When Nationalism Meets Environmentalism: An Inverted U-Curve Linking National Sentiment to Carbon Emission Intensity Through an Institutional Lens

When Nationalism Meets Environmentalism: An Inverted U-Curve Linking National Sentiment to Carbon Emission Intensity Through an Institutional Lens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article When Nationalism Meets Environmentalism: An Inverted U-Curve Linking National Sentiment to Carbon Emission Intensity Through an Institutional Lens Ling Gao, Deyin Zhang, Mengyao Xia, Helen Cai, Xuelian Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7693117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Against the backdrop of rising corporate carbon emissions, managerial attributes are garnering increasing scholarly scrutiny. Utilizing a panel dataset from China’s A-share market between 2010 and 2023, this paper employs an institutional theory perspective to examine the relationship between national sentiment and carbon emission intensity. We reveal a significant inverted U-shaped relationship between national sentiment and carbon emission intensity. Meanwhile, we also find that market competition negatively moderates the relationship between national sentiment and carbon emission intensity, whereas emission reduction target constrains and environmental penalties positively moderate it. Moreover, corporate social responsibility and green investment act as significant mediating channels in this relationship. Furthermore, national sentiment significantly influences enterprises characterized by high pollution, low subsidies, or low R&D intensity, and its impact on green patent, encompassing both green invention and green utility models still exhibits a significant inverted U-shaped relationship. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences National sentiment Carbon emission intensity Institutional theory Corporation social responsibility Green investment Figures Figure 1 Figure 2 Introduction Greenhouse gas emissions have exerted profound impacts on socio-economic systems and human well-being. According to the IPCC Sixth Assessment Report, human-induced drivers have elevated mean global temperatures by about 1.1 °C relative to pre-industrial times. This progression toward critical climate tipping points underscores an escalating existential threat to societal stability and survival. According to the World Wildlife Fund’s Living Planet Report 2024 (WWF, 2024) , climate change has been directly responsible for a 73% decline in global wildlife populations over the past five decades. Simultaneously, rising extreme temperatures are constraining human habitable zones and instigating significant migration movements (Reichman, 2022) . To solve these problems, governments worldwide have intensified their focus on climate issues and reinforced their commitments to enhancing policies aimed at emissions reduction. The implementation of corporate emission reduction strategies reflects not only managerial insight and strategic planning (López-Manuel et al., 2023) but also serves as a primary driver for mitigating emissions. Empirical research indicates that approximately two-thirds of global industrial CO₂ and CH₄ emissions originate from corporate activities (Ekwurzel et al., 2017) . Consequently, investigating corporate emission reduction mechanisms is critical for alleviating global climate pressures. Companies are increasingly integrating carbon management into their strategic planning and operational processes to mitigate their carbon footprint effectively. This commitment to decarbonization is disseminated throughout the organization via managers’ perceptual awareness, visionary foresight, and structured planning (Arruñada and Vázquez, 2013) .. Scholars find that institutional ownership (Benlemlih et al., 2023) and board diversity (Oussii and Jeriji, 2025) are associated with enhanced carbon management performance. Of course, advancements in the digital economy (Li et al., 2025a) , digital finance (Lu et al., 2023) , and environmental technologies (Şahin et al., 2025) , as well as R&D and innovation activities (Zhu and Wang, 2025) —particularly those focused on green technology (Khan et al., 2025) —along with increased green investment (Zheng and Jin, 2023) , industrial structure upgrading (Cheng et al., 2025) , and more efficient technology factor markets (Zhou et al., 2025) , have been shown to reduce carbon emission intensity. Moreover, institutional factors are also essential in carbon emission intensity. Including institutional quality (Khan et al., 2022) , institutional openness (Guo and Wang, 2023) , and stringency of environmental regulations (Liu et al., 2024) , these formal institutions have been demonstrated to mitigate corporate carbon emissions effectively, acting as critical policy instruments for governments promoting emission reductions worldwide. Simultaneously, informal institutions warrant considerable attention, and national sentiment has emerged as a distinctive informal institutional force, garnering growing academic interest and increasingly being integrated into business practices (Wang and Li, 2025) . National sentiment functions as a catalyst for corporate economic activities, affecting foreign direct investment (Li et al., 2019) , facilitating trade flows (Dow and Cuypers, 2024) , promoting green innovation (Wang et al., 2025a) , and boosting corporate ESG performance (Tan et al., 2025) . Whereas, it is managers who are instrumental in spearheading and integrating strategic carbon reduction initiatives within the corporate framework. Firms with greater CEO power (Luong et al., 2025), higher board independence, and dedicated environmental committees (Elsayih et al., 2021) are associated with reduced carbon emissions, but managerial myopia (Xie et al., 2025) increases emission levels. While extant literature has widely investigated the influence of managerial characteristics on corporate carbon behavior, little attention has been paid to their role in emissions reduction through the lens of informal institutions, such as national sentiment. Given the importance of informal institutional forces in carbon reduction, this study explores the nonlinear effects of national sentiment on carbon emission intensity. Specifically, we analyze how managers, amid dual pressures for economic legitimacy and low-carbon legitimacy, shape their firms’ emission reduction strategies in response to nuanced national sentiment impulses. This study offers three contributions to the existing literature. Firstly, we present novel evidence for a nonlinear inverted U-shaped relationship between national sentiment and carbon emission intensity, filling the theoretical gap of micro managers' cognition on corporate environmental behavior and provides a new theoretical entry point for the study of corporate carbon emission drivers. Secondly，we explore the theoretical perspective of duality of institutional theory, and verify the dual intermediary channels based on the dual perspectives of economic legitimacy and low-carbon development legitimacy. This not only provides a specific path for the transformation of the black box between national sentiment and carbon emission intensity, but also deepens the understanding of the internal mechanism between the two. Thirdly, we clarify the boundary conditions of national sentiment on carbon emission intensity, and refine the mechanism layer by layer from regulatory scenarios, normative systems, and coercive means. This enriches the application of institutional theory in the field of environmental management, and also providing a multi-level and differentiated policy perspective for the low-carbon development of enterprises. The remainder of this paper is structured as follows. Section 2 reviews the background literature and develops hypotheses. Section 3 describes the data and methodology. Section 4 presents the empirical results and analysis. Section 5 concludes with discussions. Background literature and hypotheses Institutional theory Institutional theory illuminates how organizational environments’ institutionalization drives homogeneous practice adoption (Aronson and LaFont, 2020). With the study of institutional isomorphism and decoupling grew into a substantial body of research, how individual or institutional entrepreneurs initiate institutional change and devise strategies for transforming individual agency into collective agency is beginning to attract the attention of scholars (Eitrem et al., 2024). Furthermore, institutional embeddedness fundamentally shapes institutional entrepreneurship and strategic agency (Battilana et al., 2009), where the interplay of habitual and reflexive agency elucidates institutional change trajectories (Lounsbury et al., 2021). Especially, confronting governmental policy enforcement, market uncertainties, and developmental imperatives, scholars increasingly examine the causal nexus between environmental compliance, corporate environmental practices, and firm performance through institutional theoretical lenses (Gupta and Gupta, 2021). Moreover, scholarship increasingly applies institutional theory into new fields, such as corporate social responsibility (CSR), revealing how regulatory, normative, and cognitive institutional elements shape CSR practices (Brammer et al., 2012), which is the consequences of value-driven agent selection and institutional structure (Risi et al., 2023). Then, institutional contexts fundamentally determine the form and content of geographically bounded CSR practices (Matten and Moon, 2020), compelling firms to engage in such activities to address legitimacy expectations. The moral legitimacy of these CSR initiatives is contingent upon CEO-driven stakeholder considerations manifested through responsible business conduct (Castelló and Lozano, 2011). Scholars increasingly examine how individual and collective actors agentically respond to institutional contexts. Specific CSR activities—including charitable donations and certification standards—mitigate institutional pressures (Iatridis et al., 2016; Wang et al., 2015), with behavioral change trajectories shaped by institutional process diversity (Oliver, 1991). Concurrently, firms experience compelling institutional forces: internal commitments (top management ethos, organizational culture) and external pressures (regulatory imperatives, competitive mimicry), collectively driving enhanced ethical and social responsibility (Ng et al., 2022). Drawing on institutional theory, we examine how normative and moral legitimacy imperatives for corporate decarbonization conduct shape the strategic guidance derived from patriotic corporate ethos. National sentiment and corporate carbon emission intensity As an organization embedded in the institutional environment, the behavior of enterprises is subject to both formal and informal systems (Berrone et al., 2022). Although different from the rigid constraints of formal systems, informal systems can influence organizational behavior choices through cultural identity and social expectations (Zhao et al., 2024). The specific cultural system of the enterprise is the essence of the formation and evolution of informal institutions (Pejovich, 2006). National sentiment focuses on the cultural values of the interests of the national community (Hertz, 2022), and as an important part of the cultural system, it affects the carbon emission behavior of enterprises through the legitimacy transmission mechanism of the informal system (Xu et al., 2025). When national sentiment is at a low level, the core of informal institutional pressures will fall on economic legitimacy, where corporate cultural values prioritize economic benefits and production expansion, leading to increased carbon emissions (Rottner and von Graevenitz, 2024). When a certain critical value is broken, the informal system will reconstruct the social consensus, and the corporate culture values will shift from economic legitimacy to low-carbon development legitimacy (Roh et al., 2025), thereby reducing carbon emissions. Therefore, we propose the following hypothesis: H1: There is an inverted U-shaped relationship between national sentiment and corporate carbon emission intensity. The effect of the market competition on the relationship between national sentiment and carbon intensity Enterprises perpetually competing for scarce market resources to drive their growth(Deng et al., 2024), market competition serves as the “testing ground” for this contest, intensifying the perception of resource scarcity and directly shaping the strategic choices and operational boundaries of firms as they vie for critical resources. The development of digitalization has given rise to a new form of corporate competition (Ahn, 2020). The accumulation of digital technologies within enterprises confers a competitive advantage on those firms that strategically emphasize digital production (Ye et al., 2023). Fiercely competitive environment will stimulate firms’ willingness to engage environmentally to prioritize economic gains (Sun et al., 2025), during periods of diminished national sentiment, subdued market competition fosters a more lenient institutional environment, which in turn dampens profit-seeking incentives and leads to a reduction in green innovation performance (Shi et al., 2025). When national sentiment exceeds a critical threshold, although green legitimacy gains recognition, the low-competition environment reduces firms’ pressure to seek market advantages through low-carbon practices. Consequently, enterprises may prioritize demonstrating national allegiance over pursuing stable growth, leading to a dilution of carbon reduction targets and the crowding out of green investments. Moreover, in a low-competition environment, market signals such as low-carbon and green demand remain weak, and corporate low-carbon behavior faces insufficient external institutional constraints, which further contributes to a decline in emission reduction investment. H2: Market competition negatively moderates the inverted U-shaped relationship between national sentiment and carbon emission intensity. The effect of the emission reduction target constraints on the relationship between national sentiment and carbon intensity The implementation of the emission reduction targets has significantly reduced carbon intensity, a success that is directly attributable to the target setting (Yao et al., 2019). Emission reduction is a binding target, which compels firms to boost R&D investments, cultivate low-carbon and energy-saving technologies, and enhance production efficiency (Pan et al., 2021). In the initial stage of national sentiment, stronger emission reduction targets tend to align local policy priorities with economic growth and industrial expansion, leading to a positive association with firms’ carbon emission intensity. However, once national sentiment surpasses a certain threshold, the presence of clear emission reduction targets strengthens institutional constraints. Beyond this point, heightened national sentiment motivates local governments and firms to prioritize emission reduction goals and sustainable development, thereby promoting concrete decarbonization practices and reducing carbon emission intensity (Du and Li, 2023). Therefore, we put forward the following hypothesis: H3: Emission reduction target constraints positively moderate the inverted U-shaped relationship between national sentiment and corporate carbon emission intensity. The effect of the environmental penalties on the relationship between national sentiment and carbon intensity Environmental penalties, as a consequence of stringent environmental regulation, particularly for polluting enterprises, impose substantial financial costs and operational disruptions through fines and production suspensions (Guedhami et al., 2025). During the initial phase of national sentiment, such penalties tend to encourage corporate greenwashing behavior (Zhou et al., 2024), firms may internalize environmental penalties as operational expenses, a rationalization that can perversely incentivize output expansion to offset incurred costs, and leading to higher aggregate emissions in pursuit of profit maximization. As firms place growing emphasis on institutional legitimacy, environmental penalties effectively curb greenwashing practices among both growing and mature enterprises (Li et al., 2025b). By positively moderating environmental preferences, such penalties stimulate substantive green innovation (Ma et al., 2025a), which in turn enhances the quality of environmental information disclosure (Hu and Xu, 2025) and improves environmental performance (Li et al., 2024). Moreover, augmenting penalty severity contributes to stronger synergistic effects in carbon emission reduction (Zhao et al., 2025). The public disclosure of environmental penalties also introduces the threat of social sanctions, compelling legitimacy-seeking firms to intensify their emission reduction efforts. Based on this reasoning, we propose the following hypotheses: H4: Environmental penalties positively moderates the inverted U-shaped relationship between national sentiment and carbon emission intensity. The effect of corporate social responsibility on the relationship between national sentiment and carbon intensity Corporate social responsibility constitutes a strategic imperative (Ginder et al., 2025), embodying the integration of economic, social, and environmental performance within a triple bottom line framework (Dang et al., 2022), and emerges as an outcome shaped by the confluence of agent selection and institutional structure (Risi et al., 2023).. The theoretical support for CSR as a mediator in the curvilinear relationship between national sentiment and carbon emission intensity rests on three key effects: (a) an inverted U-shaped relationship between national sentiment and corporate carbon emission intensity (consistent with H1); (b) a U-shaped relationship between national sentiment and CSR; and (c) a negative linear relationship between CSR and corporate carbon emission intensity. National sentiment serves as an institutional mechanism through which the state directs corporate ethical conduct; it fosters a profound sense of cultural identity and generates internal motivation for the fulfillment of corporate social responsibilities. Ethical behaviors relate to national sentiment are rooted in the country’s history, traditions, and modern cultural framework, and both expand and strengthen corporate social responsibility. National sentiment functions as an institutional mechanism through which the state guides corporate ethical conduct, fostering a profound cultural identity and generating intrinsic motivation for the fulfillment of social responsibilities. Ethically significant behaviors linked to national sentiment are embedded in the nation’s historical heritage, traditions, and contemporary cultural framework, thereby reinforcing and expanding the scope of corporate social responsibility (Yashalova et al., 2021). During the initial phase of rising national sentiment, firms tend to prioritize profit maximization. Although CSR consciousness persists, elevated cost constraints diminish their impetus to undertake substantive CSR initiatives. Consequently, at this early stage, intensified national sentiment may inhibit rather than promote corporate investment in CSR practices, thereby contributing to a decline in overall CSR performance. When national sentiment surpasses a critical threshold, firms encounter intensified institutional pressures from regulatory and normative systems (Lee et al., 2024), leading socio-environmental performance to elevate its priority above traditional economic objectives. Under such conditions, enterprises reorient their strategic focus toward improving social and environmental outcomes, thereby reinforcing their CSR commitments. Consequently, during this phase, heightened national sentiment is associated with strengthened corporate social responsibility. Regarding the relationship between CSR and corporate carbon emission intensity, extensive empirical research indicates a significant negative linear relationship between CSR and carbon emission intensity (Chen, 2023; Zhang et al., 2022).. Based on the foregoing analysis, the following hypothesis is proposed： H5: Corporate social responsibility mediates the inverted U-shaped relationship between national sentiment and carbon emission intensity. The effect of green investment on the relationship between national sentiment and carbon intensity Green investment embodies the significant influence of the institutional environment on capital allocation, serving as a form of institutional pressure that aligns financial objectives with environmental and social goals (Alsagr and Ozturk, 2024), which integrates environmental, social, and economic considerations as the “triple bottom line” (Guo and Zhao, 2025). The theoretical foundation for green investment as a mediator in the curvilinear relationship between national sentiment and corporate carbon emission intensity is predicated on three interrelated effects: (a) national sentiment exhibits an inverted U-shaped association with carbon emission intensity (as hypothesized in H1); (b) national sentiment demonstrates a U-shaped relationship with green investment; and (c) green investment is linearly and negatively associated with corporate carbon emission intensity. As for the relationship between national sentiment and corporate green investment. In its initial phase, rising nationalism encourages firms to prioritize economic returns, promoting economic autonomy (Liu et al., 2025), leading to a reallocation of scarce resources toward profit-driven activities and a concomitant crowding-out of environmental investments. However, at more advanced stages, as national sentiment becomes institutionalized within societal norms and expectations of corporate responsibility, firms face heightened institutional pressures—from governmental, public, and normative sources—that compel a shift toward greater social and environmental engagement (Pan et al., 2025). Consequently, green investment levels rise sharply as firms respond to these evolving demands. The relationship between green investment and carbon emissions has been the subject of considerable academic inquiry. A substantial body of research suggests that such investment significantly contributes to the reduction of carbon emissions (Ai and Yan, 2024; Kwilinski et al., 2024; Priyan, 2023; Zhong et al., 2024). Based on the above analysis, we put forward the following hypothesis: H6: Green investment mediates the inverted U-shaped relationship between national sentiment and carbon emission intensity. Building upon established theoretical foundations and prior literature, we construct the conceptual framework presented in Figure 1 to systematically examine the impact of national sentiment on carbon emission intensity. Data and Methodology Data resource To examine the relationship between national sentiment and carbon emission intensity, our analysis used an unbalanced panel dataset including 26135 observations from 3395 different firms for the period between 2010 and 2023, and firm level financial data is from CSMAR database, corporate social responsibility is from HEXUN’s net. We apply three standard filters: (1) excluding financially distressed firms (ST/*ST/PT), (2) omitting financial firms due to their distinct accounting frameworks, and (3) eliminating observations with missing data for key variables. Accordingly, the final sample comprises 3,395 firms with 26,135 firm-year observations. Dependent variables Corporate carbon emission intensity ( CI ), is quantified as the ratio of its carbon dioxide emissions to operating revenue, expressed in metric tons of CO₂ per 100 million yuan (Song et al., 2025 ). And the carbon dioxide emission is defined as the sum of combustion and fugitive emissions, production process emissions, waste emissions, and emissions from anthropogenic land-use change, specifically forest-to-industrial land conversion. A higher CI value signifies greater environmental impact from emissions, implying proportionally greater potential for emission reduction.. Independent variables National sentiment ( Nationalism ), a standardized metric for corporate nationalism was constructed by analyzing listed companies’ annual disclosures concerning expressions of patriotism, xenophobia, state nationalism, and corporate missions aligned with nationalist objectives (Wang et al., 2025a ). Elevated scores of Nationalism denote greater prevalence of rhetorical nationalism within the firm's discursive practices (Wang et al., 2025b ).. Moderating variables Market competition ( Mcompet ), is measure by Lena index. Lena Index is defined as the ratio of core operating profit to total operating revenue. A lower Lena Index value indicates higher market competition intensity, reflecting inverse proportionality between operational efficiency and competitive pressure.. Emission reduction target constraints ( Target ), using textual analysis of prefectural-level government work reports, we constructed a binary variable indicating whether municipal authorities established quantified emission reduction targets in a given year, coded as 1 if explicit numerical goals were specified and 0 otherwise. Environment penalty ( Epenalty ), constitutes administrative sanctions imposed by environmental protection agencies on regulated entities violating environmental statutes, with our dataset encompassing nationwide multi-tiered jurisdictions (provincial to county levels), and this metric quantifies coercive institutional pressures compelling corporate adherence to environmental standards (Hu and Xu, 2025 ). Mediating variables Corporate social responsibility ( Csr ), HEXUN.com's CSR ratings for listed companies. Specifically, HEXUN’s quantitative CSR metric, grounded in audited annual reports and stand-alone CSR disclosures, gauges realized rather than merely reported responsibility, thereby attenuating the typical divergence between symbolic disclosure and substantive performance (Karavitis et al., 2025 ). Green investment ( Ginvest ), is computed by aggregating environmental capital expenditures (pollution control, ecological governance, and clean production projects) from construction ledgers, scaling by period-end total assets, and applying min-max normalization. Ginvest simultaneously pursues environmental stewardship and economic resilience. Meanwhile, Ginvest ’s financial support—conventionally modeled as a mediating variable—augments internal capabilities, thereby elevating operational efficiency and environmental compliance (Haq et al., 2025 ). Control variables We firstly controlled for discretion, coded “1” if a CEO held the board chair position in the given year and “0” otherwise (Cannella and Lubatkin, 2017 ), because it may enhance the efficiency of strategic decision-making and strengthen the consistency of accountability, and promote the transformation of cognition into concrete actions for emission reduction (Jiang et al., 2022 ). We also included firm age , measured by the given year minus the firm’s founding year, Firm age plays a fundamental role in shaping the dynamics of institutional legitimacy acquisition, thereby contributing to a reduction in carbon emission intensity as the firm matures (Ma et al., 2025b ). The shareholding ratio of the largest shareholder is selected as its strategic decision-making control power and internalization mechanism of environmental responsibility (Slager et al., 2023 ). To control for the influence of board structure, we included the amount of board members, measured by the natural logarithm of the number of board members. It has been shown to influence corporate carbon intensity (Muktadir-Al‐Mukit and Bhaiyat, 2023 ). We also controlled for the amount of independent directors, because researched confirmed that boards with higher representation among independent directors are more likely to have lower carbon emissions (Elsayih et al., 2021 ). To control for firm performance, we measured debt level by total liabilities divided by total assets (Bi et al., 2025 ). Return on assets ( ROA ) is measured by dividing net profit by total assets. We also include corporate cash flow ratio, because this controls for the capacities of corporate’s green innovation and corresponding environmental protection (Makpotche et al., 2024 ). In summary, Table 1 provides the formal definitions of main variables. Table 1 Variable definitions and descriptions Variable Name Sign Measure Explained variable Carbon intensity CI We employ a natural logarithm transformation of corporate carbon intensity, calculated as the ratio of aggregate direct emissions—encompassing Scope 1 (combustion and fugitive), industrial process, waste management, and direct land-use change emissions—to total revenue, to evaluate emissions efficiency. Explanatory variable National sentiment Nationalism Nationalism was constructed by analyzing listed companies’ annual disclosures concerning expressions of patriotism, xenophobia, state nationalism, and corporate missions aligned with nationalist objectives. Moderating variables Market competition Mcompet The Lerner Index is calculated as the ratio of enterprise operating income minus operating costs, selling expenses, and administrative expenses, divided by operating income, reflecting the firm's pricing power relative to its marginal cost. Emission reduction target constraints Target Based on textual analysis of prefecture-level government work reports, we constructed a binary variable measuring the establishment of specific emission reduction targets. This indicator equals 1 if a municipal government explicitly specified quantitative environmental governance targets in its annual work report, and 0 otherwise. Environment penalty Epenalty National provincial, municipal and county environmental protection penalty case data. Mediating variables Corporate social responsibility Csr Hexun.com's CSR rating data for listed companies Green investment Ginvest Corporate green investment intensity is calculated by compiling pollution prevention, environmental remediation, ecological governance, and green production expenditures from construction in progress. This aggregate annual green investment is divided by year-end total assets and standardized to yield the final metric. Control variable Discretion Dual If a CEO held the board chair position in the given year and, the value is 1; otherwise, it is 0 Board members Director Natural logarithm of the number of board members Debt level Lev Total liabilities divided by total assets corporate cash flow ratio Cash Corporate cash ratio First The shareholding ratio of the largest shareholder Inde The proportion of independent directors Firm age Fag The time of the enterprise establish Return on assets Roa Net Profit divided by total assets Model analysis The following empirical model is estimated to test the study’s hypotheses: $$C{I_{it}}={\beta _1}Nationalis{m_{it}}+{\beta _2}Nationalis{m^2}_{{it}}+{\beta _3}\sum\limits_{{}}^{{}} {Control{s_{it}}} +{\mu _t}+{\gamma _i}+{\varepsilon _{it}}$$ 1 where subscripts i and t denote the firm and time period, respectively. CI it represent carbon emission intensity. Nationalism it refers to national sentiment. \(Contro{l_{it}}\) is a set of control variables. \({\mu _t}\) , \({\gamma _i}\) represent the year fixed effect and the company fixed effect. \({\varepsilon _{it}}\) is the regression residual term. To examine the moderating role of Mcompet , Target and Epenalty on the national sentiment and carbon emission intensity, we specify the Eq. ( 2 ) for analysis: $$\begin{gathered} C{I_{it}}{\text{=}}{\beta _{\text{0}}}{\text{+}}{\beta _1}Nationalis{m_{it}}{\text{+}}{\beta _2}Nationalis{m^2}_{{it}}{\text{+}}{\rho _{\text{1}}}Nationalis{m_{it}} \times Moderato{r_{it}} \hfill \\ {\text{ }}+{\rho _2}Nationalis{m_{it}}^{2} \times Moderato{r_{it}}+{\beta _3}\sum\limits_{{}}^{{}} {Control{s_{it}}} +{\mu _t}+{\gamma _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 2 where Moderator it is moderating effect of Mcompet , Target and Epenalty . Coefficient \({\rho _1}\) captures the linear interaction effect, while \({\rho _2}\) represents the quadratic interaction term.. \({\mu _t}\) and \({\gamma _i}\) respectively represent the year fixed effect and the company fixed effect. \({\varepsilon _{it}}\) is the regression residual term. To test the mediating pathways through which corporation social responsibility and green investment affect the nationalism and carbon emission intensity relationship, we construct the system of Eq. ( 3 )-( 4 ): $$Mediator{s_{it}}{\text{=}}{\beta _{\text{0}}}{\text{+}}{\beta _1}Nationalis{m_{it}}{\text{+}}{\beta _2}Nationalis{m^2}_{{it}}{\text{+}}{\beta _3}\sum\limits_{{}}^{{}} {Control{s_{it}}} +{\mu _t}+{\gamma _i}+{\varepsilon _{it}}$$ 3 $$\begin{gathered} C{I_{it}}{\text{=}}{\beta _{\text{0}}}{\text{+}}{\beta _1}Nationalis{m_{it}}{\text{+}}{\beta _2}Nationalis{m^2}_{{it}}{\text{+}}\delta Mediator{s_{it}} \hfill \\ {\text{ }}+{\beta _3}\sum\limits_{{}}^{{}} {Control{s_{it}}} +{\mu _t}+{\gamma _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 4 where Mediators it is mediating effect of Csr and Ginvest . \({\mu _t}\) and \({\gamma _i}\) respectively denotes year and firm fixed effects. \({\varepsilon _{it}}\) represents the residual term.. Results Descriptive statistics and correlation analysis Table 2 presents key descriptive statistics for the final sample. Variable CI ranges from 0 to 11,20 (mean = 9.502), while Nationalism exhibits a distribution from 0 to 19.41 with a substantially lower mean of 0.408. These results collectively indicate suboptimal Nationalism levels across firms, suggesting significant potential for enhancement relative to theoretical optima. Table 2 Descriptive statistics VARIABLES N Mean S.D Min Max CI 33073 9.502 1.085 0 11.20 Nationalism 47757 0.408 0.184 0 19.41 Nationalism 2 47757 0.002 0.0231 0 3.769 Mcompet 47936 -0.205 26.93 -3,404 4.730 Target 48466 0.0943 0.292 0 1 Epenalty 31094 850.8 1,203 1 6,391 Csr 31964 3.011 0.762 -4.605 4.509 Internal 43626 6.150 1.715 0 9.954 Cpolicy 48466 21.01 18.82 0 112 Ginvest 48466 0.959 3.857 0 23.53 Dual 37873 0.286 0.452 0 1 Director 47890 2.338 2.067 0 15 Lev 37873 0.362 0.253 0 4.026 Cash 37873 0.171 0.144 -0.165 1 First 44599 33.97 15.26 0.290 100 Inde 47889 3.163 0.611 0 8 Fag 47889 9.7169 7.5035 0 34 Roa 37873 0.0297 0.210 -30.69 7.445 Table 3 lists the correlations for all variables in our model. From the perspective of core variable correlation, the analysis reveals a correlation coefficient of 0.132 between Nationalism and CI , which is positively significant at the 1% level. This indicates a statistically strong positive relationship between national sentiment and corporate carbon emissions, thereby providing preliminary empirical support for subsequent main-effect tests. Table 3 Correlation analysis of main variable (1) (2) (3) (4) (5) (6) (7) (10) (11) (12) (13) (14) (15) (16) (17) (18) CI 1.000 Nationalism 0.132*** 1.000 Nationalism 2 0.009 0.778*** 1.000 Mcompet -0.005 0.003 0.000 1.000 Target -0.027*** 0.011** -0.002 -0.003 1.000 Epenalty -0.051*** -0.118*** -0.014** 0.004 0.002 1.000 Csr -0.141*** -0.059*** -0.040*** 0.031*** -0.019*** -0.021*** 1.000 Ginvest -0.090*** -0.021*** -0.004 0.003 0.022*** -0.025*** 0.006 1.000 Dual 0.133*** 0.007 -0.004 0.000 -0.013** 0.038*** -0.033*** -0.044*** 1.000 Director 0.074*** -0.048*** -0.010** 0.008* 0.003 0.029*** 0.050*** -0.007 0.126*** 1.000 Lev -0.371*** -0.147*** -0.010** 0.014*** 0.018*** 0.018*** -0.054*** 0.099*** -0.138*** -0.094*** 1.000 Cash 0.136*** 0.132*** 0.017*** -0.007 -0.012** 0.004 0.145*** -0.075*** 0.090*** 0.053*** -0.366*** 1.000 First -0.181*** 0.047*** 0.011** 0.011** 0.009* -0.002 0.154*** 0.044*** -0.039*** -0.207*** 0.032*** 0.034*** 1.000 Inde -0.238*** -0.060*** 0.001 0.004 0.015*** 0.008 0.121*** 0.024*** -0.134*** -0.007 0.087*** -0.056*** 0.031*** 1.000 Fag -0.304*** -0.097*** 0.001 -0.002 0.008 -0.022*** -0.069*** 0.063*** -0.243*** -0.340*** 0.369*** -0.238*** -0.078*** 0.121*** 1.000 Roa -0.005 0.027*** 0.003 0.024*** 0.004 -0.031*** 0.180*** 0.005 0.011** 0.039*** -0.075*** 0.088*** 0.050*** 0.007 -0.068*** 1.000 Baseline regression results Table 4 presents baseline regressions examining nationalism's impact on corporate carbon emission intensity (Model 1). Columns (1) and (3) estimate specifications without control variables while maintaining firm and year fixed effects. Column (1) reveals a statistically significant positive coefficient of 0.1408 (at the 1% level) on the linear patriotism term. Column (3) introduces a quadratic patriotism term, yielding a significantly negative coefficient of -2.4795 (at the 1% level). Columns (2) and (4) incorporate control variables, demonstrating sustained statistical significance: the linear term coefficient rises to 0.3646 (at the 1% level) in Column (2), while the quadratic term in Column (4) registers − 37.2397 (at the 1% level). The above results establish an inverted U-shaped relationship between nationalism and corporate carbon emission intensity, H1 is confirmed. From an institutional legitimacy perspective, we posit that firms demonstrating national sentiment is initially driven by socio-normative legitimacy to prioritize economic value creation as proof of national allegiance. This triggers resource reallocation toward capacity expansion, where carbon emission growth substantially outpaces potential mitigation efforts—manifested as the upward trajectory of the curve. However, beyond a critical legitimacy threshold, low-carbon ethical legitimacy restructures corporate responsibility paradigms toward “green contributions”, inducing the subsequent downward inflection of the curve. Regarding control variables, our analysis demonstrates that discretion, board structure characteristics and debt structure variables are all significantly reduce carbon intensity at the 1% significance level. Notably, cash ratio serve as robust indicators of financial resilience, while capital allocation exhibits significant carbon mitigation effects. In addition, we should not only focus on CEO dual, but also enhance awareness of the cost of environmental violations by increasing the shareholding of first shareholders. Of course, age brings higher compliance to enterprises., accumulated organizational memory forms institutional knowledge stocks in environmental management. Table 4 Baseline regression results CI (1) (2) (3) (4) Nationalism 0.1408 *** 0.3646 *** 0.4713 *** 0.9315 *** (0.0485) (0.1114) (0.0935) (0.1687) Nationalism 2 -2.4795 *** -37.2397 *** (0.6030) (10.4589) Dual -0.0451 ** -0.0448 ** (0.0193) (0.0192) Director -0.0215 *** -0.0206 *** (0.0055) (0.0054) Lev -0.2378 *** -0.2253 *** (0.0388) (0.0383) Cash -0.3450 *** -0.3531 *** (0.0533) (0.0532) First -0.0049 *** -0.0047 *** (0.0015) (0.0015) Inde -0.0638 *** -0.0619 *** (0.0192) (0.0190) Fag -0.0541 *** -0.0511 *** (0.0032) (0.0031) Roa -0.2263 -0.2231 (0.1423) (0.1431) Firm fixed effect YES YES YES YES Year fixed effect YES YES YES YES Cons 9.7522 *** 10.4940 *** 9.5907 *** 10.2783 *** (0.0312) (0.1218) (0.0496) (0.1185) N 32892 26135 32892 26135 R 2 0.0501 0.1093 0.0518 0.1123 F 82.8493 35.6321 79.8422 34.5097 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. To graphically demonstrate the established nonlinear relationship, we plot the fitted inverted U-shaped curve between nationalism and carbon emission intensity. As described in Fig. 2 , the statistically estimated turning point occurs at a nationalism level of 0.0125. Below this threshold, carbon emission intensity exhibits a positive association with nationalism. Conversely, when national sentiment exceeds 0.0125, carbon intensity demonstrates a significant negative relationship with patriotic orientation. Robustness tests Firstly, to further verify the robustness of the previously documented relationship between national sentiment and carbon emission intensity, we substitute the dependent variable with carbon performance—defined as the reciprocal of the natural logarithm of the ratio of carbon emissions to main business revenue. The regression results based on this alternative metric are reported in Table 5 . The results show a significant U-shaped relationship between nationalism and carbon performance. Given the inverse relationship between carbon performance and carbon emission intensity, these robustness tests employing carbon performance corroborate research hypothesis H1. Table 5 Robustness results of substituting the dependent variable with carbon performance Carbon performance (1) (2) Nationalism -1.0685 *** -1.9986 *** (0.1745) (0.1789) Nationalism 2 61.0957 *** (11.0663) Dual 0.0209 0.0204 (0.0228) (0.0226) Director 0.0483 *** 0.0468 *** (0.0054) (0.0053) Lev 0.7827 *** 0.7622 *** (0.0557) (0.0549) Cash -0.1931 ** -0.1797 ** (0.0766) (0.0763) First 0.0057 *** 0.0054 *** (0.0017) (0.0016) Inde 0.1208 *** 0.1177 *** (0.0217) (0.0214) Fag 0.0899 *** 0.0850 *** (0.0036) (0.0029) Roa 0.6918 * 0.6865 * (0.3724) (0.3737) Firm fixed effect YES YES Year fixed effect YES YES Cons 11.9871 *** 12.3410 *** (0.1504) (0.1209) N 26135 26135 R 2 0.3945 0.4009 F 197.1275 190.4870 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Secondly, we conducted a quadratic relationship form test, and the results are shown in Table 6 . the slope coefficient at the lower bound of patriotism is 0.9315 (statistically significant with positive sign at the 1% level), while at the upper bound it is -1444.97 (statistically significant with negative sign at the 1% level). The coefficient of overall test of presence of an inverse U shape is 3.56, and significant at the 1% level. It confirmed an inverted U-shaped relationship. Table 6 Quadratic relationship form test Items Lower bound Upper bound Interval 0 19.413 Slope 0.932 *** (5.522) -1444.97 *** (-3.560) Control variables YES YES Firm fixed effect YES YES Year fixed effect YES YES t-value 3.56 *** Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Thirdly, to mitigate potential confounding effects, we methodically incorporate six control variables into our regression specification. ①We add green cognition as control variable. We argue that managers with green cognition is a crucial factor for affecting carbon emission. In order to strip away the part of green cognition that affects corporate carbon emissions, and to more clearly identify and verify the net impact of patriotism on corporate carbon emissions, we introduce it into the model 1. In column (1), Greencogn has a significantly positive effect on CI , and the inverse U shape is stable. ②To isolate the causal channel of patriotism from corporate carbon governance frameworks, we construct a carbon management strategy index via textual analysis of annual reports—quantifying term frequencies (“low-carbon strategy”, “zero-carbon development” and etc.) to capture strategic intensity. Controlling for this institutionalized variable disentangles emission reduction behaviors attributable to structured environmental strategies versus patriotism-driven initiatives, thereby purifying the identified causal effect. As reported in Column (2) of Table 7 , the coefficient of Cmanstra demonstrates statistically significant explanatory power. Crucially, the inverted U-shaped relationship between national sentiment and carbon emissions persists after controlling for Cmanstra , with both linear and quadratic terms remaining significant at the 1% level. This robustness withstands competing explanations from corporate environmental governance, thereby providing confirmatory evidence for Hypothesis 1. ③Incorporating environmental governance investment ( Epi ) into Model 1, this study operationalizes corporate sustainability commitment through listed firms’ disclosed capital and technological allocations toward pollution control, emission abatement, and resource efficiency. Controlling for Epi disentangles competing causal pathways -environmental responsibility investments versus patriotism-driven initiatives-in explaining emission behaviors. Column (3) of Table 7 demonstrates that the inverted U-shaped relationship between national sentiment and carbon emission intensity persists with statistical significance (at the 1% level for linear/quadratic terms), thereby providing robust confirmatory evidence for Hypothesis 1 against environmental investment confounders. ④T o purify the causal effect of national sentiment on carbon abatement, we introduces an internal control variable that operationalizes corporate governance efficacy through production standardization, risk management efficiency, and compliance enforcement rigor. By controlling for this index—which mechanizes emissions reduction via energy process optimization, environmental oversight intensification, and resource allocation refinement — we disentangle institutional governance improvements from patriotism-driven initiatives, thereby isolating the net ecological impact attributable to patriotic motives. As evidenced in Column (4) of Table 7 , the inverted U-shaped relationship between patriotism and carbon emission intensity persists with statistical significance (at the 1% level for both terms) after accounting for internal control covariates, thereby reconfirming Hypothesis 1's robustness against corporate internal control confounders. ⑤To mitigate confounding from regional regulatory pressures, this study employs a provincial environmental pollution index constructed via entropy weighting — integrating wastewater discharge, industrial SO₂ emissions, and solid waste generation to quantify pollution load intensity. High values signify coercive institutional pressures that mechanically induce emission abatement through compliance costs and penalty risks, thus creating competing explanations for patriotism-driven reductions. Controlling for this variable disentangles externally regulatory pressures effects from patriotic motives. Column (5) of Table 7 demonstrates a statistically significant inverted U-shaped relationship between national sentiment and carbon emission intensity (at the 1% level for linear/quadratic terms) after controlling for environmental pollution covariates. ⑥To disentangle compliance-based abatement from patriotism-driven voluntary reductions, this study incorporates a carbon policy covariate constructed through textual mining of “carbon reduction emphasis” lexicons in corporate disclosures. This metric captures strategic responses to socio-regulatory legitimacy pressures, enabling isolation of emission effects attributable to external legitimacy demands from the impetus of patriotic motives. Column (6) of Table 7 confirms the persistent inverted U-shaped relationship between national sentiment and carbon emission intensity (at the 1% level for linear/quadratic terms) after controlling for carbon policy covariates. This robustness withstands socio-regulatory confounders, validating Hypothesis 1 against external normative pressure interference. Overall, as reported in Table 7 , the first-order term of Nationalism consistently exhibits positive coefficients significant at the 1% level, while its quadratic term shows negative coefficients statistically significant at the 1% level after adding control variables ( Greencogn , Cmanstra , Epi , Internal , EPindex and Carbon policy ). This robust pattern persists after controlling for these additional factors, thereby confirming the inverted U-shaped relationship between national sentiment and carbon emissions and further validating Hypothesis H1. Furthermore, carbon emissions exhibit positive associations with heightened managerial environmental awareness, elevated environmental pollution indices, and intensified carbon policies. Conversely, emissions demonstrate significant mitigation effects from enhanced carbon management strategies, increased environmental protection investments, and strengthened internal control systems. Table 7 The results of adding control variables Items CI (1) (2) (3) (4) (5) (6) Nationalism 0.8806 *** 0.9270 *** 0.9328 *** 1.0100 *** 0.9637 *** 0.8435 *** (0.1659) (0.1714) (0.1690) (0.1706) (0.1744) (0.1680) Nationalism 2 -35.0567 *** -36.1218 *** -37.3675 *** -38.0284 *** -37.6777 *** -32.7324 *** (9.0291) (9.9456) (10.5180) (9.9159) (10.3326) (8.6351) Greencogn 0.0011 ** (0.0005) Cmanstra -0.0129 ** (0.0064) Epi -0.0072 ** (0.0031) Internal -0.1459 *** (0.0342) EPindex 0.3528 * (0.1998) Carbon policy 0.0010 ** (0.0005) Dual -0.0297 -0.0328 * -0.0447 ** -0.0423 ** -0.0476 ** -0.0471 ** (0.0188) (0.0192) (0.0192) (0.0206) (0.0205) (0.0217) Director -0.0186 *** -0.0191 *** -0.0206 *** -0.0238 *** -0.0212 *** -0.0187 *** (0.0057) (0.0054) (0.0054) (0.0057) (0.0059) (0.0060) Lev -0.2273 *** -0.2072 *** -0.2265 *** -0.3484 *** -0.2353 *** -0.2383 *** (0.0371) (0.0388) (0.0382) (0.0545) (0.0411) (0.0413) Cash -0.3701 *** -0.3574 *** -0.3504 *** -0.2889 *** -0.4008 *** -0.3647 *** (0.0546) (0.0534) (0.0533) (0.0626) (0.0573) (0.0613) First -0.0044 *** -0.0052 *** -0.0047 *** -0.0044 *** -0.0046 *** -0.0048 *** (0.0015) (0.0014) (0.0015) (0.0015) (0.0016) (0.0017) Inde -0.0476 ** -0.0672 *** -0.0618 *** -0.0583 *** -0.0581 *** -0.0514 ** (0.0192) (0.0194) (0.0190) (0.0193) (0.0196) (0.0209) Fag -0.0512 *** -0.0511 *** -0.0507 *** -0.0547 *** -0.0479 *** -0.0536 *** (0.0034) (0.0031) (0.0031) (0.0033) (0.0043) (0.0031) Roa -0.4440 *** -0.2178 -0.2208 -0.5718 *** -0.1906 -0.1843 (0.0986) (0.1373) (0.1439) (0.1079) (0.1351) (0.1509) Cons 10.2385 *** 10.3126 *** 10.2750 *** 11.2670 *** 10.1270 *** 10.2926 *** (0.1146) (0.1156) (0.1185) (0.2547) (0.1570) (0.1252) Firm fixed effect YES YES YES YES YES YES Year fixed effect YES YES YES YES YES YES N 23621 24261 26135 23156 22522 18513 R 2 0.1076 0.1168 0.1127 0.1311 0.1168 0.1375 F 28.9198 32.2268 32.9783 33.4777 30.9078 28.6097 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Moderating effect Grounded in institutional theory, market competition constitutes the regulatory context for corporate emissions, carbon targets establish normative institutional orientations, and environmental penalties enforce coercive compliance. Consequently, we incorporate market competition, emission reduction target constraintsand environment penalty into model as the moderating variables. The regression results of Lena are shown in Table 8 . In Column (1), we introduce the interaction term Nationalism×Mcompet and Nationalism 2 ×Mcompet . The estimated coefficient of Nationalism×Mcompet is -0.9658, and is significantly negative at 5% level. The estimated coefficient of Nationalism 2 ×Mcompet is 84.4752, and is significantly positive at 10% level. We observe that attenuated market competition flattens the inverted U-shaped relationship between national sentiment and carbon emission intensity. This moderation effect arises as competitive environments foster technological diffusion and innovation incentives, whereas constrained competitive pressure engenders dual deficiency: diminished corporate motivation for economic gains and weakened initiative toward social accountability. Therefore, H2 is supported. Table 8 presents the results of the moderating effect of emission reduction target constraints on national sentiment and carbon emission intensity. In Column (2), we introduce the interaction terms of Nationalism×Target and Nationalism 2 ×Target . The regression coefficient of the Nationalism×Target is 0.3840, which is significantly positive at the 10% level. And the estimated coefficient of Nationalism 2 ×Target is -27.2525, which is significantly negative at the 10% level. This results demonstrate that Target steepen the carbon intensity curve through enhanced corporate decarbonization. Thus, hypothesis H3 is empirically substantiated. Table 8 shows the moderating results of environmental penalties. In Column (3), we introduce the interaction terms Nationalism×Epenalty and Nationalism 2 ×Epenalty . The estimated coefficient of Nationalism×Epenalty is 0.3176, which is significantly positive at the 1% level. And the estimated coefficient of Nationalism 2 ×Epenalty is -26.7371, which is significantly negative at the 5% level. It indicates that Epenalty induces a steepened gradient in the curve. And it likely stems from public disclosure of environmental penalties activates social sanction threats that force legitimacy-seeking firms to escalate emission cuts, providing empirical substantiation for Hypothesis 4. Table 8 Moderating effect results (1) (2) (3) Mcompet Target Epenalty Nationalism 1.0625 *** 0.8900 *** 0.8054 *** (0.1917) (0.1530) (0.1124) Nationalism 2 -48.9925 *** -33.5460 *** -25.8550 *** (14.3443) (8.8956) (5.0723) Nationalism×Moderator -0.9658 ** 0.3840 * 0.3176 *** (0.4034) (0.2173) (0.1045) Nationalism 2 ×Moderator 84.4752 * -27.2525 * -26.7371 ** (48.8914) (14.8755) (12.0937) Moderator 0.2385 *** -0.1168 -0.0670 *** (0.0842) (0.0723) (0.0234) Dual -0.0456 ** -0.0447 ** -0.0260 * (0.0192) (0.0192) (0.0158) Director -0.0207 *** -0.0205 *** -0.0149 *** (0.0054) (0.0054) (0.0043) Lev -0.2186 *** -0.2241 *** -0.1637 *** (0.0375) (0.0383) (0.0324) Cash -0.3610 *** -0.3527 *** -0.3690 *** (0.0538) (0.0532) (0.0584) First -0.0029 *** -0.0047 *** -0.0063 *** (0.0010) (0.0015) (0.0009) Inde -0.0615 *** -0.0618 *** -0.0552 *** (0.0190) (0.0190) (0.0152) Fag -0.0515 *** -0.0511 *** -0.0615 *** (0.0031) (0.0030) (0.0138) Roa -0.2259 -0.2216 -0.2302 *** (0.1448) (0.1434) (0.0523) Firm fixed effect YES YES YES Year fixed effect YES YES YES Cons 10.1772 *** 10.2884 *** 10.4157 *** (0.1098) (0.1155) (0.2264) N 26135 26135 16821 R 2 0.1170 0.1126 0.0842 F 34.5034 30.5832 57.6166 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Mediating effect We respectively explored the channels through which national sentiment affects the carbon intensity of enterprises under the legitimacy of social norms and the legitimacy of low-carbon morality. Thus, corporate social responsibility and green investment as mediating variables. Table 9 reports the regression results between Nationalism and CI . In column (1), Nationalism shows a statistically significant coefficient of -0.5035 at the 1% level. In linear relationships, Nationalism has a significant negative impact on Csr . In Column (2), Nationalism presents a U-shaped relationship with Csr. Nationalism will lower the Csr first, and then increase the Csr after the lowest threshold. In Column (3), the coefficient of Csr is -0.0284 and is significant at the 1% level. Simultaneous, the inverted U-shaped relationship of Nationalism to CI is significant. It indicates that Csr has a partly mediating effect of Nationalism and CI . Hypothesis 5 has been confirmed. Therefore, during economic expansions firms with national sentiment strategically reduce social responsibility expenditures; however, they subsequently increase investments in carbon emission mitigation to address legitimacy pressures arising from evolving societal norms. Then, we explore the role of green investment as a channel for Nationalism and CI . In column (4), Nationalism shows a statistically significant coefficient of -0.4329 at the 1% level. In linear relationships, Nationalism has a significant negative impact on Ginvest . In Column (5), Nationalism presents a U-shaped relationship with Ginvest. Nationalism will lower the Ginvest first, and then increase the Ginvest after the lowest threshold. In Column (6), the coefficient of Ginvest is -0.0075 and is significant at the 1% level. Simultaneous, the inverted U-shaped relationship of Nationalism to CI is significant. It indicates that Ginvest has a partly mediating effect of Nationalism and CI . Hypothesis 6 has been confirmed. From a low-carbon legitimacy perspective, firms initially reduce green resource allocation to prioritize economic development, but ultimately increase investments in environmental initiatives to fulfill legitimacy requirements. Table 9 Mediating effect results (1) (2) (3) (4) (5) (6) Csr Csr CI G invest G invest CI Nationalism -0.5035 *** -0.7290 *** 0.9150 *** -0.4329 ** -0.9185 ** 0.9397 *** (0.0821) (0.0986) (0.0849) (0.1838) (0.3799) (0.1680) Nationalism 2 16.7391 *** -34.9456 *** 6.3400 ** -37.6280 *** (3.9923) (4.4573) (2.7002) (10.2804) Csr -0.0284 *** (0.0068) Green invest -0.0075 *** (0.0024) Dual -0.0156 -0.0157 -0.0570 *** -0.0459 ** (0.0175) (0.0175) (0.0136) (0.0192) Director 0.0240 *** 0.0237 *** -0.0188 *** -0.0200 *** (0.0045) (0.0045) (0.0035) (0.0055) Lev -0.1722 *** -0.1757 *** -0.2544 *** -0.2266 *** (0.0374) (0.0375) (0.0264) (0.0386) Cash 0.4364 *** 0.4400 *** -0.3577 *** -0.3613 *** (0.0489) (0.0488) (0.0424) (0.0539) First 0.0070 *** 0.0070 *** -0.0048 *** -0.0030 *** (0.0010) (0.0010) (0.0006) (0.0010) Inde 0.0266 * 0.0261 -0.0622 *** -0.0619 *** (0.0159) (0.0159) (0.0118) (0.0190) Fag -0.0329 *** -0.0338 *** -0.0543 *** -0.0514 *** (0.0031) (0.0031) (0.0035) (0.0031) Roa 0.4268 0.4267 -0.0604 -0.2252 (0.3143) (0.3142) (0.0563) (0.1439) Firm fixed effect YES YES YES YES YES YES Year fixed effect YES YES YES YES YES YES Cons 3.1318 *** 3.2093 *** 10.3814 *** -0.0596 0.2018 10.2104 *** (0.0866) (0.0864) (0.0727) (0.2707) (0.3931) (0.1080) N 31707 31707 22291 37551 37551 26135 R 2 0.1004 0.1007 0.1054 0.0100 0.0102 0.1119 F 69.9533 67.3958 108.0643 17.4692 8.2444 33.1945 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Further research Considering firm with high-carbon emission industries are more sensitive to legality constraints, we classify companies in industries such as metal smelting, mining, petroleum processing, electricity and heat production and supply, textiles, papermaking, chemical manufacturing, rubber manufacturing and leather as high carbon emitters, and examine the impact of Nationalism on CI under the enterprises with different degrees of carbon dependence. The regression results are reported in in Table 10 . In Column (1), the coefficients of Nationalism are 0.6511 and − 26.0685, and are significant at the 1% level. It shows that in high-carbon emissions firms there is a statistically significant inverted U-shaped relationship between national sentiment and carbon emission intensity, whereas this relationship is insignificant in low-carbon emissions firms in column (2). This divergence may arise from high-carbon emissions firms facing stringent regulatory constraints where emission levels directly determine organizational survival legitimacy, so enabling national sentiment to drive mitigation behaviors. Secondly, government subsidy intensity manifests the institutional conversion efficacy whereby public resources transform into corporate endowments, we categorize sample firms into high- and low-subsidy groups based on the government subsidy mean. The regression outcomes for high-subsidy firms are reported in Column (3), while those for low-subsidy firms appear in Column (4). The coefficient of Nationalism for high-subsidy enterprises is 1.1826 (significantly positive at the 5% level), and the coefficient of Nationalism 2 is -39.9890 (significantly negative at the 10% level). Whereas, the coefficient of Nationalism for low-subsidy firms is 0.5923 (significantly positive at the 1% level), and the coefficient of Nationalism 2 is -27.3020 (significantly negative at the 1% level). Although both low- and high-subsidy firms exhibit an inverted-U relationship between national sentiment and carbon emission intensity, the inverted-U pattern demonstrates stronger statistical significance among low-subsidy firms. This difference likely origins from that resource-constrained low-subsidy enterprises institutionalize patriotism as a legitimacy tool for rigid regulatory compliance, thereby amplifying policy-response significance through passive conformity. Thirdly, R&D investment level signifies the institutional pathway toward sustainable development through innovation, enterprises are stratified into high-R&D investment and low-R&D investment groups based on whether their R&D intensity exceeds the sample mean. For low-R&D investment firms (Column (6)), the linear Nationalism coefficient (0.9842) is positive and significant at the 1% level, while the quadratic term (-38.5057) shows negative significance at 1%. While low-R&D investment firms exhibit a significant inverted U-shaped relationship between national sentiment and carbon emission intensity, this pattern is absent among high-R&D counterparts. This difference might spring from innovation-constrained low-R&D firms initially increasing emissions due to resource limitations, then internalizing national sentiment through environmental governance to pursue institutional legitimacy, which cumulatively drives emission reductions. In contrast, high-R&D firms’ abundant innovation resources make this relationship disappear. Given this result, we consider that high R&D investment may flow into non-green areas, while low R&D investment may also focus on green technologies. Therefore, we further analyzed companies’ capabilities in green technology reserves and transformation. The results are reported in the Appendix . We find that nationalism has an inverted U-shaped impact on carbon emissions only in enterprises with low-application in green patents, low-application in green invention patents, and low-application in green utility model patents. This result allows us to exclude the special case of low R&D but high proportion of green R&D. Table 10 Regression results Items (1) (2) (3) (4) (5) (6) High-carbon emission enterprises Low-carbon emission enterprises High-subsidies firms Low-subsidies firms High R&D investment Low R&D investment Nationalism 0.6511 *** 1.2065 ** 1.1826 ** 0.5923 *** -0.4448 0.9842 *** (0.1660) (0.5193) (0.5014) (0.1371) (0.7108) (0.1767) Nationalism 2 -26.0685 *** -74.3567 -39.9890 * -27.3020 *** 47.3284 -38.5075 *** (7.0519) (49.9983) (22.1820) (8.4612) (68.2285) (10.6247) Dual -0.0575 ** -0.0163 -0.0398 -0.0217 0.0512 -0.0526 ** (0.0250) (0.0253) (0.0504) (0.0160) (0.0409) (0.0210) Director -0.0228 *** -0.0179 ** -0.0482 *** -0.0134 *** -0.0085 -0.0218 *** (0.0065) (0.0074) (0.0177) (0.0051) (0.0157) (0.0058) Lev -0.1404 ** -0.2252 *** -0.2972 *** -0.1781 *** 0.0773 -0.2319 *** (0.0556) (0.0494) (0.0848) (0.0400) (0.1213) (0.0418) Cash -0.1792 ** -0.4232 *** -0.0610 -0.2668 *** -0.1518 -0.3382 *** (0.0746) (0.0695) (0.2285) (0.0512) (0.1207) (0.0593) First -0.0058 *** -0.0021 0.0027 -0.0043 *** -0.0012 -0.0046 *** (0.0021) (0.0019) (0.0034) (0.0015) (0.0035) (0.0015) Inde -0.0788 *** -0.0421 -0.0128 -0.0436 ** -0.0158 -0.0707 *** (0.0232) (0.0264) (0.0295) (0.0180) (0.0392) (0.0202) Fag -0.0509 *** -0.0497 *** -0.0738 *** -0.0388 *** -0.0217 ** -0.0533 *** (0.0036) (0.0048) (0.0130) (0.0028) (0.0106) (0.0032) Roa -0.1457 -0.3220 *** -0.6249 * -0.1267 -0.1877 -0.2091 (0.1693) (0.1062) (0.3218) (0.1155) (0.1785) (0.1513) Cons 10.3968 *** 10.0768 *** 9.1391 *** 10.3524 *** 10.2131 *** 10.2810 *** (0.1462) (0.1903) (0.3840) (0.1130) (0.2807) (0.1262) Firm fixed effect YES YES YES YES YES YES Year fixed effect YES YES YES YES YES YES N 10639 15496 4297 21838 2682 23453 R 2 0.1182 0.0975 0.1604 0.0677 0.0385 0.1217 F 18.5844 18.8840 19.4178 20.4821 2.0570 35.1289 Note: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively. Conclusions and discussion Conclusions This study investigates the relationship between national sentiment and carbon emission intensity using a panel dataset comprising 26,135 observations spanning the period from 2010 to 2023 through institutional lens. Our findings reveal an inverted U-shaped relationship between national sentiment and carbon emission intensity. During periods of weakened national sentiment, firms—under pressure to secure economic legitimacy—are more likely to prioritize economic benefits and development; this stands in consist with the extant literature which demonstrates that strong national sentiment enhances corporate green innovation (Wang et al., 2025b) and ESG performance (Arı and Sarıoğlu, 2025), which is aligned with the firm’s strategic economic interests and supports its long-term sustainable development. However, amid the realignment of social consensus, enterprises transition from pressures for economic legitimacy to those for low-carbon legitimacy, therefore driving active emission reduction efforts. This finding represents a novel contribution that not only broadens the theoretical underpinnings of national sentiment within the realm of corporate environmental behavior, but also yields fresh implications for the design and implementation of carbon mitigation strategies. Secondly, employing a dual analytical lens encompassing both market mechanisms and policy institutions, this study examines the impact of national sentiment on carbon emissions. We reveal that weaker market competition attenuates the inverted U-shaped relationship between national sentiment and carbon emissions (H2). Market competition, acting as an institutional buffer, it attenuates the direct effect of national sentiment on emission outcomes. then, policymakers can strategically shape the competitive landscape to achieve targeted reductions in carbon emissions. Meanwhile, we also find that emission reduction target constraints have a positive impact on the inverted U-shaped relationship between national sentiment and carbon emissions (H3). Emission reduction target constraints, as an institutional policy objective, intensify the linkage between national sentiment and carbon emissions. This mechanism provides a theoretical foundation for policymakers to formulate and adjust emission reduction targets in order to achieve desired climate outcomes. Moreover, our findings further indicate that environmental punishment significantly strengthens the inverted U-shaped relationship between national sentiment and carbon emissions (H4). We extend the institutional perspective on how national sentiment influences carbon emissions and offers a theoretical foundation for designing effective environmental penalty systems. Thirdly, from the dual perspectives of social-norm pressure and low-carbon moral pressure, this article examines the mediating roles of corporate social responsibility and green investment. Our findings demonstrate that firms indeed can shape carbon emissions by addressing both social and low-carbon legitimacy demands. This study examines the potential of social normative pressure to promote emissions reduction, mediated through CSR. National sentiment is found to exert a complex, U-shaped influence on CSR, ultimately encouraging firms to fulfill their social responsibilities, and CSR serves as a critical mechanism that opens new pathways for emission reductions. Both corporations and governments can achieve emission reduction objectives by strategically enhancing CSR engagement. Moreover, this paper also examines low-carbon normative pressure, which is operationalized through green investment. The relationship between national sentiment and green investment is found to be non-linear, following a U-shaped pattern. then, increased green investment facilitates progress toward carbon reduction goals. This offers a novel perspective for corporations seeking to enhance green investment by fostering national sentiment. Green investment serves as a pivotal mechanism for achieving emission reduction and holds strategic importance in advancing the sustainable development of the broader economy. Finally, building upon our initial findings, we further investigated the differential effects of national sentiment on carbon emissions across three distinct types of firms: those characterized by high pollutions, those receiving small government subsidies, and those with low research and development activities. ①In high-polluting enterprises, national sentiment exerts a significant influence on carbon emissions. High-polluting enterprises are regarded as critical targets for emission reduction efforts, and it implies that the degree of pollution intensity heightens the importance of informal institutions—such as national sentiment—in shaping corporate behavior. Then, national identity may inadvertently reinforce carbon-intensive practices. ②Meanwhile, we also find national sentiment exerts a significant influence in low subsidy enterprises. These findings indicate that low-subsidy enterprises are more inclined to leverage national sentiment as a strategy for market competition. Policy interventions should therefore aim to channel such sentiment toward emission reduction objectives through targeted incentives and regulatory guidance. At the corporate level, management should strengthen strategic integration by incorporating national sentiment into their core strategic planning, fostering constructive workplace initiatives that align national sentiment values with long-term corporate development and environmental sustainability. ③ Furthermore, this study also reveals a more pronounced association between national sentiment and carbon emissions in low-R&D enterprises, implying that such firms may leverage national sentiment to compensate for weaker innovation capacity in shaping green practices. This suggests that, within less innovation-driven contexts, national identity can become intrinsically entangled with emission outcomes. Therefore, emission reduction policies should target low-R&D firms through tailored guidance frameworks that translate national sentiment into tangible drivers of low-carbon innovation. Concurrently, corporate management should strategically align national sentiment with broader national strategies and long-term governance, ensuring it reinforces—rather than undermines—sustainable development objectives. ④What’s more, this paper further examine R&D output, national sentiment is significantly influence carbon emission in those with low levels of green patent applications, including both green invention patents and green utility model patents, This finding indicates that firms with low green patent applications may leverage more national sentiment in addressing environmental challenges. Policy interventions should target such firms by strategically channeling national sentiment toward the development of green patents through tailored incentives and institutional guidance. Meanwhile, corporate management ought to avoid superficial symbolic integration of national sentiment and instead focus on its substantive alignment with the development of green technology systems and R&D investments, thereby achieving synergy among national sentiment, green innovation, and emission reduction outcomes. Limitation future research This study is characterized by several limitations that point toward meaningful directions for future research. First, this study focuses exclusively on Chinese listed companies; therefore, generalizing the findings to unlisted firms and non-Chinese contexts would require further demonstration. These entities are often subject to different institutional pressures and governance structures, and may provide particularly relevant settings for examining the relationship between national sentiment and carbon emissions. Further research is warranted to explore this dynamic within cross-country comparative frameworks. Second, although this study investigates the influence of national sentiment from an institutional theory perspective, carbon emission intensity is likely shaped by the synergistic effects of multiple internal and external factors. Future research could thus examine how other determinants interact with national sentiment to jointly affect emission levels. Third, our findings identify CSR and green investment as mediating mechanisms between national sentiment and carbon emission intensity; however, other potential channels remain unexplored and warrant further investigation in future studies. Declarations Compliance with Ethical Standards: Funding: (1) Humanities and Social Science Foundation of the Ministry of Education of China, [24YJA630122]; (2) General Project of Social Science Fund of Fujian Province [FJ2024B114]; Conflict of Interest: No conflict exists Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors Informed consent: This article does not contain any studies with human participants performed by any of the Additional information: Correspondence and requests for materials should be addressed to Mengyao Xia. Author Contribution GL: Writing the original draft and reviewing. ZD:Reviewing and editing. XM:Conceptualisation, methodology, data curation. CH:Supervision.YX:Visualization Data Availability All data that support the findings of this study are available from the corresponding author upon reasonable request. References Ahn, S. J. (2020). 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2","display":"","copyAsset":false,"role":"figure","size":30557,"visible":true,"origin":"","legend":"\u003cp\u003eInverted U-shaped relationship between nationalism and carbon intensity\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7693117/v1/40e930d696d38a7c76727c43.jpg"},{"id":95523816,"identity":"f2da2ccb-e277-4bd9-8ada-851f22c41b12","added_by":"auto","created_at":"2025-11-10 10:01:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3504125,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7693117/v1/9dff00f5-ccf2-4d79-8b72-e157c4cd88d3.pdf"},{"id":95263068,"identity":"219ae11a-1a2f-4492-bb15-01f909babea4","added_by":"auto","created_at":"2025-11-06 05:16:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21095,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7693117/v1/fd4154539ddc202c6117ec24.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Nationalism Meets Environmentalism: An Inverted U-Curve Linking National Sentiment to Carbon Emission Intensity Through an Institutional Lens","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eGreenhouse gas emissions have exerted profound impacts on socio-economic systems and human well-being. According to the IPCC Sixth Assessment Report, human-induced drivers have elevated mean global temperatures by about 1.1 °C relative to pre-industrial times. This progression toward critical climate tipping points underscores an escalating existential threat to societal stability and survival. According to the World Wildlife Fund’s Living Planet Report 2024\u0026nbsp;\u003c/em\u003e\u003cem\u003e(WWF, 2024)\u003c/em\u003e\u003cem\u003e, climate change has been directly responsible for a 73% decline in global wildlife populations over the past five decades. Simultaneously, rising extreme temperatures are constraining human habitable zones and instigating significant migration movements\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Reichman, 2022)\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cem\u003eTo solve these problems, governments worldwide have intensified their focus on climate issues and reinforced their commitments to enhancing policies aimed at emissions reduction. The implementation of corporate emission reduction strategies reflects not only managerial insight and strategic planning\u0026nbsp;\u003c/em\u003e\u003cem\u003e(López-Manuel et al., 2023)\u003c/em\u003e\u003cem\u003e\u0026nbsp;but also serves as a primary driver for mitigating emissions. Empirical research indicates that approximately two-thirds of global industrial CO₂ and CH₄ emissions originate from corporate activities\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Ekwurzel et al., 2017)\u003c/em\u003e\u003cem\u003e. Consequently, investigating corporate emission reduction mechanisms is critical for alleviating global climate pressures.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompanies are increasingly integrating carbon management into their strategic planning and operational processes to mitigate their carbon footprint effectively. This commitment to decarbonization is disseminated throughout the organization via managers’ perceptual awareness, visionary foresight, and structured planning\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Arruñada and Vázquez, 2013)\u003c/em\u003e\u003cem\u003e..\u003c/em\u003e\u003cem\u003e\u0026nbsp;Scholars find that institutional ownership\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Benlemlih et al., 2023)\u003c/em\u003e\u003cem\u003e\u0026nbsp;and board diversity\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Oussii and Jeriji, 2025)\u003c/em\u003e\u003cem\u003e\u0026nbsp;are associated with enhanced carbon management performance. Of course, advancements in the digital economy\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Li et al., 2025a)\u003c/em\u003e\u003cem\u003e, digital finance\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Lu et al., 2023)\u003c/em\u003e\u003cem\u003e, and environmental technologies\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Şahin et al., 2025)\u003c/em\u003e\u003cem\u003e, as well as R\u0026amp;D and innovation activities\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Zhu and Wang, 2025)\u003c/em\u003e\u003cem\u003e—particularly those focused on green technology\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Khan et al., 2025)\u003c/em\u003e\u003cem\u003e—along with increased green investment\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Zheng and Jin, 2023)\u003c/em\u003e\u003cem\u003e, industrial structure upgrading\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Cheng et al., 2025)\u003c/em\u003e\u003cem\u003e, and more efficient technology factor markets\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Zhou et al., 2025)\u003c/em\u003e\u003cem\u003e, have been shown to reduce carbon emission intensity. Moreover, institutional factors are also essential in carbon emission intensity. Including institutional quality\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Khan et al., 2022)\u003c/em\u003e\u003cem\u003e, institutional openness\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Guo and Wang, 2023)\u003c/em\u003e\u003cem\u003e, and stringency of environmental regulations\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Liu et al., 2024)\u003c/em\u003e\u003cem\u003e, these formal institutions have been demonstrated to mitigate corporate carbon emissions effectively, acting as critical policy instruments for governments promoting emission reductions worldwide. Simultaneously, informal institutions warrant considerable attention, and national sentiment has emerged as a distinctive informal institutional force, garnering growing academic interest and increasingly being integrated into business practices\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Wang and Li, 2025)\u003c/em\u003e\u003cem\u003e. National sentiment functions as a catalyst for corporate economic activities, affecting foreign direct investment\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Li et al., 2019)\u003c/em\u003e\u003cem\u003e, facilitating trade flows\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Dow and Cuypers, 2024)\u003c/em\u003e\u003cem\u003e, promoting green innovation\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Wang et al., 2025a)\u003c/em\u003e\u003cem\u003e, and boosting corporate ESG performance\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Tan et al., 2025)\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhereas,\u0026nbsp;it is managers who are instrumental in spearheading and integrating strategic carbon reduction initiatives within the corporate framework. Firms with greater CEO power (Luong et al., 2025), higher board independence, and dedicated environmental committees\u0026nbsp;(Elsayih et al., 2021)\u0026nbsp;are associated with reduced carbon emissions, but managerial myopia\u0026nbsp;(Xie et al., 2025)\u0026nbsp;increases emission levels. While extant literature has widely investigated the influence of managerial characteristics on corporate carbon behavior, little attention has been paid to their role in emissions reduction through the lens of informal institutions, such as national sentiment. Given the importance of informal institutional forces in carbon reduction, this study explores the nonlinear effects of national sentiment on carbon emission intensity. Specifically, we analyze how managers, amid dual pressures for economic legitimacy and low-carbon legitimacy, shape their firms’ emission reduction strategies in response to nuanced national sentiment impulses.\u003c/p\u003e\n\u003cp\u003eThis study offers three contributions to the existing literature. Firstly, we present novel evidence for a nonlinear inverted U-shaped relationship between national sentiment and carbon emission intensity, filling the theoretical gap of micro managers' cognition on corporate environmental behavior and provides a new theoretical entry point for the study of corporate carbon emission drivers. Secondly，we explore the theoretical perspective of duality of institutional theory, and verify the dual intermediary channels based on the dual perspectives of economic legitimacy and low-carbon development legitimacy. This not only provides a specific path for the transformation of the black box between national sentiment and carbon emission intensity, but also deepens the understanding of the internal mechanism between the two. Thirdly, we clarify the boundary conditions of national sentiment on carbon emission intensity, and refine the mechanism layer by layer from regulatory scenarios, normative systems, and coercive means. This enriches the application of institutional theory in the field of environmental management, and also providing a multi-level and differentiated policy perspective for the low-carbon development of enterprises.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe remainder of this paper is structured as follows. Section 2 reviews the background literature and develops hypotheses. Section 3 describes the data and methodology. Section 4 presents the empirical results and analysis. Section 5 concludes with discussions.\u003c/em\u003e\u003c/p\u003e"},{"header":"Background literature and hypotheses","content":"\u003ch2\u003e\u003cem\u003eInstitutional theory\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eInstitutional theory illuminates how organizational environments\u0026rsquo; institutionalization drives homogeneous practice adoption (Aronson and LaFont, 2020). With the study of institutional isomorphism and decoupling grew into a substantial body of research, how individual or institutional entrepreneurs initiate institutional change and devise strategies for transforming individual agency into collective agency is beginning to attract the attention of scholars (Eitrem et al., 2024). Furthermore, institutional embeddedness fundamentally shapes institutional entrepreneurship and strategic agency (Battilana et al., 2009), where the interplay of habitual and reflexive agency elucidates institutional change trajectories (Lounsbury et al., 2021). Especially, confronting governmental policy enforcement, market uncertainties, and developmental imperatives, scholars increasingly examine the causal nexus between environmental compliance, corporate environmental practices, and firm performance through institutional theoretical lenses (Gupta and Gupta, 2021). Moreover, scholarship increasingly applies institutional theory into new fields, such as corporate social responsibility (CSR), revealing how regulatory, normative, and cognitive institutional elements shape CSR practices (Brammer et al., 2012), which is the\u0026nbsp;consequences of value-driven agent selection and institutional structure\u0026nbsp;(Risi et al., 2023). Then, institutional contexts fundamentally determine the form and content of geographically bounded CSR practices (Matten and Moon, 2020), compelling firms to engage in such activities to address legitimacy expectations. The moral legitimacy of these CSR initiatives is contingent upon CEO-driven stakeholder considerations manifested through responsible business conduct\u0026nbsp;(Castell\u0026oacute; and Lozano, 2011). Scholars increasingly examine how individual and collective actors agentically respond to institutional contexts. Specific CSR activities\u0026mdash;including charitable donations and certification standards\u0026mdash;mitigate institutional pressures\u0026nbsp;(Iatridis et al., 2016; Wang et al., 2015), with behavioral change trajectories shaped by institutional process diversity\u0026nbsp;(Oliver, 1991). Concurrently, firms experience compelling institutional forces: internal commitments (top management ethos, organizational culture) and external pressures (regulatory imperatives, competitive mimicry), collectively driving enhanced ethical and social responsibility\u0026nbsp;(Ng et al., 2022). Drawing on institutional theory, we examine how normative and moral legitimacy imperatives for corporate decarbonization conduct shape the strategic guidance derived from patriotic corporate ethos.\u003c/p\u003e\n\u003ch2\u003eNational sentiment and corporate carbon emission intensity\u003c/h2\u003e\n\u003cp\u003eAs an organization embedded in the institutional environment, the behavior of enterprises is subject to both formal and informal systems (Berrone et al., 2022). Although different from the rigid constraints of formal systems, informal systems can influence organizational behavior choices through cultural identity and social expectations (Zhao et al., 2024). The specific cultural system of the enterprise is the essence of the formation and evolution of informal institutions (Pejovich, 2006). National sentiment focuses on the cultural values of the interests of the national community (Hertz, 2022), and as an important part of the cultural system, it affects the carbon emission behavior of enterprises through the legitimacy transmission mechanism of the informal system (Xu et al., 2025). When national sentiment is at a low level, the core of informal institutional pressures will fall on economic legitimacy, where corporate cultural values prioritize economic benefits and production expansion, leading to increased carbon emissions (Rottner and von Graevenitz, 2024). When a certain critical value is broken, the informal system will reconstruct the social consensus, and the corporate culture values will shift from economic legitimacy to low-carbon development legitimacy (Roh et al., 2025), thereby reducing carbon emissions. Therefore, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003eH1: There is an inverted U-shaped relationship between national sentiment and corporate carbon emission intensity.\u003c/p\u003e\n\u003ch2\u003eThe effect of the market competition on the relationship between national sentiment and carbon intensity\u003c/h2\u003e\n\u003cp\u003eEnterprises perpetually competing for scarce market resources to drive their growth(Deng et al., 2024), market competition serves as the \u0026ldquo;testing ground\u0026rdquo; for this contest, intensifying the perception of resource scarcity and directly shaping the strategic choices and operational boundaries of firms as they vie for critical resources. The development of digitalization has given rise to a new form of corporate competition (Ahn, 2020). The accumulation of digital technologies within enterprises confers a competitive advantage on those firms that strategically emphasize digital production (Ye et al., 2023). Fiercely competitive environment will stimulate firms\u0026rsquo; willingness to engage environmentally to prioritize economic gains (Sun et al., 2025), during periods of diminished national sentiment, subdued market competition fosters a more lenient institutional environment, which in turn dampens profit-seeking incentives and leads to a reduction in green innovation performance (Shi et al., 2025). When national sentiment exceeds a critical threshold, although green legitimacy gains recognition, the low-competition environment reduces firms\u0026rsquo; pressure to seek market advantages through low-carbon practices. Consequently, enterprises may prioritize demonstrating national allegiance over pursuing stable growth, leading to a dilution of carbon reduction targets and the crowding out of green investments. Moreover, in a low-competition environment, market signals such as low-carbon and green demand remain weak, and corporate low-carbon behavior faces insufficient external institutional constraints, which further contributes to a decline in emission reduction investment.\u003c/p\u003e\n\u003cp\u003eH2: Market competition negatively moderates the inverted U-shaped relationship between national sentiment and carbon emission intensity.\u003c/p\u003e\n\u003ch2\u003eThe effect of the emission reduction target constraints on the relationship between national sentiment and carbon intensity\u003c/h2\u003e\n\u003cp\u003eThe implementation of the emission reduction targets has significantly reduced carbon intensity, a success that is directly attributable to the target setting (Yao et al., 2019). Emission reduction is a binding target, which compels firms to boost R\u0026amp;D investments, cultivate low-carbon and energy-saving technologies, and enhance production efficiency (Pan et al., 2021).\u0026nbsp;In the initial stage of national sentiment, stronger emission reduction targets tend to align local policy priorities with economic growth and industrial expansion, leading to a positive association with firms\u0026rsquo; carbon emission intensity. However, once national sentiment surpasses a certain threshold, the presence of clear emission reduction targets strengthens institutional constraints. Beyond this point, heightened national sentiment motivates local governments and firms to prioritize emission reduction goals and sustainable development, thereby promoting concrete decarbonization practices and reducing carbon emission intensity\u0026nbsp;(Du and Li, 2023). Therefore, we put forward the following hypothesis:\u003c/p\u003e\n\u003cp\u003eH3: Emission reduction target constraints positively moderate the inverted U-shaped relationship between national sentiment and corporate carbon emission intensity.\u003c/p\u003e\n\u003ch2\u003eThe effect of the environmental penalties on the relationship between national sentiment and carbon intensity\u003c/h2\u003e\n\u003cp\u003eEnvironmental penalties, as a consequence of stringent environmental regulation, particularly for polluting enterprises, impose substantial financial costs and operational disruptions through fines and production suspensions (Guedhami et al., 2025). During the initial phase of national sentiment, such penalties tend to encourage corporate greenwashing behavior (Zhou et al., 2024), firms may internalize environmental penalties as operational expenses, a rationalization that can perversely incentivize output expansion to offset incurred costs, and leading to higher aggregate emissions in pursuit of profit maximization. As firms place growing emphasis on institutional legitimacy, environmental penalties effectively curb greenwashing practices among both growing and mature enterprises (Li et al., 2025b). By positively moderating environmental preferences, such penalties stimulate substantive green innovation (Ma et al., 2025a), which in turn enhances the quality of environmental information disclosure (Hu and Xu, 2025) and improves environmental performance (Li et al., 2024). Moreover, augmenting penalty severity contributes to stronger synergistic effects in carbon emission reduction (Zhao et al., 2025). The public disclosure of environmental penalties also introduces the threat of social sanctions, compelling legitimacy-seeking firms to intensify their emission reduction efforts. Based on this reasoning, we propose the following hypotheses:\u003c/p\u003e\n\u003cp\u003eH4: Environmental penalties positively moderates the inverted U-shaped relationship between national sentiment and carbon emission intensity.\u003c/p\u003e\n\u003ch2\u003eThe effect of corporate social responsibility on the relationship between national sentiment and carbon intensity\u003c/h2\u003e\n\u003cp\u003eCorporate social responsibility constitutes a strategic imperative (Ginder et al., 2025), embodying the integration of economic, social, and environmental performance within a triple bottom line framework (Dang et al., 2022), and emerges as an outcome shaped by the confluence of agent selection and institutional structure (Risi et al., 2023).. The theoretical support for CSR as a mediator in the curvilinear relationship between national sentiment and carbon emission intensity rests on three key effects: (a) an inverted U-shaped relationship between national sentiment and corporate carbon emission intensity (consistent with H1); (b) a U-shaped relationship between national sentiment and CSR; and (c) a negative linear relationship between CSR and corporate carbon emission intensity.\u003c/p\u003e\n\u003cp\u003eNational sentiment serves as an institutional mechanism through which the state directs corporate ethical conduct; it fosters a profound sense of cultural identity and generates internal motivation for the fulfillment of corporate social responsibilities. Ethical behaviors relate to national sentiment are rooted in the country\u0026rsquo;s history, traditions, and modern cultural framework, and both expand and strengthen corporate social responsibility.\u003c/p\u003e\n\u003cp\u003eNational sentiment functions as an institutional mechanism through which the state guides corporate ethical conduct, fostering a profound cultural identity and generating intrinsic motivation for the fulfillment of social responsibilities. Ethically significant behaviors linked to national sentiment are embedded in the nation\u0026rsquo;s historical heritage, traditions, and contemporary cultural framework, thereby reinforcing and expanding the scope of corporate social responsibility (Yashalova et al., 2021). During the initial phase of rising national sentiment, firms tend to prioritize profit maximization. Although CSR consciousness persists, elevated cost constraints diminish their impetus to undertake substantive CSR initiatives. Consequently, at this early stage, intensified national sentiment may inhibit rather than promote corporate investment in CSR practices, thereby contributing to a decline in overall CSR performance. When national sentiment surpasses a critical threshold, firms encounter intensified institutional pressures from regulatory and normative systems (Lee et al., 2024), leading socio-environmental performance to elevate its priority above traditional economic objectives. Under such conditions, enterprises reorient their strategic focus toward improving social and environmental outcomes, thereby reinforcing their CSR commitments. Consequently, during this phase, heightened national sentiment is associated with strengthened corporate social responsibility.\u003c/p\u003e\n\u003cp\u003eRegarding the relationship between CSR and corporate carbon emission intensity, extensive empirical research indicates a significant negative linear relationship between CSR and carbon emission intensity (Chen, 2023; Zhang et al., 2022).. Based on the foregoing analysis, the following hypothesis is proposed：\u003c/p\u003e\n\u003cp\u003eH5: Corporate social responsibility mediates the inverted U-shaped relationship between national sentiment and carbon emission intensity.\u003c/p\u003e\n\u003ch2\u003eThe effect of green investment on the relationship between national sentiment and carbon intensity\u003c/h2\u003e\n\u003cp\u003eGreen investment embodies the significant influence of the institutional environment on capital allocation, serving as a form of institutional pressure that aligns financial objectives with environmental and social goals (Alsagr and Ozturk, 2024), which integrates environmental, social, and economic considerations as the \u0026ldquo;triple bottom line\u0026rdquo; (Guo and Zhao, 2025). The theoretical foundation for green investment as a mediator in the curvilinear relationship between national sentiment and corporate carbon emission intensity is predicated on three interrelated effects: (a) national sentiment exhibits an inverted U-shaped association with carbon emission intensity (as hypothesized in H1); (b) national sentiment demonstrates a U-shaped relationship with green investment; and (c) green investment is linearly and negatively associated with corporate carbon emission intensity.\u003c/p\u003e\n\u003cp\u003eAs for the relationship between national sentiment and corporate green investment. In its initial phase, rising nationalism encourages firms to prioritize economic returns, promoting economic autonomy (Liu et al., 2025), leading to a reallocation of scarce resources toward profit-driven activities and a concomitant crowding-out of environmental investments. However, at more advanced stages, as national sentiment becomes institutionalized within societal norms and expectations of corporate responsibility, firms face heightened institutional pressures\u0026mdash;from governmental, public, and normative sources\u0026mdash;that compel a shift toward greater social and environmental engagement (Pan et al., 2025). Consequently, green investment levels rise sharply as firms respond to these evolving demands.\u003c/p\u003e\n\u003cp\u003eThe relationship between green investment and carbon emissions has been the subject of considerable academic inquiry. A substantial body of research suggests that such investment significantly contributes to the reduction of carbon emissions (Ai and Yan, 2024; Kwilinski et al., 2024; Priyan, 2023; Zhong et al., 2024). Based on the above analysis, we put forward the following hypothesis:\u003c/p\u003e\n\u003cp\u003eH6: Green investment mediates the inverted U-shaped relationship between national sentiment and carbon emission intensity.\u003c/p\u003e\n\u003cp\u003eBuilding upon established theoretical foundations and prior literature, we construct the conceptual framework presented in Figure 1 to systematically examine the impact of national sentiment on carbon emission intensity.\u003c/p\u003e"},{"header":"Data and Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eData resource\u003c/h2\u003e\u003cp\u003eTo examine the relationship between national sentiment and carbon emission intensity, our analysis used an unbalanced panel dataset including 26135 observations from 3395 different firms for the period between 2010 and 2023, and firm level financial data is from CSMAR database, corporate social responsibility is from HEXUN\u0026rsquo;s net.\u003c/p\u003e\u003cp\u003eWe apply three standard filters: (1) excluding financially distressed firms (ST/*ST/PT), (2) omitting financial firms due to their distinct accounting frameworks, and (3) eliminating observations with missing data for key variables. Accordingly, the final sample comprises 3,395 firms with 26,135 firm-year observations.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eDependent variables\u003c/h3\u003e\n\u003cp\u003eCorporate carbon emission intensity (\u003cem\u003eCI\u003c/em\u003e), is quantified as the ratio of its carbon dioxide emissions to operating revenue, expressed in metric tons of CO₂ per 100\u0026nbsp;million yuan (Song et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). And the carbon dioxide emission is defined as the sum of combustion and fugitive emissions, production process emissions, waste emissions, and emissions from anthropogenic land-use change, specifically forest-to-industrial land conversion. A higher \u003cem\u003eCI\u003c/em\u003e value signifies greater environmental impact from emissions, implying proportionally greater potential for emission reduction..\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIndependent variables\u003c/h2\u003e\u003cp\u003eNational sentiment (\u003cem\u003eNationalism\u003c/em\u003e), a standardized metric for corporate nationalism was constructed by analyzing listed companies\u0026rsquo; annual disclosures concerning expressions of patriotism, xenophobia, state nationalism, and corporate missions aligned with nationalist objectives (Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Elevated scores of \u003cem\u003eNationalism\u003c/em\u003e denote greater prevalence of rhetorical nationalism within the firm's discursive practices (Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e)..\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eModerating variables\u003c/h2\u003e\u003cp\u003eMarket competition (\u003cem\u003eMcompet\u003c/em\u003e), is measure by Lena index. Lena Index is defined as the ratio of core operating profit to total operating revenue. A lower Lena Index value indicates higher market competition intensity, reflecting inverse proportionality between operational efficiency and competitive pressure..\u003c/p\u003e\u003cp\u003eEmission reduction target constraints (\u003cem\u003eTarget\u003c/em\u003e), using textual analysis of prefectural-level government work reports, we constructed a binary variable indicating whether municipal authorities established quantified emission reduction targets in a given year, coded as 1 if explicit numerical goals were specified and 0 otherwise.\u003c/p\u003e\u003cp\u003eEnvironment penalty (\u003cem\u003eEpenalty\u003c/em\u003e), constitutes administrative sanctions imposed by environmental protection agencies on regulated entities violating environmental statutes, with our dataset encompassing nationwide multi-tiered jurisdictions (provincial to county levels), and this metric quantifies coercive institutional pressures compelling corporate adherence to environmental standards (Hu and Xu, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMediating variables\u003c/h2\u003e\u003cp\u003eCorporate social responsibility (\u003cem\u003eCsr\u003c/em\u003e), HEXUN.com's CSR ratings for listed companies. Specifically, HEXUN\u0026rsquo;s quantitative CSR metric, grounded in audited annual reports and stand-alone CSR disclosures, gauges realized rather than merely reported responsibility, thereby attenuating the typical divergence between symbolic disclosure and substantive performance (Karavitis et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGreen investment (\u003cem\u003eGinvest\u003c/em\u003e), is computed by aggregating environmental capital expenditures (pollution control, ecological governance, and clean production projects) from construction ledgers, scaling by period-end total assets, and applying min-max normalization. \u003cem\u003eGinvest\u003c/em\u003e simultaneously pursues environmental stewardship and economic resilience. Meanwhile, \u003cem\u003eGinvest\u003c/em\u003e\u0026rsquo;s financial support\u0026mdash;conventionally modeled as a mediating variable\u0026mdash;augments internal capabilities, thereby elevating operational efficiency and environmental compliance (Haq et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eControl variables\u003c/h2\u003e\u003cp\u003eWe firstly controlled for discretion, coded \u0026ldquo;1\u0026rdquo; if a CEO held the board chair position in the given year and \u0026ldquo;0\u0026rdquo; otherwise (Cannella and Lubatkin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), because it may enhance the efficiency of strategic decision-making and strengthen the consistency of accountability, and promote the transformation of cognition into concrete actions for emission reduction (Jiang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We also included \u003cem\u003efirm age\u003c/em\u003e, measured by the given year minus the firm\u0026rsquo;s founding year, Firm age plays a fundamental role in shaping the dynamics of institutional legitimacy acquisition, thereby contributing to a reduction in carbon emission intensity as the firm matures (Ma et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The shareholding ratio of the largest shareholder is selected as its strategic decision-making control power and internalization mechanism of environmental responsibility (Slager et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To control for the influence of board structure, we included the amount of board members, measured by the natural logarithm of the number of board members. It has been shown to influence corporate carbon intensity (Muktadir-Al‐Mukit and Bhaiyat, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also controlled for the amount of independent directors, because researched confirmed that boards with higher representation among independent directors are more likely to have lower carbon emissions (Elsayih et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To control for firm performance, we measured debt level by total liabilities divided by total assets (Bi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Return on assets (\u003cem\u003eROA\u003c/em\u003e) is measured by dividing net profit by total assets. We also include corporate cash flow ratio, because this controls for the capacities of corporate\u0026rsquo;s green innovation and corresponding environmental protection (Makpotche et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In summary, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the formal definitions of main variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable definitions and descriptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbon intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWe employ a natural logarithm transformation of corporate carbon intensity, calculated as the ratio of aggregate direct emissions\u0026mdash;encompassing Scope 1 (combustion and fugitive), industrial process, waste management, and direct land-use change emissions\u0026mdash;to total revenue, to evaluate emissions efficiency.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNational sentiment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNationalism was constructed by analyzing listed companies\u0026rsquo; annual disclosures concerning expressions of patriotism, xenophobia, state nationalism, and corporate missions aligned with nationalist objectives.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerating\u003c/p\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarket competition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMcompet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe Lerner Index is calculated as the ratio of enterprise operating income minus operating costs, selling expenses, and administrative expenses, divided by operating income, reflecting the firm's pricing power relative to its marginal cost.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmission reduction target constraints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTarget\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBased on textual analysis of prefecture-level government work reports, we constructed a binary variable measuring the establishment of specific emission reduction targets. This indicator equals 1 if a municipal government explicitly specified quantitative environmental governance targets in its annual work report, and 0 otherwise.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironment penalty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEpenalty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNational provincial, municipal and county environmental protection penalty case data.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediating variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorporate social responsibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCsr\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHexun.com's CSR rating data for listed companies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen investment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGinvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorporate green investment intensity is calculated by compiling pollution prevention, environmental remediation, ecological governance, and green production expenditures from construction in progress. This aggregate annual green investment is divided by year-end total assets and standardized to yield the final metric.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiscretion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIf a CEO held the board chair position in the given year and, the value is 1; otherwise, it is 0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNatural logarithm of the number of board members\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDebt level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal liabilities divided by total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecorporate cash flow ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorporate cash ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe shareholding ratio of the largest shareholder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe proportion of independent directors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe time of the enterprise establish\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReturn on assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet Profit divided by total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eModel analysis\u003c/h2\u003e\u003cp\u003eThe following empirical model is estimated to test the study\u0026rsquo;s hypotheses:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$C{I_{it}}={\\beta _1}Nationalis{m_{it}}+{\\beta _2}Nationalis{m^2}_{{it}}+{\\beta _3}\\sum\\limits_{{}}^{{}} {Control{s_{it}}} +{\\mu _t}+{\\gamma _i}+{\\varepsilon _{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere subscripts \u003cem\u003ei\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e denote the firm and time period, respectively. \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represent carbon emission intensity. \u003cem\u003eNationalism\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e refers to national sentiment. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Contro{l_{it}}\\)\u003c/span\u003e\u003c/span\u003eis a set of control variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu _t}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma _i}\\)\u003c/span\u003e\u003c/span\u003e represent the year fixed effect and the company fixed effect. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003eis the regression residual term.\u003c/p\u003e\u003cp\u003eTo examine the moderating role of \u003cem\u003eMcompet\u003c/em\u003e, \u003cem\u003eTarget\u003c/em\u003e and \u003cem\u003eEpenalty\u003c/em\u003e on the national sentiment and carbon emission intensity, we specify the Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) for analysis:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} C{I_{it}}{\\text{=}}{\\beta _{\\text{0}}}{\\text{+}}{\\beta _1}Nationalis{m_{it}}{\\text{+}}{\\beta _2}Nationalis{m^2}_{{it}}{\\text{+}}{\\rho _{\\text{1}}}Nationalis{m_{it}} \\times Moderato{r_{it}} \\hfill \\\\ {\\text{ }}+{\\rho _2}Nationalis{m_{it}}^{2} \\times Moderato{r_{it}}+{\\beta _3}\\sum\\limits_{{}}^{{}} {Control{s_{it}}} +{\\mu _t}+{\\gamma _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eModerator\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is moderating effect of \u003cem\u003eMcompet\u003c/em\u003e, \u003cem\u003eTarget\u003c/em\u003e and \u003cem\u003eEpenalty\u003c/em\u003e. Coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _1}\\)\u003c/span\u003e\u003c/span\u003ecaptures the linear interaction effect, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _2}\\)\u003c/span\u003e\u003c/span\u003erepresents the quadratic interaction term.. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu _t}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma _i}\\)\u003c/span\u003e\u003c/span\u003e respectively represent the year fixed effect and the company fixed effect. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003e is the regression residual term.\u003c/p\u003e\u003cp\u003eTo test the mediating pathways through which corporation social responsibility and green investment affect the nationalism and carbon emission intensity relationship, we construct the system of Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)-(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$Mediator{s_{it}}{\\text{=}}{\\beta _{\\text{0}}}{\\text{+}}{\\beta _1}Nationalis{m_{it}}{\\text{+}}{\\beta _2}Nationalis{m^2}_{{it}}{\\text{+}}{\\beta _3}\\sum\\limits_{{}}^{{}} {Control{s_{it}}} +{\\mu _t}+{\\gamma _i}+{\\varepsilon _{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} C{I_{it}}{\\text{=}}{\\beta _{\\text{0}}}{\\text{+}}{\\beta _1}Nationalis{m_{it}}{\\text{+}}{\\beta _2}Nationalis{m^2}_{{it}}{\\text{+}}\\delta Mediator{s_{it}} \\hfill \\\\ {\\text{ }}+{\\beta _3}\\sum\\limits_{{}}^{{}} {Control{s_{it}}} +{\\mu _t}+{\\gamma _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eMediators\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is mediating effect of \u003cem\u003eCsr\u003c/em\u003e and \u003cem\u003eGinvest\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu _t}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma _i}\\)\u003c/span\u003e\u003c/span\u003e respectively denotes year and firm fixed effects. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003e represents the residual term..\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics and correlation analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents key descriptive statistics for the final sample. Variable \u003cem\u003eCI\u003c/em\u003e ranges from 0 to 11,20 (mean\u0026thinsp;=\u0026thinsp;9.502), while \u003cem\u003eNationalism\u003c/em\u003e exhibits a distribution from 0 to 19.41 with a substantially lower mean of 0.408. These results collectively indicate suboptimal \u003cem\u003eNationalism\u003c/em\u003e levels across firms, suggesting significant potential for enhancement relative to theoretical optima.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.D\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMcompet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3,404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTarget\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEpenalty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e850.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCsr\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInternal\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCpolicy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGinvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.7169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.5035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-30.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the correlations for all variables in our model. From the perspective of core variable correlation, the analysis reveals a correlation coefficient of 0.132 between \u003cem\u003eNationalism\u003c/em\u003e and \u003cem\u003eCI\u003c/em\u003e, which is positively significant at the 1% level. This indicates a statistically strong positive relationship between national sentiment and corporate carbon emissions, thereby providing preliminary empirical support for subsequent main-effect tests.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis of main variable\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(11)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e(13)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e(14)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e(15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e(17)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003e(18)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.132***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMcompet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTarget\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.027***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEpenalty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.118***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.014**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCsr\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.141***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.059***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.040***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.021***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGinvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.090***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.021***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.025***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.133***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.013**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.038***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.033***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.044***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.074***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.048***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.010**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.029***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.050***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.126***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.371***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.147***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.010**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.018***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.054***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.099***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.138***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.094***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.136***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.132***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.012**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.145***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.075***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.090***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.053***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e-0.366***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.181***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.047***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.154***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.044***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.207***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.032***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.034***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.238***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.060***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.121***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.134***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.087***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e-0.056***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.304***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.097***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.022***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.069***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.063***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.243***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.340***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.369***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e-0.238***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e-0.078***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.121***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.180***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.011**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e-0.075***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.088***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.050***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e-0.068***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eBaseline regression results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents baseline regressions examining nationalism's impact on corporate carbon emission intensity (Model 1). Columns (1) and (3) estimate specifications without control variables while maintaining firm and year fixed effects. Column (1) reveals a statistically significant positive coefficient of 0.1408 (at the 1% level) on the linear patriotism term. Column (3) introduces a quadratic patriotism term, yielding a significantly negative coefficient of -2.4795 (at the 1% level). Columns (2) and (4) incorporate control variables, demonstrating sustained statistical significance: the linear term coefficient rises to 0.3646 (at the 1% level) in Column (2), while the quadratic term in Column (4) registers \u0026minus;\u0026thinsp;37.2397 (at the 1% level). The above results establish an inverted U-shaped relationship between nationalism and corporate carbon emission intensity, H1 is confirmed. From an institutional legitimacy perspective, we posit that firms demonstrating national sentiment is initially driven by socio-normative legitimacy to prioritize economic value creation as proof of national allegiance. This triggers resource reallocation toward capacity expansion, where carbon emission growth substantially outpaces potential mitigation efforts\u0026mdash;manifested as the upward trajectory of the curve. However, beyond a critical legitimacy threshold, low-carbon ethical legitimacy restructures corporate responsibility paradigms toward \u0026ldquo;green contributions\u0026rdquo;, inducing the subsequent downward inflection of the curve.\u003c/p\u003e\u003cp\u003eRegarding control variables, our analysis demonstrates that discretion, board structure characteristics and debt structure variables are all significantly reduce carbon intensity at the 1% significance level. Notably, cash ratio serve as robust indicators of financial resilience, while capital allocation exhibits significant carbon mitigation effects. In addition, we should not only focus on CEO dual, but also enhance awareness of the cost of environmental violations by increasing the shareholding of first shareholders. Of course, age brings higher compliance to enterprises., accumulated organizational memory forms institutional knowledge stocks in environmental management.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline regression results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1408\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3646\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4713\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0935)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1687)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.4795\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-37.2397\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.6030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.4589)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0451\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0448\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0206\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2378\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.2253\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0388)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0383)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3450\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.3531\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0533)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0532)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0638\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0619\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0541\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.2231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1423)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1431)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7522\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.4940\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.5907\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.2783\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0496)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1185)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.8493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.6321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.8422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.5097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo graphically demonstrate the established nonlinear relationship, we plot the fitted inverted U-shaped curve between nationalism and carbon emission intensity. As described in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the statistically estimated turning point occurs at a nationalism level of 0.0125. Below this threshold, carbon emission intensity exhibits a positive association with nationalism. Conversely, when national sentiment exceeds 0.0125, carbon intensity demonstrates a significant negative relationship with patriotic orientation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eRobustness tests\u003c/h2\u003e\u003cp\u003eFirstly, to further verify the robustness of the previously documented relationship between national sentiment and carbon emission intensity, we substitute the dependent variable with carbon performance\u0026mdash;defined as the reciprocal of the natural logarithm of the ratio of carbon emissions to main business revenue. The regression results based on this alternative metric are reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The results show a significant U-shaped relationship between nationalism and carbon performance. Given the inverse relationship between carbon performance and carbon emission intensity, these robustness tests employing carbon performance corroborate research hypothesis H1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness results of substituting the dependent variable with carbon performance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCarbon performance\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.0685\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.9986\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1745)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1789)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.0957\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(11.0663)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0226)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0483\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0468\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0053)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7827\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7622\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0549)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1931\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1797\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0766)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0763)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0054\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0016)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1208\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0217)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0214)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0899\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0850\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6918\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6865\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.3724)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.3737)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.9871\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3410\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1504)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197.1275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190.4870\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSecondly, we conducted a quadratic relationship form test, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. the slope coefficient at the lower bound of patriotism is 0.9315 (statistically significant with positive sign at the 1% level), while at the upper bound it is -1444.97 (statistically significant with negative sign at the 1% level). The coefficient of overall test of presence of an inverse U shape is 3.56, and significant at the 1% level. It confirmed an inverted U-shaped relationship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQuadratic relationship form test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower bound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUpper bound\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.932\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(5.522)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1444.97\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.560)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThirdly, to mitigate potential confounding effects, we methodically incorporate six control variables into our regression specification. ①We add green cognition as control variable. We argue that managers with green cognition is a crucial factor for affecting carbon emission. In order to strip away the part of green cognition that affects corporate carbon emissions, and to more clearly identify and verify the net impact of patriotism on corporate carbon emissions, we introduce it into the model 1. In column (1), \u003cem\u003eGreencogn\u003c/em\u003e has a significantly positive effect on \u003cem\u003eCI\u003c/em\u003e, and the inverse U shape is stable. ②To isolate the causal channel of patriotism from corporate carbon governance frameworks, we construct a carbon management strategy index via textual analysis of annual reports\u0026mdash;quantifying term frequencies (\u0026ldquo;low-carbon strategy\u0026rdquo;, \u0026ldquo;zero-carbon development\u0026rdquo; and etc.) to capture strategic intensity. Controlling for this institutionalized variable disentangles emission reduction behaviors attributable to structured environmental strategies versus patriotism-driven initiatives, thereby purifying the identified causal effect. As reported in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the coefficient of \u003cem\u003eCmanstra\u003c/em\u003e demonstrates statistically significant explanatory power. Crucially, the inverted U-shaped relationship between national sentiment and carbon emissions persists after controlling for \u003cem\u003eCmanstra\u003c/em\u003e, with both linear and quadratic terms remaining significant at the 1% level. This robustness withstands competing explanations from corporate environmental governance, thereby providing confirmatory evidence for Hypothesis 1. ③Incorporating environmental governance investment (\u003cem\u003eEpi\u003c/em\u003e) into Model 1, this study operationalizes corporate sustainability commitment through listed firms\u0026rsquo; disclosed capital and technological allocations toward pollution control, emission abatement, and resource efficiency. Controlling for \u003cem\u003eEpi\u003c/em\u003e disentangles competing causal pathways -environmental responsibility investments versus patriotism-driven initiatives-in explaining emission behaviors. Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates that the inverted U-shaped relationship between national sentiment and carbon emission intensity persists with statistical significance (at the 1% level for linear/quadratic terms), thereby providing robust confirmatory evidence for Hypothesis 1 against environmental investment confounders. ④T o purify the causal effect of national sentiment on carbon abatement, we introduces an internal control variable that operationalizes corporate governance efficacy through production standardization, risk management efficiency, and compliance enforcement rigor. By controlling for this index\u0026mdash;which mechanizes emissions reduction via energy process optimization, environmental oversight intensification, and resource allocation refinement \u0026mdash; we disentangle institutional governance improvements from patriotism-driven initiatives, thereby isolating the net ecological impact attributable to patriotic motives. As evidenced in Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the inverted U-shaped relationship between patriotism and carbon emission intensity persists with statistical significance (at the 1% level for both terms) after accounting for internal control covariates, thereby reconfirming Hypothesis 1's robustness against corporate internal control confounders. ⑤To mitigate confounding from regional regulatory pressures, this study employs a provincial environmental pollution index constructed via entropy weighting \u0026mdash; integrating wastewater discharge, industrial SO₂ emissions, and solid waste generation to quantify pollution load intensity. High values signify coercive institutional pressures that mechanically induce emission abatement through compliance costs and penalty risks, thus creating competing explanations for patriotism-driven reductions. Controlling for this variable disentangles externally regulatory pressures effects from patriotic motives. Column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates a statistically significant inverted U-shaped relationship between national sentiment and carbon emission intensity (at the 1% level for linear/quadratic terms) after controlling for environmental pollution covariates. ⑥To disentangle compliance-based abatement from patriotism-driven voluntary reductions, this study incorporates a carbon policy covariate constructed through textual mining of \u0026ldquo;carbon reduction emphasis\u0026rdquo; lexicons in corporate disclosures. This metric captures strategic responses to socio-regulatory legitimacy pressures, enabling isolation of emission effects attributable to external legitimacy demands from the impetus of patriotic motives. Column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e confirms the persistent inverted U-shaped relationship between national sentiment and carbon emission intensity (at the 1% level for linear/quadratic terms) after controlling for carbon policy covariates. This robustness withstands socio-regulatory confounders, validating Hypothesis 1 against external normative pressure interference.\u003c/p\u003e\u003cp\u003eOverall, as reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the first-order term of \u003cem\u003eNationalism\u003c/em\u003e consistently exhibits positive coefficients significant at the 1% level, while its quadratic term shows negative coefficients statistically significant at the 1% level after adding control variables (\u003cem\u003eGreencogn\u003c/em\u003e, \u003cem\u003eCmanstra\u003c/em\u003e, \u003cem\u003eEpi\u003c/em\u003e, \u003cem\u003eInternal\u003c/em\u003e, \u003cem\u003eEPindex\u003c/em\u003e and \u003cem\u003eCarbon policy\u003c/em\u003e). This robust pattern persists after controlling for these additional factors, thereby confirming the inverted U-shaped relationship between national sentiment and carbon emissions and further validating Hypothesis H1. Furthermore, carbon emissions exhibit positive associations with heightened managerial environmental awareness, elevated environmental pollution indices, and intensified carbon policies. Conversely, emissions demonstrate significant mitigation effects from enhanced carbon management strategies, increased environmental protection investments, and strengthened internal control systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of adding control variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8806\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9328\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0100\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9637\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8435\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1659)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1714)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1744)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1680)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-35.0567\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-36.1218\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-37.3675\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-38.0284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-37.6777\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-32.7324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9.0291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.9456)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(10.5180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(9.9159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(10.3326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(8.6351)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGreencogn\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0011\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCmanstra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0129\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEpi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0072\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInternal\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.1459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0342)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEPindex\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3528\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCarbon policy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0010\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0328\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0423\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0476\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0471\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0217)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0191\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0206\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0238\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2273\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2265\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.3484\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.2353\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2383\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e-0.3504\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.2889\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.4008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.3647\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0546)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0534)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0533)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0626)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0573)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0613)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0044\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0044\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0476\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0672\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0618\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0583\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0581\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0514\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0196)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0512\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0507\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e(0.0986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1439)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1351)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1509)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.2385\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.3126\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.2750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.2670\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.1270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.2926\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.2547)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1570)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1252)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.9198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.2268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.9783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.4777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.9078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.6097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eModerating effect\u003c/h2\u003e\u003cp\u003eGrounded in institutional theory, market competition constitutes the regulatory context for corporate emissions, carbon targets establish normative institutional orientations, and environmental penalties enforce coercive compliance. Consequently, we incorporate market competition, emission reduction target constraintsand environment penalty into model as the moderating variables.\u003c/p\u003e\u003cp\u003eThe regression results of \u003cem\u003eLena\u003c/em\u003e are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. In Column (1), we introduce the interaction term \u003cem\u003eNationalism\u0026times;Mcompet\u003c/em\u003e and \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Mcompet\u003c/em\u003e. The estimated coefficient of \u003cem\u003eNationalism\u0026times;Mcompet\u003c/em\u003e is -0.9658, and is significantly negative at 5% level. The estimated coefficient of \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Mcompet\u003c/em\u003e is 84.4752, and is significantly positive at 10% level. We observe that attenuated market competition flattens the inverted U-shaped relationship between national sentiment and carbon emission intensity. This moderation effect arises as competitive environments foster technological diffusion and innovation incentives, whereas constrained competitive pressure engenders dual deficiency: diminished corporate motivation for economic gains and weakened initiative toward social accountability. Therefore, H2 is supported.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the results of the moderating effect of emission reduction target constraints on national sentiment and carbon emission intensity. In Column (2), we introduce the interaction terms of \u003cem\u003eNationalism\u0026times;Target\u003c/em\u003e and \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Target\u003c/em\u003e. The regression coefficient of the \u003cem\u003eNationalism\u0026times;Target\u003c/em\u003e is 0.3840, which is significantly positive at the 10% level. And the estimated coefficient of \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Target\u003c/em\u003e is -27.2525, which is significantly negative at the 10% level. This results demonstrate that \u003cem\u003eTarget\u003c/em\u003e steepen the carbon intensity curve through enhanced corporate decarbonization. Thus, hypothesis H3 is empirically substantiated.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the moderating results of environmental penalties. In Column (3), we introduce the interaction terms \u003cem\u003eNationalism\u0026times;Epenalty\u003c/em\u003e and \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Epenalty\u003c/em\u003e. The estimated coefficient of \u003cem\u003eNationalism\u0026times;Epenalty\u003c/em\u003e is 0.3176, which is significantly positive at the 1% level. And the estimated coefficient of \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Epenalty\u003c/em\u003e is -26.7371, which is significantly negative at the 5% level. It indicates that \u003cem\u003eEpenalty\u003c/em\u003e induces a steepened gradient in the curve. And it likely stems from public disclosure of environmental penalties activates social sanction threats that force legitimacy-seeking firms to escalate emission cuts, providing empirical substantiation for Hypothesis 4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerating effect results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMcompet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTarget\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eEpenalty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0625\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8900\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8054\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1917)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1124)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-48.9925\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-33.5460\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-25.8550\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(14.3443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(8.8956)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.0723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u0026times;Moderator\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.9658\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3840\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3176\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.4034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.2173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.1045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026times;Moderator\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.4752\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-27.2525\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-26.7371\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(48.8914)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(14.8755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(12.0937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModerator\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2385\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0670\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0456\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0260\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0205\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2241\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1637\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0383)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3610\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3690\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0538)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0532)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0584)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0063\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0615\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0618\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0552\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0152)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0515\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0615\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1448)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1434)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0523)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.1772\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.2884\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.4157\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.2264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.5034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.5832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.6166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eMediating effect\u003c/h2\u003e\u003cp\u003eWe respectively explored the channels through which national sentiment affects the carbon intensity of enterprises under the legitimacy of social norms and the legitimacy of low-carbon morality. Thus, corporate social responsibility and green investment as mediating variables.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reports the regression results between \u003cem\u003eNationalism\u003c/em\u003e and \u003cem\u003eCI\u003c/em\u003e. In column (1), \u003cem\u003eNationalism\u003c/em\u003e shows a statistically significant coefficient of -0.5035 at the 1% level. In linear relationships, \u003cem\u003eNationalism\u003c/em\u003e has a significant negative impact on \u003cem\u003eCsr\u003c/em\u003e. In Column (2), \u003cem\u003eNationalism\u003c/em\u003e presents a U-shaped relationship with \u003cem\u003eCsr. Nationalism\u003c/em\u003e will lower the \u003cem\u003eCsr\u003c/em\u003e first, and then increase the \u003cem\u003eCsr\u003c/em\u003e after the lowest threshold. In Column (3), the coefficient of \u003cem\u003eCsr\u003c/em\u003e is -0.0284 and is significant at the 1% level. Simultaneous, the inverted U-shaped relationship of \u003cem\u003eNationalism\u003c/em\u003e to \u003cem\u003eCI\u003c/em\u003e is significant. It indicates that \u003cem\u003eCsr\u003c/em\u003e has a partly mediating effect of \u003cem\u003eNationalism\u003c/em\u003e and \u003cem\u003eCI\u003c/em\u003e. Hypothesis 5 has been confirmed. Therefore, during economic expansions firms with national sentiment strategically reduce social responsibility expenditures; however, they subsequently increase investments in carbon emission mitigation to address legitimacy pressures arising from evolving societal norms.\u003c/p\u003e\u003cp\u003eThen, we explore the role of green investment as a channel for \u003cem\u003eNationalism\u003c/em\u003e and \u003cem\u003eCI\u003c/em\u003e. In column (4), \u003cem\u003eNationalism\u003c/em\u003e shows a statistically significant coefficient of -0.4329 at the 1% level. In linear relationships, \u003cem\u003eNationalism\u003c/em\u003e has a significant negative impact on \u003cem\u003eGinvest\u003c/em\u003e. In Column (5), \u003cem\u003eNationalism\u003c/em\u003e presents a U-shaped relationship with \u003cem\u003eGinvest. Nationalism\u003c/em\u003e will lower the \u003cem\u003eGinvest\u003c/em\u003e first, and then increase the \u003cem\u003eGinvest\u003c/em\u003e after the lowest threshold. In Column (6), the coefficient of \u003cem\u003eGinvest\u003c/em\u003e is -0.0075 and is significant at the 1% level. Simultaneous, the inverted U-shaped relationship of \u003cem\u003eNationalism\u003c/em\u003e to \u003cem\u003eCI\u003c/em\u003e is significant. It indicates that \u003cem\u003eGinvest\u003c/em\u003e has a partly mediating effect of \u003cem\u003eNationalism\u003c/em\u003e and \u003cem\u003eCI\u003c/em\u003e. Hypothesis 6 has been confirmed. From a low-carbon legitimacy perspective, firms initially reduce green resource allocation to prioritize economic development, but ultimately increase investments in environmental initiatives to fulfill legitimacy requirements.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediating effect results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCsr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCsr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eG invest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eG invest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.5035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.7290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.4329\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.9185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9397\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0821)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0849)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1838)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.3799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1680)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.7391\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-34.9456\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.3400\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-37.6280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.9923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.4573)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.7002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.2804)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCsr\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGreen invest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0570\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0459\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0240\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0237\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0188\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0200\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0055)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1722\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1757\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2544\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2266\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0374)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0386)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4400\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3577\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.3613\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0489)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0539)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0266\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0622\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0619\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0329\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0338\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0543\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0514\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.3143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.3142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1439)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1318\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3814\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.2104\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0864)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0727)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.2707)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.3931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1080)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.9533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.3958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108.0643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.4692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.2444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.1945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eFurther research\u003c/h2\u003e\u003cp\u003eConsidering firm with high-carbon emission industries are more sensitive to legality constraints, we classify companies in industries such as metal smelting, mining, petroleum processing, electricity and heat production and supply, textiles, papermaking, chemical manufacturing, rubber manufacturing and leather as high carbon emitters, and examine the impact of \u003cem\u003eNationalism\u003c/em\u003e on \u003cem\u003eCI\u003c/em\u003e under the enterprises with different degrees of carbon dependence. The regression results are reported in in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. In Column (1), the coefficients of \u003cem\u003eNationalism\u003c/em\u003e are 0.6511 and \u0026minus;\u0026thinsp;26.0685, and are significant at the 1% level. It shows that in high-carbon emissions firms there is a statistically significant inverted U-shaped relationship between national sentiment and carbon emission intensity, whereas this relationship is insignificant in low-carbon emissions firms in column (2). This divergence may arise from high-carbon emissions firms facing stringent regulatory constraints where emission levels directly determine organizational survival legitimacy, so enabling national sentiment to drive mitigation behaviors.\u003c/p\u003e\u003cp\u003eSecondly, government subsidy intensity manifests the institutional conversion efficacy whereby public resources transform into corporate endowments, we categorize sample firms into high- and low-subsidy groups based on the government subsidy mean. The regression outcomes for high-subsidy firms are reported in Column (3), while those for low-subsidy firms appear in Column (4). The coefficient of \u003cem\u003eNationalism\u003c/em\u003e for high-subsidy enterprises is 1.1826 (significantly positive at the 5% level), and the coefficient of \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is -39.9890 (significantly negative at the 10% level). Whereas, the coefficient of \u003cem\u003eNationalism\u003c/em\u003e for low-subsidy firms is 0.5923 (significantly positive at the 1% level), and the coefficient of \u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is -27.3020 (significantly negative at the 1% level). Although both low- and high-subsidy firms exhibit an inverted-U relationship between national sentiment and carbon emission intensity, the inverted-U pattern demonstrates stronger statistical significance among low-subsidy firms. This difference likely origins from that resource-constrained low-subsidy enterprises institutionalize patriotism as a legitimacy tool for rigid regulatory compliance, thereby amplifying policy-response significance through passive conformity.\u003c/p\u003e\u003cp\u003eThirdly, R\u0026amp;D investment level signifies the institutional pathway toward sustainable development through innovation, enterprises are stratified into high-R\u0026amp;D investment and low-R\u0026amp;D investment groups based on whether their R\u0026amp;D intensity exceeds the sample mean. For low-R\u0026amp;D investment firms (Column (6)), the linear Nationalism coefficient (0.9842) is positive and significant at the 1% level, while the quadratic term (-38.5057) shows negative significance at 1%. While low-R\u0026amp;D investment firms exhibit a significant inverted U-shaped relationship between national sentiment and carbon emission intensity, this pattern is absent among high-R\u0026amp;D counterparts. This difference might spring from innovation-constrained low-R\u0026amp;D firms initially increasing emissions due to resource limitations, then internalizing national sentiment through environmental governance to pursue institutional legitimacy, which cumulatively drives emission reductions. In contrast, high-R\u0026amp;D firms\u0026rsquo; abundant innovation resources make this relationship disappear.\u003c/p\u003e\u003cp\u003eGiven this result, we consider that high R\u0026amp;D investment may flow into non-green areas, while low R\u0026amp;D investment may also focus on green technologies. Therefore, we further analyzed companies\u0026rsquo; capabilities in green technology reserves and transformation. The results are reported in the \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e. We find that nationalism has an inverted U-shaped impact on carbon emissions only in enterprises with low-application in green patents, low-application in green invention patents, and low-application in green utility model patents. This result allows us to exclude the special case of low R\u0026amp;D but high proportion of green R\u0026amp;D.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-carbon emission enterprises\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow-carbon emission enterprises\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh-subsidies firms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow-subsidies firms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh R\u0026amp;D investment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow R\u0026amp;D investment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2065\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1826\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5923\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.4448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9842\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1660)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.5193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.5014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.7108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1767)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNationalism\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-26.0685\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-74.3567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-39.9890\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-27.3020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.3284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-38.5075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.0519)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(49.9983)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(22.1820)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(8.4612)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(68.2285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.6247)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0575\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0526\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0504)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0210)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDirector\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0228\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0179\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0482\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0218\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1404\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2252\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2972\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.1781\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2319\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0556)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0848)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0418)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1792\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.4232\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.2668\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.3382\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0746)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0695)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.2285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0512)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0593)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInde\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0788\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0436\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0707\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0232)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0295)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0392)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0202)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFag\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0509\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0497\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0738\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0217\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3220\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.6249\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.1267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1693)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.3218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.1785)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1513)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.3968\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.0768\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.1391\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.3524\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.2131\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.2810\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.1462)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1903)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.3840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.1130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.2807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.1262)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.5844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.8840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.4178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.4821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.1289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: The robust standard errors are reported in the parentheses. ***, **, * are significant at 1%, 5% and 10% levels, respectively.\u003c/p\u003e"},{"header":"Conclusions and discussion","content":"\u003ch2\u003eConclusions\u003c/h2\u003e\n\u003cp\u003eThis study investigates the relationship between national sentiment and carbon emission intensity using a panel dataset comprising 26,135 observations spanning the period from 2010 to 2023 through institutional lens.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings reveal an inverted U-shaped relationship between national sentiment and carbon emission intensity. During periods of weakened national sentiment, firms\u0026mdash;under pressure to secure economic legitimacy\u0026mdash;are more likely to prioritize economic benefits and development; this stands in consist with the extant literature which demonstrates that strong national sentiment enhances corporate green innovation (Wang et al., 2025b) and ESG performance (Arı and Sarıoğlu, 2025), which is aligned with the firm\u0026rsquo;s strategic economic interests and supports its long-term sustainable development. However, amid the realignment of social consensus, enterprises transition from pressures for economic legitimacy to those for low-carbon legitimacy, therefore driving active emission reduction efforts.\u0026nbsp;This finding represents a novel contribution that not only broadens the theoretical underpinnings of national sentiment within the realm of corporate environmental behavior, but also yields fresh implications for the design and implementation of carbon mitigation strategies.\u003c/p\u003e\n\u003cp\u003eSecondly, employing a dual analytical lens encompassing both market mechanisms and policy institutions, this study examines the impact of national sentiment on carbon emissions. We reveal that weaker market competition attenuates the inverted U-shaped relationship between national sentiment and carbon emissions (H2).\u0026nbsp;Market competition, acting as an institutional buffer, it attenuates the direct effect of national sentiment on emission outcomes. then, policymakers can strategically shape the competitive landscape to achieve targeted reductions in carbon emissions. Meanwhile, we also find that emission reduction target constraints have a positive impact on the inverted U-shaped relationship between national sentiment and carbon emissions (H3). Emission reduction target constraints, as an institutional policy objective, intensify the linkage between national sentiment and carbon emissions. This mechanism provides a theoretical foundation for policymakers to formulate and adjust emission reduction targets in order to achieve desired climate outcomes. Moreover, our findings further indicate that environmental punishment significantly strengthens the inverted U-shaped relationship between national sentiment and carbon emissions (H4). We extend the institutional perspective on how national sentiment influences carbon emissions and offers a theoretical foundation for designing effective environmental penalty systems.\u003c/p\u003e\n\u003cp\u003eThirdly, from the dual perspectives of social-norm pressure and low-carbon moral pressure, this article examines the mediating roles of corporate social responsibility and green investment. Our findings demonstrate that firms indeed can shape carbon emissions by addressing both social and low-carbon legitimacy demands. This study examines the potential of social normative pressure to promote emissions reduction, mediated through CSR. National sentiment is found to exert a complex, U-shaped influence on CSR, ultimately encouraging firms to fulfill their social responsibilities, and CSR serves as a critical mechanism that opens new pathways for emission reductions. Both corporations and governments can achieve emission reduction objectives by strategically enhancing CSR engagement. Moreover, this paper also examines low-carbon normative pressure, which is operationalized through green investment. The relationship between national sentiment and green investment is found to be non-linear, following a U-shaped pattern. then, increased green investment facilitates progress toward carbon reduction goals. This offers a novel perspective for corporations seeking to enhance green investment by fostering national sentiment. Green investment serves as a pivotal mechanism for achieving emission reduction and holds strategic importance in advancing the sustainable development of the broader economy.\u003c/p\u003e\n\u003cp\u003eFinally, building upon our initial findings, we further investigated the differential effects of national sentiment on carbon emissions across three distinct types of firms: those characterized by high pollutions, those receiving small government subsidies, and those with low research and development activities.\u0026nbsp;①In high-polluting enterprises, national sentiment exerts a significant influence on carbon emissions. High-polluting enterprises are regarded as critical targets for emission reduction efforts, and it implies that the degree of pollution intensity heightens the importance of informal institutions\u0026mdash;such as national sentiment\u0026mdash;in shaping corporate behavior. Then, national identity may inadvertently reinforce carbon-intensive practices.\u0026nbsp;②Meanwhile, we also find national sentiment exerts a significant influence in low subsidy enterprises.\u0026nbsp;These findings indicate that low-subsidy enterprises are more inclined to leverage national sentiment as a strategy for market competition. Policy interventions should therefore aim to channel such sentiment toward emission reduction objectives through targeted incentives and regulatory guidance. At the corporate level, management should strengthen strategic integration by incorporating national sentiment into their core strategic planning, fostering constructive workplace initiatives that align national sentiment values with long-term corporate development and environmental sustainability.\u0026nbsp;③\u0026nbsp;Furthermore, this study also reveals a more pronounced association between national sentiment and carbon emissions in low-R\u0026amp;D enterprises, implying that such firms may leverage national sentiment to compensate for weaker innovation capacity in shaping green practices. This suggests that, within less innovation-driven contexts, national identity can become intrinsically entangled with emission outcomes. Therefore, emission reduction policies should target low-R\u0026amp;D firms through tailored guidance frameworks that translate national sentiment into tangible drivers of low-carbon innovation. Concurrently, corporate management should strategically align national sentiment with broader national strategies and long-term governance, ensuring it reinforces\u0026mdash;rather than undermines\u0026mdash;sustainable development objectives.\u0026nbsp;④What\u0026rsquo;s more, this paper further examine R\u0026amp;D output, national sentiment is significantly influence carbon emission in those with low levels of green patent applications, including both green invention patents and green utility model patents,\u0026nbsp;This finding indicates that firms with low green patent applications may leverage more national sentiment in addressing environmental challenges. Policy interventions should target such firms by strategically channeling national sentiment toward the development of green patents through tailored incentives and institutional guidance. Meanwhile, corporate management ought to avoid superficial symbolic integration of national sentiment and instead focus on its substantive alignment with the development of green technology systems and R\u0026amp;D investments, thereby achieving synergy among national sentiment, green innovation, and emission reduction outcomes.\u003c/p\u003e\n\u003ch2\u003eLimitation future research\u003c/h2\u003e\n\u003cp\u003eThis study is characterized by several limitations that point toward meaningful directions for future research. First, this study focuses exclusively on Chinese listed companies; therefore, generalizing the findings to unlisted firms and non-Chinese contexts would require further demonstration. These entities are often subject to different institutional pressures and governance structures, and may provide particularly relevant settings for examining the relationship between national sentiment and carbon emissions. Further research is warranted to explore this dynamic within cross-country comparative frameworks. Second, although this study investigates the influence of national sentiment from an institutional theory perspective, carbon emission intensity is likely shaped by the synergistic effects of multiple internal and external factors. Future research could thus examine how other determinants interact with national sentiment to jointly affect emission levels. Third, our findings identify CSR and green investment as mediating mechanisms between national sentiment and carbon emission intensity; however, other potential channels remain unexplored and warrant further investigation in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompliance with Ethical Standards:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding: (1) Humanities and Social Science Foundation of the Ministry of Education of China, [24YJA630122]; (2) General Project of Social Science Fund of Fujian Province [FJ2024B114];\u003c/p\u003e\n\u003cp\u003eConflict of Interest: No conflict exists\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval: This article does not contain any studies with human participants or animals performed by any of the authors\u003c/p\u003e\n\u003cp\u003eInformed consent: This article does not contain any studies with human participants performed by any of the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional information:\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Mengyao Xia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGL: Writing the original draft and reviewing. ZD:Reviewing and editing. XM:Conceptualisation, methodology, data curation. CH:Supervision.YX:Visualization\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhn, S. J. (2020). Three characteristics of technology competition by IoT-driven digitization. \u003cstrong\u003e\u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e\u003c/strong\u003e, 157: 120062. https://doi.org/10.1016/j.techfore.2020.120062\u003c/li\u003e\n\u003cli\u003eAi, K.,Yan, X. (2024). Can Green Infrastructure Investment Reduce Urban Carbon Emissions:Empirical Evidence from China. \u003cstrong\u003e\u003cem\u003eLand\u003c/em\u003e\u003c/strong\u003e, 13(2): 226. https://doi.org/10.3390/land13020226\u003c/li\u003e\n\u003cli\u003eAlsagr, N.,Ozturk, I. (2024). 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Spatio-temporal impact of R\u0026amp;D innovation on carbon emission intensity: A comparative analysis of Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations. \u003cstrong\u003e\u003cem\u003eWorld Development Sustainability\u003c/em\u003e\u003c/strong\u003e, 7: 100247. https://doi.org/10.1016/j.wds.2025.100247\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"National sentiment, Carbon emission intensity, Institutional theory, Corporation social responsibility, Green investment","lastPublishedDoi":"10.21203/rs.3.rs-7693117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7693117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgainst the backdrop of rising corporate carbon emissions, managerial attributes are garnering increasing scholarly scrutiny. Utilizing a panel dataset from China\u0026rsquo;s A-share market between 2010 and 2023, this paper employs an institutional theory perspective to examine the relationship between national sentiment and carbon emission intensity. We reveal a significant inverted U-shaped relationship between national sentiment and carbon emission intensity. Meanwhile, we also find that market competition negatively moderates the relationship between national sentiment and carbon emission intensity, whereas emission reduction target constrains and environmental penalties positively moderate it. Moreover, corporate social responsibility and green investment act as significant mediating channels in this relationship. Furthermore, national sentiment significantly influences enterprises characterized by high pollution, low subsidies, or low R\u0026amp;D intensity, and its impact on green patent, encompassing both green invention and green utility models still exhibits a significant inverted U-shaped relationship.\u003c/p\u003e","manuscriptTitle":"When Nationalism Meets Environmentalism: An Inverted U-Curve Linking National Sentiment to Carbon Emission Intensity Through an Institutional Lens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 05:16:17","doi":"10.21203/rs.3.rs-7693117/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-20T06:37:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T04:12:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T08:52:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321329478081981831887136659968754193643","date":"2026-02-25T02:09:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T18:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196404831347301149427085388855071295539","date":"2026-02-05T15:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131162514275966591569481561854835598789","date":"2026-01-02T13:28:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322172157628836821930511624415166024286","date":"2025-12-07T03:39:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T05:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175378853854571952673635120700730487933","date":"2025-10-27T15:37:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324978869910307548668646451957781075325","date":"2025-10-27T12:11:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T11:17:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-25T08:16:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-25T02:36:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-11T08:47:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-11T08:40:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"930903f7-c7b0-4eca-9218-527423562a8a","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57200428,"name":"Business and commerce/Economics"},{"id":57200429,"name":"Social science/Economics"},{"id":57200430,"name":"Earth and environmental sciences/Environmental sciences"},{"id":57200431,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-04-11T16:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 05:16:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7693117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7693117","identity":"rs-7693117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}