Do Higher Education Research and Development Expenditures affect Environmental Sustainability? New Evidence from Thirty-One Chinese Provinces

Do Higher Education Research and Development Expenditures affect Environmental Sustainability? 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New Evidence from Thirty-One Chinese Provinces Sun Yawen, Qingquan Jiang, Shoukat Iqbal Khattak, Manzoor Ahmad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-358931/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract Higher education R&D expenditures (HEEXP) are one of the important determinants of economic growth that facilitates science, technology, new ideas, and innovation, but 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, foreign direct investment, gross domestic product, and total population for the period 2000Q1-2019Q4. Some of the key results are as follows. First, the present findings confirmed the long-run cointegration among variables. 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 exist a bi-directional 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. Environmental Engineering Environmental Policy higher education R&D expenditures electricity consumption FDI population and CO2e Figures Figure 1 Figure 2 Figure 3 1 Introduction 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 the emanation of more waste, residues, and green-house gases (GHGs) into the environment. These toxic emissions of various types are considered as primary causes of global climate change, rising temperatures, and air pollution. Among them, carbon dioxide is one of the leading pollutants, accounting for about sixty-three percent of the total GHGs (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 types of carbon-intensive 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 non-renewable sources, thereby causing CO 2 e (Rehman, Rauf, Ahmad, Chandio, & Deyuan, 2019 ). From the day China adopted the ‘opening-up policy’, its economy has sharply risen from just RMB0.365 trillion (1978) to RMB8.272 trillion (2007). With a phenomenal upsurge in the GDP (per capita) growth rate, China has now become one of the largest CO 2 emitter in the world (Li, Wu, Lei, Li, & Li, 2019 ). China has mostly relied on non-renewable 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 (Munir Ahmad & 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 at large (M. Ahmad et al., 2018 ). China, with nearly twenty percent of the global population, 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. For China, economists have extensively measured the environmental impact of CO 2 e with different indicators and different econometric techniques. Some of the these economic indicators include financial development (Manzoor Ahmad, Khan, Ur Rahman, & Khan, 2018 ), inflow of remittances (Manzoor Ahmad, Ul Haq, et al., 2019), urban population (Z. Khan, Shahbaz, Ahmad, Rabbi, & Siqun, 2019 ), innovation (Khattak, Ahmad, Khan, & Khan, 2020 ), monetary policy (Qingquan, Khattak, Ahmad, & Ping, 2020 ), globalization (Akadiri, Alola, Bekun, & Etokakpan, 2020), government expenditures (Le & Ozturk, 2020 ), foreign direct investment (Munir Ahmad, Zhao, Rehman, Shahzad, & Li, 2019 ), electricity consumption (Zhang, 2019 ), GDP (Akadiri et al., 2020 ), renewable energy consumption (Akadiri, Saint, Alola, Bekun, & Etokakpan,, 2020), information and communication technologies (Mirza, Ansar, Ullah, & Maqsood, 2020 ), tourism (Aziz, Mihardjo, Sharif, & Jermsittiparsert, 2020 ), and international trade (Boamah et al., 2017 ). This paper, however, considers 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 is central to CO 2 e mitigation strategies. Second, China has long recognized environmental pollution as an urgent threat, and therefore, it has been extensively funding higher education institutions (HEIs) for education and research projects related to energy, green economy, alternative fuel, and non-renewables. 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, public, and the environment. Figure 3 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 four percent increase in the HEEXP led to a rise in eco-related patents by twenty-one percent in 2016. From 2001–2016, an average of 21.18 percent upsurge in the HEEXP was associated with a parallel increase in eco-related patents by 20.59 percent, cueing potential implication of the HEEXP on eco-related 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 the environmental economics. Second, as of this work, the paper offers the first schematic framework that explains the precise mechanism of how the HEEXP affects environmental pollution in China. Third, the paper uses second-generation econometric techniques for robust and rigorous analysis. Fourth, through provincial data, the paper presents an in-depth insight into regional and provincial disparities vis-a-viz the effect of the HEEXP on CO 2 e. Fifth, the article has attempted to integrate two distinct paradigms into a unified framework. Most prior studies on CO 2 e in the education literature are limited to the 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 the disciplines have linked the HEEXP to CO 2 e. The rest of the paper is categorized as follows. Section 2 explores the literature review. Section 3, 4, 5, and 6, present the conceptual framework, model specifications, and data sources and variables, and estimation techniques, respectively. Section 7 focuses on the interpretations of results and discussions, followed by the conclusion, policy recommendations, future directions, and limitations in Sect. 8. 2 Literature Review 2.1 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. Environmental Kuznets curve (EKC) hypothesis is probably the most frequently tested framework that explains the link between aggregate income and environmental sustainability (Özcan & 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. Several researchers have validated the EKC hypothesis for different economies, including but not limited to, Iberia (Moutinho, Madaleno, & Bento, 2020 ); China (Jiang, Yang, & Ma, 2019 ; Mushtaq, Chen, Din, Ahmad, & Zhang, 2020 ; Zhou et al., 2018 ); India (Dar & Asif, 2017 ); Pakistan (Ur Rahman, Chongbo, & Ahmad, 2019 ); USA (Alola & Alola, 2019 ); Brazil (Ben Jebli & Ben Youssef, 2019 ); emerging economies (Wawrzyniak & Doryń, 2020 ); NAFTA and BRIC (Rahman, Cai, Khattak, & Hasan, 2019 ); Ukraine (Melnyk, Kubatko, & Kubatko, 2016 ); SEE economies (Obradović & Lojanica, 2017 ); developed and developing economies (Anser et al., 2020 ); and OECD (Manzoor Ahmad, Khan, Rahman, Khattak, & Khan, 2019 ). 2.2 Relationship between foreign direct investment and CO 2 e 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 the third-world economies where laws were either non-extant or extremely weak. As this trend has continued for long, 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 of the emerging economies (Centre, Kiichiro, Masahiro, G, & Alexandra, 2005). That said, the empirical evidence on the FDI-CO2e nexus remains controversial. Some evidence for the positive relationship between FDI and CO 2 e include studies for newly industrialized economies (Destek & Okumus, 2019 ); Cote d’Ivoire (Assamoi, Wang, Liu, & Gnangoin, 2020 ); ASEAN countries (Guzel & Okumus, 2020 ); MINT countries (Balsalobre-Lorente, Gokmenoglu, Taspinar, & Cantos-Cantos, 2019 ); Turkey (Mert & Caglar, 2020 ); MIKTA economies (Bakirtas & Cetin, 2017 ); Pakistan (Ur Rahman et al., 2019 ); Asia (M. A. Khan & Ozturk, 2020 ); European economies (Mert, Bölük, & Çağlar, 2019 ); BRI region (A. Khan, Chenggang, Hussain, & Bano, 2019 ); OIC countries (Ali, Yusop, Kaliappan, & Chin, 2020 ); and OECD countries (Manzoor Ahmad, Khattak, Khan, & Rahman, 2020 ). 2.3 The relationship between population 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 (age-structural 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 definite link between population and CO 2 e for European countries (Harper, 2013 ; Martínez-zarzoso et al., 2007 ); developed and developing economies (Dietz & Rosa, 1997 ); selected 93 economies (Shi, 2003 ); Asian economies (Qingquan et al., 2020 ); China (Z. Khan et al., 2019 ; Zhou et al., 2018 ); MENA economies (Al-mulali, Fereidouni, Lee, & Sab, 2013 ); newly industrialized nations (Sharif Hossain, 2011 ); OECD (Liddle, 2013 ); Pakistan (Ullah, Ozturk, Usman, Majeed, & Akhtar, 2020 ); OPEC economies (Murshed, Nurmakhanova, Elheddad, & Ahmed, 2020 ); and Asian countries (Abbasi, Parveen, Khan, & Kamal, 2020 ). 2.4 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 non-renewable 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, Shahbaz, Roubaud, and Farhani ( 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 non-renewable) 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 one percent increase in electricity consumption predicted an upsurge of 0.79 percent in CO 2 e. In the same way, others studies have also reported a positive connection between electricity consumption and CO 2 e for China (Akadiri et al., 2020 ; Munir & Riaz, 2020 ; Ou, Xiaoyu, & Zhang, 2011 ; Xu, Hong, Ren, Wang, & Yuan, 2015 ; Zhang, 2019 ); Spain (Zarco-Soto, Zarco-Periñán, & Sánchez-Durán, 2020 ); South Asian economies (Munir & Riaz, 2019 ); Bangladesh (Shahbaz, Salah Uddin, Ur Rehman, & Imran, 2014 ); ASEAN countries (Lean & Smyth, 2010 ); Pakistan (Rehman et al., 2019 ) BRICS (Cowan, Chang, Inglesi-Lotz, & Gupta, 2014 ; Haseeb, Xia, Saud, Ahmad, & Khurshid, 2019 ); and Kuwait (Salahuddin, Alam, Ozturk, & Sohag, 2018 ). 3 Conceptual Framework Figure 3 illustrates the conceptual framework, depicting the mechanism through which HEEXP may affects CO 2 e. For long, the HEIs have been contributing to the advancement of knowledge, economy, cultivating students, and conducting research in many fields. Whether it was the intervention of government 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, Söder, & Leppälahti, 2015 ). Governments’ funding, therefore, has been crucial for many HEIs to support basic and advanced level research, especially in fields like 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, industries, and other institutions, 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 sustainability technology, methods, processes, and products; 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, countries 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, technologies, products, and services, many countries and institutions have been investing heavily in academic research and development related to eco-innovation, 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, and 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); b) campus energy management system (CEMS). The concept of CEMS emphasizes the construction of green education and environmental-related 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, 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 to designing and implement green curricula. Thus, GC plays a vital in the implementation of the 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 GC necessitates the need for educational institutions to focus on the hardware and software of GC, simultaneously. The former pertains to the integration of green aspects in construction, building, infrastructure, and operations, and the latter refers to the development and promotion of green culture, humanity, green citizenship, and cultivation of 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, Chen, Shi, & Wang, 2014 ). In short, it is proposed that the development of GC (through HEEXP) not only helps in mitigating CO 2 e, but also play an important role in promoting sustainable consumption and production across residential and commercial sectors. 4 Model Specification Below, Equation (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. The rationale for the use of 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 eighty percent of electricity was generated from coal. Third, it is one of the largest economies in the world, vis-a-viz the GDP growth rate. Fourth, China is one of the most populous economies in the world, where population growth has created contributed to energy consumption among residential and non-residential consumers, directly and indirectly causing CO 2 e. 5 Data Sources And Variables The data for HEEXP, FDI, EC, GDP, POP, and CO 2 e were collected from the National Bureau of Statistics ( 2019 ) for the period 2000 to 2019. Consistent with the previous studies (Sbia, Shahbaz, & Hamdi, 2014 ; Shahbaz, Hoang, Mahalik, & Roubaud, 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 1 shows the data sources and descriptions. Table 1 Data description and sources. Variables Notation Units Source of data Estimation technique Expected signs Electricity Consumption EC Watt-hour National Bureau of Statistics ( 2019 ) Quadratic match-sum method Positive Foreign direct investment FDI Million Yuan National Bureau of Statistics ( 2019 ) Quadratic match-sum method Positive Gross Domestic Product GDP 100 million Yuan National Bureau of Statistics ( 2019 ) Quadratic match-sum method Positive Population POP 10000 people National Bureau of Statistics ( 2019 ) Quadratic match-sum method Positive Research & development expenditures in the higher education sector HEEXP 1000 Yuan National Bureau of Statistics ( 2019 ) Quadratic match-sum method Negative Carbon dioxide emissions CO 2 e 10000 tons National Bureau of Statistics ( 2019 ) Quadratic match-sum method 6 Estimation Techniques 6.1 Unit root testing 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 factors, including, economic union, financial shocks, demand shocks, supply shocks, pandemic diseases, globalization, and trade wars, it must be dealt with accuracy and precision. If ignored, it could be led to bias cointegration and stationarity results (Z. Khan, Ali, Jinyu, Shahbaz, & Siqun, 2020 ). The Pesaran ( 2015 ) cross-sectional dependence test (PCSDT) and the M.H Pesaran and Yamagata ( 2008 ) slope homogeneity test (SHT) were applied for addressing the CSD and homogeneity problems, respectively. In the next step, the order of integration was examined for all variables using the second-generation Pesaran and M.H (2003) (PMCADF) and Pesaran ( 2007 ) (PCIPS) unit-root tests. 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 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. For robustness check, the Clemente, Montañés, and Reyes ( 1998 ) unit root test (CMRURT), with multiple structural breaks was employed the aggregate data on EC i , FDI t , GDP t , POP it , HEEXP t , and CO 2 e it . 6.2 Cointegration testing For cointegration testing, this study adopted the Westerlund ( 2007 ) error-correction-based panel cointegration test (WECPT). The author 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) it allows unbalanced panels and unequal series length in units; 2) tests heterogeneity that is permitted in the short- and long-run parameters of the error-correction model; 3) obtains critical value using the bootstrap approach, if a correlation probability exits among units. The WECPT involves the following hypothesis: H 0 :No cointegration exists among all panels H 1 :Cointegration exists among all panels 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). 6.3 Long-run coefficients estimation 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 time-variant 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 independence and the dependent variables as surplus regressors when applying OLS to specific units, the CCEMG method allows for TVU and CSD with heterogenous effect in panel members. Identified by the averages of CS, the unobserved CF 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 non-stationary 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 group-specific 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 multi-factors error-terms and non-stationary variables, particularly considering CSD. The AMG estimator is superior to the CCEMG, in terms of creating a set of unobservable CF as a common dynamic process. Dissimilar to a scenario in which the unobservable factors are considered as a nuisance, the alternate treatment may offer helpful interpretations, depending on the context (Heshmati, 2019 ). 6.4 Panel causality testing 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 non-existence of homogenous Granger-causality 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 cross-sections 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 ). 7 Results And Discussion Table 2 depicts the results of PCSDT. As seen below, the null hypothesis of no CSD for the EC it , FDI it , GDP it , POP it , HEEXP it , and CO 2 e it was rejected at 10, 5, and 1 percent significance levels. This implied that all the provinces in China were interdependent in a way that an economic shock in one region may affect other regions, too. As reported in Table 3 , the SHT highlighted heterogeneity problems in the model. Table 2 Results of the PCSDT. Variable CD-statistic P-value Average joint T Mean \(\rho\) Mean obs ( \(\rho\) ) CO 2 e 148.205 0.000 80.00 0.77 0.86 EC 168.425 0.000 80.00 0.87 0.96 FDI 153.638 0.000 80.00 0.80 0.80 GDP 189.912 0.000 80.00 0.98 0.98 POP 56.194 0.000 80.00 0.29 0.48 HEEXP 150.963 0.000 80.00 0.78 0.81 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. Table 3 Results of the SHT. Statistics Test value P-value Delta 82.524 0.000 Adjusted Delta 86.391 0.000 Table 4 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 non-stationary at level but became stationary at the first difference, even though these tests were unable to deal with structural breaks in the data. Given that most global economies have experienced many structural changes, it is considered imperative to trace structural breaks in the data series for China. There was a high probability that the PCADF and PCIPS could be 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 4 Results of the PCADF and the PCIPs unit-root tests (without structural breaks). At level At first difference Variable PCADF PCIPS PCADF PCIPS CO 2 e 0.588 -0.335 -9.307*** -3.942*** EC 3.226 -1.866 -3.374*** -4.161*** FDI 1.140 -0.727 -4.176*** -4.467*** GDP 2.028 -0.703 -16.727*** -3.186*** POP 20.072 -1.704 -23.227*** -3.222*** HEEXP 13.596 -1.295 -12.026*** -4.250*** 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. *** indicates 1% level of significance. Table 5 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 & Zheng, 2004 ). In 2004, China faced one of the worst historic inflationary pressures, partly triggered by real-estate 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, Lenovo Group acquired the personal computer division of IBM for a hefty sum of USD1.75 billion. Indeed, this acquisition is considered as an economic breakthrough. Apart from gaining access to foreign, facilities, operations, and R&D, China strengthened its presence in the US (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 when compared with 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 two-third, i.e., USD185 billion. There was almost twenty percent contraction in outbound FDI in 2009, but the outbound FDI increased by thirty-seven percent 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 & 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 5 Unit root test with structural breaks. At level At first difference Variable t-statistic Breakpoints t-statistic Breakpoints CO 2 e 1.438 2002Q3, 2017Q3 -5.779*** 2010Q4, 2016Q4 EC 0.842 2009Q2, 2012Q3 -2.287** 2006Q4, 2009Q4 FDI 1.360 2015Q1, 2017Q2 -4.722*** 2001Q3, 2015Q1 GDP 1.442 2009Q3, 2010Q3 -4.337*** 2003Q1, 2008Q4 POP 0.050 2004Q4, 2005Q3 -8.438*** 2003Q4, 2004Q4 HEEXP 0.916 2008Q1, 2011Q3 4.187*** 2010Q4, 2011Q4 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. **, *** indicates 5% and 1% level of significance, respectively. Table 6 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 7 , the results of the cointegration analysis with structural break and regime shifts were found to be consistent with the WECPT, KRCPT, and the PCT. Table 6 Cointegration analysis without structural breaks. Test statistics CO 2 e →EC CO 2 e →FDI CO 2 e →GDP CO 2 e →POP CO 2 e →HEEXP WEPCT Gt -2.098** -2.059** -12.575*** -5.554*** -4.362*** Ga -116.59*** -44.50*** -43.088*** -29.438*** -128.47*** Pt -3.411*** 1.449 -2.087** -1.750** 0.861 Pa -7.055*** 1.738 -1.534* -1.148 0.011 KRCPT MDF t 4.57*** -18.94*** -0.21 -1.23 1.59* DF t 5.60*** -10.94*** -4.02*** 0.07 -1.85** ADF t -2.27** -13.20*** -7.08*** -3.32*** -6.26*** UMDF t 5.53*** -9.65*** 1.47*** -0.9653 2.17** UDF t 8.18*** -11.15*** -3.04*** 0.23 -1.35* PCT MDF t 4.66*** 3.92*** 3.57*** -3.24*** 2.64*** PP t 9.10*** 1.65* 7.87*** -2.78*** 6.01*** ADF t 21.11*** 2.89*** 21.29*** 3.76*** 14.52*** Note: WEPCT = Westerlund ( 2007 ) error-correction based panel cointegration tests; KRCPT = 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. *, **, *** indicates 10%, 5% and 1% level of significance, respectively. Table 7 Cointegration analysis with structural break and regime shifts. Test statistics CO 2 e →EC CO 2 e→FDI CO 2 e→GDP CO 2 e→POP CO 2 e→HEEXP Change in Level ADF -6.11*** -32.14*** -6.53*** -9.41*** -8.72*** Breakpoint 54 36 52 56 38 Break year 2013Q2 2009Q1 2012Q4 2013Q4 2009Q2 Critical values (1%) -5.13 -5.13 -5.13 -5.13 -5.13 Critical values (5%) -4.61 -4.61 -4.61 -4.61 -4.61 Critical values 10%) -4.34 -4.34 -4.34 -4.34 -4.34 Change in Regime ADF -5.56*** -5.80*** -9.54*** -8.73*** -8.92*** Breakpoint 55 61 36 36 45 Break year 2013Q3 2015Q1 2008Q4 2008Q4 2011Q1 Critical values (1%) -5.47 -5.47 -5.47 -5.47 -5.47 Critical values (5%) -4.95 -4.95 -4.95 -4.95 -4.95 Critical values 10%) -4.68 -4.68 -4.68 -4.68 -4.68 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. *** indicates a one percent level of significance. Table 8 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 one percent increase in HEEXP predicted a decline of .29 (MG), 0.24 (AMG), and 0.30 (CCEMG) percent in CO 2 e. As predicted, this result supported that spending on research and development spending in higher education has helped to mitigate CO 2 e in China. A feasible explanation is that academic institutions have been a central part of the national research framework, in terms of developing green technology, innovation, and eco-urban systems in China. In 2011 alone, the faculty and staff from HEIs constituted 11.3 percent of the overall research and development population. Using almost 8.5 percent of the total national R&D spending, these researchers have shown impressive results. These individuals conducted 62.2 percent of the all research projects and activities, received 28.8 percent of the total patents, applied for 21.6 percent of the total patents, and produced 64.4 percent of the entire scientific publications. Following the ‘new normal’ of fostering the nation with education, science, innovation, and developing a green economy, the Chinese government has placed a significant focus on green and sustainable technology research. Currently, Chinese scholars are the leading the global research related to green production, sustainability, green technology, environment, 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 eight percent of the total national spending on R&D. A total of RMB14.5 billion were allocated to basic research, accounting for nearly fifty-three percent of the total national research budget (Hu, Liang, & Tang, 2017 ). Next, China initiated the 211 Project and 985 Project to uplift the standard of its HEIs. These projects were aimed at developing globally competitive first-class universities, programs, and scientific disciplines to promote sustainable and green socio-economic development in China. Hu, Liang, and Tang ( 2017 ) argued that the fifteen years of the 211 Projects have been extremely fruitful, in terms of setting the foundations for green innovation in education, research and service, and transitioning to a green economy. China spends around two percent of its total GDP on research, an amount that is increasing at the rate of twenty percent per year (Chung, 2015 ). Under the government’s guidance, Chinese HEIs have dedicated time, resources, and money for research on green energy, economy, technology, education, and innovation to realize a green revolution (Liu, Strangway, & Feng, 2012 ). These factors have played an instrumental role in indirectly mitigating CO 2 e by raising awareness, development of green technology, and 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 establishment of zero-carbon campus (Morgan, Gu, & Li, 2017 ). Lee and Efird ( 2014 ) further explained the idea of green universities by identifying some key attributes. Firstly, these universities place acute emphasis on 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 the 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 cleans 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 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 has the potential to reduce deprivation and poverty among regions or provinces, enhance fairness, and to expand the sustainable growth concept in the 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 9 exhibits the parallel fluctuations in HEEXP and CO 2 e. Table 8 Long-run coefficients. Variables MG AMG CCEMG HEEXP -0.294*** (-4.91) [0.059] -0.242***(-4.63) [0.052] -0.303***(-3.87) [0.078] FDI 0.427***(6.59) [0.067] 0.118***(2.78) [0.042] 0.341***(6.16) [0.055] GDP 0.445***(3.19) [0.139] 0.748***(8.67) [0.086] 0.637***(6.57) [0.097] POP 0.686***(4.53) [0.151] 0.922***(3.98) [0.232] 0.683***(3.58) [0.191] EC 0.522***(3.48) [0.1444] 0.383***(6.15) [0.062] 0.308***(3.14) [0.098] C 1.169***(3.85) [0.304] 2.6223***(9.10) [0.288] 32.551***(13.31) [2.126] 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. () = t-statistic; [] = standard error; MG = Mean group; AMG = augmented mean group; CCEMG = Common correlated effect mean group. *** indicates a one percent level of significance. 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 one percent increase in FDI caused a rise in CO 2 e by 0.42 (MG), 0.12 (AMG), and 0.34 (CCEMG) percent. This result suggested that some cities, provinces, and municipalities in China, with less stringent regulations, have become pollution havens in an attempt to attract FDI and pollution-intensive industries. This result validated the previous studies conducted for China (Ur Rahman et al., 2019 ); OECD (Manzoor Ahmad et al., 2020 ); newly industrialized nations (Destek & Okumus, 2019 ); Cote d’Ivoire (Assamoi et al., 2020 ); ASEAN (Guzel & Okumus, 2020 ); MINT countries (Balsalobre-Lorente et al., 2019 ); Pakistan (Nadeem, Ali, Khan, & Guo, 2020 ; Naz et al., 2019 ); MIKTA economies (Bakirtas & Cetin, 2017 ); BRICS (Z. U. Khan, Ahmad, & Khan, 2020 ); Arab countries (Abdo, Li, Zhang, Lu, & Rasheed, 2020 ); Asian countries (M. A. Khan & Ozturk, 2020 ); and European countries (Mert et al., 2019 ). However, this results contradicts the previous studies conducted for coastal Mediterranean countries (Nathaniel, Aguegboh, Iheonu, Sharma, & Shah, 2020 ); OECD (Manzoor Ahmad, Khan, et al., 2019 ); Turkey (Mert & Caglar, 2020 ); China (Ayamba, Haibo, Ibn Musah, Ruth, & Osei-Agyemang, 2019 ; Hao, Wu, Wu, & Ren, 2020 ); and Kyoto Annex countries (Mert & Bölük, 2016 ). Third, the estimations revealed a positive association between GDP and CO 2 e—a one percent increase in GDP led to a rise in CO 2 e by 0.44 (MG), 0.75 (AMG), and 0.64 (CCEMG) percent. 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 & Asif, 2017 ); Pakistan (Chandia, Gul, Aziz, Sarwar, & Zulfiqar, 2018 ; Ur Rahman et al., 2019 ); China (Manzoor Ahmad et al., 2018 ; Mushtaq et al., 2020 ; Wei, 2020 ; Zhou et al., 2018 ); the US (Alola & Alola, 2019 ); Liberia (Moutinho et al., 2020 ); Qatar (Mrabet, AlSamara, & Hezam Jarallah, 2017 ); selected 72 countries (Inekwe, Maharaj, & Bhattacharya, 2019 ); developing countries (Wawrzyniak & Doryń, 2020 ); NAFTA and BRIC (Rahman et al., 2019 ); SEE countries (Obradović & Lojanica, 2017 ); and Asian economies (Qingquan et al., 2020 ). Table 9 Parallel fluctuations in HEEXP and CO 2 e. Province Year Quarter HEEXP (%) CO 2 e (%) Beijing 2002 I 0.03↑ 0.485↓ Beijing 2008 II 0.306↑ 0.399↓ Tianjin 2003 I 0.498↑ 0.266↓ Tianjin 2016 IV 0.002↑ 0.078↓ Hebei 2006 I 2.151↑ 0.209↓ Hebei 2009 I 0.373↑ 0.177↓ Shanxi 2006 I 0.146↑ 0.245↓ Shanxi 2013 II 0.093↑ 0.117↓ Inner Mongolia 2003 I 0.842↑ 1.319↓ Inner Mongolia 2013 I 0.126↑ 0.607↓ Liaoning 2000 I 0.881↑ 2.189↓ Liaoning 2019 I 0.714↑ 0.655↓ Jilin 2012 I 0.070↑ 0.261↓ Heilongjiang 2004 IV 1.012↑ 0.946↓ Heilongjiang 2016 III 0.187↑ 0.188↓ Shanghai 2011 I 0.504↑ 0.121↓ Shanghai 2014 I 0.354↑ 0.548↓ Jiangsu 2017 II 0.178↑ 0.139↓ Jiangsu 2018 I 0.806↑ 0.992↓ Zhejiang 2017 III 0.0081↑ 0.268↓ Zhejiang 2019 I 0.522↑ 0.686↓ Anhui 2004 I 0.832↑ 0.303↓ Anhui 2018 I 1.414↑ 1.071↓ Fujian 2015 IV 0.357↑ 0.183↓ Fujian 2018 II 0.189↑ 0.817↓ Jiangxi 2012 I 0.113↑ 0.604↓ Jiangxi 2014 I 0.212↑ 0.390↓ Shandong 2006 I 0.888↑ 0.191↓ Shandong 2019 I 1.136↑ 0.133↓ Henan 2012 I 0.744↑ 0.317↓ Henan 2015 I 0.055↑ 0.184↓ Hubei 2008 I 0.101↑ 0.222↓ Hubei 2018 I 2.135↑ 1.042↓ Hunan 2004 I 0.650↑ 0.168↓ Hunan 2013 II 0.112↑ 0.104↓ Guangdong 2013 I 0.472↑ 0.137↓ Guangdong 2018 I 0.638↑ 1.117↓ Guangxi 2002 I 3.644↑ 0.388↓ Guangxi 2015 I 0.080↑ 0.444↓ Hainan 2015 IV 1.253↑ 0.185↓ Chongqing 2003 II 0.744↑ 0.253↓ Chongqing 2013 III 0.449↑ 0.131↓ Sichuan 2006 I 0.814↑ 0.205↓ Sichuan 2017 II 0.159↑ 0.154↓ Guizhou 2004 I 1.6099↑ 0.142↓ Guizhou 2010 I 0.795↑ 0.211↓ Yunnan 2003 III 0.484↑ 0.307↓ Yunnan 2010 I 0.779↑ 0.135↓ Xizang 2006 II 0.489↑ 0.105↓ Xizang 2016 III 0.125↑ 0.191↓ Shanxi 2009 I 0.839↑ 0.171↓ Shanxi 2019 I 1.722↑ 0.763↓ Gansu 2011 I 0.849↑ 0.182↓ Gansu 2016 I 0.284↑ 0.194↓ Qinghai 2013 I 2.716↑ 0.114↓ Qinghai 2017 I 3.029↑ 0.379↓ Ningxia 2004 II 0.293↑ 0.459↓ Ningxia 2016 I 0.668↑ 0.713↓ Xinjiang 2012 I 1.439↑ 0.607↓ Xinjiang 2017 II 0.111↑ 0.303↓ Fourth, the long-term coefficients demonstrated a positive connection between population and CO 2 e—a one percent increase in population contributed to a rise in CO 2 e by 0.69 (MG), 0.92 (AMG), and 0.68 (CCEMG) percent. This finding implied although the growing aging populace would lower the rate of future CO 2 e, it would also create the need for developing alternative models of economic growth for a smooth transition into a green economy. Nonetheless, this result supported the previous results for China (Z. Khan et al., 2019 ; Zhou et al., 2018 ); Asian economies (Khoshnevis Yazdi & Dariani, 2019 ; Qingquan et al., 2020 ); developing economies (Martínez-zarzoso et al., 2007 ); MENA countries (Al-mulali et al., 2013 ); newly industrialized nations (Sharif Hossain, 2011 ); and the EU nations (Kasman & Duman, 2015 ). Fifth, the results revealed a positive electricity use-CO 2 e nexus, implying that the irresponsible consumption of electricity (by educational, residential, and industrial consumers) had significantly enhanced CO 2 e in China. This finding points towards 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 over-dependency 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 conventual 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 ; Munir & Riaz, 2020 ; Xu et al., 2015 ; Zhang, 2019 ); Spain (Zarco-Soto et al., 2020 ); South Asian economies (Munir & Riaz, 2019 ); Bangladesh (Shahbaz et al., 2014 ); ASEAN countries (Lean & Smyth, 2010 ); Pakistan (Rehman et al., 2019 ) and BRICS (Haseeb et al., 2019 ). Finally, Table 10 exhibits the results of the DHPCT. The causality estimates revealed a bi-directional 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. Table 10 Results of the DHPCT. Relationship W-Stat Zbar-Stat EC→CO 2 e 15.5470*** 35.5125*** CO 2 e →EC 11.0489*** 23.6718*** FDI→ CO 2 e 8.90131*** 18.0186*** CO 2 e →FDI 14.9115*** 33.8397*** GDP→ CO 2 e 15.3180*** 34.9098*** CO 2 e →GDP 13.8654*** 31.0861*** POP→ CO 2 e 16.9784*** 39.2807*** CO 2 e →POP 18.1091*** 42.2570*** HEEXP → CO 2 e 7.11697*** 13.3215*** CO 2 e →HEEXP 12.0309*** 26.2569*** 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. *** indicates a one percent level of significance. 8 Conclusion And Policy Implications The main objective of this study was to explore potential long-run connections between the HEEXP and CO 2 e for thirty-one provinces in China from 2000(Q1) to 2019(Q4). The panel data were analysed 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 implication were drawn from current findings. Firstly, the current findings assert the need for the policymakers to design specific policies for green education, green campus, 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., non-renewable 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 non-linear models (e.g., NARDL) can be used to explore the same relationship and variables in a unified framework. Third, the current has adopted the EKC framework for examining different relationships. Researchers are encouraged to tests the current findings using the STIRPAT framework for new insights. Declarations Ethical Approval Not applicable Consent to Participate Not applicable Consent to Publish Not applicable Authors Contributions Sun YAWEN: Conceptualization; Data curation; Formal analysis Qingquan JIANG: Investigation; Methodology; Project administration Shoukat I KHATTAK: Software; Supervision; Validation Manzoor AHMAD: Writing - original draft; Writing - review & editing Hui LI: Writing - original draft; Writing - review & editing Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors . Competing Interests The authors declare that they have no competing interests. Data availability The datasets used and/or analyzed during the current study are variability from the corresponding author on reasonable request. References Abbasi, M. A., Parveen, S., Khan, S., & Kamal, M. A. (2020). 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Environmental and Ecological Statistics , 25 (4), 429–442. https://doi.org/10.1007/s10651-018-0412-8 Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 13 May, 2021 Reviewers invited by journal 03 Apr, 2021 Reviews received at journal 03 Apr, 2021 Editor assigned by journal 24 Mar, 2021 First submitted to journal 23 Mar, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-358931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":19920793,"identity":"51b33ee8-a0df-44b4-b6d6-48684ed7c523","order_by":0,"name":"Sun Yawen","email":"","orcid":"","institution":"The Education University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Yawen","suffix":""},{"id":19920794,"identity":"8aec8d5a-a4d4-4fea-9b00-12ae43b570ba","order_by":1,"name":"Qingquan Jiang","email":"","orcid":"","institution":"Xiamen University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qingquan","middleName":"","lastName":"Jiang","suffix":""},{"id":19920795,"identity":"9b5dafde-5425-40a9-b601-23c59f45960c","order_by":2,"name":"Shoukat Iqbal Khattak","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Shoukat","middleName":"Iqbal","lastName":"Khattak","suffix":""},{"id":19920796,"identity":"3d7673ab-5e7e-4128-b45c-d1cb815e464f","order_by":3,"name":"Manzoor Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDADAyCW+MBjw8PP30CCFskZMmkykjMOkKBFmsfmsI1BQwJ+lbrtZw9/+FBxT95c7PDD2zw553kMGA4wfviYg1uL2Zm8BMMZZ4oNd85OM7acc+Y2jzlzA7PkzG14tBzIMUjmbUtg3HA7wUzibc9tHsuGA2zMvPi0nH9jcBioxX7D7fRvErz/zvEYHEggoOVGjmEzUEvihts5ZpI8PAeI0fLGmHHGmYRkoJZiyxk8yTySMw424/fL+RxjYIgl2AIdtvHGBx47e37+5oMfPuLRgg0wNpCmfhSMglEwCkYBBgAA5FFWa71kh4gAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1152-2096","institution":"Nanjing University Business School","correspondingAuthor":true,"prefix":"","firstName":"Manzoor","middleName":"","lastName":"Ahmad","suffix":""},{"id":19920797,"identity":"edb2129c-7e3e-4d9d-9b6d-8e9debe28ecc","order_by":4,"name":"Hui Li","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2021-03-24 19:53:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-358931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-358931/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-021-14685-w","type":"published","date":"2021-07-07T15:01:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":7685401,"identity":"ba27c2d2-3f2c-4820-b6a4-5105fc5d4a94","added_by":"auto","created_at":"2021-04-05 20:18:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44769,"visible":true,"origin":"","legend":"A comparison of CO2e, population, and GDP growth in China (1981-2019).","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-358931/v1/eed366e7584ca4e1ce5b7521.jpg"},{"id":7685704,"identity":"35270dcd-640c-4d51-b95d-ca2dbe8a64b2","added_by":"auto","created_at":"2021-04-05 20:21:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53670,"visible":true,"origin":"","legend":"Proportionate changes (%) in HEEXP and eco-related patents.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-358931/v1/3ff818ce29fb762e2e11cd4d.jpg"},{"id":7685400,"identity":"c41c6e94-0b3d-433b-a2f8-1fdf4acd8417","added_by":"auto","created_at":"2021-04-05 20:18:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61606,"visible":true,"origin":"","legend":"The conceptual framework.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-358931/v1/6f901bfb0a25cf8ff5b084e8.jpg"},{"id":13684084,"identity":"f05c7c0b-fbad-42c1-8ce0-8fec24d86d9c","added_by":"auto","created_at":"2021-09-17 12:06:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-358931/v1/8865f646-fea3-4d41-a444-9bcefa729114.pdf"}],"financialInterests":"","formattedTitle":"Do Higher Education Research and Development Expenditures affect Environmental Sustainability? New Evidence from Thirty-One Chinese Provinces","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEnvironmental 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 the emanation of more waste, residues, and green-house gases (GHGs) into the environment. These toxic emissions of various types are considered as primary causes of global climate change, rising temperatures, and air pollution. Among them, carbon dioxide is one of the leading pollutants, accounting for about sixty-three percent of the total GHGs (Sharif Hossain, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wei, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Wei (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) 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\u003csub\u003e2\u003c/sub\u003ee is directly linked to the consumption of different types of carbon-intensive natural resources, especially fossil fuels. Many scholars have argued that CO\u003csub\u003e2\u003c/sub\u003ee, 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 non-renewable sources, thereby causing CO\u003csub\u003e2\u003c/sub\u003ee (Rehman, Rauf, Ahmad, Chandio, \u0026amp; Deyuan, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFrom the day China adopted the \u0026lsquo;opening-up policy\u0026rsquo;, its economy has sharply risen from just RMB0.365 trillion (1978) to RMB8.272 trillion (2007). With a phenomenal upsurge in the GDP (per capita) growth rate, China has now become one of the largest CO\u003csub\u003e2\u003c/sub\u003e emitter in the world (Li, Wu, Lei, Li, \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). China has mostly relied on non-renewable energy resources (i.e., coal) to drive its economic growth and industrialization at the cost of high CO\u003csub\u003e2\u003c/sub\u003ee, even though it is now cleaning its energy mix (Munir Ahmad \u0026amp; Zhao, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). 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 at large (M. Ahmad et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). China, with nearly twenty percent of the global population, has significantly affected the economic and environmental landscape of the world. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the historical growth in population, GDP growth, and CO\u003csub\u003e2\u003c/sub\u003ee in China.\u003c/p\u003e\n\u003cp\u003eFor China, economists have extensively measured the environmental impact of CO\u003csub\u003e2\u003c/sub\u003ee with different indicators and different econometric techniques. Some of the these economic indicators include financial development (Manzoor Ahmad, Khan, Ur Rahman, \u0026amp; Khan, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), inflow of remittances (Manzoor Ahmad, Ul Haq, et al., 2019), urban population (Z. Khan, Shahbaz, Ahmad, Rabbi, \u0026amp; Siqun, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), innovation (Khattak, Ahmad, Khan, \u0026amp; Khan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), monetary policy (Qingquan, Khattak, Ahmad, \u0026amp; Ping, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), globalization (Akadiri, Alola, Bekun, \u0026amp; Etokakpan, 2020), government expenditures (Le \u0026amp; Ozturk, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), foreign direct investment (Munir Ahmad, Zhao, Rehman, Shahzad, \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), electricity consumption (Zhang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), GDP (Akadiri et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), renewable energy consumption (Akadiri, Saint, Alola, Bekun, \u0026amp; Etokakpan,, 2020), information and communication technologies (Mirza, Ansar, Ullah, \u0026amp; Maqsood, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), tourism (Aziz, Mihardjo, Sharif, \u0026amp; Jermsittiparsert, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and international trade (Boamah et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). This paper, however, considers higher education R\u0026amp;D expenditures (HEEXP) as another unexplored determinant of CO\u003csub\u003e2\u003c/sub\u003ee for several reasons. First, the HEEXP serves as a core of science, technology, and innovation, which boosts industrialization and economic growth. Thus, this factor is central to CO\u003csub\u003e2\u003c/sub\u003ee mitigation strategies. Second, China has long recognized environmental pollution as an urgent threat, and therefore, it has been extensively funding higher education institutions (HEIs) for education and research projects related to energy, green economy, alternative fuel, and non-renewables. 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, public, and the environment. Figure\u0026nbsp;3 depicts the parallel development in the HEEXP and environment-related patents for China for the period 2001\u0026ndash;2019, signaling the potential role of HEEXP in eco-related patents. As seen above, a four percent increase in the HEEXP led to a rise in eco-related patents by twenty-one percent in 2016. From 2001\u0026ndash;2016, an average of 21.18 percent upsurge in the HEEXP was associated with a parallel increase in eco-related patents by 20.59 percent, cueing potential implication of the HEEXP on eco-related 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.\u003c/p\u003e\n\u003cp\u003eThe key purpose of this study is to fill this knowledge void by comprehensively analyzing the nexus between HEEXP and CO\u003csub\u003e2\u003c/sub\u003ee 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\u003csub\u003e2\u003c/sub\u003ee, thereby opening a possible research avenue in the environmental economics. Second, as of this work, the paper offers the first schematic framework that explains the precise mechanism of how the HEEXP affects environmental pollution in China. Third, the paper uses second-generation econometric techniques for robust and rigorous analysis. Fourth, through provincial data, the paper presents an in-depth insight into regional and provincial disparities vis-a-viz the effect of the HEEXP on CO\u003csub\u003e2\u003c/sub\u003ee. Fifth, the article has attempted to integrate two distinct paradigms into a unified framework. Most prior studies on CO\u003csub\u003e2\u003c/sub\u003ee in the education literature are limited to the 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 the disciplines have linked the HEEXP to CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e\n\u003cp\u003eThe rest of the paper is categorized as follows. Section 2 explores the literature review. Section 3, 4, 5, and 6, present the conceptual framework, model specifications, and data sources and variables, and estimation techniques, respectively. Section 7 focuses on the interpretations of results and discussions, followed by the conclusion, policy recommendations, future directions, and limitations in Sect.\u0026nbsp;8.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\n\u003cp\u003e\u003cstrong\u003e2.1 The relationship between income and CO\u003csub\u003e2\u003c/sub\u003ee\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003cp\u003eThe 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\u003csub\u003e2\u003c/sub\u003ee levels to prevent the harmful impact of global warming problems. Environmental Kuznets curve (EKC) hypothesis is probably the most frequently tested framework that explains the link between aggregate income and environmental sustainability (\u0026Ouml;zcan \u0026amp; \u0026Ouml;zt\u0026uuml;rk, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Grossman and Krueger (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e) 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. Several researchers have validated the EKC hypothesis for different economies, including but not limited to, Iberia (Moutinho, Madaleno, \u0026amp; Bento, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); China (Jiang, Yang, \u0026amp; Ma, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mushtaq, Chen, Din, Ahmad, \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); India (Dar \u0026amp; Asif, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); Pakistan (Ur Rahman, Chongbo, \u0026amp; Ahmad, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); USA (Alola \u0026amp; Alola, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Brazil (Ben Jebli \u0026amp; Ben Youssef, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); emerging economies (Wawrzyniak \u0026amp; Doryń, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); NAFTA and BRIC (Rahman, Cai, Khattak, \u0026amp; Hasan, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Ukraine (Melnyk, Kubatko, \u0026amp; Kubatko, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e); SEE economies (Obradović \u0026amp; Lojanica, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); developed and developing economies (Anser et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); and OECD (Manzoor Ahmad, Khan, Rahman, Khattak, \u0026amp; Khan, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Relationship between foreign direct investment and CO\u003csub\u003e2\u003c/sub\u003ee\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe positive link between FDI and CO\u003csub\u003e2\u003c/sub\u003ee 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 the third-world economies where laws were either non-extant or extremely weak. As this trend has continued for long, 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 of the emerging economies (Centre, Kiichiro, Masahiro, G, \u0026amp; Alexandra, 2005). That said, the empirical evidence on the FDI-CO2e nexus remains controversial. Some evidence for the positive relationship between FDI and CO\u003csub\u003e2\u003c/sub\u003ee include studies for newly industrialized economies (Destek \u0026amp; Okumus, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Cote d\u0026rsquo;Ivoire (Assamoi, Wang, Liu, \u0026amp; Gnangoin, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); ASEAN countries (Guzel \u0026amp; Okumus, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); MINT countries (Balsalobre-Lorente, Gokmenoglu, Taspinar, \u0026amp; Cantos-Cantos, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Turkey (Mert \u0026amp; Caglar, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); MIKTA economies (Bakirtas \u0026amp; Cetin, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); Pakistan (Ur Rahman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Asia (M. A. Khan \u0026amp; Ozturk, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); European economies (Mert, B\u0026ouml;l\u0026uuml;k, \u0026amp; \u0026Ccedil;ağlar, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); BRI region (A. Khan, Chenggang, Hussain, \u0026amp; Bano, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); OIC countries (Ali, Yusop, Kaliappan, \u0026amp; Chin, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); and OECD countries (Manzoor Ahmad, Khattak, Khan, \u0026amp; Rahman, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 The relationship between population and CO\u003csub\u003e2\u003c/sub\u003ee\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, 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 (age-structural change or population aging), distribution (migration), and density (urbanization). Harper (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) stated that three sub-factors of the population had played an important role in increasing or decreasing CO\u003csub\u003e2\u003c/sub\u003ee. Mart\u0026iacute;nez-zarzoso et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) 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 definite link between population and CO\u003csub\u003e2\u003c/sub\u003ee for European countries (Harper, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mart\u0026iacute;nez-zarzoso et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e); developed and developing economies (Dietz \u0026amp; Rosa, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e); selected 93 economies (Shi, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e); Asian economies (Qingquan et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); China (Z. Khan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); MENA economies (Al-mulali, Fereidouni, Lee, \u0026amp; Sab, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e); newly industrialized nations (Sharif Hossain, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e); OECD (Liddle, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e); Pakistan (Ullah, Ozturk, Usman, Majeed, \u0026amp; Akhtar, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); OPEC economies (Murshed, Nurmakhanova, Elheddad, \u0026amp; Ahmed, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); and Asian countries (Abbasi, Parveen, Khan, \u0026amp; Kamal, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 The relationship between electricity consumption and CO\u003csub\u003e2\u003c/sub\u003ee\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectricity is one of the primary sources of energy for all industries. Even though electricity consumption is not directly associated with CO\u003csub\u003e2\u003c/sub\u003ee, the vast quantities of non-renewable fossil fuels used for power generation emit high CO\u003csub\u003e2\u003c/sub\u003ee (Zhang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previously, few academics have examined the relationship between electricity consumption and CO\u003csub\u003e2\u003c/sub\u003ee. For example, Zhang (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) 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\u003csub\u003e2\u003c/sub\u003ee. Balsalobre-Lorente, Shahbaz, Roubaud, and Farhani (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) concluded that electricity consumption increased CO\u003csub\u003e2\u003c/sub\u003ee in the long-run across the European nations. B\u0026eacute;la\u0026iuml;d, and Youssef (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) tested the association between energy (renewable and non-renewable) consumption and CO\u003csub\u003e2\u003c/sub\u003ee for Algeria. The ARDL estimates validated the renewable energy consumption-CO\u003csub\u003e2\u003c/sub\u003ee led hypothesis. Yorucu and Varoglu (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the nexus among industrial production, electricity consumption, economic growth, and CO\u003csub\u003e2\u003c/sub\u003ee in selected small island states. Based on the FMOLS and DOLS estimations, the authors found that a one percent increase in electricity consumption predicted an upsurge of 0.79 percent in CO\u003csub\u003e2\u003c/sub\u003ee. In the same way, others studies have also reported a positive connection between electricity consumption and CO\u003csub\u003e2\u003c/sub\u003ee for China (Akadiri et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Munir \u0026amp; Riaz, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ou, Xiaoyu, \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xu, Hong, Ren, Wang, \u0026amp; Yuan, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Spain (Zarco-Soto, Zarco-Peri\u0026ntilde;\u0026aacute;n, \u0026amp; S\u0026aacute;nchez-Dur\u0026aacute;n, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); South Asian economies (Munir \u0026amp; Riaz, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Bangladesh (Shahbaz, Salah Uddin, Ur Rehman, \u0026amp; Imran, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e); ASEAN countries (Lean \u0026amp; Smyth, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e); Pakistan (Rehman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) BRICS (Cowan, Chang, Inglesi-Lotz, \u0026amp; Gupta, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Haseeb, Xia, Saud, Ahmad, \u0026amp; Khurshid, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); and Kuwait (Salahuddin, Alam, Ozturk, \u0026amp; Sohag, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Conceptual Framework","content":"\u003cp\u003eFigure 3 illustrates the conceptual framework, depicting the mechanism through which HEEXP may affects CO\u003csub\u003e2\u003c/sub\u003ee. For long, the HEIs have been contributing to the advancement of knowledge, economy, cultivating students, and conducting research in many fields. Whether it was the intervention of government 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, S\u0026ouml;der, \u0026amp; Lepp\u0026auml;lahti, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Governments\u0026rsquo; funding, therefore, has been crucial for many HEIs to support basic and advanced level research, especially in fields like 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, industries, and other institutions, 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 sustainability technology, methods, processes, and products; ii) developing and promoting green campuses (GC).\u003c/p\u003e\n\u003cp\u003eCongruent 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, countries 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, technologies, products, and services, many countries and institutions have been investing heavily in academic research and development related to eco-innovation, 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, and carbon footprint, and low energy efficiency.\u003c/p\u003e\n\u003cp\u003eBeyond 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); b) campus energy management system (CEMS). The concept of CEMS emphasizes the construction of green education and environmental-related 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, land; promote the use of green and clean energy sources during official hours; encourage sustainable development in higher education; improve R\u0026amp;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 to designing and implement green curricula. Thus, GC plays a vital in the implementation of the 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 GC necessitates the need for educational institutions to focus on the hardware and software of GC, simultaneously. The former pertains to the integration of green aspects in construction, building, infrastructure, and operations, and the latter refers to the development and promotion of green culture, humanity, green citizenship, and cultivation of 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, Chen, Shi, \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). In short, it is proposed that the development of GC (through HEEXP) not only helps in mitigating CO\u003csub\u003e2\u003c/sub\u003ee, but also play an important role in promoting sustainable consumption and production across residential and commercial sectors.\u003c/p\u003e"},{"header":"4 Model Specification","content":"\u003cp\u003eBelow, Equation (1) represents the dynamic relationship between higher education R\u0026amp;D expenditures (HEEXP), foreign direct investment (FDI), electricity consumption (EC), gross domestic product (GDP), total population (POP), and CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1617649379.jpg\"\u003e\u003c/p\u003e\u003cp\u003eThe rationale for the use of 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 eighty percent of electricity was generated from coal. Third, it is one of the largest economies in the world, vis-a-viz the GDP growth rate.\u0026nbsp; Fourth, China is one of the most populous economies in the world, where population growth has created contributed to energy consumption among residential and non-residential consumers, directly and indirectly causing CO\u003csub\u003e2\u003c/sub\u003ee. \u0026nbsp;\u003c/p\u003e"},{"header":"5 Data Sources And Variables","content":"\u003cp\u003eThe data for HEEXP, FDI, EC, GDP, POP, and CO\u003csub\u003e2\u003c/sub\u003ee were collected from the National Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the period 2000 to 2019. Consistent with the previous studies (Sbia, Shahbaz, \u0026amp; Hamdi, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shahbaz, Hoang, Mahalik, \u0026amp; Roubaud, 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\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the data sources and descriptions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eData description and sources.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNotation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnits\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource of data\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimation technique\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExpected signs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectricity Consumption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWatt-hour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForeign direct investment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMillion Yuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGross Domestic Product\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u0026nbsp;million Yuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10000 people\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResearch \u0026amp; development expenditures in the higher education sector\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1000 Yuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarbon dioxide emissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10000 tons\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Bureau of Statistics (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuadratic\u003c/p\u003e\n\u003cp\u003ematch-sum\u003c/p\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6 Estimation Techniques","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.1 Unit root testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTesting 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 factors, including, economic union, financial shocks, demand shocks, supply shocks, pandemic diseases, globalization, and trade wars, it must be dealt with accuracy and precision. If ignored, it could be led to bias cointegration and stationarity results (Z. Khan, Ali, Jinyu, Shahbaz, \u0026amp; Siqun, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Pesaran (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) cross-sectional dependence test (PCSDT) and the M.H Pesaran and Yamagata (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) slope homogeneity test (SHT) were applied for addressing the CSD and homogeneity problems, respectively. In the next step, the order of integration was examined for all variables using the second-generation Pesaran and M.H (2003) (PMCADF) and Pesaran (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) (PCIPS) unit-root tests. 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 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. For robustness check, the Clemente, Monta\u0026ntilde;\u0026eacute;s, and Reyes (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) unit root test (CMRURT), with multiple structural breaks was employed the aggregate data on EC\u003csub\u003ei\u003c/sub\u003e, FDI\u003csub\u003et\u003c/sub\u003e, GDP\u003csub\u003et\u003c/sub\u003e, POP\u003csub\u003eit\u003c/sub\u003e, HEEXP\u003csub\u003et\u003c/sub\u003e, and CO\u003csub\u003e2\u003c/sub\u003ee\u003csub\u003eit\u003c/sub\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Cointegration testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor cointegration testing, this study adopted the Westerlund (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) error-correction-based panel cointegration test (WECPT). The author 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) it allows unbalanced panels and unequal series length in units; 2) tests heterogeneity that is permitted in the short- and long-run parameters of the error-correction model; 3) obtains critical value using the bootstrap approach, if a correlation probability exits among units. The WECPT involves the following hypothesis:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e\u003cem\u003eH\u003csub\u003e0\u003c/sub\u003e :No cointegration exists among all panels\u003c/em\u003e\u003c/div\u003e\n\u003cdiv class=\"mathdisplay\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e\u003cem\u003eH\u003csub\u003e1\u003c/sub\u003e :Cointegration exists among all panels\u003c/em\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe paper adopted three cointegration tests for checking robustness\u0026mdash;Kao (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) residual-based cointegration test (KRCPT), Pedroni (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) cointegration test (PCT), and the Gregory and Hansen (\u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) cointegration test (GHCT) (with structural breaks and regime shifts).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Long-run coefficients estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral 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 (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) common correlated effects mean group (CCEMG) estimator, and the Eberhardt and Bond (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) 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 time-variant 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 independence and the dependent variables as surplus regressors when applying OLS to specific units, the CCEMG method allows for TVU and CSD with heterogenous effect in panel members. Identified by the averages of CS, the unobserved CF 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 non-stationary 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 group-specific 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 multi-factors error-terms and non-stationary variables, particularly considering CSD. The AMG estimator is superior to the CCEMG, in terms of creating a set of unobservable CF as a common dynamic process. Dissimilar to a scenario in which the unobservable factors are considered as a nuisance, the alternate treatment may offer helpful interpretations, depending on the context (Heshmati, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Panel causality testing\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cp\u003eFor panel data, Dumitrescu and Hurlin (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) 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 non-existence of homogenous Granger-causality relationship. In this way, the Dumitrescu and Hurlin (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) panel-causality test (DHPCT) accounts for the CSD between the sample province or countries. Moreover, the DHPCT is insensitive to the variance among the cross-sections 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, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"7 Results And Discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the results of PCSDT. As seen below, the null hypothesis of no CSD for the EC\u003csub\u003eit\u003c/sub\u003e, FDI\u003csub\u003eit\u003c/sub\u003e, GDP\u003csub\u003eit\u003c/sub\u003e, POP\u003csub\u003eit\u003c/sub\u003e, HEEXP\u003csub\u003eit\u003c/sub\u003e, and CO\u003csub\u003e2\u003c/sub\u003ee\u003csub\u003eit\u003c/sub\u003e was rejected at 10, 5, and 1 percent significance levels. This implied that all the provinces in China were interdependent in a way that an economic shock in one region may affect other regions, too. As reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the SHT highlighted heterogeneity problems in the model.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the PCSDT.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCD-statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003cp\u003ejoint T\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean obs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e148.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e168.425\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e153.638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e189.912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote. CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the SHT.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStatistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDelta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82.524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdjusted Delta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e 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 non-stationary at level but became stationary at the first difference, even though these tests were unable to deal with structural breaks in the data. Given that most global economies have experienced many structural changes, it is considered imperative to trace structural breaks in the data series for China. There was a high probability that the PCADF and PCIPS could be given bias results, if structural changes were underestimated. This problem was addressed through the CMRURT that allowed for multiple structural breaks in the data.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the PCADF and the PCIPs unit-root tests (without structural breaks).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAt level\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAt first difference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCADF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCIPS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCADF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCIPS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.307***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.942***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.374***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.161***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.176***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.467***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-16.727***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.186***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23.227***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.222***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-12.026***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.250***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote. CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures. *** indicates 1% level of significance.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e 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\u0026mdash;often linked to global or local events\u0026mdash;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 \u0026amp; Zheng, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). In 2004, China faced one of the worst historic inflationary pressures, partly triggered by real-estate 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, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). In 2005, Lenovo Group acquired the personal computer division of IBM for a hefty sum of USD1.75\u0026nbsp;billion. Indeed, this acquisition is considered as an economic breakthrough. Apart from gaining access to foreign, facilities, operations, and R\u0026amp;D, China strengthened its presence in the US (Morrison, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). From 2008\u0026ndash;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 when compared with the total shrinkage in the world output (Lardy, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although the incoming FDI experienced a sharp decline, the inbound foreign investments reached an all-time high in 2010, increasing by around two-third, i.e., USD185\u0026nbsp;billion. There was almost twenty percent contraction in outbound FDI in 2009, but the outbound FDI increased by thirty-seven percent and touched an all-time high of USD60\u0026nbsp;billion (Lardy, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). 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 \u0026amp; W\u0026oacute;jcik, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). With all the study variables exhibiting the same integration order, the study applied the cointegration analysis, including the WECPT, KRCPT, PCT, and the GHCT.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnit root test with structural breaks.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAt level\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAt first difference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreakpoints\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreakpoints\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2002Q3, 2017Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.779***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010Q4, 2016Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.842\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009Q2, 2012Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.287**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2006Q4, 2009Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015Q1, 2017Q2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.722***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2001Q3, 2015Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009Q3, 2010Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.337***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2003Q1, 2008Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004Q4, 2005Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.438***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2003Q4, 2004Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.916\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2008Q1, 2011Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.187***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010Q4, 2011Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote. CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures. **, *** indicates 5% and 1% level of significance, respectively.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the outcomes of the cointegration analysis without structural breaks. The first two columns (G\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, G\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e) 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\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the results of the cointegration analysis with structural break and regime shifts were found to be consistent with the WECPT, KRCPT, and the PCT.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCointegration analysis without structural breaks.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;EC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;FDI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;GDP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;POP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;HEEXP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWEPCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.098**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.059**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.575***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.554***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.362***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-116.59***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-44.50***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-43.088***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-29.438***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-128.47***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.411***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.087**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.750**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.861\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.055***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.738\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.534*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKRCPT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMDF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.57***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-18.94***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.59*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.60***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10.94***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.02***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.85**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.27**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-13.20***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.08***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.32***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-6.26***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUMDF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.53***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.65***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.47***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.9653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUDF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.18***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.15***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.04***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.35*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMDF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.66***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.92***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.57***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.24***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.64***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePP\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.10***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.65*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.78***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.01***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADF\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.11***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.89***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.29***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.76***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.52***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: WEPCT\u0026thinsp;=\u0026thinsp;Westerlund (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) error-correction based panel cointegration tests; KRCPT\u0026thinsp;=\u0026thinsp;Kao (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) residual-based tests for cointegration in panel data; MDF\u003csub\u003et\u003c/sub\u003e= Modified Dickey-Fuller t; DF\u003csub\u003et\u003c/sub\u003e= Dickey-Fuller t; ADF\u003csub\u003et\u003c/sub\u003e= Augmented Dickey-Fuller t; UMD\u003csub\u003et\u003c/sub\u003e= Unadjusted Modified Dickey- Fuller t; UDF\u003csub\u003et\u003c/sub\u003e= Unadjusted Dickey-Fuller t; PCT\u0026thinsp;=\u0026thinsp;Pedroni (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) cointegration test; PP\u003csub\u003et\u003c/sub\u003e= Phillips-Perron t; ADF\u003csub\u003et\u003c/sub\u003e= Augmented Dickey-Fuller t; CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*, **, *** indicates 10%, 5% and 1% level of significance, respectively.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCointegration analysis with structural break and regime shifts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;EC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u0026rarr;FDI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u0026rarr;GDP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u0026rarr;POP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u0026rarr;HEEXP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eChange\u003c/em\u003e in Level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.11***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-32.14***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.53***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.41***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.72***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreakpoint\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreak year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013Q2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009Q2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values 10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eChange in Regime\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.56***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.80***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.54***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.73***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.92***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreakpoint\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreak year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2008Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2008Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2011Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical values 10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*** indicates a one percent level of significance.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e 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\u003csub\u003e2\u003c/sub\u003ee\u0026mdash;a one percent increase in HEEXP predicted a decline of .29 (MG), 0.24 (AMG), and 0.30 (CCEMG) percent in CO\u003csub\u003e2\u003c/sub\u003ee. As predicted, this result supported that spending on research and development spending in higher education has helped to mitigate CO\u003csub\u003e2\u003c/sub\u003ee in China. A feasible explanation is that academic institutions have been a central part of the national research framework, in terms of developing green technology, innovation, and eco-urban systems in China. In 2011 alone, the faculty and staff from HEIs constituted 11.3 percent of the overall research and development population. Using almost 8.5 percent of the total national R\u0026amp;D spending, these researchers have shown impressive results. These individuals conducted 62.2 percent of the all research projects and activities, received 28.8 percent of the total patents, applied for 21.6 percent of the total patents, and produced 64.4 percent of the entire scientific publications. Following the \u0026lsquo;new normal\u0026rsquo; of fostering the nation with education, science, innovation, and developing a green economy, the Chinese government has placed a significant focus on green and sustainable technology research. Currently, Chinese scholars are the leading the global research related to green production, sustainability, green technology, environment, and green energy. More so, the government has been allocating a considerable amount of funds for sustainability-oriented R\u0026amp;D projects. From 2000\u0026ndash;2009, these funds have increased from just RMB7.67\u0026nbsp;billion to RMB46.7\u0026nbsp;billion, constituting almost eight percent of the total national spending on R\u0026amp;D. A total of RMB14.5\u0026nbsp;billion were allocated to basic research, accounting for nearly fifty-three percent of the total national research budget (Hu, Liang, \u0026amp; Tang, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNext, China initiated the \u003cem\u003e211 Project\u003c/em\u003e and \u003cem\u003e985 Project\u003c/em\u003e to uplift the standard of its HEIs. These projects were aimed at developing globally competitive first-class universities, programs, and scientific disciplines to promote sustainable and green socio-economic development in China. Hu, Liang, and Tang (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) argued that the fifteen years of the \u003cem\u003e211 Projects\u003c/em\u003e have been extremely fruitful, in terms of setting the foundations for green innovation in education, research and service, and transitioning to a green economy. China spends around two percent of its total GDP on research, an amount that is increasing at the rate of twenty percent per year (Chung, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Under the government\u0026rsquo;s guidance, Chinese HEIs have dedicated time, resources, and money for research on green energy, economy, technology, education, and innovation to realize a green revolution (Liu, Strangway, \u0026amp; Feng, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These factors have played an instrumental role in indirectly mitigating CO\u003csub\u003e2\u003c/sub\u003ee by raising awareness, development of green technology, and green urbanization, and green education.\u003c/p\u003e\n\u003cp\u003eIn 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 establishment of zero-carbon campus (Morgan, Gu, \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Lee and Efird (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) further explained the idea of green universities by identifying some key attributes. Firstly, these universities place acute emphasis on 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 the 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 cleans and green campus environment. Gou (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) 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 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 has the potential to reduce deprivation and poverty among regions or provinces, enhance fairness, and to expand the sustainable growth concept in the Chinese society. All these measures, if implemented correctly, can decrease CO\u003csub\u003e2\u003c/sub\u003e-related energy consumption and increase the use of clean technologies across China. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e exhibits the parallel fluctuations in HEEXP and CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLong-run coefficients.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAMG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCCEMG\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.294*** (-4.91)\u003c/p\u003e\n\u003cp\u003e[0.059]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.242***(-4.63)\u003c/p\u003e\n\u003cp\u003e[0.052]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.303***(-3.87)\u003c/p\u003e\n\u003cp\u003e[0.078]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.427***(6.59)\u003c/p\u003e\n\u003cp\u003e[0.067]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118***(2.78)\u003c/p\u003e\n\u003cp\u003e[0.042]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.341***(6.16)\u003c/p\u003e\n\u003cp\u003e[0.055]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.445***(3.19)\u003c/p\u003e\n\u003cp\u003e[0.139]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.748***(8.67)\u003c/p\u003e\n\u003cp\u003e[0.086]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637***(6.57)\u003c/p\u003e\n\u003cp\u003e[0.097]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.686***(4.53)\u003c/p\u003e\n\u003cp\u003e[0.151]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.922***(3.98)\u003c/p\u003e\n\u003cp\u003e[0.232]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.683***(3.58)\u003c/p\u003e\n\u003cp\u003e[0.191]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.522***(3.48)\u003c/p\u003e\n\u003cp\u003e[0.1444]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.383***(6.15)\u003c/p\u003e\n\u003cp\u003e[0.062]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308***(3.14)\u003c/p\u003e\n\u003cp\u003e[0.098]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.169***(3.85)\u003c/p\u003e\n\u003cp\u003e[0.304]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6223***(9.10)\u003c/p\u003e\n\u003cp\u003e[0.288]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.551***(13.31)\u003c/p\u003e\n\u003cp\u003e[2.126]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNote. CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population, HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures. ()\u0026thinsp;=\u0026thinsp;t-statistic; []\u0026thinsp;=\u0026thinsp;standard error; MG\u0026thinsp;=\u0026thinsp;Mean group; AMG\u0026thinsp;=\u0026thinsp;augmented mean group; CCEMG\u0026thinsp;=\u0026thinsp;Common correlated effect mean group.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e*** indicates a one percent level of significance.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, the long-run coefficients indicated a significant positive linkage between FDI and CO\u003csub\u003e2\u003c/sub\u003ee, offering empirical evidence for the acceptance of the PHH in China. A one percent increase in FDI caused a rise in CO\u003csub\u003e2\u003c/sub\u003ee by 0.42 (MG), 0.12 (AMG), and 0.34 (CCEMG) percent. This result suggested that some cities, provinces, and municipalities in China, with less stringent regulations, have become pollution havens in an attempt to attract FDI and pollution-intensive industries. This result validated the previous studies conducted for China (Ur Rahman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); OECD (Manzoor Ahmad et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); newly industrialized nations (Destek \u0026amp; Okumus, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Cote d\u0026rsquo;Ivoire (Assamoi et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); ASEAN (Guzel \u0026amp; Okumus, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); MINT countries (Balsalobre-Lorente et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Pakistan (Nadeem, Ali, Khan, \u0026amp; Guo, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Naz et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); MIKTA economies (Bakirtas \u0026amp; Cetin, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); BRICS (Z. U. Khan, Ahmad, \u0026amp; Khan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Arab countries (Abdo, Li, Zhang, Lu, \u0026amp; Rasheed, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Asian countries (M. A. Khan \u0026amp; Ozturk, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); and European countries (Mert et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, this results contradicts the previous studies conducted for coastal Mediterranean countries (Nathaniel, Aguegboh, Iheonu, Sharma, \u0026amp; Shah, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); OECD (Manzoor Ahmad, Khan, et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Turkey (Mert \u0026amp; Caglar, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); China (Ayamba, Haibo, Ibn Musah, Ruth, \u0026amp; Osei-Agyemang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hao, Wu, Wu, \u0026amp; Ren, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); and Kyoto Annex countries (Mert \u0026amp; B\u0026ouml;l\u0026uuml;k, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThird, the estimations revealed a positive association between GDP and CO\u003csub\u003e2\u003c/sub\u003ee\u0026mdash;a one percent increase in GDP led to a rise in CO\u003csub\u003e2\u003c/sub\u003ee by 0.44 (MG), 0.75 (AMG), and 0.64 (CCEMG) percent. This result suggested that GDP growth\u0026mdash;driven by low energy efficiency and coal consumption\u0026mdash;had enhanced CO\u003csub\u003e2\u003c/sub\u003ee in China. This result is consistent with the previous findings for India (Dar \u0026amp; Asif, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); Pakistan (Chandia, Gul, Aziz, Sarwar, \u0026amp; Zulfiqar, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ur Rahman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); China (Manzoor Ahmad et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mushtaq et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wei, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); the US (Alola \u0026amp; Alola, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Liberia (Moutinho et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Qatar (Mrabet, AlSamara, \u0026amp; Hezam Jarallah, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); selected 72 countries (Inekwe, Maharaj, \u0026amp; Bhattacharya, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); developing countries (Wawrzyniak \u0026amp; Doryń, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); NAFTA and BRIC (Rahman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); SEE countries (Obradović \u0026amp; Lojanica, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); and Asian economies (Qingquan et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eParallel fluctuations in HEEXP and CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProvince\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQuarter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHEEXP (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeijing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.485\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeijing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.306\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.399\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTianjin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.498\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.266\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTianjin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.078\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHebei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.151\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.209\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHebei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.373\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.177\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.146\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.245\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.093\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.117\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInner Mongolia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.842\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.319\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInner Mongolia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.126\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.607\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiaoning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.881\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.189\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiaoning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.714\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.655\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJilin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.070\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.261\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.012\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.946\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.187\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.188\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.504\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.121\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.354\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.548\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.178\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.139\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.806\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.992\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZhejiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0081\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.268\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZhejiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.522\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.686\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnhui\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.832\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.303\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnhui\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.414\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.071\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.357\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.183\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.189\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.817\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.113\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.604\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.212\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.390\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShandong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.888\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.191\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShandong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.136\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.133\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.744\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.317\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.184\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHubei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.101\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHubei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.135\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.042\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.650\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.168\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.112\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.104\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangdong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.472\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.137\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangdong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.638\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.117\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.644\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.388\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.080\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.444\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHainan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.253\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.185\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChongqing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.744\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.253\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChongqing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.449\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.131\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSichuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.814\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.205\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSichuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.159\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.154\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuizhou\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6099\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.142\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuizhou\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.795\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.211\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYunnan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.484\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.307\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYunnan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.779\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.135\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXizang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.489\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.105\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXizang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.125\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.191\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.839\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.171\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.722\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.763\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGansu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.849\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.182\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGansu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.284\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.194\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQinghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.716\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.114\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQinghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.029\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.379\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNingxia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.293\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.459\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNingxia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.668\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.713\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXinjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.439\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.607\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXinjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.111\u0026uarr;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.303\u0026darr;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, the long-term coefficients demonstrated a positive connection between population and CO\u003csub\u003e2\u003c/sub\u003ee\u0026mdash;a one percent increase in population contributed to a rise in CO\u003csub\u003e2\u003c/sub\u003ee by 0.69 (MG), 0.92 (AMG), and 0.68 (CCEMG) percent. This finding implied although the growing aging populace would lower the rate of future CO\u003csub\u003e2\u003c/sub\u003ee, it would also create the need for developing alternative models of economic growth for a smooth transition into a green economy. Nonetheless, this result supported the previous results for China (Z. Khan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); Asian economies (Khoshnevis Yazdi \u0026amp; Dariani, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qingquan et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); developing economies (Mart\u0026iacute;nez-zarzoso et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e); MENA countries (Al-mulali et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e); newly industrialized nations (Sharif Hossain, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e); and the EU nations (Kasman \u0026amp; Duman, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFifth, the results revealed a positive electricity use-CO\u003csub\u003e2\u003c/sub\u003ee nexus, implying that the irresponsible consumption of electricity (by educational, residential, and industrial consumers) had significantly enhanced CO\u003csub\u003e2\u003c/sub\u003ee in China. This finding points towards 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 over-dependency on fossil-fuels will reduce significantly in the future, thereby decreasing CO\u003csub\u003e2\u003c/sub\u003ee. The commercial sector (e.g., tech companies) is also setting the foundations for responsible energy consumption by switching from conventual 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., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Munir \u0026amp; Riaz, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Spain (Zarco-Soto et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); South Asian economies (Munir \u0026amp; Riaz, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Bangladesh (Shahbaz et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e); ASEAN countries (Lean \u0026amp; Smyth, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e); Pakistan (Rehman et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and BRICS (Haseeb et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFinally, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e exhibits the results of the DHPCT. The causality estimates revealed a bi-directional causality between EC and CO\u003csub\u003e2\u003c/sub\u003ee; FDI and CO\u003csub\u003e2\u003c/sub\u003ee; GDP and CO\u003csub\u003e2\u003c/sub\u003ee; POP and CO\u003csub\u003e2\u003c/sub\u003ee and HEEXP, and CO\u003csub\u003e2\u003c/sub\u003ee. 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\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab10\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the DHPCT.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelationship\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eW-Stat\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eZbar-Stat\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u0026rarr;CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.5470***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.5125***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;EC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.0489***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.6718***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFDI\u0026rarr; CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.90131***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.0186***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;FDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.9115***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.8397***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP\u0026rarr; CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.3180***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.9098***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;GDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.8654***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.0861***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePOP\u0026rarr; CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.9784***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.2807***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;POP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.1091***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.2570***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEEXP \u0026rarr; CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.11697***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.3215***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee \u0026rarr;HEEXP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.0309***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.2569***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: CO\u003csub\u003e2\u003c/sub\u003ee\u0026thinsp;=\u0026thinsp;Carbon dioxide emissions; EC\u0026thinsp;=\u0026thinsp;Electricity consumption; FDI\u0026thinsp;=\u0026thinsp;Foreign direct investment; GDP\u0026thinsp;=\u0026thinsp;Gross domestic product; POP\u0026thinsp;=\u0026thinsp;Population; HEEXP\u0026thinsp;=\u0026thinsp;Higher education R\u0026amp;D expenditures. *** indicates a one percent level of significance.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"8 Conclusion And Policy Implications","content":"\u003cp\u003eThe main objective of this study was to explore potential long-run connections between the HEEXP and CO\u003csub\u003e2\u003c/sub\u003ee for thirty-one provinces in China from 2000(Q1) to 2019(Q4). The panel data were analysed 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\u003csub\u003e2\u003c/sub\u003ee, while EC, FDI, POP, and GDP had a positive interaction with CO\u003csub\u003e2\u003c/sub\u003ee in the long run. Third, the DHPCT reflected that a two-way causal relationship existed between CO\u003csub\u003e2\u003c/sub\u003ee and all other study variables\u0026mdash;FDI, EC, GDP, POP, and HEEXP.\u003c/p\u003e\n\u003cp\u003eThe following important implication were drawn from current findings. Firstly, the current findings assert the need for the policymakers to design specific policies for green education, green campus, 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.\u003c/p\u003e\n\u003cp\u003eThird, 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\u003csub\u003e2\u003c/sub\u003ee 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\u003csub\u003e2\u003c/sub\u003ee. 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., non-renewable 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.\u003c/p\u003e\n\u003cp\u003eFifth, 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\u003csub\u003e2\u003c/sub\u003ee, 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\u003csub\u003e2\u003c/sub\u003ee, 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.\u003c/p\u003e\n\u003cp\u003eThis 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\u003csub\u003e2\u003c/sub\u003ee. Perhaps, some non-linear models (e.g., NARDL) can be used to explore the same relationship and variables in a unified framework. Third, the current has adopted the EKC framework for examining different relationships. Researchers are encouraged to tests the current findings using the STIRPAT framework for new insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSun YAWEN: Conceptualization; Data curation; Formal analysis\u003c/p\u003e\n\u003cp\u003eQingquan JIANG: Investigation; Methodology; Project administration\u003c/p\u003e\n\u003cp\u003eShoukat I KHATTAK: Software; Supervision; Validation\u003c/p\u003e\n\u003cp\u003eManzoor AHMAD: Writing - original draft; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eHui LI: Writing - original draft; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors\u003cstrong\u003e. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are variability from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAbbasi, M. 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The influences of industrial gross domestic product, urbanization rate, environmental investment, and coal consumption on industrial air pollutant emission in China. \u003cem\u003eEnvironmental and Ecological Statistics\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(4), 429\u0026ndash;442. https://doi.org/10.1007/s10651-018-0412-8\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"higher education R\u0026D expenditures, electricity consumption, FDI, population, and CO2e","lastPublishedDoi":"10.21203/rs.3.rs-358931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-358931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigher education R\u0026amp;D expenditures (HEEXP) are one of the important determinants of economic growth that facilitates science, technology, new ideas, and innovation, but its effect on environmental sustainability remains unexplored. This paper examines the nexus between HEEXP and carbon dioxide emissions (CO\u003csub\u003e2\u003c/sub\u003ee), followed by control variables such as electricity consumption, foreign direct investment, gross domestic product, and total population for the period 2000Q1-2019Q4. Some of the key results are as follows. First, the present findings confirmed the long-run cointegration among variables. Second, the finding showed significant long-term negative nexus between HEEXP and CO\u003csub\u003e2\u003c/sub\u003ee. 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\u003csub\u003e2\u003c/sub\u003ee. Fourth, the results indicated that there exist a bi-directional causality between EC and CO\u003csub\u003e2\u003c/sub\u003ee; FDI and CO\u003csub\u003e2\u003c/sub\u003ee; GDP and CO\u003csub\u003e2\u003c/sub\u003ee; POP and CO\u003csub\u003e2\u003c/sub\u003ee and HEEXP, and CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e","manuscriptTitle":"Do Higher Education Research and Development Expenditures affect Environmental Sustainability? New Evidence from Thirty-One Chinese Provinces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-05 20:18:36","doi":"10.21203/rs.3.rs-358931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2021-05-13T07:24:22+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-04T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-04T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-03-25T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-03-23T11:36:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a55bc13-5ec1-4b7a-9270-f0f8baac038b","owner":[],"postedDate":"April 5th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3424968,"name":"Environmental Engineering"},{"id":3424969,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-08-17T16:09:40+00:00","versionOfRecord":{"articleIdentity":"rs-358931","link":"https://doi.org/10.1007/s11356-021-14685-w","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2021-07-07 15:01:25","publishedOnDateReadable":"July 7th, 2021"},"versionCreatedAt":"2021-04-05 20:18:36","video":"","vorDoi":"10.1007/s11356-021-14685-w","vorDoiUrl":"https://doi.org/10.1007/s11356-021-14685-w","workflowStages":[]},"version":"v1","identity":"rs-358931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-358931","identity":"rs-358931","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}