key: cord-0018856-km09t63p authors: Su, Yawen; Jiang, Qingquan; Khattak, Shoukat Iqbal; Ahmad, Manzoor; Li, Hui title: Do higher education research and development expenditures affect environmental sustainability? New evidence from Chinese provinces date: 2021-07-07 journal: Environ Sci Pollut Res Int DOI: 10.1007/s11356-021-14685-w sha: e77340367eb111db0f4955d9e3ed4ffa4de2a34e doc_id: 18856 cord_uid: km09t63p Even though higher education R&D expenditures (HEEXP) are important determinants of economic growth that facilitate science, technology, new ideas, and innovation, yet its effect on environmental sustainability remains unexplored. This paper examines the nexus between HEEXP and carbon dioxide emissions (CO(2)e), followed by control variables such as electricity consumption (EC), foreign direct investment (FDI), gross domestic product (GDP), and total population (TP) for the period 2000Q1–2019Q4. Data were evaluated using different tests, e.g., the cross-sectional dependence test, cross-sectionally augmented Dickey-Fuller unit root test, Westerlund error-correction–based panel cointegration test, mean group, augmented mean group, common correlated effects mean group, and Dumitrescu-Hurlin panel causality test. First, the results validated the cointegration association among HEEXP, EC, FDI, GDP, TP, and CO(2)e. Second, the finding showed significant long-term negative nexus between HEEXP and CO(2)e. Third, the findings indicated that electricity consumption, foreign direct investment, gross domestic product, and total population are the important factors that intensify the overall situation of CO(2)e. Fourth, the results indicated that there exists bidirectional causality between EC and CO(2)e; FDI and CO(2)e; GDP and CO(2)e; POP and CO(2)e; and HEEXP and CO(2)e. This paper’s findings call for devising policies and strengthening financial support to induce higher education for developing green patents. Environmental pollution is a major threat to the environment of the world. Rising economic growth and industrialization in emerging economies have fuelled the irresponsible consumption of fossil fuels. Apart from the speedy depletion of natural resources, this situation has contributed to more waste, residues, and greenhouse gases (GHGs) in the environment. These toxic emissions of various types are considered primary causes of global climate change, rising temperatures, and air pollution. Among them, carbon dioxide is one of the leading pollutants, accounting for about 63% of GHG emissions (Sharif Hossain 2011; Wei 2020) . Wei (2020) further reported that the global mean temperature has upsurge by 0.74 centigrade during the last ten decades. Theoretically, the association between gross domestic per capita (GDP) and CO 2 e is directly linked to the consumption of different carbonintensive natural resources, especially fossil fuels. Many scholars have argued that CO 2 e, fossil fuel consumption, and economic progress are intimately correlated. Researchers have stated that massive industrialization, resulting from an increase in economic activities, escalates the rate of energy consumption from various nonrenewable sources, thereby causing CO 2 e (Rehman et al. 2019) . From the day China adopted the "opening-up policy," its economy sharply rose from just RMB0.365 trillion (1978) to RMB8.272 trillion (2007) . With a phenomenal upsurge in the GDP (per capita) growth rate, China has become one of the largest CO 2 emitter in the world (Q. Li et al. 2019) . China has mostly relied on nonrenewable energy resources (i.e., coal) to drive its economic growth and industrialization at the cost of high CO 2 e, even though it is now cleaning its energy mix (Ahmad and Zhao 2018) . Nonetheless, an overdependency on coal has significantly contributed to global warming, climate change, water contamination, soil erosion, and air pollution in China and the world (Ahmad et al. 2018a) . With nearly 20% of the global population, China has significantly affected the economic and environmental landscape of the world. Figure 1 presents the historical growth in population, GDP growth, and CO 2 e in China. Besides, previous studies have identified various determinants of CO 2 e. These factors include financial development (Ahmad et al. 2018b) , technological innovation (Ahmad et al. 2019a) , fiscal decentralization (Du and Sun 2021) , freight and passenger transportation (Godil et al. 2020) , economic growth (Nathaniel et al. 2021d; Solarin et al. 2021 ), institutional quality (Wawrzyniak and Doryń 2020) , urbanization (Ahmad and Zhao 2018; Nathaniel et al. 2021c) , exports (Anser et al. 2021 ), renewable energy consumption (Nathaniel et al. 2021b) , corruption (Ren et al. 2021) , public-private partnership investment in energy (Raza et al. 2021) , aggregate consumption , human capital (Asghar et al. 2020) , shadow economy (Sohail et al. 2021) , higher education , imports (Adewuyi and Awodumi 2020) , gross fixed capital formation (Nathaniel and Adeleye 2021), energy demand (Vo and Zaman 2020) , foreign direct investment , green technology innovation (Meirun et al. 2021) , democratic transition (Mao 2018) , inflow of remittances (Ahmad et al. 2019a; , tourism (Aziz et al. 2020) , international cooperation , financial instability (Baloch et al. 2018) , fuel tax (Akkaya and Hepsag 2021) , income inequality , premature deindustrialization (Ullah et al. 2020) , information and communication technologies (Anser et al. 2021) , natural resources rents Nathaniel et al. 2021e) , military expenses (Isiksal 2021) , globalization , real interest rate (Isiksal et al. 2019) , trade openness (Iheonu et al. 2021; Nathaniel 2020) , commercial policies (Jiang et al. 2021a) , globalization (Nathaniel et al. 2021a) , and nonrenewable energy (Nathaniel and Iheonu 2019). This paper focuses on higher education R&D expenditures (HEEXP) as another unexplored determinant of CO 2 e for several reasons. First, the HEEXP serves as a core of science, technology, and innovation, which boosts industrialization and economic growth. Thus, this factor could be central to CO 2 e mitigation strategies. Second, China recognizes environmental pollution as an urgent threat. It has been extensively funding higher education institutions (HEIs) for education and research projects related to energy, green economy, alternative fuel, and nonrenewables. In response, the HEIs have actively engaged in the education, research, and development activities by developing new ideas, technologies, products, and processes for the benefit of industry, the public, and the environment. Figure 2 depicts the parallel development in the HEEXP and environment-related patents for China for the period 2001-2019, signaling the potential role of HEEXP in eco-related patents. As seen above, a 4% increase in the HEEXP led to a rise in eco-related patents by 21% in 2016 21% in . From 2001 21% in -2016 , an average of 21.18% upsurge in the HEEXP was associated with a parallel increase in eco-related patents by 20.59%, cueing potential implication of the HEEXP on ecorelated patent development and environmental pollution in China. Despite that, the existing literature fails to offer any published study that sheds light on how shifts in the HEEXP are shaping environmental pollution dynamics. The key purpose of this study is to fill this knowledge void by comprehensively analyzing the nexus between HEEXP and CO 2 e by using data from thirty-one provinces in China. Some significant contributions of this work are as follows. First, the paper provides an initial insight into the potential nexus between HEEXP and CO 2 e, thereby opening a possible research avenue in environmental economics. Second, the article offers the first schematic framework explaining the precise mechanism of how HEEXP affects environmental population, and GDP growth in China (1981-2019) pollution in China. Third, the paper uses second-generation econometric techniques for robust and rigorous analysis. Fourth, through provincial data, the paper presents an indepth insight into regional and provincial disparities vis-avis the HEEXP-CO 2 e nexus. Fifth, the article has attempted to integrate two distinct paradigms into a unified framework, i.e., higher education and environmental economics. Most prior studies on CO 2 e in the education literature are limited to campus-level surveys. Of the few studies in the economics literature, scholars have used education as a control variable, predominantly using student numbers or percentage of students as proxies. None of the prior studies in both disciplines has linked the HEEXP to CO 2 e. The rest of the paper is categorized as follows. The second section is the "Literature review" section. The third section is the "Conceptual framework, model specification, data, and estimation techniques" section. The "Results and discussions" section focuses on the interpretations of results and discussions, followed by the conclusion, policy recommendations, future directions, and limitations in the "Conclusion and policy implications" section. The relationship between income and CO 2 e The close inverted U-shape association between environmental sustainability and economic progress has gained considerable significance among scholars, especially during the last three decades. Many believe that rapid economic progress and industrialization affect the environment through the excessive consumption of fossil fuels. Intellectuals have conducted extensive research to find potential determinants of environmental pollution. Past empirical studies have established that dirty and cheap fuel sources (e.g., coal, oil, and natural gas) have been a significant source of increasing global temperature. After the first industrial revolution, entrepreneurs and economies have been striving to control the CO 2 e levels to prevent the harmful impact of global warming problems. The environmental Kuznets curve (EKC) hypothesis is probably the most frequently tested framework that explains the link between aggregate income and environmental sustainability (Özcan and Öztürk 2019) . Grossman and Krueger (1991) argued that ecological pollution escalates in the initial stage of economic progress due to intense industrial consumption of cheap energy. This situation, however, improves with increased income as more efficient and clean technologies are used in the production process in the latter stages of economic development. This relationship is commonly referred to as the EKC hypothesis. Researchers have validated the EKC hypothesis for different economies, e.g., Iberia (Moutinho et al. 2020) ; China Jiang et al. 2019; Mushtaq et al. 2020; Xiaosan et al. 2021; Zhou et al. 2018) ; India (Dar and Asif 2017) ; Pakistan (Ur Rahman et al. 2019) ; the USA (Alola and Alola 2019); Brazil (Ben Jebli and Ben Youssef 2019); emerging economies (Wawrzyniak and Doryń 2020) ; NAFTA and BRIC Rahman et al. 2019a ); Ukraine (Melnyk et al. 2016) ; SEE economies (Obradović and Lojanica 2017) ; developed and developing economies (Anser et al. 2020) ; and OECD (Ahmad et al. 2019a The positive link between FDI and CO 2 e is known as the pollution haven hypothesis (PHH). This concept explains how sources of pollution transfer between countries and regions due to asymmetries in environmental regulations and industrial locations. Prior evidence indicates that pollution-intensive units, factories, or plants facing stringent regulations and policies in first-world economies moved and sought refuge in developing and third-world economies where laws were either nonextant or extremely weak. As this trend has continued for a long time, many developing and third-world nations have become pollution havens due to imported pollution-intensive industries from the developed countries. Besides international trade and foreign investments, weak regulations in these economies have also attracted dirty technologies in most emerging economies (Centre et al. 2005) . That said, the empirical evidence on the FDI-CO 2 e nexus remains controversial. Some proof of the positive relationship between FDI and CO 2 e include the newly industrialized economies ( Table 2 depicts the summary of the selected studies on the nexus between FDI and CO 2 e. Globally, the historical shifts in demographics have not only resulted in falling fertility, mortality, and population size, but it is also linked to the developments in composition (agestructural change or population aging), distribution (migration), and density (urbanization). Harper (2013) stated that three sub-factors of the population had played an important role in increasing or decreasing CO 2 e. Martínez-zarzoso et al. (2007) believed that although economic activity initiates wealth creation in a society, it damages the environment. The authors further added that the production systems in developed economies had generated massive water, air, and soil pollution, while simultaneously depleting precious global natural resources. The detrimental environmental impact of economic activities on the environment has worsened over the past years due to unparalleled demographic growth. With the global population increasing at an unprecedented rate, the resulting expansion in energy consumption has created higher risks for the environment. Researchers have established a positive link between population and CO 2 e for the European countries (Harper 2013; Martínez-zarzoso et al. 2007 ); developed and developing economies (Dietz and Rosa 1997) ; selected 93 economies (Shi 2003) ; Asian economies (Qingquan et al. 2020 Pakistan (Ullah et al. 2020) ; OPEC economies (Murshed et al. 2020) ; and Asian countries (Abbasi et al. 2020) . Table 3 depicts the summary of the selected studies on the nexus between population and CO 2 e. The relationship between electricity consumption and CO 2 e Electricity is one of the primary sources of energy for all industries. Even though electricity consumption is not directly associated with CO 2 e, the vast quantities of nonrenewable fossil fuels used for power generation emit high CO 2 e (Zhang 2019) . Previously, few academics have examined the relationship between electricity consumption and CO 2 e. For example, Zhang (2019) investigated the relationship between electricity consumption and carbon intensity among twenty-seven firms in China using a STIRPAT framework. The results indicated that electricity consumption played a mitigating role in CO 2 e. Balsalobre-Lorente et al. (2018) concluded that electricity consumption increased CO 2 e in the long run across the European nations. Bélaïd and Youssef (2017) tested the association between energy (renewable and nonrenewable) consumption and CO 2 e for Algeria. The ARDL estimates validated the renewable energy consumption-CO 2 e led hypothesis. Yorucu and Varoglu (2020) studied the nexus among industrial production, electricity consumption, economic growth, and CO 2 e in selected small island states. Based on the FMOLS and DOLS estimations, the authors found that a 1% increase in electricity consumption predicted an upsurge of 0.79% in CO 2 e. In the same way, other studies have also reported a positive connection between electricity consumption and CO 2 e for China (Akadiri et al. 2020 Table 4 depicts the summary of the selected studies on the nexus between electricity consumption and CO 2 e. Conceptual framework, model specification, data, and estimation techniques Conceptual framework Figure 3 illustrates the conceptual framework, depicting the mechanism through which HEEXP may affect CO 2 e. The HEIs have contributed to the advancement of knowledge, economy, cultivating students, and conducting research in many fields. Whether it was government intervention or a self-driven agenda, HEIs around the world have undergone enormous transformation and restructuring in areas like organizational practices, research focus, controls, funding structures, and autonomy (Wendt et al. 2015) . Governments' funding, therefore, has been crucial for many HEIs to support primary and advanced level research, especially in fields like Destek and Okumus ( Generalized method of moments environmental sciences, energy and resources efficiency, sustainability, and other similar areas. Many academic institutions have set up separate departments for energy economics, sustainability, green technology, and eco-innovation while simultaneously initiating programs and activities to achieve green education, green campus, and green economy. With the support of their respective governments, many industries and academic institutions are actively conducting research and developing solutions for sustainable production, responsible consumption, and environmental preservation. These projects reflect two facets: (i) research on green and sustainable technology, methods, processes, and products and (ii) developing and promoting green campuses (GC). Congruent with the above, academic institutions and governments are equally focused on addressing various crucial issues related to energy consumption and production. A possible explanation resides in the energy resources possessed by a country. If the energy demand exceeds the supply, governments are left with no choice but to import expensive energy from other countries that undermine their security and environment. With the potential role of renewable and green energy, green technologies, green products, and green services, many governments and institutions have been investing heavily in academic research and development related to ecoinnovation, green technologies, and renewable energy solutions. As a result, the number of eco-related patent applications and green research has increased manifolds in the past few decades across developed and developing nations. In terms of environmental benefits, these patents have been used across many industries to solve problems, including energy shortages, fossil fuel dependency, carbon footprint, and low energy efficiency. Beyond that, academic institutions have been developing and institutionalizing the concept of green campus (GC) and green education. Simply put, GC embodies the development of two critical aspects in an academic institution: (a) energy- and resource-efficient campus (ERSC) and (b) campus energy management system (CEMS). The concept of CEMS emphasizes the construction of green education and environmentrelated technologies for ERSC. The ERSC, however, requires the integration of green ideology into capital operation, infrastructure, logistics, and other departments. The primary purpose of GC is to achieve energy and resources efficiency by saving materials, water, energy, and land; promote the use of green and clean energy sources during official hours; encourage sustainable development in higher education; improve R&D for faculty, staff, students, and society at large; enhance stakeholder engagement on sustainable decision-making; sponsor students and faculty participation in green and sustainability-related activities; and design and implement green curricula. Thus, GC plays a vital role in the implementation of sustainable development goals and green policies. Above all, the exchange and cooperation activities among academic institutions for the advancement of GC ideology offer multiple benefits, in terms of national policy formulation for GC development; attainment of Strategic Development Goals; encouraging collaborative research; enabling the diffusion of carbon-and energy-saving programs, innovation, and carbon-reduction technology in HEIs; initiating training programs for faculty members; and establishing real-time experiment, labs, and demonstration centers for green research, education, green campus development, and strategy implementation. Through the proper utilization of HEEXP, the GC can find a new way to set the foundations for disseminating the soft power of eco-protection, achieving low-carbon goals, and enabling a smooth transition to a green economy and campus. That said, the development of the GC necessitates the need for educational institutions to focus on the hardware and software of the GC simultaneously. The former pertains to the integration of green aspects in construction, building, infrastructure, and operations. The latter refers to developing and promoting green culture, humanity, green citizenship, and talent for social entrepreneurship. This process, if properly executed, will result in the formation of core green values at all levels (economy, education, society, business), enabling sustainable progress (Tan et al. 2014) . In short, it is proposed that the development of GC (through HEEXP) not only helps in mitigating CO 2 e but also plays an essential role in promoting sustainable consumption and production across residential and commercial sectors. Below, Eq. (1) represents the dynamic relationship between higher education R&D expenditures (HEEXP), foreign direct investment (FDI), electricity consumption (EC), gross domestic product (GDP), total population (POP), and CO 2 e. where CO 2 e it is the carbon dioxide emissions; HEEXP it the higher education R&D expenditures; FDI it the foreign direction investment; EC it the electricity consumption; GDP it the gross domestic product; POP it the total population; ϵ it the error terms; ψ o the constant; and ψ 1 , ψ 2 , ψ 3 , ψ 4 , and ψ 5 are the unknown parameters of each variable. The rationale for using variables, including FDI, EC, GDP, and POP (as control variables), is briefly discussed henceforth. First, China has become one of the most attractive FDI destinations due to low labor costs and weak environmental regulations. Many multinational companies from developed nations have transferred their technologies (FDI), converting China into a pollution haven. Second, China is among the top energy generation countries, where almost 80% of electricity was generated from coal. Third, it is one of the largest economies in the world, vis-a-vis the GDP growth rate. Fourth, China is one of the most populous economies globally, where population growth has contributed to energy consumption among residential and nonresidential consumers, directly and indirectly causing CO 2 e. The data for HEEXP, FDI, EC, GDP, POP, and CO 2 e were collected from the National Bureau of Statistics (2019) for 2000-2019. Consistent with the previous studies (cf. Sbia et al. 2014; Shahbaz et al. 2017) , the accuracy and frequency of the data were enhanced through the quadratic match-sum method. All variables were converted into logarithmic forms for added reliability and consistent results. Table 5 shows the data sources and descriptions. Following Nathaniel et al. (2021b) and Ahmed et al. (2021) , this study employs panel econometric approaches. These methods are appropriate for panels with a large number of cross-section and years. As noted earlier, the study's methodology consists of cross-sectional dependence (CSD) test, slope homogeneity test (SHT), second-generation panel unit root tests, Westerlund (2007) error-correction-based panel cointegration test (WECPT). Next, the estimation of longrun coefficients was conducted through the following estimators, i.e., mean group (MG), augmented mean group (AMG), and common correlated effects mean group (CCEMG) estimator. Finally, the causality was checked using the Dumitrescu and Hurlin (2012) panel causality test (DHPCT). Testing the cross-sectional dependence (CSD) among the series was the first step in the panel data analysis. This test was conducted to identify and deal with the problems of unit root and CSD in the data series. As the CSD is associated with economic unions, financial shocks, demand shocks, supply shocks, pandemic diseases, globalization, and trade wars, it must be dealt with accuracy and precision. If ignored, it could lead to biased cointegration and stationarity results . Based on prior recommendations (Ahmed et al. 2021; Nathaniel et al. 2021b) , the Pesaran (2015) crosssectional dependence test (PCSDT) was applied for addressing the CSD problem. The CSD statistics can be represented as follows: where p ij denotes the cross-sectional correlation of error between j and i. T and N represent time horizon and cross-sections, respectively. The selection of this approach is due to the dataset size, i.e., a smaller number of cross-sections compared to the time period (Nathaniel et al. 2021c). Moreover, the Pesaran and Yamagata (2008) slope homogeneity test (SHT) was applied for addressing the homogeneity problems. As per the authors, the "slope homogeneity (H 0 : β i = β for all (i)" in the null hypothesis and "slope heterogeneity (H 0 : β i ≠ β)" (Balsalobre-Lorente et al. 2020). The Pesaran and Yamagata (2008) SHT involve estimation of the following equations: where S depicts Swamy test statistic, N signifies the cross- , and k represents finite positive constant. If the CSD problem is detected, the first-generation unit root approaches are not applicable because they cannot deal with the problem of CSD. Therefore, the second-generation teststhe Pesaran (2003) cross-sectionally augmented Dickey-Fuller (PCADF) and the Pesaran (2007) cross-sectionally augmented IPS (CIPS) unit root tests-are used to sort out CSD issues. Conventional or first-generation panel unit root tests are based on the hypothesis of cross-sectional independence (CSI). The second-generation unit root tests, however, allow for the assumption of CSD in the data series. With the results of the second-generation tests providing strong evidence on the existence of CSD across the provinces in China, these tests were appropriate for estimating the order of integration. The PCADF test statistics can be estimated using the following generalized regression function: where Δy indicates the averages of the cross-sectional outcome variables at first differences and y shows the averages of the cross-sectional outcome variables at lagged levels. The PCIPS statistic is then computed using the approximate tstatistic from Eq. (5), which can be defined as: The null hypothesis of both PCIPS and PCADF indicates whether every panel's cross-section is stationary or nonstationary. For robustness check, the Clemente et al. (1998) unit root test (CMRURT) was applied with multiple structural breaks. After determining the order of integration among EC, FDI, GDP, POP, HEEXP, and CO 2 e, it was important to look for the long-term nexus between them using a suitable cointegration method. Westerlund (2007) error-correction-based panel cointegration test (WECPT) was employed to inspect the dynamic cointegration connection between EC, FDI, GDP, POP, HEEXP, and CO 2 e. This test deals with the problem of common factor restriction (Nathaniel et al. 2021d). Westerlund (2007) proposed four cointegration tests to examine the presence of long-run cointegration in the panel data. These tests are based on the error-correction (EC) model and offer three distinct advantages: (1) allow unbalanced panels and unequal series length in units; (2) test heterogeneity that is permitted in the short-and long-run parameters of the error-correction model; and (3) obtain critical value using the bootstrap approach, if a correlation probability exits among units. The WECPT involves the following hypothesis: The paper adopted three cointegration tests for checking robustness- Kao (1999) residual-based cointegration test (KRCPT), Pedroni (2004) cointegration test (PCT), and the Gregory and Hansen (1996) cointegration test (GHCT) (with structural breaks and regime shifts). Several economic techniques have been introduced in the past decades for addressing the CSD and parameter heterogeneity problems. Some of the widely accepted methods include the M Hashem Pesaran and Smith (1995) mean group (MG) estimator, M Hashem Pesaran (2006) common correlated effects mean group (CCEMG) estimator, and the Eberhardt and Bond (2009) augmented mean group (AMG). Technically, the MG method separately applies times series ordinary least square (OLS) to each panel, including a linear trend to estimate timevariant unobservable (TVU) and an intercept to deal with fixed components. Then, this estimator averages the computed individual-specific slope (without or with wrights). For dynamic cases, this estimator proves to be reliable for large N and T if the coefficients exhibit heterogeneity in groups. This estimator, however, fails to offer information about common factors (CFs), which may exist in the panel data. The CFs are referred to as "time-specific effects," which are common in provinces, countries, or regions. By incorporating the averages of the cross-sections of the independent and the dependent variables as surplus regressors (when applying OLS to specific units), the CCEMG method allows for TVU and CSD with heterogeneous effect in panel members. Identified by the averages of CS, the unobserved CFs can be any fixed digit. With superior small sample characteristics and short-run estimation properties, the CCEMG technique is relatively robust to non-cointegrated and nonstationary CF, structural breaks, and some serial correlations. As an alternate method, the AMG initially computes an augmented pooled model (with year dummies) through the first difference OLS. The calculated year dummies are then compiled to construct a new variable, representing the common dynamic process. This new variable is used as an extra regressor for single groupspecific regressor model, along with an intercept for capturing the time-variant fixed impacts. Similar to the CCEMG technique, the AMG method helps in dealing with multifactor error terms and nonstationary variables, particularly considering CSD. The AMG estimator is superior to the CCEMG estimator in creating a set of unobservable CF as a common dynamic process. Dissimilar to a scenario in which the unobservable factors are considered a nuisance, the alternate treatment may offer helpful interpretations, depending on the context (Heshmati 2019). The AMG consists of the estimation of the following equations: Stage 2 : where b b AMG is the AMG estimator, y it denotes the observables, d i signifies the coefficient of the time dummies, b i is the countryspecific estimates of coefficients, and f t represents the unobserved common factor, while x it and y it show the observables. The CCEMG introduced by Pesaran (2006) comprises the estimation of the following equations: where b b CCEMG indicates the panel CCEMG estimator and b b i denotes the individual CCEMG estimation for each of the cross-sectional unit. For panel data, Dumitrescu and Hurlin (2012) proposed a test to examine causal relationships between variables. This test outperforms the traditional causality tests by allowing for the hypothesis of causality existence in at least one cross-section against the nonexistence of the homogenous Grangercausality relationship. In this way, the Dumitrescu and Hurlin (2012) panel causality test (DHPCT) accounts for the CSD between the sample province or countries. Moreover, the DHPCT is insensitive to the variance among the crosssections and the time difference in the panel. It generates efficient results, even if the size of the cross-sections and time series are smaller or larger than others (Ceyhun 2019). The DHPCT involves the estimation of the following standardized statistics: where π p ð Þ i denotes the regression coefficient, ξ p ð Þ i shows the autoregressive parameter, ϕ i is the intercept, and π i represents the coefficient. The DHPCT follows the testing of the following hypothesis: Results and discussion Table 6 depicts the results of PCSDT. As seen below, the null hypothesis of no CSD for the EC, FDI, GDP, POP, HEEXP, and CO 2 e was rejected at 10, 5, and 1% significance levels, implying that all the provinces in China were interdependent, i.e., an economic shock in one region may affect other regions. As reported in Table 7 , the SHT highlighted heterogeneity problems in the model. Table 8 displays the results of the PCADF and PCIPS unit root tests. These tests were used to check the integration order of all the study variables. The results confirmed that all the study variables were nonstationary at level but became stationary at the first difference, even though these tests could not deal with structural breaks in the data. Given that most global economies have experienced many structural changes, it was considered imperative to trace structural breaks in the data series for China. There was a high probability that the PCADF and PCIPS could have given bias results if structural changes were underestimated. This problem was addressed through the CMRURT that allowed for multiple structural breaks in the data. Table 9 illustrates the results of the CMRURT. The test indicated that all variables were stationary at the first difference, with two break years in each series. The estimated structural breaks-often linked to global or local events-had potential positive or negative implications for the Chinese economy. In 2002, a deadly virus named SARS emerged in Guangdong and severely impacted industrial production (Wong and Zheng 2004) . In 2004, China faced one of the worst historic inflationary pressures, partly triggered by realestate speculations. With an increase in the costs of raw material and energy and over-investments in some industries, China raised interest rates and applied administrative control to abate the pace of investment in some sectors and industries (Morrison 2010) . In 2005, the Lenovo Group acquired IBM's computer division for a hefty sum of USD1.75 billion, which was considered an economic breakthrough. Apart from gaining access to foreign facilities, operations, and R&D, China strengthened its presence in the USA (Morrison 2010 ). From 2008 -2009 , the global financial crisis pushed China to revisit its economic policies to sustain economic growth. While the economic growth rate was disrupted in 2009 relative to the past years, this slowdown in growth was reasonably modest, especially compared to the total shrinkage in the world output (Lardy 2012) . Although the incoming FDI experienced a sharp decline, the inbound foreign investments reached an all-time high in 2010, increasing by around twothird, i.e., USD185 billion. There was almost a 20% contraction in outbound FDI in 2009, but the outbound FDI increased by 37% and touched an all-time high of USD60 billion (Lardy 2012) . Moreover, the inclusion and internationalization of RMB in the special drawing rights currency basket by the IMF in 2010 was another important milestone, which enabled China to expand its financial presence in the global financial markets (Cassis and Wójcik 2018) . With all the study variables exhibiting the same integration order, the study applied the cointegration analysis, including the WECPT, KRCPT, PCT, and the GHCT. Table 10 depicts the outcomes of the cointegration analysis without structural breaks. The first two columns (G t , G a ) indicate the group means statistics for the total cointegration, whereas the remaining two columns (Pa, Pt) show panel statistics. The WECPT outputs confirmed a sustainable long- term association among all the study variables. In Table 11 , the results of the cointegration analysis (with structural break and regime shifts) were found to be consistent with the WECPT, KRCPT, and PCT. Table 12 displays the long-run coefficients based on three different econometric methods, including the MG, AMG, and CCMEG. The main findings are as follows. First, the estimates showed a significant negative linkage between HEEXP and CO 2 e-a 1% increase in HEEXP predicted a decline of 0.29 (MG), 0.24 (AMG), and 0.30% (CCEMG) in CO 2 e. As expected, this result supported that spending on research and development spending in higher education has helped mitigate CO 2 e in China. A feasible explanation is that academic institutions have been a central part of the national research framework in developing green technology, innovation, and eco-urban systems in China. In 2011 alone, the faculty and staff from HEIs constituted 11.3% of the overall research and development population. Using almost 8.5% of the total national R&D spending, these researchers have shown impressive results. These individuals conducted 62.2% of all research projects and activities, received 28.8% of the total patents, applied for 21.6% of the total patents, and produced 64.4% of all scientific publications. Following the "new normal" of fostering the nation with education, science, innovation, and developing a green economy, the Chinese government has focused on green and sustainable technology research. Currently, Chinese scholars are leading global research related to green production, sustainability, green technology, environmental science, and green energy. More so, the government has been allocating a considerable amount of funds for sustainability-oriented R&D projects. From 2000-2009, these funds have increased from just RMB7.67 billion to RMB46.7 billion, constituting almost 8% of the total national spending on R&D. A total of RMB14.5 billion was allocated to basic research, accounting for nearly 53% of the total national research budget (Hu et al. 2017). Note. CO 2 e, carbon dioxide emissions; EC, electricity consumption; FDI, foreign direct investment; GDP, gross domestic product; POP, population; HEEXP, higher education R&D expenditures. ** and *** indicate 5% and 1% level of significance, respectively Kao (1999) residual-based tests for cointegration in panel data; MDF t , modified Dickey-Fuller t; DF t , Dickey-Fuller t; ADF t , augmented Dickey-Fuller t; UMD t , unadjusted modified Dickey-Fuller t; UDF t , unadjusted Dickey-Fuller t; PCT, Pedroni (2004) cointegration test; PP t , Phillips-Perron t; ADF t , augmented Dickey-Fuller t; CO 2 e, carbon dioxide emissions; EC, electricity consumption; FDI, foreign direct investment; GDP, gross domestic product; POP, population; HEEXP, higher education R&D expenditures *, **, and *** indicate 10%, 5% and 1% level of significance, respectively Next, China initiated the 211 Project and 985 Project to uplift the HEI standard. These projects focused on developing globally competitive first-class universities, programs, and scientific disciplines to promote sustainable and green socioeconomic development in China. Hu et al. (2017) argued that the 15 years of the 211 Projects have been extremely fruitful in setting the foundations for green innovation in education, research and service, and transitioning to a green economy. China spends around 2% of its total GDP on research, an amount that is increasing at the rate of 20% per year (Chung 2015) . Under the government's guidance, Chinese HEIs have dedicated time, resources, and money to research green energy, economy, technology, education, and innovation to realize a green revolution (Liu et al. 2012 ). These factors have played an instrumental role in indirectly mitigating CO 2 e by raising awareness, green technology development, green urbanization, and green education. In the same vein, China has been investing heavily in the green university/campus project. Many top-ranking and globally recognized universities have joined hands with the government to realize the Sustainable Development Goals. For instance, Tsinghua University has been championing the idea of green campus (GC), green technology, and green education. Peking University initiated the Green University project in April 2009. As an initial step, the planning department was rebranded as the Campus Planning and Sustainable Development Office. Beijing University has set four key objectives for achieving the GC and educational goals: (1) spatial design augmentations of the university; (2) improved and continued excellence of scientific research and teaching; (3) propagation and restoration of culture and environmental heritage; and (4) establishment of zerocarbon campus (Morgan et al. 2017) . Lee and Efird (2014) further explained the idea of green universities by identifying some key attributes. Firstly, these universities emphasize environmental education and integrate environmental aspects in the teaching, research, and curriculum. Secondly, the student and faculty master the knowledge, skills, and expertise on topics related to environmental protection, sustainable development, and environmental awareness. Thirdly, the members of the green universities actively engage in society-focused programs for environmental publicity, evaluation, and education. Fourthly, the environment becomes an important part of the campus culture, and it is integrated into all campus policies to develop a clean and green campus environment. Gou (2019) added that green campus operations are linked to all areas, including labs, classrooms, transportation, dormitories, and other facilities. Thus, the idea of green campus and green education entails several economic benefits, especially for a massively populated country like China. The GC can help to save energy, water, and other precious resources in China, particularly if the consumption of energy and water among HEIs is higher than that among the residential consumers. Apart from enabling the generation of new ideas and patents for green production, innovation, technology, and economy, the macro impact of the GC resides in improved efficiency and social fairness in the usage of natural resources. For ecological advantages, all HEIs need to revisit their effects on energy efficiency by transforming their facilities to preserve the environment. Beyond that, the social benefits of the GC include the conversion of students and teachers into conscious and eco-friendly consumers. Thus, the GC holds the potential to reduce deprivation and poverty among regions or provinces, enhance fairness, and expand the sustainable growth concept in Chinese society. All these measures, if implemented correctly, can decrease CO 2 -related energy consumption and increase the use of clean technologies across China. Table 13 exhibits the parallel fluctuations in HEEXP and CO 2 e. Second, the long-run coefficients indicated a significant positive linkage between FDI and CO 2 e, offering empirical evidence for the acceptance of the PHH in China. A 1% increase in FDI caused a rise in CO 2 e by 0.42 (MG), 0.12 (AMG), and 0.34% (CCEMG). This result suggested that some cities, provinces, and municipalities in China, with less (Mert and Bölük 2016) . Third, the current estimations revealed a positive association between GDP and CO 2 e-a 1% increase in GDP led to a rise in CO 2 e by 0.44 (MG), 0.75 (AMG), and 0.64% (CCEMG). This result suggested that GDP growth-driven by low energy efficiency and coal consumption-had enhanced CO 2 e in China. This result is consistent with the previous findings for India (Dar and Fourth, the long-term coefficients demonstrated a positive connection between population and CO 2 e-a 1% increase in population contributed to a rise in CO 2 e by 0.69 (MG), 0.92 (AMG), and 0.68% (CCEMG). This finding implied that although the growing aging populace would lower the rate of future CO2e, it would also create the need to develop alternative models of economic growth for a smooth transition into a green economy. Nonetheless, this result supported the previous results for China Zhou et al. 2018 Fifth, the results revealed a positive electricity use-CO 2 e nexus, implying that the irresponsible electricity consumption (by educational, residential, and industrial consumers) had significantly enhanced CO 2 e in China. This finding points toward the heavy reliance on carbon-intensive energy sources (e.g., coal and oil) for domestic and industrial consumers by the power generation sector. That said, the new energy policies and installed capacity forecast suggest that the overdependency on fossil fuels will reduce significantly in the future, thereby decreasing CO 2 e. The commercial sector (e.g., tech companies) is also setting the foundations for responsible energy consumption by switching from conventional to renewable energy sources. As some tech companies have started using solar and wind for power generation, other sectors will also follow this campaign to reduce their carbon footprint. This result validates the previous studies conducted for China (Akadiri et al. 2020 Finally, Table 14 exhibits the results of the DHPCT. The causality estimates revealed a bidirectional causality between EC and CO 2 e; FDI and CO 2 e; GDP and CO 2 e; POP and CO 2 e; and HEEXP and CO 2 e. These results suggested that government policies that target EC, FDI, GDP, POP, and HEEXP have, directly and indirectly, led to an increase or decrease in CO 2 e. The main objective of this study was to explore potential longrun connections between the HEEXP and CO 2 e for thirty-one provinces in China from 2000(Q1) to 2019(Q4). The panel data were analyzed using the multiple econometric techniques. First, the results of the WECPCT, KRCPT, and PCT indicated that a long-term cointegration existed between all the study variables. Second, the MG, AMG, and CCEMG supported that the HEEXP had disrupted CO 2 e, while EC, FDI, POP, and GDP had a positive interaction with CO 2 e in the long run. Third, the DHPCT reflected that a two-way causal relationship existed between CO 2 e and all other study variables-FDI, EC, GDP, POP, and HEEXP. The following important implications were drawn from the current findings. First, the current findings assert the need for the policymakers to design specific policies for green education, green campus, and green economy. Chinese government should extend financial support to encourage its academic institutions for developing green patents and conducting research on projects related to energy efficiency, sustainable production, green consumption, and preservation of land, soil, and environment. With the nascent awareness of environmental standards and norms, an extensive capacity building is across all academic institutions to align these institutions with global standards, eco-innovation, and sustainability practices. Second, the current results also require the need for the adjustment of research themes with the national energy and sustainable development plans. For this purpose, the HEEXP policy should be designed in a manner that the rewards, incentives, bonuses, and funding for academic institutions are based on the quality and quantity of eco-related patents and research. These institutions should be directed to develop matrices aligned with national themes and sustainability targets, including but not limited to clean and efficient transport technologies, solar thermal technology, solar cells, wind power, new nuclear power systems, carbon capture and sequestration, clean coal, ecological conservation, grassland development, recycling economy, biofuels, bioproducts, and integrated gasification combined systems. Third, the acceptance of the PHH in this study has strengthened the previous argument that FDI in developing countries have enhanced dirty technologies. Thus, policymakers are expected to tighten the environmental regulations, ensure that foreign enterprises transfer clean technologies, and improve green investment. Fourth, the positive connection between CO 2 e and electricity use calls for not only revisiting the existing energy mix but also asserts the need for devising energy efficiency strategies to curb CO 2 e. Policymakers should, therefore, continue to clean and expand the energy mix with more renewables for electricity generation to meet future demand. While encouraging and supporting the commercial sector to deploy solar and wind for power generation, the government should formulate energy efficiency policies for resources management, regardless of its types, i.e., nonrenewable or renewable energy. If inefficiently managed, these resources face the risk of depletion. Thus, the future policies for a green economy should incorporate efficient resources management, solar and wind energy development, technology improvements, carbon-taxing, and green urbanization. Of particular significance, all these policies should be designed, integrated, and coordinated with multiple stakeholders (i.e., community, government, academia, and administration) for effective execution and results. Fifth, the current findings concerning the adverse effect of the population on the environment assert the need for developing a responsible and eco-driven aging sector. This argument stems from the fact that a significant majority of the existing population in China is predicted to experience aging, leaving a wide gap in the workforce in the future. While this phenomenon may decrease the level of CO 2 e, it necessitates the need policies that guarantee better healthcare, social justice, social security, and other related facilities across all provinces. If this issue is underestimated, the socially deprived and unsatisfied populace may contribute to CO 2 e, thereby disrupting the green transformation. Thus, policymakers should devise policies to encourage investments in the aging sector to address the potential future disruption in economic growth. That said, this new sector should be built on the foundations of energy-saving, responsible consumption, social equality, income equality, old-age security, and equal access to quality healthcare for all provinces. This study has some limitations that open new doors for future research. First, this study had only focused on China. The same model can be used for other developing and developed economies. Second, this study applied linear econometric techniques (MG, AMG, and CCEMG) to explore the relationship between HEEXP and CO 2 e. Perhaps, some nonlinear models (e.g., NARDL) can be used to explore the same relationship and variables in a unified framework. Third, the current study has adopted the EKC framework for examining different relationships. Researchers are encouraged to tests the current findings using the STIRPAT framework for new insights. Urbanization and energy consumption effects on carbon dioxide emissions: evidence from Asian-8 countries using panel data analysis Influence of FDI on environmental pollution in selected Arab countries: a spatial econometric analysis perspective Determinants of CO2 emissions: empirical evidence from Egypt Environmental pollution, energy import, and economic growth: evidence of sustainable growth in South Africa and Nigeria Is aggregate domestic consumption spending (ADCS) per capita determining CO2 emissions in South Africa? A new perspective Empirics on linkages among industrialization, urbanization, energy consumption, CO2 emissions and economic growth: a heterogeneous panel study of China Impact of environmental quality variables and socioeconomic factors on human health: empirical evidence from China Does financial development asymmetrically affect CO2 emissions in China? An application of the nonlinear autoregressive distributed lag (NARDL) model Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective Does the inflow of remittances cause environmental degradation? Empirical evidence from China Innovation, foreign direct investment (FDI), and the energy-pollution-growth nexus in OECD region: a simultaneous equation modeling approach Heterogeneous links among urban concentration, non-renewable energy use intensity, economic development, and environmental emissions across regional development levels Do inward foreign direct investment and economic development improve local environmental quality: aggregation bias puzzle Heterogeneity of pollution haven/ halo hypothesis and environmental Kuznets Curve hypothesis across development levels of Chinese provinces The criticality of information and communication technology and human capital in environmental sustainability: evidence from Latin American and Caribbean countries Does electricity consumption and globalization increase pollutant emissions? Implications for environmental sustainability target for China Does fuel tax decrease carbon dioxide emissions in Turkey? Evidence from an asymmetric nonlinear cointegration test and error correction model Urbanization and carbon dioxide emissions in Singapore: evidence from the ARDL approach Dynamic common correlated effects of trade openness, FDI, and institutional performance on environmental quality: evidence from OIC countries Exploring the relationship between urbanization, energy consumption, and CO2 emission in MENA countries Carbon emissions and the trilemma of trade policy,migration policy and health care in the US Relationship of environment with technological innovation, carbon pricing, renewable energy, and global food production The role of information and communication technologies in mitigating carbon emissions: evidence from panel quantile regression The effects of deforestation and urbanization on sustainable growth in Asian countries Nonrenewable energy-environmental and health effects on human capital: empirical evidence from Pakistan Investigating the pollution haven hypothesis in Cote d'Ivoire: evidence from autoregressive distributed lag (ARDL) approach with structural breaks An empirical model on the impact of foreign direct investment on China's environmental pollution: analysis based on simultaneous equations The role of tourism and renewable energy in testing the environmental Kuznets curve in the BRICS countries: fresh evidence from methods of moments quantile regression A new look at the FDI-income-energy-environment nexus: dynamic panel data analysis of ASEAN Revisiting the environmental Kuznets curve and pollution haven hypotheses: MIKTA sample How economic growth, renewable electricity and natural resources contribute to CO2 emissions? An approach to the pollution haven and pollution halo hypotheses in MINT countries Strategies in sustainable tourism, economic growth and clean energy Environmental degradation, renewable and non-renewable electricity consumption, and economic growth: assessing the evidence from Algeria Combustible renewables and waste consumption, agriculture, CO 2 emissions and economic growth in Brazil International financial centres after the global financial crisis and Brexit Development centre studies policy coherence towards East Asia development challenges for OECD countries: development challenges for OECD countries Does international cooperation affect CO2 emissions? Evidence from OECD countries Assessing China's power Testing for a unit root in variables with a double change in the mean The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries Is financial development good for carbon mitigation in India? A regime shift-based cointegration analysis Does pollution haven hypothesis hold in newly industrialized countries? Evidence from ecological footprint Effects of population and affluence on CO2 emissions The nonlinear impact of fiscal decentralization on carbon emissions: from the perspective of biased technological progress Testing for Granger non-causality in heterogeneous panels Cross-section dependence in nonstationary panel models: a novel estimator The effects of international tourism, electricity consumption, and economic growth on CO2 emissions in North Africa Consumption-based carbon emissions and trade nexus: Evidence from nine oil exporting countries Rethinking electricity consumption and economic growth nexus in Turkey: environmental pros and cons The asymmetric role of freight and passenger transportation in testing EKC in the US economy: evidence from QARDL approach Green building in developing countries: policy, strategy and technology Residual-based tests for cointegration in models with regime shifts Environmental impacts of a North American free trade agreement CO 2 emissions, income inequality, and country risk: some international evidence Revisiting the pollution haven hypothesis in ASEAN-5 countries: new insights from panel data analysis How do FDI and technical innovation affect environmental quality? Evidence from China Population-environment interactions: European migration, population composition and climate change Does information and communication technologies improve environmental quality in the era of globalization? An empirical analysis Exploring the dynamic interaction of CO2 emission on population growth, foreign investment, and renewable energy by employing ARDL bounds testing approach Does economic growth, international trade, and urbanization uphold environmental Environ Sci Pollut Res sustainability in sub-Saharan Africa? Insights from quantile and causality procedures Testing the effect of sustainable energy and military expenses on environmental degradation: evidence from the states with the highest military expenses Testing the impact of real interest rate, income, and energy consumption on Turkey's CO2 emissions Coal production and consumption analysis, and forecasting of related carbon emission: evidence from China Mitigation pathways to sustainable production and consumption: examining the impact of commercial policy on carbon dioxide emissions in Australia Measuring the simultaneous effects of electricity consumption and production on carbon dioxide emissions (CO2e) in China: new evidence from an EKC-based assessment Spurious regression and residual-based tests for cointegration in panel data CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis Examining foreign direct investment and environmental pollution linkage in Asia Does energy consumption, financial development, and investment contribute to ecological footprints in BRI regions? Total retail goods consumption, industry structure, urban population growth and pollution intensity: an application of panel data analysis for China On the remittances-environment led hypothesis: empirical evidence from BRICS economies The cyclical and asymmetrical impact of green and sustainable technology research (GSTR) on carbon dioxide emissions (CO2) in BRICS economies: role of renewable energy consumption, foreign direct investment, and exports CO2 emissions, urbanisation and economic growth: evidence from Asian countries Sustaining China's economic growth after the global financial crisis. Peterson Institute for International Economics, Washington Lean HH, Smyth R (2010) CO2 emissions, electricity consumption and output in ASEAN Schooling for sustainable development across the Pacific China's provincial CO2 emissions and interprovincial transfer caused by investment demand Measuring the impact of higher education on environmental pollution: new evidence from thirty provinces in China Population, affluence, and environmental impact across development: Evidence from panel cointegration modeling Environmental innovation in China Does democratic transition reduce carbon intensity? Evidence from Indonesia using the synthetic control method The impact of population on CO2 emissions: evidence from European countries The dynamics effect of green technology innovation on economic growth and CO2 emission in Singapore: new evidence from bootstrap ARDL approach Were Ukrainian regions too different to start interregional confrontation: economic, social and ecological convergence aspects? Do foreign direct investment and renewable energy consumption affect the CO2 emissions? New evidence from a panel ARDL approach to Kyoto Annex countries Testing pollution haven and pollution halo hypotheses for Turkey: a new perspective Interrelationships among foreign direct investments, renewable energy, and CO2 emissions for different European country groups: a panel ARDL approach Handbook of education in china China's economic conditions Cointegration and causality: considering Iberian economic activity sectors to test the environmental Kuznets curve hypothesis The impact of economic development on environmental degradation in Qatar Energy consumption and environmental quality in South Asia: evidence from panel non-linear ARDL Asymmetric impact of energy consumption on environmental degradation: evidence from Australia, China, and USA Value addition in the services sector and its heterogeneous impacts on CO2 emissions: revisiting the EKC hypothesis for the OPEC using panel spatial estimation techniques Income inequality, innovation and carbon emission: perspectives on sustainable growth Relationship between inward FDI and environmental degradation for Pakistan: an exploration of pollution haven hypothesis through ARDL approach s11356-020-08083-x consumption, FDI inflows, and economic growth on carbon dioxide emissions: evidence from robust least square estimator Energy use, CO2 emissions and economic growth -causality on a sample of SEE countries Life-cycle energy consumption and greenhouse gas emissions for electricity generation and supply in China Environmental Kuznets curve (EKC): a manual Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis A simple panel unit root test in the presence of cross section dependence. Cambridge Working Papers in Economics 0346, Faculty ofEconomics, University of Cambridge Estimation and inference in large heterogeneous panels with a multifactor error structure A simple panel unit root test in the presence of crosssection dependence Testing weak cross-sectional dependence in large panels testing weak cross-sectional dependence in large panels Estimating long-run relationships from dynamic heterogeneous panels Testing slope homogeneity in large panels A new approach to environmental sustainability: assessing the impact of monetary policy on CO 2 emissions in Asian economies Carbon dioxide emissions, economic growth, energy use, and urbanization in Saudi Arabia: evidence from the ARDL approach and impulse saturation break tests Energy productionincome-carbon emissions nexus in the perspective of An (a)symmetric analysis of the pollution haven hypothesis in the context of Pakistan: a non-linear approach The impact of public-private partnerships investment in energy on carbon emissions: evidence from nonparametric causality-in-quantiles The effect of carbon dioxide emission and the consumption of electrical energy, fossil fuel energy, and renewable energy, on economic performance: evidence from Pakistan Responses of carbon emissions to corruption across Chinese provinces The effects of electricity consumption, economic growth, financial development and foreign direct investment on CO2emissions in Kuwait The effects of urbanization and globalization on CO2 emissions: evidence from the sub-Saharan Africa (SSA) countries A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE Industrialization, electricity consumption and CO2 emissions in Bangladesh Energy consumption, financial development and economic growth in India: new evidence from a nonlinear and asymmetric analysis Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries The role of tourism, transportation and globalization in testing environmental Kuznets curve in Malaysia: new insights from quantile ARDL approach The impact of population pressure on global carbon dioxide emissions, 1975-1996: evidence from pooled cross-country data The shadow economy in South Asia: dynamic effects on clean energy consumption and environmental pollution Towards achieving environmental sustainability: environmental quality versus economic growth in a developing economy on ecological footprint via dynamic simulations of ARDL Development of green campus in China Revisiting the impacts of economic growth on environmental degradation: new evidence from 115 countries On the asymmetric effects of premature deindustrialization on CO2 emissions: evidence from Pakistan An (a)symmetric analysis of the pollution haven hypothesis in the context of Pakistan: a nonlinear approach Relationship between energy demand, financial development, and carbon emissions in a panel of 101 countries: "go the extra mile" for sustainable development Does the quality of institutions modify the economic growth-carbon dioxide emissions nexus? Evidence from a group of emerging and developing countries EKC test study on the relationship between carbon dioxide emission and regional economic growth A guide to understanding higher education R&D statistics in the Nordic countries. Nordic Institute for Studies in Innovation, Research and Education (NIFU) Working Paper No Testing for error correction in panel data Achieving sustainability and energy efficiency goals: assessing the impact of hydroelectric and renewable electricity generation on carbon dioxide emission in China Approaches for controlling air pollutants and their environmental impacts generated from coal-based electricity generation in China Empirical investigation of relationships between energy consumption, industrial production, CO2 emissions, and economic growth: the case of small island states Influence of climate on energy consumption and CO2 emissions: the case of Spain Effects of electricity consumption on carbon intensity across Chinese manufacturing sectors The influences of industrial gross domestic product, urbanization rate, environmental investment, and coal consumption on industrial air pollutant emission in China The heterogeneous effects of urbanization and income inequality on CO2 emissions in BRICS economies: evidence from panel quantile regression Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Nathaniel SP (2020) Modelling urbanization, trade flow, economic growth and energy consumption with regards to the environment in Nigeria. GeoJournal 85 (6) The authors declare no competing interests.