The Role of Education and Green Innovation in Green Transition: Advancing the United Nations Agenda on Sustainable Development

Abstract

Educating the masses about the dire consequences of climate change in high-polluting countries is essential to achieving the United Nations’ sustainable development agenda. This study investigates the role of education in environmental sustainability and influencing mechanisms of green innovation and government intervention in China. Using panel data of 31 provinces from 2008 to 2020, our analysis documents the significant impact of education in reducing pollutant emissions. Further, green innovation and government intervention can enhance this impact significantly. Regional heterogeneity tests show a more pronounced effect of education and government intervention in the eastern and western provinces, while the western region is indifferent in this regard. The results are robust to the replacement of core variables and shifting mean values. The likely endogeneity issue is resolved through the system GMM approach, which provides similar estimates as in the baseline regression. The study offers several implications for the government, researchers, policymakers, environmentalists, and industrialists.

1. Introduction

At present, environmental anomalies are being observed globally, which have endangered humans as well as economies. On one side, humans are making numerous accomplishments economically, industrially, and technologically by exploiting their knowledge and skills. On the other side, they have brought themselves to the verge of destruction by ruining the environment through harmful processes and materials for development purposes. The sudden climatic changes, increasing pollution, the higher levels of emission of greenhouse gases (GHGs), the continuous rise in the global temperature, and the reduction in air and water quality have all been caused by the lethal industrial progress globally [1,2,3]. Considering the adversities, it is essential to work on innovative strategies to protect the environment by suppressing the effects of climatic aberrations and simultaneously making economic and technological progress.

In this perspective, the Sustainable Development Goals (SDGs) designed by the United Nations in 2012 were a huge step toward educating countries worldwide to alleviate greenhouse gas emissions, especially CO₂, and reduce the consumption of natural resources by switching to alternative and eco-friendly means. Accordingly, China has also announced that it will combat carbon emissions by peaking it in 2030 and reaching neutrality by 2060 [4]. This is because China has made tremendous technological progress quickly; however, it emits 11.9 billion tons of CO₂ (33% of global emissions; see IEA, 2021, Figure 1) annually. China initiated the journey towards greening the environment by implementing various reforms and strategies, bringing about many structural and economic changes [5]. The country promoted green technological innovations, redesigned its industrial structure, favored renewable energy technology innovations, and introduced environmental regulation for industries (voluntary control, carbon taxes, market incentives), with significant capacity to curb emissions [6,7,8,9,10]. Indeed, the factors mentioned above or steps taken by China have proved to be environmental saviors. However, the role of education in green transition has been observed in limited studies in this regard. Researchers have placed immense importance on the education of people in the literature. Education makes people informed and concerned about environmental problems. Fisher and McAdams [11] suggested that students who study the subject of the environment are more inclined toward preserving the environment. Alekjeseva [12] also believes that higher education and labor are crucial to scientific progress and becoming a prosperous country. When human capital is provided with proper knowledge, skills, and training, their productivity increases through a deep understanding of the technology and processes used [13]. Moreover, high workforce education makes them energy efficient [14], and focuses their attention on adopting environmentally friendly technologies, means of communication, and pollution-free transportation. Higher education limits the energy consumption of households and further exacerbates renewable energy consumption [15]. It also amplifies economic growth [16]. Also, highly educated human capital utilizes technologies innovatively and efficiently to produce green and clean products [17]. Education makes human capital aware of the country’s capacity to follow environmental standards and helps promote green innovation [18].

Figure 1 shows the share of industry in emissions in China compared with other countries. Although the overall percentage of industrial emissions is higher, cement and steelmaking is less energy-intensive compared with the G20 average. In China, education is observed to mitigate CO₂ emissions [19], as the proportion of services in the GDP is rising. It is reported that the total number of students who graduated from public colleges and universities in China is 8.3 million, and the enrolment rate in tertiary education was 57.8% in 2021, which is a remarkable increment compared to 1990, i.e., 3.4% (Ministry of Education, 2022). The government education expenditure has been inflating in recent years and reached CNY 3.95 trillion in 2022. Considering CO₂ emissions and education together, the actual figures have opposed the contextual notion that education is mitigating the emissions as described by Li et al. [19]. Alongside this, the impact of education on pollutant emissions (not only CO₂) has not been witnessed extensively in the Chinese context.

Upon realizing the importance of education in this regard, this study aims to investigate the impact of education on environmental sustainability. Another reason for studying this influence is that the Chinese government has been focusing seriously on environmental, industrial, technological, and financial policies and regulations regarding green transition. However, systematic guidelines for promoting green education have not been observed to be the point of focus of the government. The study incorporates the influence mechanisms of green innovation and government intervention to diversify the framework and the empirical results further. Green innovation is the leading environmental improvement factor. It helps control pollution and recycling through modern and advanced procedures and technologies [20]. Figure 2 displays how patents related to climate change have been intensified in China compared with other countries recently. Henceforth, green innovation can help industries to invest in educated, talented, and environmentally informed human capital to ensure a green transition. Likewise, government intervention in terms of environmental regulations, educational policies, educational expenditure, training of labor, etc., enhances education’s influence on environmental sustainability [21]. Furthermore, the study also examines how the level of education varies in the eastern, central, and western regions of China. The heterogeneity test is necessary to provide policy implications to induce talent and skills in the less developed area to achieve the SDGs and dual carbon goals promptly.

The research is conducted to unearth how educating people can benefit the environment in regional China and the influence of green innovations and government interventions on the influence of education on green transition. In this regard, the findings of this study highlight the hidden contribution and potential of education in resisting the debilitating impact of pollutant emissions on the environment. Secondly, it will open new insights for industry owners in different regions about utilizing educated human capital to produce innovative products and grasping the talented labor for increasing productivity. Thirdly, the heterogeneity among the three areas will guide the government to explore the benefits of this hidden treasure of education and formulate impactful policies for promoting education and environmental awareness in the weak-performing regions of China. Investing in developing labor skills can increase the competition between the firms, and the goal of reducing pollutant emissions can be achieved in this way.

First, the study provides empirical evidence that education significantly negatively impacts carbon emissions in provincial China, which confirms and extends previous studies that have suggested a positive relationship between education and environmental sustainability. Second, the study shows that government intervention can enhance the impact of education on the environment, which suggests that policy measures can effectively promote the green transition. Third, the study highlights the regional heterogeneity of the relationship between education and the environment in China, indicating that education’s benefits for the green transition may vary across different regions. Fourth, the study suggests that green innovation can play a supportive role in enhancing the impact of education on the environment, which provides a new perspective on the interaction between education and technological innovation. Finally, the study offers several policy implications for the government, researchers, policymakers, environmentalists, and industrialists, which can inform future efforts to promote the green transition in China and other countries. Therefore, the study presented in this paper adds several new insights to what was already known on the relationship between education and the environment and provides a valuable contribution to the literature on this topic.

The rest of the article is organized as follows: Section 2 provides the literature on essential variables of the study in detail. Section 3 represents the methodology, and Section 4 displays the empirical analysis and results. The last section provides the conclusion, limitations, and policy implications.

2. Related Studies

This section mainly reviews the literature on the nexus between human capital and green transition and critically analyzes the interactional effects of green innovation and government intervention in this regard.

2.1. Education and Green Transition

The prevailing globalization has encouraged modernization and urbanization, so the countries are developing quickly. But unfortunately, the process of advancement has hugely damaged the natural environment. The rise in the global temperature, increase in pollution emissions, deforestation, and extravagant use of coal and other natural resources all contribute to poor environmental quality and climate change [22]. Scholars are concerned about the catastrophic consequences of the adversity of climatic conditions. As mentioned above, especially CO₂ emissions have ruined the quality of life globally. Therefore, there is a dire need to improve the ecosystem to ensure stability and sustainability in the world. The United Nations’ sustainable development goals are critical to protect the environment and derive intelligent and innovative methods to transform the environment and mitigate the negative impacts of GHG (greenhouse gas) emissions. This can be referred to as green transition. Green transition (G.T.) is also studied on cultivated land regarding green utilization efficiency evaluation, which can promote economic growth and benefit the ecosystem and society [23].

A plethora of studies in the past have discussed various factors that have influenced G.T. widely, for example, foreign direct investment, industrial structure, and government policies. Among them, human capital or the education of people is observed to be the major influencer. Industries are now technologically advanced. They are investing in a resource that is knowledgeable and skillful at the same time. Educated people can easily comprehend the fatality of environmental degradation and the need to save the environment. Moreover, they can simultaneously develop new innovative business ideas that are profitable and green [24]. Hiring educated resources is essential for the firms as they can help those fulfilling environmental targets. For instance, Khan et al. [25] assert that incorporating human capital can augment renewable energy consumption in G-7 countries. Moreover, awareness of the environmental risk will restrict the buyer from buying and investors from investing in harmful activities. People will become socially responsible if they are properly guided about environmental protection.

Boca and Siracili [26] studied students’ perceptions, attitudes, and behaviors toward environmental education (as a subject) in universities and determined how the students prioritized ecological activities. Based on primary research, they conclude that the students are involved in green activities like recycling materials and volunteering to protect the environment. They further emphasize that education about the environment can encourage students to be creative in reducing, reusing, and recycling products and materials. Moreover, they are taught and persuaded to opt for green energies like solar and wind energy. Ali and Anufriev [27] find that Russian universities are taking sustainability initiatives and that education, research, transportation, and waste influence environmental quality. Voumik and Ridwan [18] investigate how foreign direct investment (FDI), education, and industrialization affect the environment in Argentina. The results show a cointegration association between CO₂, population, automation, and education. However, there is an inverse association between CO₂ and education. Specifically considering education, it is suggested that investment in education is essential for building a greener economy.

Humans also contribute to the physical environment through poor air quality, soil erosion, and changes in climate change. Omri and Afi [28] discover that tertiary education and government expenditure positively impact environmental quality, thus paving the way toward a green transition. In Pakistan, there is a significant long-term relationship between human capital and CO₂ emissions, as human capital can help reduce carbon emissions without decreasing economic growth [13]. Ahmed et al. [29] articulate that China has a remarkably grown economy and human capital is immensely responsible for urbanization, environmental development, and sustainable society. Hui et al. [19] studied 30 provinces in China to examine the relationship between higher education and environmental sustainability and found that higher education and FDI can reduce carbon emissions.

Although there is sound literature available supporting human capital alleviating the dire effects of CO₂ emissions, a few studies negate the very relationship. For instance, Lui et al. [17] examine linear and nonlinear impacts of natural resources and education on CO₂ emissions. The empirical results provide that both variables intensify CO₂ emissions in Latin America. Furthermore, Hassan et al. [30] prove that natural resources and urbanization significantly impact the ecological footprint in Pakistan, while human capital is indifferent in this regard. Nevertheless, these few cases cannot deny the influential role of human capital in the green transition. Hence, in the presence of human capital theory [31], it is hypothesized that:

H1.

Human capital significantly influences green transition (pollutant emissions) in China.

2.2. Green Innovation, Education, and Environment

Recently, the world has diverted its attention, capacities, and capabilities toward greening the environment. To achieve environmental sustainability, traditional ways of developing new products, processes, and materials are being transformed into green products, processes, and materials. Moreover, green innovation also contributes to economic growth and financial development. Numerous studies discuss the importance of green innovation in environmental sustainability. For instance, Ahmed et al. [29] suggest that we cannot ignore the tremendous role of green technological innovation in increasing the green energy supply and guaranteeing sustainability by putting excessive CO₂ emissions at a halt in the long run in G-7 economies. Su and Fan [32] discover that renewable energy technology innovation significantly impacts China’s green development (G.D.) level. Further, the mutual effect of renewable energy technology innovation and industrial structure also positively influences China’s G.D. level.

Correspondingly, Ramzan et al. [33] use the Moments Quantile Regression (MMQR) technique to analyze the environmental factors affecting ecological neutrality in the top 10 green economies from 1980 to 2019. It is proposed that green innovation potentially curbs carbon emissions and environmental footprints but information technology does the opposite. Thus, investment in green innovation will escalate the greening of economies. The scholars focus on how financial inclusion, green innovation, and energy efficiency impacted sustainable development in developed and emerging countries from 2000 to 2018 [5]. The findings demonstrate that financial inclusion and industrial growth positively induce ecological footprints. However, if developed countries majorly work on green patents, the environment can be saved. However, in the industrial sector, this transition is quite tricky. Likewise, Obobisa et al. [34] research African countries and point out that GTI and renewable energy consumption negatively impact CO₂ emissions. Therefore, they suggest that nations invest in green technological innovations to inhibit environmental destruction.

Similarly, Xie and Jammani [35] investigate the relationship between green innovation and environmental taxes in limiting carbon emissions, using the data of G-7 countries from 1990 to 2020. After applying rigorous techniques, it is confirmed that green innovation and environmental taxes help in diminishing carbon emissions. Alongside this, energy productivity and greener energy resources are observed to be the key factors in mitigating carbon emissions. Hassan et al. [30] use the GMM model to analyze the relationships among G.I., financial development, and financial regulation in 30 provinces of China. The results support the argument that green technological and financial innovation can impact environmental deterioration.

Even though G.I. is crucial for environmental sustainability, knowing about innovations or developing greener ones is also immensely important. Only an educated and trained person can adopt change, make informed decisions, and embrace green innovations [36]. Consequently, the role of human capital in greening the environment through the interactional capability of green innovation cannot be overlooked.

Human capital is a part of green intellectual capital and can also be inferred as an organization’s capacity [37]. Wang & Zatzick [38] state that human capital, which shows an organization’s ability, is mainly responsible for various innovations. For a greener environment, the members of the organization, especially the top management, play a vital role. Wang et al. [39] explain that eco-innovation or green innovation is driven by CEOs who are environmentally friendly leaders. Abbas and Sagsan [40] study knowledge management practices’ impact on green innovation and corporate sustainable development (CSD) in Pakistani manufacturing firms. It is concluded that K.M. significantly affects G.I. and CSD.

Accordingly, human capital increases labor productivity if they are educated, have sound knowledge, and can use technology effectively [41]. Xiao and You [42] state that human capital accumulation poses a heterogeneous but positive effect on Green Total Factor Productivity (GTFP) in the regional context of China. Ibrahim [43] studies the interactive effects of human capital on the finance–growth nexus in Sub-Saharan Africa (SSA). They infer that a higher level of human capital accumulation encourages innovation and the development of modern technology that spurs financial intermediation and economic growth. Anik and Sulistyo [44] also support the view that green innovation and competitive advantage are attained due to human capital.

It is plausible that green innovation can only be brought about by knowledgeable, experienced, and dexterous human capital [45]. The role of education in greening innovation and improving environmental performance is crucial [46]. Therefore, in the presence of the above-mentioned factors, the following hypothesis is proposed:

H2.

Green innovation moderates the relationship between human capital and green transition in China.

2.3. Government Intervention, Education, and Environment

The world is experiencing anomalies in climatic conditions, possibly due to failing to limit the emissions of greenhouse gases into the atmosphere. Despite this misfortune, all the developing and developed countries are still trying hard to cope with the adversities of climatic change. The nations have employed multiple techniques and methods to save the environment. The most significant achievement is creating awareness among citizens, students, and industries, which induces a concern for environmental stability in human beings. Another critical factor, less debated in the prior literature, is the intervention of government bodies in alleviating environmental destruction and smoothing the road toward a green transition.

From this perspective, environmental policies designed by governments can successfully drive innovation activities by introducing clean technologies in the firms [38]. Li et al. [47] argue that governments should help firms produce high-quality products and services by motivating and facilitating them. They can also encourage firms to consume few natural resources by promoting green innovation. Zhang et al. [48] observe that the environmental policy stringency influences green innovation for geothermal, hydro, and marine energy in OECD and high-income countries. Furthermore, government intervention to support environmental sustainability can be expressed in educational policies, science and technology, and enterprise-level policies [49]. Hsu, Quang-Thanh, Chien, Li, and Mohsin [50] explored the mediating mechanism of environmental regulations in achieving the financial development goals in China. The data from 28 provinces of China were gathered and analyzed. The results depicted that environmental regulations enhanced the GTI, and financial development and GTI could cut carbon emissions. Wang et al. (2021) used a panel threshold effect model to determine government intervention’s and market development’s collective impact on China’s provincial pollution emission efficiency [51]. They indicated that both variables were complementary; however, the impact of government intervention was dynamic.

It is plausible that the transition towards green development requires a considerable investment. Small and Medium Enterprises (SMEs) cannot afford to adapt to new changes in traditional systems. Government subsidies are a possible way to facilitate green transition [52]. In addition, these subsidies motivate enterprises to perform science and technology activities to achieve policy goals [53]. Feng et al. [54] focus on vegetation subsidies and explain that despite local degradation in some areas, China is experiencing large-scale greening. Yu and Haung [55] apply econometric techniques to discourse local government incentives in China and check how these affect governing practices and the environment. The research postulates that late-developing cities in China have the potential to lower carbon emissions. Also, the study proposes no convincing role of environmental incentives. The municipal governments are inclined towards the economic benefits of promoting low-carbon innovations and green activities.

Similarly, Feng and Yang [54] evaluate that government spending has a minor impact on human capital. However, it negatively impacts carbon emissions. According to Zhao et al. [56], knowledge stock can be transitioned to technological innovation due to government intervention in R&D spending. Chen et al. [57] support that local governments have begun focusing on R&D activities, exacerbating green technological innovation. Deng et al. [58] add that local governments keep fiscal and tax incentives for green technological innovation to accelerate economic growth. In the Chinese context, Xu et al. [59] emphasize avoiding unproductive competition among governments and supporting the earlier achievement of the “dual carbon” goal. Policymakers should also create a regionally linked low-carbon emission reduction system alongside increased research and development investment in green technologies. Feng et al. [21] analyze the data from selected BRI (The Green Belt and Road Initiative) countries from 2000 to 2018. They provide a positive impact of government expenditure on green economic performance such that public spending on human capital and renewable energy leads to a green economy with varying features in different BRI countries.

Based on the above literature, the following hypothesis is proposed:

H3.

Government intervention significantly impacts the nexus between education and the environment in provincial China.

3. Methodology

3.1. Data and Description of Variables

Our study is based on regional China, so the data are gathered from various sources. Most of the variables are from the National Bureau of Statistics, China Energy Statistical Year Book, China Environmental Statistical Year Book, China Statistical Year Book, CSMAR database, and Provincial Statistical Year Book. Given the data availability, the educational data at the regional level are disclosed from 2008, and data for Carbon Emissions are updated to 2020. Therefore, the employed data span from 2008 to 2020.

3.1.1. Dependent Variable

Green transition is our explained variable, which is measured by the “pollutant emissions”. Prior studies have utilized CO₂ emissions as a proxy for green transition [60]. However, CO₂ alone cannot explain all the other types of pollutants. Moreover, the simple summation average cannot reflect the real pollution levels of different regions due to differences in the dimensions and harm degrees of specific pollutants. Referring to Wang and Sun [61], the entropy weight method is adopted in this study to calculate the pollution emission of industrial wastewater, waste gas, and solid waste. Afterward, the pollutant emission index is formed.

3.1.2. Core Explanatory Variables

Education is our primary variable of interest, which influences environmental sustainability. Education must help foster technological innovation and green advancements. Being educated, skillful, and informed about environmental safety can guide human capital to develop brilliant, eco-friendly, sustainable ideas. Various studies have used education expenditure [62], literacy rate [63], and average years of schooling as a proxy for education. The most suitable proxy in our case is “the number of students enrolled in colleges each year”. Green innovation is measured by the “number of green patent applications” in the relevant regions. Government intervention is measured by “the ratio of government financial expenditure to GDP”.

3.1.3. Control Variables

Considering the risk of omitted variable bias, several other factors, other than education, are also incorporated in the study that can influence the green transition. Referring to Lanoie et al., Zhang and Wen, and Xu and Cui [64,65,66], the following variables are selected as control variables: economic development level (GDP), measured by per capita GDP growth rate; population density (Dens), measured by the ratio of population number at the end of the year to regional land area, which reflects the impact of population agglomeration changes on green transition; opening degree (Open), measured by the ratio of regional import and export volume to GDP; foreign direct investment (FDI), measured by the natural logarithm of regional FDI; industrial structure (Stru), measured by the ratio of the secondary industry to GDP; environmental regulation intensity (Regu), measured by the natural logarithm of the investment in environmental pollution control by the government.

4. Empirical Analysis and Results

4.1. Empirical Model

To investigate the impact of education on the environment/green transition, we first carry out an OLS panel data model with time and individual fixed effects. Before entering into detailed estimations, it is vital to find the descriptive statistics, correlations among variables, and the unit root test results to know about the behavior of the observed variables, the association among variables, and the stationarity of the data. Afterward, the basic regression model is carried out on the panel data. Researchers like Long et al. [67] have supported the idea that panel data deals with individuals and time simultaneously to provide us with the best statistical results and coefficient estimates. The panel basic regression model based on fixed effects is as follows:

Trans_it = α + β edu_it + β control_it + ε_it

(1)

In Equation (1), Trans_it is the pollutant emissions for province i at time t, α is constant, β is the parameter to be estimated, edu_it is the education level in province i at time t, control_it represents a set of control variables, and finally, ε_it is the error term.

$$Tran{s}_{it}=\alpha +{\alpha }_{1}Ed{u}_{it}+{\alpha }_{2}GD{P}_{it}+{\alpha }_{3}Den{s}_{it}+{\alpha }_{4}Ope{n}_{it}+{\alpha }_{5}FD{I}_{it}+{\alpha }_{6}Str{u}_{it}+{\alpha }_{7}Reg{u}_{it}+{\epsilon }_{it}$$

(2)

Equation (2) shows all the control variables used in the model.

$$\begin{array}{ll}Tran{s}_{it}& =\alpha +{\alpha }_{1}Ed{u}_{it}+{\alpha }_{2}Gree{n}_{it}+{\alpha }_{3}Ed{u}_{it}*Gree{n}_{it}+{\alpha }_{4}GD{P}_{it}+{\alpha }_{5}Den{s}_{it}+{\alpha }_{6}Ope{n}_{it}\\ & +{\alpha }_{7}FD{I}_{it}+{\alpha }_{8}Str{u}_{it}+{\alpha }_{9}Reg{u}_{it}+{\epsilon }_{it}\end{array}$$

(3)

Equation (3) shows the impact of the interaction between education and green innovation on green transition.

$$\begin{array}{ll}Tran{s}_{it}& =\alpha +{\alpha }_{1}Ed{u}_{it}+{\alpha }_{2}In{t}_{it}+{\alpha }_{3}Ed{u}_{it}*In{t}_{it}+{\alpha }_{4}GD{P}_{it}+{\alpha }_{5}Den{s}_{it}+{\alpha }_{6}Ope{n}_{it}\\ & +{\alpha }_{7}FD{I}_{it}+{\alpha }_{8}Str{u}_{it}+{\alpha }_{9}Reg{u}_{it}+{\epsilon }_{it}\end{array}$$

(4)

Equation (4) shows the impact of the interaction between education and government intervention on green transition. Where Edu_it∗Int_it refers to the interaction term between government intervention and education, the subscript shows that the data are a panel. ε_it is the error term.

Table 1. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
Trans	390	0.311	0.18	0.018	0.716
Edu	390	13.41	0.819	10.714	14.645
GDP	390	10.687	0.527	9.44	11.956
Dens	390	5.457	1.28	2.063	8.255
Open	390	0.278	0.391	0.017	1.473
FDI	390	14.577	1.654	9.776	16.79
Stru	390	44.405	8.758	19.014	58.4
Regu	390	11.831	1.009	8.236	13.937

Notes: Trans means green transition, represented by the pollutant emissions index calculated by the entropy weight method, Edu is the number of new students enrolled in colleges, GDP is gross domestic product, Dens is population density, Open is trade openness, FDI is foreign direct investment, Stru is industrial structure represented by the ratio of tertiary industry to the GDP, Regu is governmental regulations measured by the natural logarithm of the investment in environmental pollution control. All variables are estimated/included on a provincial level.

displays the descriptive statistics of the variables used in the estimations. The mean value of pollutant emission on the provincial level is 0.031, which fluctuates between 0.018 and 0.716, with a standard deviation of 0.18. On the other hand, the mean value Edu is 13.41, ranging from 10.714 to 14.645, with a standard deviation of 0.819. To ensure data stationarity before applying regression models, Levin, Lin, Chu, and Im, Pesaran, Shin, and Augmented Dickey–Fuller unit root tests are used [68,69,70]. The results from

Table 2. Unit root tests.

Variable	LLC		ADF		IPS
	Stat. Value	p Value	Stat. Value	p Value	Stat. Value	p Value
Trans	−13.07	0.00	11.416	0	−6.36	0
Edu	−25.33	0.00	24.337	0	−4.90	0
GDP	−9.29	0.00	27.058	0	−5.18	0
Dens	5.43	1.00	20.25	0	−2.32	0.01
Open	−26.63	0.00	28.991	0	−8.54	0
FDI	−13.12	0.00	17.33	0	−6.74	0
Stru	−12.10	0.00	13.7401	0	−6.58	0
Regu	−21.77	0.00	28.223	0	−9.13	0

Notes: If p value = 0.00, the assumption that there is a unit root is rejected. For abbreviations, please refer to the notes in Table 1.

indicate that the data are stationary, and there is no issue of unit root in the panel data. Before running our main regression models, we performed various diagnostic tests, like multicollinearity, cross-sectional dependence, and cross-group heterogeneity. The mean value of VIF is 2.11, and the maximum variance inflation factor value is 3.04, which is much less than 10. So, there is no severe multicollinearity problem between the variables. The cross-group heteroscedasticity test is performed, and the result shows that the p-value is 0.000, which is less than 0.1. So, the null hypothesis is significantly rejected at 1%, and there is heteroscedasticity in the panel data. This paper uses the Pesaran cross-section correlation test to check for significant cross-section correlation in panel data [71]. The result shows that the p-value is 0.3500, indicating no cross-sectional dependence problem in our data. (We thank an anonymous reviewer for recommending these tests. The detailed results of the diagnostic test are available from the author upon request).

4.2. Basic Regression

The regression results of the first model, representing the impact of education on green transition, are shown in

Table 3. Impact of education on pollutant emissions.

Variable	Model (1)
Edu	−0.001 *** (0.000)
GDP	0.000 (0.000)
Dens	−0.001 *** (0.000)
Open	0.001 (0.000)
FDI	−0.000066
Stru	−0.002 *** (0.001)
Regu	−0.002 (0.003)
Cons	0.741 *** (0.084)
Time FE	YES
Individual FE	YES
N	390
R2	0.14

Note: Robust standard errors in parentheses; *** p < 0.01. Please refer to Table 1 for abbreviations. Cons is constant, and N represents the number of observations.

. The coefficient of the core variable is −0.718, and it is significant at the 1% level, indicating that an increase in education significantly inhibits regional pollution emissions. According to the human capital theory, education is an essential driving factor for technological progress, which promotes the redistribution and efficient combination of various production factors [72]. It improves the efficiency of resource allocation and helps realize a green transition. Meanwhile, with the improvement in labor education, human capital quality and regional talent structure are improved, accelerating the accumulation of knowledge, technology, and related factors [73].

Consequently, education promotes the smooth development of innovative activities and the promotion and application of green technologies. The traditional path of resource-based industries can be changed to realize energy conservation and emissions reduction. Therefore, the higher the education level of the labor force, the lower the level of pollutant emissions, which promotes green transition. Most control variables have significant coefficients, with Dens, FDI, Stru, and Regu showing negative coefficients. In short, these controls capture the likely influence of confounders to provide us with reliable estimates.

4.3. Robustness Tests

To verify the reliability of the above-stated results, we performed some robustness tests that reveal the extent to which the coefficient estimates of basic regression are consistent.

4.3.1. Replacing Core Variables

We replace the pollutant emissions index with the CO₂ emissions to measure green transition, and the regression results are shown in Model 1 of

Table 4. Robustness tests.

Variable	Replacing Core Variable	Shifting Mean Values
	Model (1)	Model (2)
Edu	−0.702 *** (0.232)	−0.001 *** (0.000)
GDP	−0.001 (0.000)	−0.000 (0.000)
Dens	−0.022 (0.023)	−0.001 *** (0.000)
Open	1.196 (0.781)	0.001 (0.000)
FDI	11.389 (8.601)	−0.012 (0.006)
Stru	5.254 *** (1.339)	−0.004 *** (0.001)
Regu	−11.094 (9.409)	−0.003 (0.004)
Cons	106.978 (143.440)	0.720 *** (0.087)
N	390	390
Time FE	YES	YES
Area FE	YES	YES
R2	0.149	0.238

Note: Robust standard errors in parentheses; *** p < 0.01. Please refer to Table 1 for the abbreviations. Cons is constant and N represents number of observations.

. The coefficient of education on CO₂ emissions is still significantly negative at the 1% level. With the improvement in education level, regional carbon emissions can be reduced considerably, which verifies the positive role of education in the green transition. Thus, the results from Table 3 are robust.

4.3.2. Shifting Mean Values

Considering that the data fluctuate wildly every year, which may affect the results obtained, the moving average method is adopted to process data again, and the results are shown in Model 2 of Table 4. The coefficient of education is −0.001, and it is significant at the 1% level. So, education plays a positive role in reducing pollutant emissions, which promotes regional green transition. The results are consistent with the previous estimations in Table 3.

4.4. Further Analysis

4.4.1. Testing the Interaction Effect of Green Innovation

Green transition is a new and sustainable way to coordinate economy, society, and ecology. In addition, green innovation is a core driving force for the green transition (Song et al., 2019), reduces energy consumption, and improves resource allocation efficiency. In regions with different green innovation levels, the impact of education on green transition may be different. Contemplating the notion, this paper further explores the influence mechanism of green innovation on the relationship between education and green transition. The “number of regional green patents” is selected to measure green innovation, and its interaction with education is introduced into the model. The results are shown in Model 1 in

Table 5. Influencing mechanism.

Variables	Green Innovation	Government Intervention
	Model (1)	Model (2)
Edu	0.001 (0.001)	−0.003 *** (0.001)
Green	0.025 *** (0.005)
Edu*Green	−0.002 *** (0.000)
Int		0.031 (0.034)
Edu*Int		−0.002 *** (0.000)
GDP	−0.000 (0.000)	0
Dens	−0.000 *** (0.000)	−0.000 *** (0.000)
Open	−0.001 (0.000)	−0.001 (0.000)
FDI	−0.011 ** (0.005)	−0.002 (0.005)
Stru	−0.002 *** (0.001)	−0.004 *** (0.001)
Regu	−0.001 (0.003)	−0.002 (0.003)
Cons	0.553 *** (0.089)	0.622 *** (0.088)
Area FE	YES	YES
Time FE	YES	YES
N	390	390
R2	0.2	0.185

Note: Robust standard errors in parentheses; ** p < 0.05, *** p < 0.01. Edu*Green is the interaction term between education and a green transition, Int is government intervention, and Edu*Int is the interaction term between education and government intervention. Other variables are the same as in Table 1.

. It can be found that the impact of education on green transition is not significant, but the coefficient of the interaction term between education and green innovation is −0.002, which is significant at the 1% level. It indicates that green innovation enhances the promotion effect of education on emission reduction. In regions with higher green innovation levels, the positive impact of education on green transition is more significant.

4.4.2. The Influence Mechanism of Government Intervention

The regional green transition has the attribute of public goods, which often requires the guidance and support of government departments [74]. The Organization Department of the Central Committee of China recently proposed constructing a green performance evaluation system. The administrative intervention of the government departments effectively makes up for the market flaws. It encourages enterprises to conduct R&D and innovation activities, introduce green technologies, and update environmental protection equipment. It will ultimately influence the optimal allocation of production factors and the level of environmental governance [75]. Moreover, when the authorities implement stringent environmental policies to protect the environment, the industries are forced to adopt environmentally friendly methods and eco-innovate themselves [48]. Therefore, government intervention may affect the relationship between education and green transition. This paper selects the ratio of government financial expenditure to GDP to measure government intervention and further examines its influencing mechanism. The government intervention and its interaction with education are introduced into the model, and the results are shown in Model 2 of Table 5. The coefficient of education on the green transition is −0.003, and that of the interaction term is −0.002, both of which are significant at the 1% level. It indicates that government intervention enhances the negative impact of education on pollutant emissions.

4.5. Endogeneity Checks

4.5.1. Considering the Predetermined Variables

Due to the possible feedback interaction between education, some control variables, and a green transition, there might be some variable variation that is endogenous. Considering this issue, all explanatory variables are lagged by one period to alleviate endogenous problems. This lag is simple, intuitively appealing, and requires less additional data. Referring to

Table 6. Endogeneity test.

Variable	Model (1)	Model (2)	Model (3)
Edu	−0.001 *** (0.000)	0.001 (0.001)	0.001 (0.001)
L.Green		0.020 *** (0.006)
L.Edu*Green		−0.000 *** (0.000)
L.Int			0.214 ** (0.108)
L.Edu*Int			−0.005 ** (0.002)
L.GDP	0.000 (0.000)	0.000 ** (0.000)	−0.000 (0.000)
L.Dens	−0.001 *** (0.000)	−0.000 *** (0.000)	−0.000 *** (0.000)
L.Open	0.000 (0.000)	−0.000 (0.000)	0.000 (0.000)
L.FDI	0.001 (0.006)	0.001 (0.006)	0.004 (0.006)
L.Stru	−0.003 *** (0.001)	−0.002 *** (0.001)	−0.003 *** (0.001)
L.Regu	−0.001 (0.003)	−0.001 (0.003)	−0.002 (0.003)
Cons	0.573 *** (0.090)	0.355 *** (0.095)	0.487 *** (0.099)
Area FE	YES	YES	YES
Time FE	YES	YES	YES
N	360	360	360
R2	0.114	0.193	0.129

Notes: This table shows the lagged impact of all independent variables, including interaction terms on pollutant emissions. ** p < 0.05, *** p < 0.01.

, the regression results reveal that the assumption that the variables are endogenous is rejected, and it is inferred that the relationships/effects are observed before retaining their directions and significance.

4.5.2. The System GMM Model

Another effective and reliable technique to test for endogeneity is the system GMM model. Zhao et al. [56] have regarded this approach as the most appropriate for testing the uncertain effects. It can test the weak instrument variables as well. Edu_t-1 and Edu_t-2 are used as instrumental variables and are introduced into the model, following Wintoki et al. [76], who state that the explanatory variable should be delayed for two periods to meet the requirements of the exogeneity of instrumental variables. The GMM model is adopted for regression, and the results are displayed in

Table 7. System GMM model.

Variable	Model (1)	Model (2)	Model (3)
Edu	−0.001 *** (0.000)	0.004 (0.001)	−0.004 *** (0.001)
Green		−0.019 ** (0.008)
Edu*Green		−0.000 *** (0.000)
Int			−0.019 (0.046)
Edu*Int			−0.002 *** (0.000)
GDP	0.000 (0.000)	−0.000 ** (0.000)	−0.000 *** (0.000)
Dens	−0.000 *** (0.000)	−0.001 ** (0.000)	−0.000 *** (0.000)
Open	0.001 *** (0.000)	0.001 *** (0.000)	0.000 (0.000)
FDI	−0.018 *** (0.005)	−0.005 (0.005)	−0.018 *** (0.005)
Stru	0.001 (0.001)	0.007 *** (0.001)	0.000 (0.001)
Regu	−0.001 (0.002)	0.004 ** (0.002)	−0.002 (0.002)
Cons	0.6851 *** (0.137)	0.942 *** (0.172)	0.988 *** (0.156)
N	330	330	330
AR(1)	−3.25 ***	−1.84 *	−2.82 ***
AR(2)	−1.44	−1.3	−1.2
Sargan	0.49	33.77	0.79

Notes: AR(1) and AR(2) are the p-values of the first-order and second-order disturbance terms, respectively. * p < 0.1,** p < 0.05, *** p < 0.01.

. Firstly, the values of AR(1) < 0.1 and AR(2) > 0.1 and Sargan test results revealed that GMM is the most suitable test. Further, the coefficient estimates in Models 2 and 3 are statistically significant at the 1% level, which certainly shows that education of green innovation in moderation will indeed promote green transition and the same is for the impact of the interaction between government intervention and education on green transition. Hence, it is proved that no endogeneity issue is observed in the data.

4.6. Regional Heterogeneity

The above-stated empirical analysis has already proved that education has a powerful influence on the green transition in China. It is also revealed that green innovation and government intervention enhance the impact of education on greening the environment. However, it is crucial to investigate which part of China is reaping the rewards of educated labor, eco-innovation, and government policies. For this purpose, we have segregated the country into three parts: eastern, western, and central. Afterward, benchmark regression and influence mechanisms of G.I. and GINT are applied for regional heterogeneity determination.

Firstly, the test results of the eastern region indicate that the coefficient estimate of education is negatively significant at the 1% level in the model (1). The same is valid with Models (2) and (3), where the interactional effects of G.I. and GINT on education are examined. The coefficient of interaction between Edu and G.I. is −0.023, which infers that green innovation magnifies the influence of education on green transition. The coefficient of the other interaction (GINT) −0.002 also symbolizes promoting green transition in the eastern region through increased education (see

Table 8. Eastern regions.

Variable	Model (1)	Model (2)	Model (3)
Edu	−0.001 *** (0.000)	−0.004 *** (0.001)	−0.000002
Green		−0.023 ** (0.009)
Edu*Green		0.000 *** (0.000)
Int			−0.081 (0.069)
Edu*Int			−0.002 *** (0.001)
GDP	−0.000 (0.000)	0.000 (0.000)	−0.000 *** (0.000)
Dens	−0.000 ** (0.000)	0.000 ** (0.000)	0.000 (0.000)
Open	−0.000 (0.000)	−0.000 (0.000)	−0.001 *** (0.000)
FDI	−0.022 ** (0.011)	−0.021 ** (0.010)	0.001 (0.011)
Stru	0.002 (0.001)	0.001 (0.001)	0.000 (0.001)
Regu	−0.003 (0.005)	−0.003 (0.004)	−0.001 (0.005)
Cons	0.877 *** (0.168)	1.095 *** (0.179)	0.127 (0.172)
N	156	156	156
R2	0.342	0.387	0.369

Notes: This table shows the impact of education, green innovation, and government intervention on green transition in the eastern provinces. ** p < 0.05, *** p < 0.01.

).

Secondly, the test results of the central region are calculated similarly, revealing that the coefficient estimate of education is negatively significant at the 1% level in Model (1). The same is true with Models (2) and (3), where the interactional effects of G.I. and GINT on education are examined. The coefficient of interaction between Edu and G.I. is −0.004, indicating that green innovation magnifies the influence of education on the green transition. Following the footsteps of G.I., the coefficient of the other interaction (GINT) −0.005 narrates the acceleration effect of Edu on green transition (

Table 9. Central regions.

Variable	Model (1)	Model (2)	Model (3)
Edu	−0.002 *** (0.000)	0.002 * (0.001)	−0.009 ** (0.004)
Green		0.058 *** (0.011)
Edu*Green		−0.004 *** (0.000)
Int			0.703 *** (0.229)
Edu*Int			−0.005 ** (0.002)
GDP	0.000 * (0.000)	−0.000 (0.000)	0.000 (0.000)
Dens	0.002 *** (0.001)	0.002 *** (0.001)	0.000 ** (0.000)
Open	0.003 (0.002)	0.002 (0.002)	−0.018 *** (0.004)
FDI	−0.017 (0.010)	−0.000153	−0.006 (0.021)
Stru	−0.005 *** (0.001)	−0.003 *** (0.001)	0.006 ** (0.002)
Regu	−0.001 (0.003)	0.001 (0.003)	0.013 (0.011)
Cons	0.491 ** (0.192)	−0.172 (0.213)	1.231 *** (0.353)
N	117	117	117
R2	0.434	0.387	0.051

Notes: This table shows the impact of education, green innovation, and government intervention on green transition in the central provinces. * p < 0.1,** p < 0.05, *** p < 0.01.

).

On the contrary, the impact of education on green transition is not significant. Testing the influence mechanism of green innovation and government intervention is not required on that account. The reason may be that education in the western region is still low and economic development is more of a concern. So, it is hard to play the positive effects of education (

Table 10. Western regions.

Variable	Model (1)
Edu	0.000 (0.000)
Green
Edu*Green
Int
Edu*Int
GDP	0.000 *** (0.000)
Dens	−0.000 (0.001)
Open	−0.004 *** (0.001)
FDI	−0.027 *** (0.007)
Stru	0.001 (0.001)
Regu	0.005 (0.004)
Cons	0.497 *** (0.165)
N	117
R2	0.364

Notes: This table shows the impact of education, green innovation, and government intervention on green transition in the western provinces. *** p < 0.01.

).

4.7. Discussion

The impact of education on green transition, the interactional effects of green innovation and government intervention, and the role of education in reducing carbon emissions are examined in this study. We conclude that education significantly negatively impacts carbon emissions in provincial China, which indicates that improving the education level can contribute to the green transition. Government intervention substantially affects the relationship between education and the environment, which suggests that policy measures can effectively promote the green transition.

We add several new insights to what was already known on the topic of the relationship between education and the environment. First, we provide empirical evidence that education significantly negatively impacts carbon emissions in provincial China, which confirms and extends previous studies that have suggested a positive relationship between education and environmental sustainability. Second, we show that government intervention can enhance the impact of education on the environment, which means that policy measures can effectively promote the green transition. Third, we highlight the regional heterogeneity of the relationship between education and the environment in China, indicating that education’s benefits for the green transition may vary across different regions. Fourth, we suggest that green innovation can play a supportive role in enhancing the impact of education on the environment, which provides a new perspective on the interaction between education and technological innovation. Finally, we offer several policy implications for the government, researchers, policymakers, environmentalists, and industrialists, which can inform future efforts to promote the green transition in China and other countries.

5. Conclusions and Policy Implication

Undoubtedly, education is the treasure for all the world economies to make economic progress, make technological innovations, and preserve the environment. China is the world’s industrial hub, striving for a technologically advanced economy and struggling to lessen the catastrophic consequences of increasing pollutant emissions. In the context of Human Capital Theory, the major objective of this study is to investigate the impact of education on the green transition. The study considers the dataset of 30 provinces of China from 2008 to 2020, employing panel econometric techniques of fixed effects and system GMM modeling. The robustness tests are carried out by shifting mean values, replacing core variables, and using the Generalized Method of Moments technique to ensure the consistency of the results of the benchmark regression. This research further examines the influences of green innovation and government intervention in promoting the impact of education on green transition. Finally, the heterogeneity analysis is conducted to obtain a clear picture of the versatile effect of education on different regions of China.

Our findings show that education significantly reduces harmful pollutant emissions. Moreover, the influencing mechanisms of green innovation and government intervention support education’s positive impact on green transition in China. This suggests that increasing education levels should be supplemented with supportive policies for green innovation to fully realize the dividends of quality human capital for green transition. The heterogeneity tests demonstrate that China’s central and eastern regions are fortunate to exploit the human capital in fulfilling the SDGs; however, the western part needs special attention. The benefits of quality human capital in facilitating green transition are yet to be gained in the western region.

Accordingly, this research provides practical and theoretical insights for the government, industrialists, policymakers, and environmentalists to achieve dual carbon emissions peaking and neutrality emissions goals. The government should focus on education, innovation, and environmental stability in the underserved western region. The people of this region should be encouraged to obtain higher education and work to protect the environment in innovative ways. The students should be given scholarships, environmental knowledge, and proper training to use their dexterity and talent in developing eco-friendly processes and products. Industrialists should employ all their tactics to grab the raw talent for abiding by the environmental policies imposed by the government. The policymakers should facilitate eco-innovation projects initiated by high-quality human resources and venture capitalists and design robust policies regarding environmental sustainability education at each level of schooling to achieve a green transition. In this regard, the “education modernization 2035 plan” is correct. The authorities should further emphasize the collaboration of industry and universities, research and innovation, and the mid-west region development, which is already mentioned in the plan. Further, the Ministry of Education, the national development and reform commission, and the state council-issued circulars and policies in 2017, 2018, and 2022 should be used to promote artificial intelligence in the country’s primary, secondary, and higher education sectors. The relevant authorities should monitor the practical implementations of these policies and ensure that COVID-19 and aftershocks do not hinder these measures.