Influence of Regional Air Pollution Pressure on the Green Transformation of Higher Education: An Empirical Study Based on PM2.5 in Chinese Cities

Abstract

Against the backdrop of the pressing challenge of global climate change and the framework of the United Nations’ Sustainable Development Goals (SDGs), this study focuses on examining how regional air pollution pressures drive the transition towards green practices within higher education institutions (HEIs). This research begins with the painstaking manual collection and organization of green transformation data from 113 Chinese HEIs spanning the years 2017 to 2022. We construct a comprehensive green transformation index for higher education, including four dimensions: Education and Research, Operational Management Efficiency, Green Campus Construction, and Social Participation, along with 14 foundational indicators. Empirical analysis demonstrates a significant positive correlation between air pollution pressures and the green transformation of HEIs, confirming the facilitative role of government policy support and public environmental awareness in this transformative process. This study further uncovers that the timeliness and continuity of policies are crucial for HEIs in responding to environmental pressures and accelerating their green transition. Moreover, it highlights the impact of regional characteristics, revealing that HEIs in economically more advanced regions exhibit a stronger response to air pollution pressures compared with those in less developed areas. This research not only enhances understanding of the interplay among environmental policies, public engagement, and behavioral changes in HEIs but also furnishes policymakers, HEI administrators, and environmental advocates with robust empirical evidence. It underscores the urgency for multi-stakeholder collaboration, policy incentives, and the consideration of regional specifics, thereby providing strategic guidance for facilitating green transformations in HEIs and advancing the attainment of global sustainability objectives.

1. Introduction

In an increasingly globalized 21st century, environmental issues have emerged as a shared challenge confronting nations worldwide [1], with climate change, one of the gravest global problems, exerting profound impacts on natural ecosystems, socioeconomic development, and human lifestyles [2,3]. Against this backdrop, the United Nations adopted the 2030 Agenda for Sustainable Development in 2015, outlining 17 Sustainable Development Goals (SDGs) aimed at fostering inclusive, sustainable economic growth globally [4], while safeguarding the planet’s environment and ensuring peace and prosperity for all [5]. This agenda not only charts a course of action for governments but also presents new demands and opportunities for the education sector, particularly higher education [6,7].

Initially, scholarly attention centered on the implications of environmental pollution for human health issues, notably exacerbating respiratory diseases and metabolic disorders caused by particulate matter [8]. Subsequently, the reach of pollution’s effects broadened, implicating educational performance, work productivity, and individual economic outcomes negatively [9,10,11]. Of particular concern is the escalating global air pollution crisis, exemplified by rising concentrations of fine particulate matter (PM2.5), which has significantly threatened the normal functioning of education systems amidst intensifying climate change. Guo and colleagues, leveraging a dataset from educational institutions in Shandong, China, found that a one-unit increase in PM2.5 concentration led to a 3.9 percentage point decrease in parents’ likelihood to invest in their children’s education. This finding intimates that environmental degradation, specifically air pollution, might impede human capital accumulation by influencing family resource allocation for education [12].

Building upon this foundation, subsequent research has delved into the intricate dynamics between environmental pollution and educational development. Liu et al., utilizing panel data from 30 Chinese provinces and municipalities, empirically demonstrated that deepening educational inequality weakens individual environmental behaviors, deteriorating environmental quality and elevating PM2.5 concentrations, highlighting spatial spillover effects as a critical characteristic of air pollutants [13]. Das and Sethi analyzed 74 low- and middle-income countries (LMICs) from 1996 to 2018, and employed advanced panel data methodologies like the Westerlund cointegration test and the system GMM approach, uncovering an ascending trend in pollution levels alongside improvements in higher education attainment in these developing nations [14]. Li et al., through a comprehensive analysis of higher education panel data across 30 Chinese provinces from 2000 to 2018, employed cross-sectional correlations and least squares regression, among other statistical methods, robustly validating the pivotal role of higher education in advancing the implementation of Sustainable Development Goals [15]. Lastly, Giesenbauer and Müller-Christ, drawing from the Graves system development model, advocated for the promotion of holistic growth in higher education systems and reinforced cross-organizational networking to enhance responses to complex challenges and foster sustainable development strategies, thereby offering a novel perspective on the modernization of higher education [16].

Higher education, as the core driver of knowledge innovation and societal progress, plays an indispensable part in advancing the implementation of the SDGs [17]. Educational institutions serve as both repositories and conduits of knowledge, and act as crucibles nurturing future leaders, researchers, and policymakers, making them vital for global environmental governance and the realization of the SDGs [18,19]. The greening transformation and sustainability capability of higher education are fundamentally important for addressing climate change, promoting environmental justice, and facilitating the green transition of economies and societies [16]. Nonetheless, despite significant strides in exploring the link between higher education and sustainable development, gaps persist in understanding how higher education systems directly confront environmental challenges such as air pollution and the underlying mechanisms triggering their transformation towards green sustainability.

This study aims to extend and deepen this discourse by focusing on a specific environmental stressor—PM2.5 concentration. Adopting quantitative analytical methods, including but not limited to multiple regression analyses, it explores the relationship between PM2.5 concentration, its variations, and the progression of green transformation in higher education institutions. Initially, this study constructs a framework for the green transformation of higher education in the Chinese context, encompassing four dimensions: Education and Research, Operational Management Efficiency, Green Campus Development, and Social Engagement, with 14 foundational indicators outlined. Sequentially, Z-score standardization and the entropy weight method are employed to calibrate a green transformation index for higher education. Further, this study examines the influence of regional air pollution pressures and the moderating effects of government policy support and public environmental awareness, and also investigates the impact of policy response time and regional disparities. The objective is to establish a comprehensive assessment model quantitatively illustrating the dynamic interplay between environmental pressures and the green transformation of higher education. Ultimately, this study intends to propose effective strategies that not only propel the green transition of higher education institutions but also set replicable and scalable examples for other developing countries, collectively fostering a harmonious coexistence between global education and the natural environment. Through these endeavors, this study contributes knowledge and practical experiences to expedite the global endeavor towards attaining the United Nations’ Sustainable Development Goals.

The potential contributions of this study are multifaceted: First, it extends and enriches the theoretical framework by intertwining environmental stress, particularly air pollution epitomized by PM2.5, with the sustainable development of higher education, thereby broadening the interdisciplinary research frontier between environmental science and educational economics and enhancing our understanding of how environmental factors catalyze transformations within educational systems. Second, this study constructs a comprehensive green transformation index for higher education institutions to evaluate their green transition, marking a significant shift from qualitative to quantitative analysis. This provides a scientific basis for assessing the progress of green transformation in higher education institutions. The index encompasses four dimensions: Education and Research, Operational Management Efficiency, Green Campus Construction, and Social Participation, and includes 14 foundational indicators, such as the proportion of green courses and the quantity of sustainable development research outcomes. Through regression analysis of PM2.5 concentration and green practices in higher education institutions, we aim to reveal the static and dynamic impacts of PM2.5 concentration on the green transformation of higher education. Furthermore, this study explores whether the relationship between air quality pressure and the green transformation of higher education is influenced by formal institutions (such as government policies), informal institutions (such as public environmental awareness), and regional heterogeneity. Finally, this study’s findings have direct implications for policy and practice, furnishing valuable insights for higher education institutions, government departments, and other stakeholders, encouraging more precise and efficacious actions towards addressing climate change and promoting sustainability. In summary, this paper innovates both theoretically and methodologically, and significantly contributes to practical applications and policy formulation, having far-reaching implications for fostering a green transition in the global higher education system and realizing the SDGs.

The subsequent structure of this paper is outlined as follows: Section 2 engages in theoretical analysis and hypothesis formulation, reviewing existing literature and theories to subsequently posit the research hypotheses. Section 3 conducts empirical tests and analyzes the results, mathematically validating the hypotheses presented in Section 2. Section 4 undertakes robustness checks, verifying the stability of the model from two aspects: explanatory variables and sample selection. Lastly, the conclusion summarizes the study’s findings, offers pertinent recommendations, and outlines directions for future research.

2. Theoretical Analysis and Hypothesis

2.1. Regional Air Pollution Pressure and Green Transformation of Higher Education

Amidst the escalating urgency of global environmental challenges, higher education institutions (HEIs), as pivotal forces behind knowledge evolution and societal progress, increasingly hold the key to advancing environmental sustainability. Air pollution, an acute contemporary environmental issue, not only gauges HEIs’ agility and responsiveness, but also emerges as a pivotal criterion for assessing their dedication to societal obligations and competence in cultivating the next generation of sustainability-focused professionals [20]. Contemporary academic and practical discourses have unveiled a multifaceted, evolving nexus between environmental stressors and institutional adaptation [21], suggesting that the gravity of pollution is intertwined with educational disparities [13] and the reach of higher education [14]. However, environmental pressure theories highlight a compelling adaptation scenario, where deteriorating external conditions impel institutions to embrace more assertive tactics in countering or reversing environmental decline [22].

Within the higher education landscape, this paradigm suggests that, under the duress of air pollution, HEIs must react to ecological adversities via a comprehensive reform strategy. This encompasses enriching curriculums with environmental and sustainability components to cultivate students’ eco-awareness and troubleshooting abilities [23]; boosting research funding, especially in environmental tech and policy research, to catalyze tech innovations and inform policy drafting [24,25]; deploying eco-conscious campus management practices, including energy conservation measures, emissions reduction, and eco-friendly infrastructure development, to shrink the institution’s environmental impact [26]; and reinforcing community ties through environmental awareness programs and collaborative conservation efforts, thereby elevating public engagement in environmental stewardship [27,28].

Faced with intensified air pollution, HEIs are poised to embrace holistic green transformation strategies. These strategies transcend mere curricular adjustments and research realignment towards sustainability, incorporating a broader integration of environmentally considerate practices into daily operations and management paradigms. The dual objective is to alleviate environmental stress and elevate both educational excellence and societal impact in unison. Grounded in this theoretical framework, this paper posits the following hypothesis:

H1. 

There is a significant positive correlation between regional air pollution pressures and the extent of green transformation observed in higher education institutions.

2.2. The Moderating Effect of Policy Support Intensity

In the decision-making processes of higher education institutions (HEIs), government policy support stands as a pivotal factor, with its intensity directly influencing the depth and breadth of green transformation efforts [21]. Rooted in policy instrument theory and incentive frameworks, this external motivator operates through a blend of direct and indirect means, shaping institutional pathways towards sustainability. Public policies leverage tools such as fiscal subsidies [29], tax incentives [30], and research funding [31], reducing the financial barriers to green transitions, while concurrently establishing a favorable legal landscape for eco-innovation through legislative and policy frameworks [32]. These interventions not only alleviate the financial strain on HEIs in upgrading green infrastructure and pursuing environmental research projects but also significantly enhance the appeal and viability of green initiatives, thereby stimulating greater transformational momentum.

Of particular note is the fact that when governments escalate annual funding for environmental research, the impact of such policy support on HEIs becomes even more pronounced. It not only furnishes HEIs with the necessary economic backbone to propel advancements in environmental governance technologies but also ensures the smooth execution of campus greening renovations and energy efficiency enhancements, bolstering HEIs’ adaptive capacity and responsiveness to air pollution issues, including PM2.5 concentrations. Amidst high pollution pressures, this policy encouragement acts as a formidable catalyst, hastening the transition of HEIs towards more environmentally friendly and sustainable models [33]. Consequently, government policy support assumes a dual role in modulating and fostering the dynamic relationship between environmental pressures and the green transformation of HEIs. Accordingly, this article proposes the following hypothesis:

H2. 

Government policy support enhances the positive correlation between regional air pollution pressure and the green transformation of higher education institutions.

2.3. The Moderating Effect of Public Environmental Awareness

Social constructivist theory posits that societal realities, inclusive of perceptions about environmental issues, are fashioned through social interactions and discourse. An elevated public consciousness about environmental protection not only fosters a conducive socio-cultural milieu for the greening of higher education institutions (HEIs) but also accelerates and deepens their transformative journey through indirect mechanisms [34]. When a strong environmental consciousness permeates society, it cultivates a collective “green consensus”, which influences HEIs to integrate environmental preservation and social responsibility more prominently into policy formulations and future planning [35]. Therefore, the significance of public environmental awareness cannot be underestimated.

Environmental psychology studies demonstrate that public attitudes towards environmental issues and behavioral intentions are closely linked to their perceptions of environmental problems [36]. High levels of public environmental awareness not only stimulate broader societal attention and support for environmental issues but also promote overall societal progress towards sustainability. In the realm of higher education, the enhancement of public environmental awareness helps create a supportive social and cultural atmosphere, providing necessary external conditions for the green transformation of higher education institutions. Additionally, this awareness can exert various forms of pressure on higher education institutions, prompting them to adopt more proactive measures to address environmental challenges, thereby achieving greener and more sustainable development goals. Specifically, in a society with strong environmental awareness, the public expresses expectations for environmental protection through direct participation in environmental activities, discussions on social media, public protests, and other means. The public opinion pressure generated by these behaviors and statements can indirectly influence the decision-making processes of higher education institutions. As focal points of societal attention, higher education institutions are more likely to positively respond to societal environmental expectations and accelerate their pace of green transformation in order to maintain their social image and reputation.

Furthermore, students and parents, as key stakeholders of higher education institutions, can also exert direct expectation pressure on schools due to heightened environmental awareness. Student groups often exhibit high enthusiasm for environmental protection, expressing aspirations for green education and sustainable campus life through course selection preferences and student organization activities [37]. Parents may apply indirect pressure by participating in school–parent cooperation activities or considering a school’s environmental performance when choosing a school. This internal driving force prompts higher education institutions to place greater emphasis on environmental protection and sustainability in curriculum design, campus operations, and research directions. In summary, public environmental awareness, as an essential component of the social environment, significantly promotes the green transformation of higher education institutions by fostering a supportive social and cultural atmosphere, exerting public opinion pressure, and influencing the expectations of key stakeholders. This enhances the determination and speed of transformation faced with air pollution pressures. Grounded in these observations, this paper formulates the following hypothesis.

H3. 

Public environmental awareness positively moderates the correlation between regional air pollution pressures and the green transformation of higher education institutions, enhancing their responsiveness and acceleration in the face of such pressures.

3. Empirical Test and Result Analysis

3.1. Data Source and Sample Determination

This study uses Chinese higher education institutions from 2017 to 2022 as the research sample. Given that the data primarily come from official publications such as the “China Higher Education Quality Development Report for Undergraduate Teaching and Learning” and the “Annual Reports of Chinese Higher Education Institutions”, the manual data collection process imposes certain limitations on the sample size. Therefore, data were collected from only 113 higher education institutions, including 79 general undergraduate colleges, 10 higher vocational colleges, and 24 private universities, providing a total of 678 observations. Following the preliminary delineation of the analytical sample, a series of refinements were conducted to better align with the research requirements: (1) Exclusion of Institutions with Missing Data (those HEIs with missing data for key variables were removed from the sample) and (2) Winsorization of Continuous Variables (to mitigate the impact of outliers, all continuous variables were winsorized at the 1st and 99th percentiles on an annual basis).

The environmental data pertaining to Chinese HEIs were meticulously gathered from sources such as the Annual Report on the Development of Undergraduate Teaching Quality in Ordinary Chinese Higher Education Institutions and the Annual Reports of Chinese HEIs. Additional required data were primarily sourced from the China Statistical Yearbook, China Environmental Yearbook, China Industrial Statistical Yearbook, and the CSMAR database. This meticulous data collation process, albeit labor-intensive, ensured a comprehensive and robust dataset for the ensuing analysis.

3.2. Research Design

To examine the impact of regional air pollution pressure on the extent of green transformation in higher education institutions, this paper formulates Model (1). Additionally, Models (2) and (3) are constructed respectively to validate the moderating effects of government policy support and public environmental awareness, as follows:

$${GT}_{i,t}={\alpha }_0+{\alpha }_1{PM25}_{i,t}+{\alpha }_j\sum {Controls}_{i,t}+\sum Year+{\epsilon }_{i,t}$$

(1)

$${GT}_{i,t}={\alpha }_0+{\alpha }_1{PM25}_{i,t}+{\alpha }_2{Enpolicy}_{i,t}{+\alpha }_3{{PM25}_{i,t}\times Enpolicy}_{i,t}+{\alpha }_j\sum {Controls}_{i,t}+\sum Year+{\epsilon }_{i,t}$$

(2)

$${GT}_{i,t}={\alpha }_0+{\alpha }_1{PM25}_{i,t}+{\alpha }_2{Enaware}_{i,t}{+\alpha }_3{{PM25}_{i,t}\times Enaware}_{i,t}+{\alpha }_j\sum {Controls}_{i,t}+\sum Year+{\epsilon }_{i,t}$$

(3)

where i is the institution of higher education in China, t is the year, GT is the green transformation index, Enpolicy is the government policy support, Enaware is the public environmental awareness; Controls is the control variable, and Year is the year dummy variable. α₀ is the constant term, α₁~α₃ and α_j are the regression coefficients, j = 4, 5, ..., 11, and ε_i,t is the residual term.

3.3. Measurement of Green Transformation in Higher Education

3.3.1. Methodology Basis

The advancement of green transformation in higher education (GT) has seen significant contributions from scholarly literature. Alghamdi and colleagues set a methodological precedent by examining assessment tools for institutional sustainability [38], while Abad-Segura and González-Zamar outlined a framework to assess sustainability-focused research in academia, refining metrics for educational and research domains [39]. Both Koç and Alshuwaikhat and Abubakar highlighted continuous improvement strategies for campus sustainability, emphasizing operational practices [40,41]. Chen’s team and Shuqin Chen et al. delved into indicator development for eco-friendly campuses, broadening the scope to include various facets of green infrastructure and operations [42,43]. Curriculum integration of sustainability was addressed by Kapitulčinová et al. to inform educational and research dimension indicators [44], and Marrone et al. comprehensively reviewed greening initiatives and performance metrics in university settings [45].

3.3.2. Qualitative Analysis

Building upon existing research, we incorporated interviews with university administrators and students when constructing the index for higher education green transformation. We conducted interviews with 30 university administrators and 100 university students, with each interview lasting approximately one hour, in order to gain a deeper understanding of the actual motivations and challenges faced during the green transformation process.

1.

Semi-Structured Interview Design

The interviews were structured around several core questions:

Administrators of higher education:

• How do you view the impact of current air quality conditions on the campus environment?
• What measures have your institution taken to address air pollution?
• What challenges did you encounter while implementing these measures?
• What factors do you believe motivated your institution to adopt green initiatives?

Students:

• What are your thoughts on the environmental activities organized by your school?
• Have you participated in any environmental activities at your school? If so, which ones?
• What do you think your school could do to promote green transformation?
• What environmental actions have you taken in your personal life?

2.

Interview Results

Through the interview results, the following key points are found. First of all, administrators of higher education generally realize that improving the quality of campus environment is not only a responsibility for the health of teachers and students, but also an important way to enhance the school’s reputation and social responsibility. Some managers specifically cited government policies and growing public concern for environmental protection as the main drivers of the green transition. In the process of implementing green transformation, the main challenges faced by universities include limited funds, difficulties in the application of technology, lack of sufficient professionals, and institutional barriers. Especially in some cash-strapped universities, the lack of adequate financial support has become a major obstacle to promoting green transformation. In order to overcome these challenges, universities have taken a series of measures, including the introduction of energy-saving and emission reduction technologies and products, environmental protection education activities, and the establishment of campus green funds. In addition, some universities also cooperate with other institutions to share resources and technology to achieve win–win results.

The majority of students said they cared deeply about environmental issues and were willing to take practical action to support the green transformation of their schools. Students actively participated in various environmental protection activities, such as garbage sorting, energy conservation, and emission reduction competitions, showing a high enthusiasm for participation. The main factors that affect student engagement include the attractiveness of the activity, personal interest, and the incentives offered by the school. The students hope that the school can provide more practical environmental training and practice opportunities, and encourage the school to take more green measures to improve the campus environment.

In summary, administrators of higher education focus more on the macro goals and long-term planning of green transformation, including policy support, financial security, and technological applications. They particularly emphasize the importance of government policies and public attention in driving green transformation. In contrast, students are more concerned with the practical actions and participation opportunities of green transformation, including specific green activities, personal involvement, and environmental training provided by the school. Students wish for the school to create more opportunities for them to engage in real-world environmental work.

3.3.3. Construction of Higher Education Green Transformation System

Based on existing research results, this paper combines interview results and China’s national conditions to construct the green transformation index system of China’s higher education. This system is structured around four pivotal dimensions: Educational and Research, Operational Management Effectiveness, Green Campus Construction, and Social Participation. These four dimensions are inter-related and collectively form the core framework of the green transformation of higher education, which is crucial for advancing higher education institutions towards sustainable development goals. Specific indicators are detailed in

Table 1. Green transformation system of higher education.

Primary Indicators	Secondary Indicators	Tertiary Indicators
Green Transition Index for Higher Education	Education and Research	Proportion of green courses
		Number of sustainable development research results
		Number of interdisciplinary sustainability research projects
		Investment in environment-related scientific research
	Operation Management Effectiveness	Energy consumption per unit area
		Water resource consumption per unit area
		Proportion of use of low-carbon means of transport, such as bicycles
		Waste recovery rate
	Green Campus Construction	Proportion of investment in green buildings
		Campus green space rate
		Number of biodiversity conservation projects
	Social Participation	Number of environmental publicity and education activities
		Number of participants in environmental protection activities
		Number of community cooperative environmental projects

.

The dimension of Education and Research focuses on whether educational content and research activities embody the principles of sustainable development. The dimension of Operational Management Efficiency concerns the optimization of internal management processes within higher education institutions to enhance resource utilization efficiency and reduce environmental pollution. The dimension of Green Campus Construction involves the greening of campus infrastructure through renovation and new construction projects to ensure that the campus environment meets sustainability requirements. The dimension of Social Participation emphasizes the interaction and cooperation between higher education institutions and various aspects of society, including government agencies, businesses, non-governmental organizations, and communities. These four dimensions are mutually reinforcing.

Education and Research provide theoretical guidance and technical support for Operational Management Efficiency and Green Campus Construction. Operational Management Efficiency and Green Campus Construction, in turn, represent the process of putting theory into practice, providing practical cases and feedback information for Education and Research. Social Participation runs throughout the entire process, enabling higher education institutions to better understand societal needs through external interactions and to adjust and develop their strategies for green transformation accordingly.

In summary, these four dimensions together constitute a comprehensive framework for the green transformation of higher education, which is of significant importance for advancing higher education institutions towards sustainable development goals. Through the mutual interaction of these dimensions, higher education institutions can not only enhance their own level of greening but also promote the development of the entire society towards a more sustainable direction.

The following steps are performed in sequence when fitting:

(1) To neutralize the impact of varying scales and ensure comparability across different dimensions, Z-scores are employed [46]. This standardization process is mathematically represented as z = (x − μ)/σ, wherein x signifies the individual data point, μ denotes the mean of all data points for the respective indicator, and σ represents the standard deviation of those data points. This transformation adjusts the data such that the mean becomes zero and the standard deviation, unity, effectively stripping the measurement units and facilitating a direct comparison of deviations from the mean.

(2) Entropy weighting, a technique rooted in information theory, is employed for index prioritization [47]. The process initiates with the computation of entropy values for each indicator, encapsulated by $Ej=-k\sum _{i=1}^{n}pij\mathrm{l}\mathrm{n}\left(pij\right)$, wherein pij symbolizes the fraction of the ith sample’s observation on the jth indicator relative to the total observations for the said indicator. Here, n denotes the sample size, and k, typically set as 1/ln(n), ensures that entropy values are confined within the interval of 0 to 1. Subsequently, the entropy weights of the indicators are derived through $wj={\displaystyle \frac{1-Ej}{\sum _{m=1}^m(1-Em)}}$, where wj signifies the weight accorded to the jth indicator, and m is the aggregate count of indicators. This methodology assigns higher weights to indicators with lower entropy values, indicative of greater information density and discriminatory power within the dataset.

(3) Holistic assessment: Following the standardization of indicators via Z-scores to neutralize scale variances, each normalized value is multiplied by its respective weight, ascertained through the entropy weighting approach. Aggregating these weighted scores yields the green transformation index for every individual higher education institution. This index spans a spectrum of 0 to 1, with a more elevated GT index implying a heightened level of ecological transition and sustainability integration within the educational institution.

3.3.4. Measurement of Other Variables

(1) Explanatory variable: Regional air pollution pressure (PM2.5), measured by the annual average concentration of PM2.5 in Chinese provinces and municipalities.

(2) Moderating variables: Government policy support (Enpolicy), measured by the annual expenditure on environment-related scientific research in Chinese provinces and municipalities.

Public environmental awareness (Enaware) is quantified by the total number of environmental proposals submitted to local People’s Congresses and Political Consultative Conferences in a given year. Due to the right-skewed nature of Enpolicy and Enaware data, logarithmic transformations are applied to approximate normal distribution.

(3) Control variables: The selection of control variables in this study encompasses several aspects:

Initially, we incorporate controls to account for the unique attributes of higher education institutions: Institutional identity (Pub): Differentiating between public and private HEIs, this variable considers disparities in resource acquisition and decision-making frameworks under distinct governance structures. Degree awarding scope (Deg): It reflects how the spectrum of degrees conferred by an institution influences its resource demands, ecological footprint, and management complexity. Foundation era (Est): Spanning until 2022, this variable captures the duration since the institution’s inception, potentially impacting educational standards, research capabilities, and green education initiatives’ maturation. National importance (Key): It denotes institutions receiving substantial government funding and research support, which can expedite and sustain green transformation endeavors.

Next, we consider geographical and socioeconomic contextual factors: Geographical placement (Geo): Acknowledging regional variations in pollution sensitivity, policy backing, and public green consciousness, this binary variable (1 for eastern Chinese universities, otherwise 0) accounts for regional idiosyncrasies. Eastern China encompasses 10 provinces and municipalities. Economic prosperity (GDP): Utilizing the natural log of regional per capita GDP, it controls for economic prowess’s influence on green investment capability, technology accessibility, and environmental policy stringency. R&D intensity (RD): Calculated as the ratio of internal R&D expenditures to GDP, it isolates the impact of R&D focus on institutional green transitions. Educational attainment (Edu): Measured by the enrollment ratio in higher education, it adjusts for the influence of human capital and talent pools on green transformation.

Additionally, industrial context dynamics are factored in: Industrial structure (Str): Represented by the tertiary to secondary industry output ratio, it directly affects air quality and sets external pressure or benchmarks for HEI green transitions. Industrialization level (Ind): The proportion of industrial value added in GDP, it underscores the industrial sector’s direct impact on environmental conditions.

Finally, temporal dynamics are considered: Temporal shift (Year): It captures the evolving landscape over time, including policy dynamics, technological progress, and rising environmental consciousness, influencing green transformation trajectories.

Regression analyses employ clustered standard errors at the institutional and annual levels (Cluster), mitigating potential inter-institutional and inter-temporal correlation biases, thereby fortifying the robustness and precision of our model estimates. This clustering methodology permits nuanced understanding of green transformation drivers within HEIs, acknowledging institutional heterogeneity and temporal trends, thus bolstering the findings’ reliability and generalizability. A comprehensive lexicon defining the study’s variables is detailed in

Table 2. Variable definition.

Variable Type	Variable Name	Abbreviation	Variable Definition and Calculation
Explained variable	Green transformation of higher education	GT	Green transition index for higher education
Explanatory variable	Regional air pollution pressure	PM2.5	Measurement of PM2.5 concentration in the current year at the location of the institution
Regulating variable	Government policy support	Enpolicy	General budget expenditure/gross regional product
	Public awareness of environmental protection	Enaware	The natural logarithm of the total number of environmental proposals of the Chinese People’s Political Consultative Conference and the National People’s Congress
Control variables	Institutional identity	Pub	If the school is a public school, the value is 1; otherwise, it is 0.
	Degree awarding scope	Deg	If an undergraduate degree is awarded, the value is 1; if an associate degree is awarded, the value is 0.
	Foundation era	Est	Duration of establishment of university
	National importance	Key	If it is a national key university, the value is 1; otherwise, it is 0.
	Geographical placement	Geo	If the school is in the eastern region, the value is 1; otherwise, it is 0.
	Economic prosperity	GDP	The natural logarithm of GDP per capita in the location of the institution
	R&D intensity	RD	Internal expenditure on R&D funds/gross regional product
	Educational attainment	Edu	Number of students enrolled in higher education institutions/total population
	Industrial structure	Str	Output value of tertiary industry/output value of secondary industry
	Industrialization level	Ind	Industrial value added/gross regional product
	Temporal shift	Year	Year dummy

.

3.4. Empirical Analysis

3.4.1. Descriptive Analysis

Table 3. Descriptive statistical analysis.

Variable	N	Avg	Med	Max	Min	Std
GT	678	0.4755	0.4539	0.7933	0.3167	0.2933
PM2.5	678	39.5541	36.3747	85.9842	4.1994	21.0281
Enpolicy	678	0.3035	0.2280	1.3098	0.1363	0.2165
Enaware	678	9.6473	9.8544	12.5738	3.9420	1.4257
Pub	678	0.8172	1.0000	1.0000	0.0000	0.4266
Deg	678	0.5791	1.0000	1.0000	0.0000	0.5135
Est	678	0.1366	0.0000	1.0000	0.0000	0.3224
Key	678	56.5057	66.0000	129.0000	19.0000	27.4945
Geo	678	0.4234	0.0000	1.0000	0.0000	0.4953
GDP	678	11.1917	11.0055	12.1566	10.4359	0.3866
RD	678	0.0529	0.0262	0.0655	0.0344	0.0134
Edu	678	0.0584	0.0328	0.0378	0.0424	0.0038
Str	678	1.5776	1.3431	5.2880	0.7885	0.7875
Ind	678	0.3334	0.1962	0.4858	0.1093	0.0719

consolidates the fundamental descriptive statistics for the study’s core variables, presenting metrics such as sample size (N), mean (Avg), median (Med), extremities represented by the maximum (Max) and minimum (Min) values, and the standard deviation (Std). This statistical synopsis is based on a dataset comprising 678 observations following the meticulous handling of missing data entries. A rigorous scrutiny of data integrity was conducted, involving the assessment of variance inflation factors (VIFs), with the uppermost VIF recorded at a modest 2.43—substantially beneath the prevalent multicollinearity threshold of 10. This low figure confidently substantiates the minimal presence of problematic multicollinearity among the study’s prime variables, thereby affirming the logical coherence and precision of examining the individual relationships between explanatory variables independently. Consequently, this sound statistical footing paves the way for a thorough, nuanced exploration of the intricate interplay among these variables in subsequent analyses.

The green transformation index (GT) averages at 0.4755, indicative of a fairly advanced mean level of eco-transition among the surveyed higher education institutions (HEIs), nearing the optimal midpoint of 0.5, which symbolizes full sustainability. This finding attests to tangible advancements these institutions have made in their green sustainability journey. A median of 0.4539, marginally less than the mean, suggests a subtle positive skew in the distribution, implying a segment of HEIs outperforming the average in green transformation efforts, counterbalanced by a significant cohort trailing, which pulls the median downwards. The broad spectrum spanning from a peak value of 0.7933 to the nadir at 0.3167 underscores the extensive disparity in transformation stages across HEIs, ranging from highly progressive (approximately 0.8) to considerably nascent (below 0.4). A standard deviation of 0.2933 highlights the scattered progression of green transformation efforts among institutions, confirming that while the overall average transformation level is moderate, individual institutions exhibit considerable diversity, pointing to an evident discrepancy. These insights resonate with the broader context of China’s green transition narrative. Zhai et al. proposed an entropy-weight-based framework to gauge green transformation and uncovered a relatively low green transformation index for China. They reported that the eastern region outpaced the central and western regions in terms of both mean level and growth rate, with substantial variation observed among data from 30 provinces. Furthermore, their analysis highlighted the positive influence of government intervention and environmental regulations on fostering green development [48].

In conclusion, this descriptive overview illustrates that while higher education institutions (HEIs) have indeed made strides in green transformation, a conspicuous diversity in progress is evident. A dual reality emerges: a vanguard of institutions spearheading transformative efforts contrasted by a multitude advancing at a more gradual pace. This dichotomy accentuates to policymakers and educational administrators the pressing necessity to tackle the inconsistency in transformation rates and foster a uniformly progressive trajectory towards enhanced environmental sustainability throughout the HEI ecosystem. Targeted policy interventions and judicious resource allocations are thereby underscored as pivotal strategies to bridge this gap and cultivate a comprehensive green transformation across the board.

3.4.2. Correlation Analysis

Table 4. Correlation statistics.

	GT	PM2.5	Enpolicy	Enaware	Pub	Deg	Est
GT	1
PM2.5	0.106 ***	1
Enpolicy	0.113 ***	0.252 ***	1
Enaware	0.114 ***	0.075 ***	−0.079 ***	1
Pub	0.161	0.123	0.129	0.058 ***	1
Deg	0.113 ***	−0.037 *	−0.085 ***	0.016	0.062 ***	1
Est	−0.003	0.003	0.003	0.143 **	−0.051 ***	0.081 ***	1
Key	0.058 ***	−0.085 ***	0.185 ***	0.149 ***	0.139	0.085 ***	0.132 ***
Geo	0.062 ***	−0.098 ***	0.198 ***	0.152 ***	−0.036 *	0.087 ***	0.138 ***
GDP	0.069 ***	0.039 **	0.202 ***	0.158 ***	0.042 ***	0.098 ***	0.141 ***
RD	0.075 ***	−0.084 ***	0.206 ***	0.169 ***	0.123	0.099 ***	0.220 ***
Edu	−0.087 ***	−0.013	−0.023	0.171 ***	0.001	0.229	0.117 ***
Str	−0.037 **	0.171 ***	0.099 ***	0.178 ***	0.014	0.238 ***	0.122 ***
Ind	0.223 ***	0.411 ***	0.314 ***	0.183 ***	0.082	0.025 ***	0.029
	Key	Geo	GDP	RD	Edu	Str	Ind
Key	1
Geo	0.009 ***	1
GDP	0.211 ***	0.367 ***	1
RD	0.313 ***	0.323 ***	0.230 ***	1
Edu	0.217 ***	0.127 **	0.233 ***	−0.104 ***	1
Str	−0.162 ***	−0.169 ***	−0.087 ***	−0.119 ***	−0.037 **	1
Ind	−0.154 ***	−0.127 ***	0.052 ***	−0.116 ***	−0.047 **	0.393 ***	1

Note: *, **, and *** indicate significance levels at 10%, 5%, and 1%, respectively.

presents a detailed overview of the correlations between variables, specifically illustrating the Pearson correlation coefficients between the green transformation index (GT) of higher education institutions and its explanatory variables—environmental policy support (Enpolicy), public environmental awareness (Enaware), and other control elements, as detailed in Table 2. Observations reveal that the correlation coefficients between any pair of variables remain below 0.5, indicating that, within the framework of this study, the set of variables does not exhibit strong signs of multicollinearity. This statistically validates the relative independence of variables in the model, providing a reliable basis for subsequent multivariate analysis.

Pearson correlation analysis results show that the correlation coefficient between PM2.5 concentration and the GT of higher education institutions is 0.106, which is statistically significant at the 1% level. This finding, albeit indicating a subtle yet noteworthy association, underscores that even with a small correlation coefficient, the underlying relationship can attain high credibility and certainty when supported by a large sample size or highly precise data.

From an economic standpoint, the analysis uncovers a profound revelation: while the overt influence of environmental quality, particularly air pollution, on the green transformation agendas of higher education institutions appears marginal, it subtly intertwines with their sustainability planning at a more profound stratum. This insight cautions policymakers and academic administrators that even subtle signs of environmental decline carry the potential to subtly spur institutions towards more assertive eco-friendly transformations, thereby accentuating the pervasive and prospective implications of environmental conditions for economic strategy formulation. Thus, in shaping environmental policy and sustainable growth blueprints for the realm of higher education, this nuanced yet vital nexus between environmental stewardship and economic prosperity must be meticulously factored in to cultivate a synergistic relationship that bolsters both environmental conservation and educational prosperity.

3.4.3. Regression Analysis

Column 1 of

Table 5. Regression statistics.

	(1)	(2)	(3)
	GT	GT	GT
PM2.5	0.3199 ***	0.2146 *	0.2496 *
	(5.3036)	(1.6545)	(1.8367)
Enpolicy		0.3953 *
		(1.7732)
PM2.5 × Enpolicy		0.3920 ***
		(6.3025)
Enaware			0.8252 **
			(2.6724)
PM2.5 × Enaware			0.6233 *
			(1.8725)
Pub	0.3025 ***	1.3402 **	0.9283 *
	(4.2052)	(2.4824)	(1.8232)
Deg	0.4643	1.2432	0.9274
	(0.7433)	(0.2356)	(0.9281)
Est	−6.3684 **	−6.3222 **	−4.2573
	(−2.3954)	(−5.2425)	(−0.9274)
Key	0.3688	0.0924	0.2632 **
	(0.2865)	(0.3522)	(2.2474)
Geo	0.5334 ***	0.7214 ***	0.7924 **
	(5.3794)	(5.2532)	(2.0881)
GDP	−0.7432	−0.2477	−0.5283
	(−1.4953)	(−0.9893)	(−0.8921)
RD	4.7322	4.2732	4.6398
	(0.9274)	(1.3502)	(0.1932)
Edu	−0.2053 ***	−1.0923 ***	−0.0982 ***
	(−5.3743)	(−6.2378)	(−7.9287)
Str	0.6433 **	0.7599 **	0.8826
	(2.4222)	(2.2912)	(1.0814)
Ind	0.0322	0.0298	−0.0224
	(0.3028)	(0.8232)	(−0.0872)
_cons	53.2227 ***	38.0742 ***	43.0632 ***
	(11.9243)	(10.9324)	(13.2942)
Year	Yes	Yes	Yes
N	678	678	678
r2	0.3287	0.2342	0.3982
r2_a	0.3218	0.2298	0.3727

Note: *, **, and *** represent significance levels at 10%, 5%, and 1%, respectively, with t values in parentheses.

outlines the regression scenario concerning regional air pollution pressure and the green transformation of higher education, revealing a positive correlation with a regression coefficient of 0.3199, statistically significant at the 1% level. This finding not only establishes a positive association between rising PM2.5 concentrations and an increase in the green transformation index of higher education institutions but also underscores the profound impact of environmental pressures, particularly air pollution, as an external driver of behavioral shifts within the higher education sector. This conclusion implies that deteriorating environmental conditions, especially declining air quality, not only pose immediate threats to human health and ecological balance but also act as a catalyst for transforming higher education institutions towards greener, more sustainable models. In response, institutions have likely adopted a range of proactive measures, including reinforcing environmental content in curricula, implementing green campus projects, investing in renewable energy technologies, and enhancing energy efficiency, collectively contributing to their elevated green transformation indices.

The second column of Table 5 demonstrates that government policy support significantly enhances the positive correlation between regional air pollution pressure and green transformation in higher education, with a regression coefficient of 0.3920, also statistically significant at the 1% level. This indicates that policy intervention not only recognizes environmental pressures as a driver of change but also amplifies their impact through targeted measures. The emphasis here is on policy support functioning as a pivotal external incentive mechanism, crucial for encouraging higher education institutions to confront environmental challenges and adopt more proactive green initiatives. Through financial subsidies, tax incentives, and policy guidance, the government bolsters institutions’ capacity and willingness to respond to environmental pressures. Moreover, as environmental issues often entail market failures where the private sector lacks sufficient incentives for environmental action, government policy fills this gap, incentivizing institutions to adopt long-term sustainable strategies beyond short-term economic interests. This proactive policy engagement also fosters cross-sector collaborations, enhancing synergies in addressing environmental challenges and promoting green transitions.

In the third column, it is shown that public environmental awareness significantly boosts the positive relationship between regional air pollution pressure and higher education’s green transformation, with a regression coefficient of 0.8252, significant at the 5% level. This implies that as public understanding of environmental protection grows, so do their expectations and demands for environmental action from higher education institutions, thereby significantly positively impacting their shift towards green and sustainable models. The empirical significance of this result underscores the importance of public awareness as a societal driver, complementing and reinforcing environmental pressures as triggers for transformation. Increased environmental awareness reflects not only society’s heightened concern for environmental quality but also a shift in values towards a more holistic consideration that balances economic development with environmental protection and sustainability. The significance of the regression coefficient highlights the undeniable impact of this societal mindset shift on the higher education system, prompting institutions to integrate more green strategies and practices into curriculum development, research directions, and campus management. This further supports the theoretical proposition that societal forces, particularly elevated public environmental awareness, can function as a complementary informal institution [21] to formal policy directives. It motivates institutions to respond not just to direct policy mandates but also to public expectations for environmental protection, accelerating their green transition under multifaceted pressures. Additionally, this bottom–up push contributes to a broader societal consensus, fostering a more favorable external environment for sustainable development in higher education and facilitating the comprehensive infiltration of environmental responsibility from concept to practice.

4. Robustness Checks

4.1. Analysis Regarding Variations in PM2.5

Empirical investigation has uncovered a constructive association between the intensification of regional air pollution burdens and the magnitude of eco-friendly reforms in higher education institutions, employing PM2.5 concentration as the gauge for pollution intensity. In pursuit of fortifying these findings, this research incorporates the annual rate of change in PM2.5 as an alternative variable, thereby incorporating both the instant pollution load and the evolving impact of pollution alterations. This multidimensional lens on the interplay between pollution stress and ecological metamorphosis in tertiary education is elucidated in the initial three columns of

Table 6. Robustness test.

	(1)	(2)	(3)	(4)	(5)	(6)
	GT	GT	GT	GT	GT	GT
ΔPM2.5	0.4253 ***	0.1843 ***	0.5322 **
	(5.2934)	(6.3533)	(2.2732)
PM2.5				0.0184 **	0.0984 ***	0.7924 **
				(2.0824)	(5.3928)	(2.1812)
Enpolicy		0.8432 ***			0.2945 ***
		(7.0823)			(5.3053)
PM2.5 × Enpolicy		0.3923 ***			0.0824 ***
		(4.2053)			(5.3937)
Enaware			0.9865 ***			0.8763 ***
			(3.6893)			(3.9724)
PM2.5 × Enaware			2.3789 ***			0.2042 ***
			(3.9876)			(4.9732)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
N	678	678	678	534	534	534
r2	0.3118	0.2932	0.3872	0.2442	0.3921	0.2919
r2_a	0.3171	0.2873	0.3689	0.2214	0.3782	0.2752

Note: ** and *** represent significance levels at 5% and 1%, respectively, with t values in parentheses.

, confirming the steadfastness of propositions H1 through H3 and underscoring the pivotal role of escalating pollution pressures in catalyzing green transformations.

By pivoting from static pollution thresholds to annual variability, this study not merely contemplates the instant ecological exigencies but also probes how the longitudinal pattern shapes institutional strategizing. This focus on temporal dynamics accentuates how incremental shifts in pollution levels can exert formidable sway over institutional decisions for green evolution, accentuating the paramountcy of adapting to evolving conditions in the greening process.

In mapping out their green transition blueprints, institutions of higher learning appear to prioritize congruence with the projected course of environmental improvement, implying that their environmental adaptability transcends mere compliance with extant policies or the satiation of growing public eco-consciousness. Instead, it embodies a proactive commitment to future-proofing sustainability. Regardless of the absolute level of pollution or the rate of change, the internal logic of green transformation in higher education institutions—as an adaptive response to environmental challenges and its interaction with government policy support and public awareness of environmental protection—demonstrates a high degree of consistency and stability. This reinforces the depth and universal value of the research conclusions.

4.2. Examination of Sample Selection Bias

In the first phase of sampling, data were collected from 89 public and 24 private higher education institutions. However, given that private higher education institutions have significant differences in funding sources, management structures, or operational models compared with public institutions, these differences may affect their responses to environmental policies or strategies for green transition. Private higher education institutions typically have greater autonomy and may respond differently to policy changes than public institutions. Such differences could impact the generalizability and applicability of the study’s conclusions. Specifically, private higher education institutions primarily rely on tuition fees and donations for funding, which contrasts with public institutions that depend on government grants. This difference in funding sources implies that private institutions may face different financial pressures and incentive structures, potentially influencing their behavior and decisions in the context of green transition. Furthermore, the management structures and operational models of private higher education institutions may be more flexible, allowing them to adopt different strategies in response to changes in environmental policies.

To ensure the robustness and reliability of our study’s conclusions, we need to consider the potential impact of these differences on the research results. If excluding private higher education institutions leads to a change in the study’s conclusions, it suggests that the original findings may be biased. Therefore, we decided to limit the sample to public higher education institutions and conduct a robustness check on the results. This approach not only avoids the uncertainty in the study’s conclusions due to increased sample heterogeneity but also ensures that our findings accurately reflect the characteristics and trends of green transition in public higher education institutions. The outcomes, depicted in Table 6’s columns 4 through 6, uphold that hypotheses H1, H2, and H3 persist as valid under this refined scope.

Our original dataset, dominated by public institutions, encapsulates prevalent patterns in environmental responsiveness throughout the higher education landscape. Hence, confining the study to these institutions does not fundamentally change the overarching inferences. Moreover, the fundamental mechanisms guiding responses to environmental challenges, policy directives, and societal expectations are shared to a considerable extent by both public and private institutions. Air pollution, a universal issue affecting public health and the environment, uniformly pressures all higher education institutions towards eco-friendly transitions. Environmental protection policies, with their broad reach, apply not just to public but also to private institutions, which must adhere to identical regulatory frameworks.

Additionally, the heightened public consciousness about environmental issues impacts the entire higher education sector indiscriminately. Operating within a shared societal context, where there is a mounting public urge for environmental preservation, both types of institutions experience parallel incentives to bolster their green initiatives. This exploration underscores that the impetus for green transformation in higher education transcends ownership specifics, reinforcing the idea that the transformative dynamics are universally relevant.

5. Further Analysis

5.1. Analysis of Time Lag in Policy Response

To further scrutinize how government policy support modulates the connection between regional air pollution pressures and the greening of higher education institutions, we recognize the existence of a potential temporal discrepancy between policy deployment and institutional reactions. In acknowledgment of this delay, we employed an enhanced analytical methodology, integrating a temporally lagged iteration of the policy support metric (LEnpolicy) into Model (2). This methodological adjustment is purposed to better mimic the actual time span from policy initiation to institutional adaptation, shedding light on how this interval shapes the evolving dynamics between the two variables. Incorporating the lag variable enables the research not just to finely delineate the evolving interplay between policy backing and ecological transformation but also to affirm the regulatory potency of policy effects, even when delayed. As evident in column 1 of

Table 7. Further analysis.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	GT	Developed	Developing	Developed	Developing	Developed	Developing
PM2.5	0.3912 ***	0.0823 ***	0.1083 **	0.4329 ***	0.4532 **	0.3982 ***	0.2945 **
	(4.6932)	(4.2095)	(2.1181)	(5.4935)	(2.1032)	(5.9255)	(2.3639)
LEnpolicy	0.1142 ***			0.8353 **	0.0842 **
	(4.2065)			(2.0931)	(2.0942)
PM2.5×LEnpolicy	0.6322 ***			0.1938 ***	0.9832 **
	(6.2596)			(4.7893)	(2.2942)
Enaware						0.1093 ***	0.4278 *
						(6.3926)	(1.6485)
PM2.5 × Enaware						0.0257 ***	0.3744 **
						(3.9426)	(2.2053)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	678	552	126	552	126	552	126
r2	0.3633	0.3942	0.2978	0.2894	0.2791	0.2904	0.3913
r2_a	0.3412	0.3822	0.2892	0.2213	0.2382	0.2879	0.3792
Suest		F = 8.8342, p = 0.000		F = 8.0243, p = 0.000		F = 12.0032, p = 0.000

Note: *, **, and *** represent significance levels at 10%, 5%, and 1%, respectively, with t values in parentheses.

, the identified moderating impact both is statistically significant and concurs with our anticipations, signifying that the time-lagged influence of policy support bolsters the affirmative link between air pollution levels and the drive for green transformation. This finding arms policymakers with vital information, accentuating the necessity for well-timed and enduring policy measures that can effectively spur higher education’s responsiveness to ecological challenges and expedite the journey towards sustainability.

5.2. Analysis of Regional Variations

China’s eastern region, distinguished by its prosperous economy, industrial density, and advanced urban landscape, confronts a uniquely pressing issue with air pollution. This scenario elevates public anticipation for environmentally conscious actions from tertiary education establishments, while concurrently furnishing them with robust policy support, substantial resource reserves, and an elevated societal consciousness regarding eco-protection. Such synergistic elements propel institutions in the eastern belt to demonstrate swifter reactions to atmospheric pollution pressures, thereby hastening their transition towards eco-friendliness, even amidst escalated expenses and amplified resource rivalry. Geographic location, being a pivotal contextual determinant, introduces a varied influence on the predictive outcomes of the research’s analytical model, emphasizing the significance of spatial dynamics in shaping the relationship between pollution challenges and institutional responses.

By dividing the regional attributes of higher education institutions, this study classifies those located in eastern China as the developed group and the rest as the developing group. The results of regression analysis are presented in detail in columns 2 to 7 of Table 7. The analysis shows that there is a positive correlation between air pollution pressure and green transformation of higher education in both developed and developing groups, and the supporting power of government policies and the improvement of public environmental awareness have a significant effect on strengthening this positive correlation.

Nonetheless, through a meticulous examination using the Seemingly Unrelated Regression Equations (SURE) test, a revelation emerges: the beneficial impacts are notably more conspicuous and potent within the higher education sector of the eastern region. This observation underscores how the distinctive economic and sociocultural milieu of the east amplifies the immediacy of environmental issues and cultivates a more streamlined and proactive approach to green transformation.

In the resource-abundant eastern context, where policy directives are unequivocal and eco-consciousness is ingrained, higher education institutions have proven exceptionally adaptable and effective in their transformations amidst exterior pressures, including air pollution. Thus, geographical placement serves as a crucial regulatory lens in examining the intricate mechanics of air pollution’s influence on green transitions in higher education. It accentuates the imperative of acknowledging regional disparities when devising environmental policies and orchestrating sustainability blueprints for educational institutions, thereby enriching our understanding of the complex interplay between locale, policy, and ecological responsiveness.

5.3. Dynamic Association between Air Pollution Pressure and Higher Education Green Transformation

To better understand the dynamics and effectiveness of green transformation in higher education institutions, it is necessary to compare the dynamic changes in higher education green transformation with variations in air pollution levels. Such a comparison can help us gain deeper insights into how environmental pressures influence the green transformation of higher education institutions and evaluate the actual effectiveness of transformation measures. We aimed to investigate how much unit change in PM2.5 would cause a change in the green transformation of higher education institutions. To this end, we applied first differencing to both the explanatory and dependent variables to more accurately capture the dynamic relationship between them. By performing first differencing on these variables, we can eliminate trend components in the time series and focus on short-term dynamic relationships between the variables. The model is shown in Equation (4) as follows:

$$∆{GT}_{i,t}={\alpha }_0+{\alpha }_1∆{PM25}_{i,t}+{\alpha }_j\sum {Controls}_{i,t}+\sum Year+{\epsilon }_{i,t}$$

(4)

where $∆{GT}_{i,t}$ represents the first difference of the higher education green transformation index, $∆{PM25}_{i,t}$ represents the first difference of PM2.5 concentration, α₀ is the intercept term, α₁ indicates the impact of a unit change in PM2.5 concentration on the change in the higher education green transformation index, α_j is the regression coefficients for the control variables, and ${\epsilon }_{i,t}$ is the error term.

The research results, as shown in

Table 8. The dynamic association between air pollution pressure and higher education green transformation.

	(1)	(2)	(3)
	ΔGT	ΔGT	ΔGT
ΔPM2.5	0.2143 ***	0.1372 ***	0.2359 **
	(8.2942)	(6.2931)	(2.1954)
Enpolicy		0.2942 ***
		(8.9273)
PM2.5 × Enpolicy		0.2183 ***
		(4.1042)
Enaware			0.6843 ***
			(4.9282)
PM2.5 × Enaware			3.9284 ***
			(7.3924)
Controls	Yes	Yes	Yes
Year	Yes	Yes	Yes
N	565	565	565
r2	0.2913	0.2943	0.3927
r2_a	0.2749	0.2551	0.3728

Note: ** and *** represent significance levels at 5%, and 1%, respectively, with t values in parentheses.

, indicate that every one-unit increase in PM2.5 concentration leads to an average increase of 21.43% in the higher education green transformation index. This suggests that, in the face of severe air pollution problems, higher education institutions are capable of taking proactive measures. These measures not only are in response to environmental pressures but also serve to fulfill social responsibilities and enhance their reputations. Furthermore, this finding implies that the pressure of air pollution can act as an external driving force, pushing higher education institutions towards more sustainable development.

6. Conclusions

6.1. Summary of Findings

This study, through an in-depth analysis of the process of green transformation in higher education institutions and its multidimensional influencing factors, reveals the significant role of environmental pressure, policy support, and social cultural factors in promoting sustainable development in education. The following summarizes the main findings of this research:

• The Role of Regional Air Pollution Pressure

The study confirms a positive correlation between air pollution pressure and the degree of green transformation in higher education institutions, indicating that environmental pressure has become an important external force driving educational institutions to adopt sustainable actions. This finding not only elucidates the impact mechanism of environmental issues on the education system but also underscores the active role of educational institutions in addressing global environmental challenges.

• The Critical Nature of Government Policy Support

Government policy support is proven to have a significant facilitative effect on accelerating this transformation process. Clear policy guidance, financial incentives, and regulation formulation can effectively enhance the ability of educational institutions to cope with environmental pressures and promote their transition to more sustainable operational models.

• Influence of Social Cultural Factors

The enhancement of public environmental awareness has a positive impact on the green transformation of higher education institutions, indicating that social cultural factors play an indispensable role in environmental governance. Therefore, higher education institutions should place greater emphasis on community interaction and utilize educational platforms to raise public environmental awareness, thereby fostering a supportive external environment.

• Robustness Testing

Through the application of rigorous analytical strategies, including the substitution of key explanatory variables and flexible adjustment of sample ranges, we successfully validated the stability and generalizability of our research conclusions. These tests further solidified the positive correlation between air pollution pressure and the green transformation of higher education institutions and reaffirmed the positive roles of government policy support and public environmental awareness in this process.

• Impact at the Policy Level

The study finds that the timeliness and continuity of policies are critical elements driving higher education institutions to effectively respond to environmental challenges and accelerate the green transformation process. This suggests that policymakers, in designing environmental policies, should consider not only the innovation and strength of policies but also the timing and consistency of implementation to ensure that policies can rapidly and continuously guide and incentivize higher education institutions to take environmental actions.

• Geographical Differences

Compared with higher education institutions in central and western regions, those in eastern China show a more pronounced response to air pollution pressure in terms of green transformation. This finding not only emphasizes the promoting role of unique developmental backgrounds in eastern China (such as high industrialization, economic development, rich policy resources, and strong public environmental awareness) in driving green transformation but also suggests that, in advancing green transformation strategies, it is necessary to fully consider regional differences and formulate more targeted and differentiated policy measures to maximize the environmental potential of higher education institutions in various regions.

Collectively, our findings deepen the comprehension of the drivers behind green transformations in higher education institutions and offer invaluable insights and strategic recommendations to policymakers, education administrators, and society at large. By leveraging environmental pressures constructively, securing robust policy backing, and actively engaging societal and cultural forces, a concerted effort is encouraged to advance green transitions in the realm of higher education, thereby contributing to global sustainable development goals. Looking ahead, the continuous exploration and refinement of these enabling mechanisms will be pivotal in advancing the sustainability agenda in education.

6.2. Research Recommendations

To facilitate advancements in green transformation among higher education institutions (HEIs) and the achievement of Sustainable Development Goals (SDGs), this study proposes comprehensive recommendations across three dimensions: HEIs, government, and the general public.

6.2.1. At the Higher Education Institution Level

It is recommended that higher education institutions (HEIs) incorporate the Sustainable Development Goals (SDGs) into their educational curricula. Education in HEIs plays a pivotal role in advancing the SDGs, particularly by strategically embedding environment-focused targets from the United Nations SDG framework into institutional planning [49], serving as a potent mechanism to swiftly steer universities towards greener, environmentally sustainable entities [50]. By integrating SDGs into course designs, via dedicated courses, workshops, and seminars, institutions can elevate student awareness and engagement with the SDGs, with a particular emphasis on areas such as climate action, clean water and sanitation, and sustainable cities and communities.

Moreover, HEIs should strive to establish themselves as exemplars of green campuses. Utilizing the campus as a living laboratory for sustainability, initiatives promoting energy efficiency, water management, green transportation, and zero-waste practices not only diminish the institution’s environmental footprint but also furnish students with hands-on learning experiences, thereby fostering progress towards SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production).

Lastly, fostering interdisciplinary research collaborations around SDG themes, including climate change mitigation strategies, biodiversity conservation, and sustainable agricultural techniques, can drive innovation through research projects. This contributes actionable, sustainable solutions to society, thereby bolstering SDG 9 (Industry, Innovation, and Infrastructure) and SDG 13 (Climate Action). Through these concerted efforts, HEIs can effectively align their educational, operational, and research activities with global sustainability aspirations.

For example, Columbia University has introduced a “Sustainability Science” major and a series of interdisciplinary courses, enhancing students’ awareness and engagement with the Sustainable Development Goals (SDGs). Specifically, in areas such as climate action, clean water and sanitation, and sustainable cities and communities, Lund University in Sweden has effectively promoted student participation through workshops and seminars. Additionally, efforts should be made to create green campus demonstration sites. Arizona State University in the United States has implemented several energy-saving and emission-reduction projects, such as solar power systems and rainwater harvesting systems, which not only reduce the environmental footprint of the campus but also provide students with opportunities for hands-on learning. Finally, interdisciplinarity in research collaboration should be increased; for instance, the University of Toronto in Canada has promoted innovation around themes such as climate change solutions and biodiversity conservation through research projects, providing actionable sustainable solutions for society.

6.2.2. At the Government Level

It is advisable for governments to furnish unambiguous policy direction and concrete assistance, devising and enforcing policies that expedite the green transformation of higher education institutions (HEIs). Integral to this approach are incentives like fiscal subsidies, tax relief, and favorable procurement policies for eco-friendly goods and services, all designed to reinforce alignment with the Sustainable Development Goals (SDGs), notably SDG 4 (Quality Education) and SDG 17 (Partnerships for the Goals). Concurrently, a sturdy legal and regulatory infrastructure ought to be put in place for HEI green transitions, encompassing benchmarks for environmentally sound campus development and an assessment system for green Research and Development (R&D), thereby furnishing clear benchmarks and norms for adherence and synchronizing legal structures with the SDG agenda. Furthermore, governments should encourage the establishment of cooperative platforms engaging HEIs, enterprises, and civil society groups to enhance resource pooling and collaborative ventures. By doing so, they can advance SDGs through public–private partnerships (PPPs) that spur innovation and the deployment of green technologies, fostering a collaborative ecosystem geared towards sustainable development objectives. Such a multi-stakeholder approach not only accelerates green transitions within HEIs but also strengthens the broader societal commitment to achieving a sustainable future.

For example, the German government encourages green transitions in higher education institutions by providing fiscal subsidies and tax reductions as incentives, ensuring that policies align with the SDGs. At the same time, it establishes and improves a legal framework related to the green transition of higher education, such as the UK government’s issuance of the “Green Campus Construction Guidelines”, which provides clear guidance and standards for higher education institutions. Additionally, it builds cooperation platforms among governments, universities, businesses, and social organizations; for instance, Denmark has successfully promoted the development of sustainable agricultural technologies through public–private partnerships (PPPs) that drive the research and application of green technologies.

6.2.3. At the Societal and Public Level

The promotion of synergies between higher education institutions (HEIs) and local communities is encouraged to execute public environmental literacy programs, such as seminars on environmental topics, practical workshops, and environmental volunteer initiatives. These activities aim to amplify understanding of the SDGs and nurture environmental mindfulness, with a special focus on SDG 11 (Sustainable Cities and Communities) and SDG 12. A participatory framework should be instituted, empowering a diverse array of stakeholders—including learners, educators, parents, and community members—to actively engage in the green governance and initiatives of HEIs, thereby cultivating a grassroots-driven impetus in favor of the SDGs.

Additionally, HEIs are urged to enhance the visibility of their eco-transformation journeys by consistently communicating progress, hurdles encountered, and accomplishments through sustainability reports, social media engagement, and other public interfaces. This fosters greater societal scrutiny and encouragement, thereby bolstering the execution of SDG 16 (Peace, Justice, and Strong Institutions). For example, Tsinghua University in China collaborates with surrounding communities to host lectures and volunteer activities on environmental themes, enhancing public awareness of the SDGs and environmental consciousness. It is recommended to establish effective public participation mechanisms, such as Harvard University in the United States inviting community members to participate in the university’s sustainability plans, jointly discussing and implementing green decisions. Finally, higher education institutions should increase transparency in their green transition processes; for instance, the National University of Singapore regularly publishes sustainability reports and actively engages on social media, thereby enhancing societal oversight and support.

The execution of these holistic strategies will not only expedite the green metamorphosis within HEIs but also intensify cross-sectoral collaboration, collectively propelling progress towards the universal aims of sustainable development.