledge exchange, and technology sharing [ 8 ]. These factors contribute to the spatial spillover effects of the digital economy, influencing the innovation process and green development in neighboring regions [ 42 ]. The digital economy also promotes the formation of collaborative and cooperative industrial alliances among various market participants, including the government, enterprises, and individuals. This dynamic engenders a mechanism of resource-sharing and synergistic leveraging of advantages among these stakeholders, engendering a positive impact on the caliber of economic development in neighboring regions [ 12 ]. The presence of such alliances positively impacts the quality of economic development in nearby regions. Due to the spatial spillover effects facilitated by the digital economy, the efficiency gap in green economy development among Chinese provinces is gradually narrowing. This convergence demonstrates “Club Convergence” characteristics, exhibiting positive spatial correlation and a strong degree of spillover [ 43 ]. The flow of production factors, resource sharing, and capital enabled by the digital economy promotes the spatial spillover of green technologies between regions, facilitating the sharing and development of green technological innovation among different areas. Based on these observations, we propose the following hypothesis.

H2 : There is a significant positive spatial spillover effect from the digital economy to urban green economic development.

The digital economy has significant indirect impacts on green economic development, primarily through driving industrial structure upgrading and promoting regional innovation output. The transformation of labor and capital-intensive industries into highly technology-intensive sectors, driven by the digital economy, leads to an advanced industrial structure and improved ecological efficiency [ 44 ]. Industries associated with the digital economy rely on knowledge, information, and digital technology as key factors of production, reducing reliance on natural resources and achieving sustainable development with low inputs and minimal pollution [ 45 ]. Technological innovation, enabled by the digital economy, plays a crucial role in achieving cleaner production by reducing resource consumption and energy intensity [ 46 ]. At the micro level, technologies like big data, artificial intelligence, internet of things, and cloud computing break down information barriers, driving competition and the development of new technologies and products [ 47 ]. At the meso level, high-tech industries facilitate the diffusion of new technologies across sectors, enabling innovation in traditional industries influenced by the digital economy [ 48 ]. At the macro level, the digital economy allows for the reorganization of cross-regional innovation resources through online platforms, promoting coordinated development and elevating cross-regional collaborative innovation [ 49 ]. The enhancement of regional innovation output effectively addresses the contradiction between economic growth and the ecological environment, serving as a driver for sustainable development [ 50 ]. Based on these observations, we propose the following hypothesis.

H3 : The digital economy enhances urban green economic development by driving the optimization and upgrading of industrial structures and improving regional technological innovation output.

Methodology and data

Measures of green economy and digital economy.

The core of green economic development is to promote the coordination of the ecological environment and economic development, and to achieve common progress in terms of ecology, economy, and society. With reference to existing studies combined with data availability, this study constructs city-level index systems for measuring the development of the green economy (GE) and digital economy (DE) in China. The GE index system includes 18 indicators related to resource utilization, environmental governance, growth quality, and environmental quality. The DE index system considers factors such as hardware support and service scenarios and consists of five indicators [ 51 ]. Table 1 lists the variables for the construction of urban GE and DE indicator systems, both of which adopt PCA.

https://doi.org/10.1371/journal.pone.0289826.t001

Data source

This study uses panel data from 280 prefecture-level cities in China (2010–2019). Data sources include the China City Statistical Yearbook, China Energy Statistical Yearbook, and Social Development Bulletin. Missing values were filled using the mean, and monetary indicators were adjusted to the 2010 base year. The full names, definitions and symbols of all variables are displayed in Table 2 . Descriptive statistics ( Table 3 ) indicate stable and non-volatile data. Multicollinearity tests show variance inflation factors below 10, suggesting no multicollinearity issues. Correlation coefficients (Table 1 in S1 Appendix ) are mostly significant at the 1% level, confirming that there are no problems with the degree of correlation among the variables. Consequently, multicollinearity is not a concern in the subsequent analysis.

https://doi.org/10.1371/journal.pone.0289826.t002

https://doi.org/10.1371/journal.pone.0289826.t003

Empirical research

The measure results of ge and de.

To examine the temporal change characteristics of GE and DE, we conducted an analysis of annual average statistics for 280 cities in China from 2010 to 2019. The results, as illustrated in Fig 2 , reveal that both GE and DE exhibit relatively stable patterns with a consistent upward trend over the study period. Specifically, GE shows an increase from 5.59 to 6.22, representing a notable growth of 11.29%. This positive development, coupled with China’s concerted efforts to address environmental challenges amidst economic progress, suggests the effectiveness of environmental control policies and management practices. Moreover, DE demonstrates a remarkable surge from 0.71 to 1.32, indicating a substantial growth rate of 84.12%. This surge can be attributed to the rapid advancements in new-generation information technologies such as big data, cloud computing, internet of things, and artificial intelligence. The integration of these technologies with various sectors of the economy and society has enabled a robust expansion of DE. In addition, we categorized the data according to the 12th Five-Year Plan (2011–2015) and the 13th Five-Year Plan (2016–2019). A noteworthy observation is that during the 12th Five-Year Plan period, the regions with high levels of GE were predominantly concentrated in the eastern coastal areas, with prominent urban agglomerations like the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei region. However, in the subsequent 13th Five-Year Plan period, there was a noticeable shift of high GE levels toward inland areas, particularly the Chengdu–Chongqing city cluster. Similarly, during the 12th Five-Year Plan period, high DE levels were concentrated in the eastern coastal regions, but during the 13th Five-Year Plan period, there was a discernible trend of DE expansion toward inland regions. The analysis conducted above reveals a clear spatial correlation between GE and DE, underscoring the interplay between environmental factors and the development of the digital economy.

https://doi.org/10.1371/journal.pone.0289826.g002

Empirical results

Panel unit root tests were conducted to ensure data smoothness (Table 2 in S1 Appendix ). The presence of significant positive spatial correlation among the variables was confirmed using the Moran’s index test and local Moran index scatter plots (Table 3 and Fig 1 in S1 Appendix ). Based on the results of the LM, Wald, and LR tests, the the dual spatial and time-double fixed SDM was selected for regression analysis (Table 4 in S1 Appendix ). The regression results, presented in Table 4 , indicate consistent coefficients and significance across the five models. The coefficients of DE in all models are significantly positive at the 1% level. This suggests that DE, with its focus on digital knowledge and information, enhances the efficiency of traditional resource utilization and enables digitalization, intelligence, and networking of production processes. Consequently, it contributes significantly to GE development, supporting the verification of H1 . The DE also exhibits spatial externality, as evidenced by the positive coefficient in model (5) at the 1% significance level. This spatial externality promotes cross-regional flow and integration of production factors through information dissemination, enhancing technological innovation capability, improving factor allocation efficiency, and effectively driving green development in both the focal city and neighboring cities. Therefore, H2 is confirmed. In terms of control variables, innovation input and economic development have a positive impact on urban GE development, while the effects of openness to the outside world, infrastructure construction, and the advanced labor market are not significant. It is important to note that while the coefficients estimated by the spatial econometric model indicate the variables’ effects and their spatial lagged terms, the true effects, both direct and indirect, should be derived using partial derivatives [ 52 , 53 ].

https://doi.org/10.1371/journal.pone.0289826.t004

Table 5 presents the decomposition results of the SDM, revealing spatial spillover effects for each variable. The development of the DE in cities fosters the integration and growth of digital industries in neighboring cities, leading to improved production efficiency and reduced pollution emissions. The significant direct effect (0.055) indicates that the DE surpasses a critical point, enabling the growth of network value and economies of scale [ 38 ]. This contributes significantly to GE development in cities by promoting economic growth, cost reduction, and environmental sustainability. While the indirect effect is not significant, it remains positive, suggesting that GE development in the city is also beneficial for neighboring cities. The direct and indirect effects of an advanced industrial structure are both significantly positive, indicating that high-tech and efficient enterprises attract more production factors, such as human and financial capital, and drive GE development through improved production efficiency. The rise of technology-intensive and knowledge-intensive industries enhances production efficiency and stimulates GE development in the city, while also benefiting neighboring cities through technology and scale advantages. The direct effect of regional innovation output is significantly positive, demonstrating that it promotes information exchange between regions through advanced technologies like the internet, information and communication, and big data. This spurs research and development activities and facilitates the provision of new technologies and products, leading to improvements in regional human capital and labor productivity. Cities with high innovation output drive urban green total factor productivity improvement through various effects, such as technology, agglomeration, and pushback effects [ 19 ], ultimately achieving GE development. Among the control variables, the direct effect of environmental regulation is significantly positive, indicating its role in promoting GE development. Urban infrastructure has a positive total effect on the GE development of the city and neighboring cities, indicating its catalytic impact on GE development.

https://doi.org/10.1371/journal.pone.0289826.t005

To test whether the above conclusions are reliable, four methods are used to test the robustness of the empirical results. (1) Replace the spatial weight matrix . The spatial weight matrix was replaced with two alternative matrices: the economic geographic distance weight matrix ( W 2 ) and the economic gravity weight matrix ( W 3 ). The results from models (6) and (7) in Table 6 confirmed that the direction and significance of the DE’s influence on GE development remained consistent with the original results using W 1 , indicating the robustness of the conclusions to changes in the weight matrix measurement method. (2) Replacement of explanatory variables . The explanatory variables were replaced by using the SBM-ML index method to measure GE development from an input-output perspective. The results from model (8) remained robust after substituting the results calculated by this method as the core explanatory variables. (3) Regression by period . Regression analysis was conducted for different periods corresponding to the Chinese economy’s cyclical evolution of the Five-Year Plan. The estimation results from models (9) and (10) indicated that the DE made a significant contribution to the development of urban GE during both the 12th and 13th Five-Year Plan periods, thereby confirming the robustness of the main findings. (4) Instrumental variables approach . Regression analysis was conducted for different periods corresponding to the Chinese economy’s cyclical evolution of the Five-Year Plan. The estimation results from models (9) and (10) indicated that the DE made a significant contribution to the development of urban GE during both the 12th and 13th Five-Year Plan periods, thereby confirming the robustness of the main findings. An instrumental variables approach was employed to address potential endogeneity issues. One- and two-period lags of the DE were introduced as instrumental variables, and two-stage least squares estimation was performed. The tests showed that there were no weak instrumental variable problems, unidentifiability problems, or over-identification problems. The results from model (11) demonstrated the robustness of the conclusions when considering endogeneity.

https://doi.org/10.1371/journal.pone.0289826.t006

Mediating effect analysis

In this study, GE is denoted as Y, DE as X, and IND, INNOV as M. The mediating effect model is constructed by incorporating control variables. Models (13) and (16) in Table 7 reveal significant coefficient values for the DE in relation to advances in industrial structure (0.147) and regional innovation output (0.267) at the 1% level. Models (14) and (17) demonstrate that the DE contributes significantly to GE development through advances in industrial structure and innovation output. The Sobel test results indicate that the mediating effect of advances in industrial structure accounts for 38.185% of the total effect, with a Z-value of 11.950 that is significant at the 1% level. Likewise, the mediating effect of regional innovation output accounts for 30.848% of the total effect, with a Z-value of 6.356 that is significant at the 1% level. Both mediating variables exhibit partial mediating effects, suggesting that the DE enhances GE development by optimizing industrial structures and promoting regional technological innovation output. This confirms hypothesis H3 .

https://doi.org/10.1371/journal.pone.0289826.t007

Heterogeneity analysis

Geographic heterogeneity..

Regional heterogeneity in the relationship between DE development and urban GE development is evident due to objective differences in economic, historical, and geographical factors across China. To analyze this heterogeneity, the 280 cities in this study are divided into four regions: east, central, west, and northeast. The results of direct and indirect effect decomposition obtained through spatial econometric model regression are presented in Table 8 . In the eastern, central, and northeastern regions, the direct effect of the DE is significantly positive. However, in the western region, the direct effect of DE development is positive but not significant, while the indirect effect is significantly positive. In terms of impact magnitude, the promotion effect of the DE on urban GE development is significantly stronger in the northeastern region compared to the eastern, central, and western regions. The order of DE dividends released for urban GE development can be ranked as follows: northeast—central—east—west. This regional heterogeneity can be explained by two factors. Firstly, based on the concept of the “long tail effect,” the DE primarily targets the “long tail” groups, including low-income individuals and small- and medium-sized enterprises. By lowering barriers to entry, the DE effectively reduces costs and generates greater output benefits for these groups. The northeastern region, with relatively lower economic development, benefits more from DE development in terms of improving eco-efficiency. Secondly, the marginal effect of the DE in enhancing eco-efficiency is higher in the northeast. Although the level of DE development in the eastern region is significantly higher than in other regions, the law of diminishing marginal effects suggests that the improvement in eco-efficiency due to the DE is more pronounced in the northeastern region. The growth potential of DE dividends in the eastern region is limited. Additionally, the western region lags behind in digital economy development due to weaker network infrastructure, leading to a less noticeable impact on urban green economy development.

https://doi.org/10.1371/journal.pone.0289826.t008

City development type heterogeneity.

Resource-based cities primarily rely on heavy industry and factor inputs like labor and mineral resources for economic development, resulting in an industrial structure dominated by these sectors. However, they often face challenges in technological innovation, factor allocation, and the “resource curse” when pursuing green development. To investigate this issue, this study utilizes the National Sustainable Development Plan for Resource-based Cities (2013–2020) issued by the State Council and categorizes the sample into resource-based and non-resource-based cities. Analyzing the regression results presented in Table 9 , it becomes evident that the digital economy plays a significant role in enhancing green economic development in both types of cities, particularly in non-resource-based cities. The limited integration of traditional high-pollution and high-energy-consuming industries with the digital economy in resource-based cities may explain this observation. Conversely, non-resource-based cities exhibit a more balanced industrial structure, with higher investment in scientific and technological talents and green innovation research and development. Consequently, they can harness the benefits of the digital economy to a greater extent.

https://doi.org/10.1371/journal.pone.0289826.t009

Policy intensity heterogeneity.

The digital economy heavily relies on policy support as an emerging industry. Developed countries like the United States have been formulating and endorsing digital economy development policies since 2010. In China, the attention towards the digital economy has also been growing. The State Council introduced the “Broadband China” strategy and implementation plan in 2013, followed by the identification of three batches of “Broadband China” pilot cities in 2014, 2015, and 2016. These pilot cities have played a crucial role in promoting urban network infrastructure construction and upgrade. To assess the impact of the digital economy on urban green economic development under different policy intensities, this study divides the sample into pilot cities and non-pilot cities based on the pilot city list. Regression analysis in Table 9 reveals that the digital economy significantly contributes to improving the green economy development level in pilot cities. However, its effect on non-pilot cities is not statistically significant. This highlights the importance of policy support in leveraging the digital economy for urban green economic development. Policy preferences and supervision received by pilot cities stimulate digital economy development and enhance its positive impact on the urban economy. Furthermore, the overall development of network infrastructure facilitates the growth of the electronic information industry and accelerates economic structural transformation.

Conclusion and policy implications

Based on panel data from 280 prefecture-level and above cities in China from 2010–2019, this paper has empirically demonstrated the impact of the digital economy on green economic development and the mechanism of action using a spatial Durbin model and a mediating effects model. The main conclusions are as follows: (1) The fixed-effects-based baseline regression model and the geographic weight matrix-based spatial Durbin model show that the digital economy effectively enhances the green economic development of cities, and the conclusions are robust to changes in the spatial weight matrix, core explanatory variables, phased regressions, and instrumental variables. (2) There is a significant positive spatial effect of the digital economy on the green economic development of cities. While enhancing the green economic development of cities, the digital economy also promotes the green economic development of neighboring cities through the knowledge spillover effect, which contributes to synergistic development among cities. (3) Regional heterogeneity is evident in the impact of the digital economy on green economic development, with the highest impact observed in the northeast region, followed by the central region, and then the western region. Moreover, the digital economy plays a more prominent role in promoting green economic development in non-resource-based cities and “broadband China” pilot cities. (4) The promotion of an advanced industrial structure and regional innovation output are the mechanisms whereby the digital economy acts to improve urban green economic development. Mediating effect analysis shows that the proportion of the total effect is 38.185% for advances in industrial structure and 30.848% for regional innovation output. This means that the optimization of industrial structures and regional innovation output driven by the digital economy are important ways of improving the green economic development of cities.

Based on the above conclusions, this paper puts forth the following recommendations. Firstly, expedite the digital penetration of traditional industries and explore new pathways for green development. Promote the integrated development of 5G communication, big data, the Internet of Things (IoT), and artificial intelligence (AI) with traditional industries, facilitating their green and digital transformation. This comprehensive approach aims to enhance energy and resource efficiency, reduce production costs, and further unleash the driving force of the digital economy in fostering urban green development. Additionally, actively guide capital flow towards emerging industries that prioritize resource conservation and environmental friendliness. By improving factor allocation efficiency, a comprehensive green transformation of urban economic and social development can be achieved.

Secondly, accelerate the construction of regional cyberspace to fully leverage the spatial spillover effects of the digital economy. There exist significant regional disparities in China’s digital economy development. Therefore, it is crucial to expedite the construction of regional cyberspace, effectively enhancing the radiating effect of regions with advanced digital technology on their neighboring areas. This will promote the development of the digital economy in different regions and facilitate regional synergistic development strategies. The integration and development of digital technology among regions serve as essential means to establish regional networks, harness the spatial spillover effects of the digital economy on green economic development, and drive development in adjacent regions.

Thirdly, optimize the overall layout of building a Digital China and foster new engines for green development. From a governmental perspective, a holistic approach should be adopted to further refine the top-level design for digital economic development. Clear goals and directions for China’s future digital economy development should be established, accompanied by the formulation of rational planning schemes. Simultaneously, taking into account the resource endowment and comparative advantages of each city, the comprehensive carrying capacity and overall level of digital economic development should be improved to curb the widening “digital divide.” Accelerate the process of digitally-driven urban green coordinated development, allowing cities with different geographical locations and development types to share the green benefits brought about by the digital economy.

Lastly, strengthen industrial structure upgrades and increase investment in regional technological innovation. This approach will optimize the promoting role of the data economy in industrial structure upgrades and the enhancement of regional innovation output, thereby advancing regional green economic development. Overall, the aim is to promote green and sustainable development, gradually establish a green, low-carbon circular economy, and achieve high-quality economic development.

Supporting information

S1 appendix..

https://doi.org/10.1371/journal.pone.0289826.s001

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Evolutionary game analysis of emission-dependent supply chain carbon abatement behaviours considering cap-and-trade mechanism

• Original Paper
• Published: 09 May 2024

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• Z. J. Ying 1 &
• Y. S. Liu 1  

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Green development has become the focus of the global industrial competition. In order to explore the emission-dependent supply chain carbon abatement behaviours, this paper constructs an evolutionary game model of high-carbon emission manufacturers and carbon quota suppliers to study the comparisons of the carbon abatement strategy of manufacturers and the trading path of suppliers. Moreover, the influence of related parameters: emission reduction cost coefficient, proportion of carbon quota purchase and transaction cost on the evolutionary game is further analyzed. The results show that: (1) the combination of green technology and carbon quota purchase strategy is the best choice for manufacturers; (2) The proportion of carbon quota purchase is the key parameter, which mainly affects the evolutionary trend of emission-dependent supply chain carbon abatement behaviours; (3) Manufacturers play a critical role in the emission-dependent supply chain system. Numerical simulation is conducted to illustrate the models and validate the findings.

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Acknowledgements

This research was funded by the National Social Science Fund of China [18BGL182].

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J. Wei, Z. J. Ying & Y. S. Liu

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All the authors have contributed to developing the current study. WJ provided the direction and gave guidance to the paper. YZJ analyzed the evolutionary game and data simulation and was a major contributor to the manuscript writing. LYS determined the content including background, object, and method of the research in the early stage of the manuscript. All authors read and approved the final manuscript.

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Wei, J., Ying, Z.J. & Liu, Y.S. Evolutionary game analysis of emission-dependent supply chain carbon abatement behaviours considering cap-and-trade mechanism. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05648-y

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Received : 23 June 2023

Revised : 15 December 2023

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Published : 09 May 2024

DOI : https://doi.org/10.1007/s13762-024-05648-y

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* Corresponding authors

a National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide & Agricultural Bioengineering, Ministry of Education, State-Local Joint Laboratory for Comprehensive Utilization of Biomass, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, Guizhou, China E-mail: [email protected]

b Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive, NW, Calgary, Alberta, Canada E-mail: [email protected]

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