Environmental regulation tools, innovative human capital and industrial green and low-carbon transformation

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Systematically clarifying the role of innovative human capital in promoting industrial green and low-carbon transformation through environmental regulation tools is crucial for achieving the "dual carbon" goals. This paper, based on endogenous growth theory and relevant theories of environmental economics, uses provincial panel data from China's industry from 1997 to 2021 as a sample to empirically examine the impact of environmental regulation tools and innovative human capital on industrial green and low-carbon transformation. The study finds that different types of environmental regulation tools exhibit an inverted "U" -shaped characteristic, with effects initially increasing and then decreasing, or a "U" -shaped characteristic with effects initially decreasing and then increasing; innovative human capital can promote industrial green and low-carbon transformation and also plays a moderating role in the relationship between environmental regulation tools and industrial green and low-carbon transformation, although this moderating effect varies across different regions. environmental regulation tools innovative human capital industrial green and low-carbon transformation regulatory effect regional differences Figures Figure 1 Introduction Since the reform and opening up, China has relied on an industrial-led development strategy to drive rapid economic expansion, successfully making a significant leap from being among the poorest countries to becoming one of the upper-middle-income nations. However, while China's industrial sector has fueled rapid economic growth, its extensive development model has also led to substantial resource consumption and pollution emissions, increasingly highlighting the contradiction between economic development and environmental protection. This has become a bottleneck constraining China's efforts to achieve its "dual carbon" goals and promote high-quality economic development[1]. In order to solve the increasingly severe environmental problems, the Chinese government has clearly pointed out that "we should accelerate the green transformation of development mode. We will make economic and social development green and low-carbon, and take "coordinated efforts to reduce carbon, reduce pollution, expand green and grow" as an important way to build an ecological civilization. It is necessary to "accelerate the green transformation of development mode" and "build a green, low-carbon and circular economic system", which provides a clear policy guidance for industrial green transformation. As the primary sector responsible for energy and resource consumption and carbon emissions in China's economic operations and development landscape, whether the industrial sector can achieve a green and low-carbon transformation in the new era is not only crucial for driving the green transformation of development methods and achieving high-quality economic development but also a matter of responsibility in addressing global climate change and building a community with a shared future for mankind. Therefore, how to promote the green and low-carbon transformation of industry has become a hot topic in the field of environmental economics both domestically and internationally, and an important issue in the process of China's high-quality economic development. On the one hand, environmental regulation, as a core strategy and institutional support in the environmental governance system, is widely recognized as a key mechanism and effective tool for driving green technological innovation and promoting the transition to a green or low-carbon economy. This assertion has been fully validated in academic research, with studies by Hou et al.(2018)[2], Du et al. (2021)[3], and Zhang et al. (2022) [4]providing strong evidence. These studies collectively demonstrate that environmental regulation plays an irreplaceable role in guiding companies to adopt more environmentally friendly production methods and promote technological innovation. On the other hand, innovative human capital, as a higher form of human capital, not only helps optimize resource allocation and improve efficiency but also significantly promotes the innovation and development of green technology, thereby having a profound impact on the green transformation and high-quality development of the economy. The research findings by Jin et al. (2022)[5] provide an in-depth analysis, revealing the critical role of innovative human capital in driving green transformation. Based on the above policy background and existing research foundation, this study proposes three core issues: First, do different types of environmental regulatory tools (command-type environmental regulation, investment-type environmental regulation, expense-type environmental regulation, non-governmental environmental organizations, and public environmental supervision) have differentiated characteristics and nonlinear relationships in their impact on industrial green transformation? Second, how does innovative human capital play a moderating role in the process of promoting industrial green transformation through environmental regulatory tools, and does its mechanism vary depending on the type of environmental regulatory tool? Third, considering the reality of uneven regional development in China, does the moderating effect of innovative human capital exhibit heterogeneous characteristics across eastern, central, and western regions? To address these questions, this study uses provincial panel data from Chinese industry from 1997 to 2021 as the research sample. After empirically testing the direct impacts of environmental regulatory tools and innovative human capital on industrial green and low-carbon transformation, we further delve into the moderating role of innovative human capital and conduct a detailed analysis of its heterogeneous effects on different environmental regulatory tools and in different regions, aiming to provide more precise theoretical support and empirical evidence for policy formulation and practice. 1. Theoretical analysis and research hypothesis 1.1 Environmental regulation tools and industrial green and low-carbon transformation Environmental regulation refers to the regulatory power exercised by governments or the public for the purpose of environmental protection, using tangible systems or intangible awareness as means to constrain enterprises or individuals that cause environmental pollution. Depending on the implementing entity, environmental regulation can be divided into formal environmental regulation, which is primarily government-led, and informal environmental regulation, which is primarily led by the public [6]. Formal environmental regulation can be further categorized into three tools: Command-Type environmental regulation, investment-type environmental regulation, and cost-based environmental regulation; informal environmental regulation can be divided into two tools: non-governmental environmental organizations and public participation in environmental protection. The impact of command-type environmental regulations on corporate green and low-carbon transformation can either promote or inhibit it, depending on the intensity of these regulations. When the government first formulates environmental regulation policies, the intensity of command-type environmental regulations is relatively low. If companies improve their production and pollution emission indicators through green technological innovation and apply the results of such innovations to industrial processes, they can reduce pollution emissions while also gaining more environmental economic benefits, corporate competitiveness, and social benefits[7], creating "compensatory gains" that exceed the cost of environmental regulation, thus driving corporate green and low-carbon transformation. However, once the intensity of command-type environmental regulations exceeds a certain threshold, the strong constraints, high costs, and low efficiency associated with these regulations make production costs excessively high for companies, "crowding out" a significant amount of resources. The cost of environmental regulation becomes greater than the "compensatory gains," making it unfavorable for companies to engage in green R&D activities [8], thereby inhibiting corporate green and low-carbon transformation. The impact of investment-type environmental regulation on corporate green and low-carbon transformation can vary depending on its intensity. When investment-type environmental regulation is first implemented, the negative environmental externalities of enterprises are addressed, leading them to shift part of their pollution control investments to the research and development of green technologies and products, thereby improving production efficiency and promoting green and low-carbon transformation[9]; as the intensity of investment-type environmental regulation exceeds a critical point, government involvement in environmental governance largely eliminates the negative environmental externalities generated by enterprises, which may lead them to avoid technological reforms and instead develop a dependency mindset that undervalues the research and development of green technologies and products[10], which is detrimental to industrial green and low-carbon transformation. The impact of expense-type environmental regulations on corporate green and low-carbon transformation varies depending on their intensity. When expense-type environmental regulations are first implemented, they act as a short-term measure directly affecting the production process of enterprises. Companies must allocate funds to pay pollution fees, taxes, or purchase emission rights, which diverts resources from research and development, leading to reduced innovation [11], thereby suppressing green and low-carbon transformation; as the intensity of expense-type environmental regulations gradually increases and passes a critical point, the profit margin formed by companies paying pollution fees decreases, even turning negative. This forces companies to make early strategic moves, adopting green technological innovations to enhance product competitiveness and production efficiency while reducing emissions, thus promoting corporate green and low-carbon transformation[12]. The impact of non-governmental environmental organizations on the green and low-carbon transformation of industries varies depending on their level of development. In the early stages of their development, due to a lack of effective supervision and internal management mechanisms, these organizations often suffer from low efficiency, which can negatively affect companies' green technological innovation[13], hindering the transition to green and low-carbon practices; as they mature, under the backdrop of natural selection, the professionalism, impartiality, and non-profit nature of these organizations become evident. Measures such as publishing corporate pollution emission lists and analyzing government policies can promote the green and low-carbon transformation of enterprises[14]. When the public first began to participate in environmental governance, due to their small numbers, the government could provide precise feedback and regulate polluting enterprises, effectively curbing corporate emissions through supervision and reporting [15], promoting the green and low-carbon transformation of industry; however, when public participation in environmental governance becomes excessive, it may lead to false reports or even defamation, which increases the government's scrutiny of reported information, slows down feedback, and increases pressure on enterprises. This, in turn, can have negative impacts on production activities, dampening companies' enthusiasm for green technological innovation and diverting R&D funds [16], hindering the green and low-carbon transition of industry. H1: Environmental regulation tools have a significant impact on the green and low-carbon transformation of industry 1.2 Innovative human capital and industrial green and low-carbon transformation Innovative human capital, also known as advanced human capital, refers to the human resources that possess certain innovative capabilities and potential, bringing about innovation benefits for enterprises through increasing marginal returns and output multiplier effects [17]. Some empirical studies have found that innovative human capital serves as a crucial driving force for technological progress and economic growth, playing an important role in the green development of the economy. Pargal et al. (1996) [6]found that the level of regional pollution emissions is related to the local human capital level. Avvisati et al. (2013)[18] discovered that companies with more highly educated labor tend to implement environmental standards and increase their efforts in environmental protection, and whether the "pollution haven hypothesis" exists is also associated with the level of local human capital. The theoretical foundation for the impact of human capital on green development mainly stems from Romer's (1990) endogenous growth model[19]: human capital in the R&D sector is a key factor in achieving technological progress and efficiency. As a more advanced form of human capital, R&D human capital not only possesses the general attributes of human capital but also has the unique capability of innovative scarcity, characterized by increasing marginal returns and output multiplier effects. This not only creates conditions for green technology research and development but also facilitates the introduction, use, and absorption of advanced clean production technologies from abroad. Therefore, innovative human capital can influence the industrial transition to green and low-carbon through these two pathways of technological innovation. First, The level of innovative human capital directly affects the success rate of technological innovation in enterprises, which in turn impacts the green and low-carbon transformation of industries. Due to the uncertainty associated with new technology development, there is a certain probability of failure for companies engaging in green technological innovation; it does not necessarily lead to green innovation outcomes. An increase in the level of innovative human capital can enhance the likelihood of green technological innovation, increase both the quantity and variety of such innovations, and provide a greater advantage and higher success rate when developing new technologies. Conversely, if a company has a lower level of innovative human capital, this indicates a lower efficiency in utilizing knowledge and technology, making it difficult to convert knowledge and technical equipment into technological progress [20]. This results in increased investment in green innovation without a noticeable improvement in innovation output, leading to slow progress in the green and low-carbon transformation of industries. Second, the level of innovative human capital directly affects a company's ability to imitate and absorb technology, which in turn impacts the green and low-carbon transformation of industries. When companies choose to introduce green production technologies or purchase equipment to reduce pollution emissions, the efficiency of using these green technologies and equipment does not immediately reach its optimal state. It requires innovative human capital to learn and absorb the use of technology and equipment, fully mastering their usage methods. Companies with high levels of innovative human capital can effectively absorb and master introduced equipment and technologies, quickly achieving green production and reducing pollution emissions. However, if the level of innovative human capital fails to meet the requirements for absorbing and assimilating introduced technologies, it will lead to low utilization rates of green technologies, preventing their advantages from being fully realized. The extent to which new production models reduce pollution emissions and improve production efficiency may be very low, even lower than those of traditional production models [21]. H2: Innovative human capital will promote the green and low-carbon transformation of industry 1.3 The moderating effect of innovative human capital The green and low-carbon transformation of industry requires substantial resource investment, often accompanied by certain risks. This makes companies tend to prioritize pollution control over green innovation when facing environmental regulations. Therefore, achieving the green and low-carbon transformation of industry is crucial for both resources and risk resistance. Innovative human capital can help reduce R&D risks and improve resource utilizatio[5], potentially moderating the relationship between environmental regulatory tools and the green and low-carbon transformation. First, innovative human capital moderates the relationship between Command-Type environmental regulation and industrial green and low-carbon transformation. When the intensity of Command-Type environmental regulation is low, the "innovation compensation effect" generated by environmental regulatory constraints outweighs the "resource crowding-out effect," thus promoting green technological innovation in industrial enterprises. If the level of innovative human capital in these enterprises increases at this time, it will lead to a higher success rate for green technological innovation and an accelerated pace of green and low-carbon transformation. However, when the intensity of Command-Type environmental regulation exceeds a certain threshold, the regulatory constraints exert a "resource crowding-out effect" on normal production inputs and R&D investments, making the additional production costs higher than the "innovation compensation effect" brought about by green technological innovation. If the level of innovative human capital in enterprises is increased, it provides more room for buffering, thereby mitigating the inhibitory effects of Command-Type environmental regulation on industrial green and low-carbon transformation [22]. Second, innovative human capital moderates the relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. When the intensity of investment-oriented environmental regulation is low, companies can address their environmental negative externalities, shifting part of their pollution control investments to research and development of green technologies and products, thereby improving production efficiency and promoting green and low-carbon transformation. If the level of innovative human capital increases at this time, it will help companies improve R&D success rates, accelerating the pace of industrial green and low-carbon transformation; as the intensity of investment-oriented environmental regulation gradually increases, the effectiveness of government environmental governance improves, and most of the environmental negative externalities generated by companies are eliminated, they may not reform their existing technologies but instead develop a dependency mindset that undervalues the research and development of green technologies and products, which is detrimental to industrial green and low-carbon transformation. If the level of innovative human capital increases at this time, companies will engage in new rounds of technological innovation based on their existing technologies, thus reducing the negative impact of investment-oriented regulation on industrial green and low-carbon transformation[23]. Third, innovative human capital moderates the relationship between cost-based environmental regulation and industrial green and low-carbon transformation. When the intensity of cost-based environmental regulation is low, it acts as a short-term measure directly impacting the production process of enterprises. As a result, companies tend to allocate funds primarily for paying pollution fees, taxes, or purchasing emission rights, which diverts research and development funds, leading to reduced innovation and suppressed green and low-carbon transformation. If the level of innovative human capital increases at this time, it can help companies secure more R&D funding for innovation, thereby mitigating the negative effects of cost-based environmental regulation. As the intensity of cost-based environmental regulation gradually increases and passes a critical point, the profit margin formed by companies paying pollution fees decreases or even turns negative. This forces companies to make early strategic moves, shifting towards green technological innovations that enhance product competitiveness and reduce pollution emissions, thus promoting their green and low-carbon transformation. If the level of innovative human capital increases at this time, it will increase the success rate of green technological innovations and reduce R&D risks, further facilitating industrial green and low-carbon transformation[24]. Fourth, innovative human capital moderates the relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. When the number of non-governmental environmental organizations is small, due to the lack of effective supervision and internal management mechanisms, work efficiency is low, which in turn negatively impacts companies' green technological innovation behaviors, hindering the transition to green and low-carbon [25]. If at this point the level of innovative human capital increases, companies, having had their credibility and income depleted, need to engage in certain green production activities, thus mitigating the negative impact of non-governmental environmental organizations on industrial green and low-carbon transformation; when the number of non-governmental environmental organizations increases, under the backdrop of survival of the fittest, the professionalism, fairness, and non-profit nature of these organizations become evident. Measures such as publishing corporate pollution emission lists and analyzing government policies can promote corporate green and low-carbon transformation. If at this point the level of innovative human capital rises, it will be even more conducive to corporate green technological innovation and the promotion of industrial green and low-carbon transformation[26]. Fifth, innovative human capital moderates the relationship between public environmental participation and industrial green and low-carbon transformation. When the public first begins to participate in environmental governance, due to their small numbers, the government can provide precise feedback and regulate polluting enterprises, effectively curbing corporate pollution emissions through supervision and reporting, thus promoting industrial green and low-carbon transformation. If at this point the level of innovative human capital in enterprises increases, they will place greater emphasis on the research and development of green technologies, with higher success rates and faster green and low-carbon transformation. However, when public participation in environmental governance becomes excessive, it may lead to false reports or even defamation, which increases the government's scrutiny of reported information, slows down feedback, and increases pressure on enterprises, thereby negatively impacting their production activities, suppressing their enthusiasm for green technology innovation, and diverting R&D funds, which is detrimental to industrial green and low-carbon transformation. If the level of innovative human capital in enterprises rises, it will give them more confidence to continue green technology research and development, thus reducing the negative impact of public environmental participation on industrial green and low-carbon transformation[27]. Based on this, the following research hypotheses are proposed: H3: Innovative human capital moderates the relationship between environmental regulation tools and industrial green and low-carbon transformation 2. Model design, variable selection and descriptive statistics 2.1 Model design Firstly, in order to test the impact of environmental regulation tools on industrial green and low-carbon transformation, the quadratic term of environmental regulation tools is introduced. The model is constructed as follows: Secondly, according to the theoretical analysis above, in order to test the impact of innovative human capital on industrial green and low-carbon transformation, the benchmark model is constructed as follows: Subsequently, combining Eq. ( 1 ) and Eq. ( 2 ), the interaction term of innovative human capital and environmental regulation tools and the interaction term of square of innovative human capital and environmental regulation tools are introduced to explore the moderating effect of innovative human capital: Wherere LGL it presents industrial green and low-carbon transformation, and represents environmental regulation tools, including command-type environmental regulation (cer it ), investment-type environmental regulation (ier it ), expense-type environmental regulation (cber it ), non-governmental environmental organizations (ngo it ), and public participation in environmental protection (pu it s); thr it Represents innovative human capital and control it represents the control variable; i is the province, t is the time; a 0 is the constant term, u t which represents the unobservable provincial individual effect, v t is the time effect, and ε it is the random disturbance term. Due to the lag in the impact of formal and informal environmental regulation on industrial green and low-carbon transformation, LGL it starts from the leading first-period item of Y. 2.2 Variable selection (1) Dependent variable Industrial green and low-carbon transformation (LGL it ). The industrial green and low-carbon transformation index is measured by the green and low-carbon total factor productivity measured by the super-efficiency EBM model and GML model based on the common frontier and considering the undesirable output, and accumulated. The larger the value is, the better the effect of industrial green and low-carbon transformation is. (2) Core explanatory variables Environmental Regulation Tools:① Command-Type Environmental Regulation (cer): This paper combines the measurement methods of Du et al.(2021)[ 3 ], using the entropy value method to calculate command-type environmental regulation based on the comprehensive utilization rate of "three wastes." ② Expense-type Environmental Regulation (cber). Drawing on Guo et al. (2020) 's research[ 28 ], the ratio of pollution discharge fee revenue to GDP is used as a measure. Specifically, from 1997 to 2006, it represents the pollution discharge fee revenue; from 2007 to 2017 and thereafter, it represents the amount collected into the treasury; from 2018 to 2021, it represents the environmental protection tax amount. The original data comes from the "China Tax Yearbook." ③ Investment-Type Environmental Regulation (ier). Drawing on Dong et al. (2024) ' s research[ 10 ], the ratio of investment in environmental pollution control to GDP is used as a measure. The original data comes from the "China Environmental Statistics Yearbook." ④ Non-Governmental Environmental Organizations (engo). The number of non-governmental organizations in each province, the total of actual social organizations at year-end, actual funds at year-end, and actual private non-enterprise units at year-end is used to measure [ 13 ].⑤ Public Environmental Participation (sup). This paper draws on Dong et al. (2024) 's approach[ 10 ], using the number of letters related to environmental issues to measure public environmental participation. It is a direct indicator reflecting citizens' environmental participation, where residents can directly report environmental problems to local environmental protection agencies through letters. The more letters there are, the higher the level of public environmental participation and the stronger the willingness to supervise. Innovative Human Capital (thr). Innovative human capital refers to human resources with certain innovation capabilities and potential that can bring innovative benefits to enterprises. Existing research has not established a unified standard for measuring innovative human capital. Some studies measure it from an educational perspective by selecting the proportion of people employed in higher education or the proportion of those with a bachelor's degree or above. Some scholars measure it from an investment perspective by using actual R&D expenditure or multiplying the number of graduates with a bachelor's degree or above and professional technicians by the average monetary wage of employees. Other scholars focus on the quality of output from innovative human capital, choosing indicators such as the number of faculty members, educational expenditure, research funding, and fixed assets of universities to measure its input. This chapter draws on the research of Jin et al.[ 5 ], using the ratio of R&D personnel to total staff to measure the stock of innovative human capital. At the same time, in order to intuitively reflect the changing trend of innovative human capital, the time change map of innovative human capital by province is drawn. As can be seen from Fig. 1 , on the whole, the proportion of innovative human capital in each province shows an upward trend, but there are obvious differences in the changes of innovative human capital among different regions. This indicates that with the improvement of education level and the popularization of compulsory education, China's stock of innovative human capital is gradually increasing, but the imbalance between regions still exists. (3) Control variables ① R&D investment (ede) uses the perpetual inventory method to convert internal R&D expenditures into R&D capital stock, then measures R&D investment as the ratio of R&D capital stock to industrial output value. ② Foreign direct investment (fdi) is measured by the proportion of foreign direct investment in each province's actual GDP. ③ Urbanization level (ul) reflects urbanization through the ratio of urban population to the total regional population. ④ Industrial structure (is) reflects the level of industrial structure by the proportion of the tertiary industry's output value to local GDP in each province. 2.3 Data sources and variable descriptive statistics Based on the possibility and completeness of data, this paper selects panel data of China's industrial provinces from 1997 to 2021, covering 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan). The relevant data for the dependent and independent variables mentioned above are all sourced from the "China Statistical Yearbook," "China Industrial Statistical Yearbook," "China Science and Technology Statistical Yearbook," "China Environmental Yearbook," "China Tax Yearbook," "China Environmental Statistics Yearbook," and relevant national and provincial statistical bureaus. Missing values in the data consolidation process were uniformly filled using the mean method. In order to make the data more stable, weaken the heteroscedasticity and introduce the concept of elasticity, we log-transformed the above variable data. The descriptive statistical results after processing are shown in Table 1 . Table 1 Descriptive statistics variable observed value average value standard error least value crest value lnLGL 750 0.625 0.417 -0.318 1.755 lncer 750 7.549 1.398 -0.594 10.718 lnier 750 9.067 1.250 4.669 11.861 lncber 750 10.382 1.029 6.648 12.791 lnengo 750 9.075 1.098 5.524 11.547 lnsup 750 9.446 1.385 3.912 12.556 lnthr 750 -5.421 0.873 -9.779 -1.620 lnede 750 13.422 1.630 7.411 17.184 lnfdi 750 5.599 1.923 0.019 10.720 lnul 750 3.816 0.421 2.476 4.495 lnis 750 -0.883 0.202 -1.373 -0.176 3. Empirical analysis Firstly, the impact of environmental regulation tools on industrial green and low-carbon transformation is analyzed; secondly, the impact of innovative human capital on industrial green and low-carbon transformation is studied; finally, the moderating effect of innovative human capital on environmental regulation tools and industrial green and low-carbon transformation and regional heterogeneity are further analyzed. 3.1 The impact of environmental regulation tools on the green and low-carbon transformation of industry Table 2 Impact of environmental regulation tools on industrial green and low-carbon transformation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) variable lncer lncer lnier lnier lncber lncber lnengo lnengo lnpus lnpus lner 0.018*** 0.025 0.089*** 0.077*** -0.259*** 0.332 -0.265*** -0.344*** 0.027*** 0.015 (4.200) (1.527) (4.503) (4.877) (-11.887) (1.626) (-7.723) (-8.917) (3.595) (0.346) lner2 -0.009*** -0.064*** 0.040*** 0.060*** 0.032*** (-5.113) (-4.936) (2.910) (7.335) (6.007) lnede 0.177*** 0.176*** 0.177*** 0.199*** 0.121*** 0.128*** 0.127*** 0.155*** 0.176*** 0.171*** (9.938) (9.883) (9.963) (11.081) (7.290) (7.710) (7.020) (8.786) (10.024) (9.332) lnfdi 0.163*** 0.162*** 0.162*** 0.154*** 0.137*** 0.132*** 0.129*** 0.102*** 0.165*** 0.167*** (12.277) (12.160) (12.029) (11.579) (11.466) (10.969) (9.701) (7.697) (12.586) (12.611) lnul 0.386*** 0.403*** 0.388*** 0.345*** 0.044 0.085 0.117 0.211** 0.293*** 0.301*** (4.200) (4.329) (4.217) (3.807) (0.511) (0.977) (1.242) (2.325) (3.104) (3.176) lnis -0.669*** -0.670*** -0.691*** -0.709*** -0.715*** -0.766*** -0.731*** -0.690*** -0.663*** -0.665*** (-11.180) (-11.207) (-10.785) (-11.287) (-13.517) (-13.831) (-12.885) (-12.693) (-11.270) (-11.304) Constant -4.796*** -4.833*** -4.902*** -1.237 -4.520*** -2.637*** -5.388*** -10.769*** -4.711*** -4.514*** (-15.131) (-15.169) (-14.090) (-1.514) (-16.003) (-3.741) (-17.463) (-13.625) (-14.997) (-12.184) time effect control control control control control control control control control control Provincial effect control control control control control control control control control control F statistics 395.01*** 329.55*** 393.90*** 346.65*** 525.90*** 445.78*** 449.48*** 420.51*** 405.76*** 338.31*** Hausman 78.55*** 75.13*** 63.22*** 66.74*** 36.19*** 36.94*** 83.84*** 89.00*** 79.06*** 77.49*** Observations 750 750 750 750 750 750 750 750 750 750 R-squared 0.787 0.787 0.786 0.796 0.831 0.834 0.808 0.825 0.791 0.792 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses. The first term of the command-type environmental regulation in column (1) of Table 2 is positive and significant, while the coefficient of the second term of the command-type environmental regulation in column (2) is negative at the 1% significance level. This indicates a significant inverted "U" relationship between command-type environmental regulation and industrial green and low-carbon transformation. Command-type environmental regulation initially promotes industrial green and low-carbon transformation, but when its intensity exceeds a certain threshold, it begins to inhibit this transformation. The results in column (3) show that the first term of investment-type environmental regulation is positive and significant, while the coefficient of the second term of investment-type environmental regulation in column (4) is negative and passes the 1% statistical significance test. This suggests an significant inverted "U" relationship between investment-type environmental regulation and industrial green and low-carbon transformation. The results in column (5) indicate that the first term of expense-type environmental regulation is negative and significant, while the coefficient of the second term of expense-type environmental regulation in column (6) is positive and passes the 1% statistical significance test. This suggests a significant "U" relationship between expense-type environmental regulation and industrial green and low-carbon transformation. The results in column (7) show that the first term of non-governmental environmental organizations is negative and significant, while the coefficient of the second term of non-governmental environmental organizations in column (8) is positive and passes the 1% statistical significance test. This suggests a significant "U" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. The results in column (9) show that the first-order term of public environmental participation is positive and significant, while the second-order term coefficient of public environmental participation in column (10) is negative. Both have passed the 1% statistical significance test, indicating a possible inverted "U" relationship between public environmental participation and industrial green and low-carbon transformation. The above results also confirm H1. 3.2 Impact effect of innovative human capital on industrial green and low-carbon transformation Table 3 reports the impact of innovative human capital on industrial green and low-carbon transformation under the fixed effect model, where models (1), (2), (3) and (4) are the regression results of national data, eastern region, central region and western region respectively. Table 3 The impact of innovative human capital on industrial green and low-carbon transformation variable nationwide east central section west (1) (2) (3) (4) lnthr 0.570** 0.394* 0.974*** 0.708** (2.600) (2.227) (4.156) (2.209) lnede 0.147*** 0.174*** 0.190* 0.177** (3.476) (3.488) (2.311) (2.436) lnfdi 0.150*** 0.078 0.156* 0.143*** (4.887) (1.372) (2.327) (5.506) lnul 0.247 -0.662* 0.328 -0.151 (1.031) (-2.065) (0.675) (-0.422) lnis -0.720*** -0.412** -1.050*** -0.505*** (-6.205) (-2.334) (-9.083) (-3.172) Constant -5.298*** -2.317* -6.931*** -4.721*** (-6.623) (-2.232) (-5.477) (-4.956) time effect control control control control Provincial effect control control control control F statistics 116.36*** 99.50*** 47.85*** 71.18*** Hausman 103.23*** 142.37*** 94.19*** 81.29*** Observations 750 250 150 350 R-squared 0.800 0.758 0.888 0.829 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses The regression results in Table 3 show that the coefficient of the core explanatory variable, innovative human capital, is significant at least at the 10% level in all models, confirming H2, which states that innovative human capital has a significant and robust positive impact on the green and low-carbon transformation of industry. This means that innovative human capital has a significant positive effect on the green and low-carbon transformation of industry both nationally and regionally, indicating that following the strategy of "innovation-driven development and talent-oriented" helps achieve this transformation. Other control variables, combined with the results from regression using national data in Model (2), show that R&D investment is significantly positive at the 1% level, suggesting that increasing R&D investment has a positive impact on the green and low-carbon transformation of industry, which is consistent with the previous regression results; foreign direct investment is also positive and significant at the 1% level, indicating that from a national perspective, foreign direct investment has a technology spillover effect that effectively promotes the green and low-carbon transformation of industry; the coefficient for urbanization level is not significant; the coefficient for industrial structure is significantly negative at the 1% level, indicating that an increase in the output value of the tertiary sector is detrimental to the green and low-carbon transformation of industry. 3.3 Test of the moderating effect of innovative human capital To study the moderating effect of innovative human capital on the relationship between environmental regulation tools and industrial green and low-carbon transformation, the model introduces interaction terms between environmental regulation tools and innovative human capital, as well as the quadratic term of innovative human capital. Models (1) to (5) use Command-Type environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, with specific regression results shown in Table 4 : Table 4 The moderating effect of innovative human capital (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.128*** 0.352*** 0.211*** 0.311*** 0.271*** (4.186) (3.741) (2.748) (4.362) (5.092) lner 0.098*** 0.074** -0.461*** 0.410*** 0.130*** (3.296) (1.988) (-4.558) (5.747) (2.966) lner2 -0.018*** -0.026*** 0.097*** -0.808*** -0.019*** (-8.318) (-3.972) (4.212) (-10.202) (-6.059) lnthr*lner 0.055 0.117 0.270 -0.218 -0.054 (1.087) (0.988) (0.752) (-1.420) (-0.926) lnthr*lner2 -0.012*** -0.025** 0.024** -0.018*** -0.013** (-3.152) (-2.027) (2.239) (-3.155) (-2.581) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -5.306*** -6.836*** -6.017*** -7.413*** -6.204*** (-16.869) (-8.155) (-9.053) (-11.268) (-12.210) F statistics 312.39*** 307.52*** 381.25*** 337.08*** 313.66*** Hausman 265.10*** 118.99*** 205.87*** 255.64*** 227.60*** Observations 750 750 750 750 750 R-squared 0.804 0.802 0.834 0.816 0.805 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses For command-type environmental regulations, the coefficient of lnthr × lncer2 in Model (1) of Table 4 is negative and passes the 1% significance test (-0.012, p < 0.01), indicating that innovative human capital moderates the inverted "U" relationship between command-type environmental regulations and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make this inverted "U" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between command-type environmental regulations and industrial green and low-carbon transformation within the extreme value range, while beyond this extreme value, the negative correlation becomes more evident. For investment-oriented environmental regulation, the coefficient of lnthr × lnier2 in model (2) of Table 4 is negative and significant (-0.025, p < 0.05), indicating that innovative human capital moderates the inverted "U" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted "U" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation within the extreme value range. Beyond this extreme value, the negative correlation becomes more evident. For cost-based environmental regulation, the coefficient of lnthr × lncber2 in model (3) of Table 4 is positive at the 5% significance level (0.024, p < 0.05), indicating that innovative human capital moderates the "U" -shaped relationship between cost-based environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this "U" -shaped relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the negative correlation between cost-based environmental regulation and industrial green and low-carbon transformation within the extreme value range, and the more pronounced the positive correlation beyond the extreme value range. For non-governmental environmental organizations, the coefficient of lnthr × lnengo2 in model (4) of Table 4 is negative at the 1% significance level (-0.018, p < 0.01), indicating that innovative human capital moderates the inverted "U" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make the "U" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation more gradual. Specifically, the higher the level of innovative human capital, the stronger the negative effect of non-governmental environmental organizations on industrial green and low-carbon transformation will be within the extreme value range, and after exceeding this extreme value, the promoting effect of non-governmental environmental organizations on industrial green and low-carbon transformation will weaken. For public environmental participation, the coefficient of lnthr × lnsup2 in Model (5) of Table 4 is negative at the 5% significance level (-0.013, p < 0.05), indicating that innovative human capital moderates the inverted "U" relationship between public environmental participation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted "U" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between public environmental participation and industrial green and low-carbon transformation within the extreme value range. Beyond this extreme value, the negative correlation becomes more evident. These results also confirm Hypothesis H3. 3.4 Robustness test (1) Replace the dependent variable. The industrial green and low-carbon transition is derived from the cumulative total factor productivity of industrial green practices. Therefore, using the total factor productivity of industrial green and low-carbon practices as a proxy for the industrial green and low-carbon transition to test the robustness of the regression results. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. Specific regression results are shown in Table 5 : Table 5 Robustness test of substitution of dependent variable (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.219*** 0.397*** 0.253*** 0.369*** 0.303*** (5.323) (4.188) (3.285) (5.106) (4.871) lner 0.110*** 0.065*** -0.443*** 0.531*** 0.099*** (5.747) (3.998) (-3.882) (9.977) (3.218) lner2 -0.045*** -0.033*** 0.064*** -0.874*** -0.014*** (-3.901) (-5.280) (4.873) (-5.332) (-3.776) lnthr*lner 0.021 0.003 0.101 -0.330 -0.009 (1.339) (0.019) (0.228) (-0.945) (-0.028) lnthr*lner2 -0.059*** -0.049*** 0.019*** -0.045*** -0.056*** (-4.964) (-3.901) (3.943) (-3.784) (-3.447) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -5.306*** -6.836*** -6.017*** -7.413*** -6.204*** (-16.869) (-8.155) (-9.053) (-11.268) (-12.210) F statistics 85.98*** 67.99*** 33.16*** 86.65*** 84.88*** Hausman 265.10*** 118.99*** 205.87*** 255.64*** 227.60*** Observations 750 750 750 750 750 R-squared 0.804 0.802 0.834 0.816 0.805 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses From the regression results in Table 5 , it can be seen that the coefficients of innovative human capital are all positive and significant at the 1% level. The coefficient of environmental regulation instrumental variable is also significant at the 1% level and consistent with the sign of the previous regression results. Moreover, the interaction term coefficient between innovative human capital and environmental regulation instrument is significant, which is also consistent with the sign of the previous regression results, indicating that the regression results are robust. (2) Sample Reduction. Generally, municipalities have a higher level of economic development and receive more favorable policies from the government. Therefore, municipalities can attract innovative talents independently, leading to a higher level of innovative human capital. To verify that municipalities do not substantially affect the estimation results, this paper excludes the municipalities (Beijing, Shanghai, Tianjin, Chongqing) from the sample and re-estimates the model. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. The specific results are shown in Table 6 . From the regression results, it is evident that the coefficients of innovative human capital and environmental regulatory tools, as well as the interaction terms, show no significant changes in sign or significance, indicating that the baseline estimation results remain robust. Table 6 Robustness test of sample deletion (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.288*** 0.380*** 0.231*** 0.334*** 0.258*** (2.914) (4.053) (2.996) (4.812) (4.964) lner 0.135*** 0.089*** -0.502*** 0.395*** 0.149*** (3.480) (3.660) (-5.293) (6.731) (3.398) lner2 -0.059*** -0.061*** 0.155*** -0.756*** -0.023*** (-5.218) (-3.772) (4.318) (-5.128) (-7.412) lnthr*lner 0.004 0.105 0.109 -0.190 -0.018 (0.093) (1.307) (0.302) (-1.003) (-1.437) lnthr*lner2 -0.028*** -0.034*** 0.045*** -0.035*** -0.025*** (-4.368) (-2.789) (8.320) (-3.495) (-5.626) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -5.463*** -7.149*** -6.195*** -7.845*** -6.370*** (-17.338) (-8.597) (-9.315) (-12.187) (-12.687) F statistics 96.80*** 76.23*** 34.88*** 94.97*** 93.76*** Hausman 664.35*** 128.64*** 230.76*** 244.03*** 219.67*** Observations 650 650 650 650 650 R-squared 0.819 0.814 0.834 0.827 0.816 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses 3.5 Endogeneity test (1) Two-way causality The previous empirical results show that the effect of formal and informal environmental regulation on China's industrial green and low-carbon transformation is nonlinear. Therefore, when using the instrumental variable method, the two-stage residual intervention method (2SRI) which is more suitable for the estimation of nonlinear model should be adopted. Drawing on the research of Ou et al. (2023) [ 29 ], air circulation coefficient is chosen as an instrumental variable for environmental regulation. The air circulation coefficient is measured by the product of wind speed and boundary layer height. Therefore, the magnitude of the regional air circulation coefficient mainly depends on natural phenomena such as climate conditions, meeting the exogeneity requirement of an instrumental variable. This approach can, to some extent, overcome the endogeneity issues caused by the existence of reverse causality. Models (1) to (5) use command-type environmental regulation, investment-type environmental regulation, expense-type environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables. Under the assumption of endogeneity, according to the empirical judgment rule of "weak instrument variables," if the F-statistic in the first stage is greater than 10, there is no need to worry about the weak instrument variable issue. In this paper, the F-statistics are all greater than 10 and significant at the 0.01 level. The Sargan-statistic further indicates that the instrument variable is exogenous (p > 10%), thus satisfying the conditions of relevance and exogeneity. As shown in Table 7 , the regression results are highly consistent with those discussed earlier. Therefore, it further demonstrates the robustness of the empirical conclusions of this paper. Table 7 Endogeneity test of bidirectional causality (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.201*** 0.374*** 0.198*** 0.282*** 0.223*** (4.330) (5.398) (4.904) (3.473) (5.341) lner 0.107*** 0.066*** -0.459*** 0.353*** 0.106*** (5.239) (5.440) (-6.322) (4.288) (4.114) lner2 -0.041*** -0.038*** 0.118*** -0.642*** -0.015*** (-3.774) (-5.390) (5.213) (-3.903) (-5.303) lnthr*lner 0.013 0.092 0.079 -0.163 -0.009 (0.107) (0.904) (0.201) (-0.143) (-1.236) lnthr*lner2 -0.017*** -0.026*** 0.034*** -0.022*** -0.018*** (-5.264) (-3.019) (4.420) (-9.935) (-4.021) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control residual 0.159*** 0.133*** 0.124*** 0.115*** 0.137*** (9.221) (4.201) (10.046) (5.338) (4.210) F statistics 42.14*** 16.75*** 24.31*** 31.07*** 19.88*** Sagan 0.412 0.339 0.297 0.553 0.218 Observations 750 750 750 750 750 R-squared 0.901 0.835 0.897 0.746 0.796 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses (2) Missing variables To test the impact of omitted variables on endogeneity issues and regression results, trade openness was added as a control variable. Models (1) to (5) use command environmental regulation, investment environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. The specific correlation regression results are shown in Table 8 . From the regression results, it can be seen that the coefficients of innovative human capital and environmental regulation tools, as well as the interaction terms, show no significant changes in sign or significance. This indicates that the model fits well and the regression results are robust. Table 8 Endogeneity test for the addition of control variables (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.119*** 0.332*** 0.198** 0.305*** 0.260*** (3.785) (3.481) (2.555) (4.213) (4.858) lner 0.079*** 0.091*** -0.499*** 0.390*** 0.141*** (4.001) (2.784) (-4.368) (5.292) (3.022) lner2 -0.010*** -0.031*** 0.118*** -0.717*** -0.025*** (-2.957) (-5.455) (3.275) (-5.323) (-3.900) lnthr*lner 0.067 0.036 0.144 -0.104 -0.010 (0.841) (1.330) (0.082) (-1.011) (-0.215) lnthr*lner2 -0.023*** -0.016*** 0.055** -0.040*** -0.038*** (-3.990) (-3.560) (2.117) (-3.028) (-9.197) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -5.380*** -6.788*** -6.041*** -7.409*** -6.284*** (-16.854) (-8.095) (-9.092) (-11.252) (-12.308) F statistics 43.64*** 44.10*** 25.36*** 51.72*** 48.10*** Hausman 74.22*** 52.10*** 145.58*** 86.97*** 148.14*** Observations 750 750 750 750 750 R-squared 0.805 0.802 0.834 0.816 0.805 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses 4. Heterogeneity test To analyze whether the moderating effect of innovative human capital varies across different regions, national data were divided into eastern, central, and western regions for re-verification. Tables 9 , 10 , and 11 present the regression results for the eastern, central, and western regions, respectively. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as core explanatory variables. Table 9 The moderating effect of innovative human capital in eastern China (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.177*** 0.200*** 0.184*** 0.291*** 0.306*** (3.531) (3.248) (4.266) (3.269) (4.442) lner 0.023 0.113*** -0.071*** -0.260** 0.179*** (1.011) (3.582) (-5.956) (-2.411) (2.809) lner2 0.069 0.009 0.068*** 0.109*** -0.017*** (1.321) (0.618) (6.623) (6.017) (-5.993) lnthr*lner 0.028 0.121 -0.029 -0.015 0.004 (0.927) (0.460) (-1.541) (-1.444) (0.438) lnthr*lner2 -0.018 0.026 0.098*** -0.076*** -0.016** (-1.335) (1.278) (4.343) (-4.522) (-2.241) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -3.263*** -3.778*** -3.054*** -3.794*** -3.388*** (-5.512) (-5.333) (-4.097) (-3.885) (-4.591) F statistics 89.34*** 80.68*** 83.52*** 82.75*** 86.38*** Hausman 114.89*** 129.44*** 107.33*** 105.80*** 165.02*** Observations 250 250 250 250 250 R-squared 0.783 0.766 0.772 0.770 0.778 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses The coefficient of the squared term (lnthr×lner2) between innovative human capital and command environmental regulation in model (1) in Table 9 is not significant, indicating that innovative human capital in eastern China does not play a moderating role in the inverted "U" relationship between command environmental regulation and industrial green and low-carbon transformation. The coefficient of the squared term (lnthr×lner2) of innovative human capital and investment-oriented environmental regulation in model (2) in Table 9 is not significant, indicating that innovative human capital in eastern China does not play a moderating role in the inverted "U" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. In the model (3) of Table 9 , the coefficient of the squared term (lnthr × lner2) for innovative human capital and cost-based environmental regulation is positive at the 1% significance level, indicating that innovative human capital moderates the "U" -shaped relationship between cost-based environmental regulation and industrial green and low-carbon transformation in the eastern region. An increase in the level of innovative human capital makes this "U" -shaped relationship steeper. In other words, before the intensity of cost-based environmental regulation in the eastern region exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between cost-based environmental regulation and industrial green and low-carbon transformation. After the intensity of cost-based environmental regulation in the eastern region exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between cost-based environmental regulation and industrial green and low-carbon transformation. In the model (4) of Table 9 , the coefficient of the squared term for innovative human capital and non-governmental environmental organizations (lnthr × lner²) is negative at the 1% significance level, indicating that innovative human capital moderates the "U" -shaped relationship between non-governmental environmental organizations and industrial green and low-carbon transformation in the eastern region. An increase in the level of innovative human capital can make this "U" -shaped relationship more gradual. Specifically, before the number of non-governmental environmental organizations in the eastern region exceeds the turning point, the higher the level of innovative human capital, the less pronounced the negative correlation between non-governmental environmental organizations and industrial green and low-carbon transformation; after the number of non-governmental environmental organizations in the eastern region exceeds the turning point, the higher the level of innovative human capital, the less pronounced the positive correlation between non-governmental environmental organizations and industrial green and low-carbon transformation. The coefficient of the square term (lnthr×lner2) between innovative human capital and public environmental protection participation in model (5) in Table 9 is not significant, indicating that in eastern China, innovative human capital does not play a moderating role in the inverted "U" relationship between public environmental protection participation and industrial green and low-carbon transformation. Table 10 The moderating effect of innovative human capital in central China (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.191** 0.226*** 0.235*** 0.223*** 0.288*** (1.998) (3.640) (4.122) (3.148) (2.664) lner 0.049*** 0.088 -0.319*** 0.149 0.176** (5.778) (0.359) (-5.904) (1.413) (2.173) lner2 -0.032*** 0.017 -0.150 -0.332 0.011 (-3.992) (0.772) (-0.788) (-0.178) (0.757) lnthr*lner 0.040 0.038 -0.017 -0.160 -0.063 (0.923) (0.429) (-0.416) (-0.774) (-1.105) lnthr*lner2 -0.074*** -0.099 0.178 0.069 -0.032 (5.178) (0.814) (0.733) (0.810) (0.803) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -7.487*** -8.902*** -2.889*** -20.807*** -12.512*** (-9.573) (-5.484) (-3.988) (-4.082) (-4.391) F statistics 121.28*** 115.43*** 211.16*** 124.14*** 121.57*** Hausman 120.49*** 105.46*** 128.80*** 47.51*** 191.68*** Observations 150 150 150 150 150 R-squared 0.894 0.890 0.936 0.896 0.894 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses The coefficient of the squared term (lnthr × lner²) for innovative human capital in Model (1) of Table 10 is negative and passes the 1% significance test (-0.074, p < 0.01). This indicates that in central China, innovative human capital moderates the inverted "U" relationship between command environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted "U" relationship steeper. Specifically, before the intensity of command environmental regulation in central China exceeds the turning point, the higher the level of innovative human capital, the stronger the positive correlation between command environmental regulation and industrial green and low-carbon transformation. After the intensity of command environmental regulation in central China exceeds the turning point, the higher the level of innovative human capital, the stronger the negative correlation between command environmental regulation and industrial green and low-carbon transformation. The coefficient of the squared term (lnthr×lner2) between innovative human capital and investment-oriented environmental regulation in model (2) in Table 10 is not significant, indicating that innovative human capital in central China does not play a moderating role in the inverted "U" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. The coefficient of the squared term (lnthr×lner2) between innovative human capital and cost-based environmental regulation in model (3) of Table 10 is not significant, indicating that innovative human capital in central China does not play a moderating role in the "U" relationship between cost-based environmental regulation and industrial green and low-carbon transformation. In the model (4) of Table 10 , the coefficient of the square term of innovative human capital and non-governmental environmental organizations (lnthr×lner2) is not significant, indicating that the "U" -shaped relationship between innovative human capital and non-governmental environmental organizations and industrial green and low-carbon transformation in central China does not play a moderating role. In the model (5) of Table 10 , the coefficient of the square term of innovative human capital and public environmental protection participation (lnthr×lner2) is not significant, indicating that the inverted "U" relationship between innovative human capital and public environmental protection participation and industrial green and low-carbon transformation in central China does not play a moderating role. Table 11 The moderating effect of innovative human capital in western China (1) (2) (3) (4) (5) variable lncer lnier lncber lnengo lnpus lnthr 0.232*** 0.405*** 0.369*** 0.326*** 0.433*** (4.220) (3.340) (3.583) (2.844) (5.257) lner 0.047*** 0.079*** -0.312*** -0.340*** 0.126* (2.681) (11.483) (-5.040) (-3.263) (1.946) lner2 -0.033*** -0.051*** -0.133 -0.119 -0.005*** (10.135) (-5.383) (-1.096) (-1.184) (-4.920) lnthr*lner 0.113 0.015 0.045 -0.012 0.015 (0.911) (0.970) (0.937) (-0.936) (1.334) lnthr*lner2 -0.023** -0.167*** 0.151 0.331 -0.015* (-2.315) (-9.281) (1.179) (1.181) (-1.883) controlled variable YES YES YES YES YES time effect control control control control control Provincial effect control control control control control Constant -4.375*** -6.369*** -7.395*** -5.775*** -5.942*** (-8.693) (-5.622) (-7.091) (-5.306) (-7.205) F statistics 177.38*** 178.35*** 244.50*** 186.64*** 172.73*** Hausman 113.35*** 120.47*** 53.50*** 202.81*** 36.82*** Observations 350 350 350 350 350 R-squared 0.835 0.836 0.875 0.842 0.832 Note: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses In the model (1) of Table 11 , the coefficient of the squared term for innovative human capital and command-type environmental regulation (lnthr × lner²) is negative and passes the 5% significance test (-0.023, p < 0.05). This indicates that in western regions, innovative human capital moderates the inverted "U" relationship between command-type environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make this inverted "U" relationship steeper. Specifically, before the intensity of command-type environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between command-type environmental regulation and industrial green and low-carbon transformation. After the intensity of command-type environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between command-type environmental regulation and industrial green and low-carbon transformation. In the model (2) of Table 11 , the coefficient of the squared term for innovative human capital and investment-oriented environmental regulation (lnthr × lner²) is negative and significant (-0.167, p < 0.01). This indicates that in western regions, innovative human capital moderates the inverted "U" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted "U" relationship steeper. Specifically, before the intensity of investment-oriented environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation. After the intensity of investment-oriented environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation. The coefficient of the square term (lnthr×lner2) of innovative human capital and cost-based environmental regulation in model (3) in Table 11 is not significant, indicating that in western China, innovative human capital does not play a moderating role in the "U" relationship between cost-based environmental regulation and industrial green and low-carbon transformation. In the model (4) of Table 11 , the coefficient of the square term of innovative human capital and non-governmental environmental organizations (lnthr×lner2) is not significant, indicating that in western China, innovative human capital does not play a moderating role in the "U" -shaped relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. In the model (5) of Table 11 , the coefficient of the squared term for innovative human capital and public environmental participation (lnthr × lner²) is negative at the 5% significance level (-0.015, p < 0.10). This indicates that in western regions, innovative human capital moderates the inverted "U" relationship between public environmental participation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted "U" relationship steeper. Specifically, before the turning point of public environmental participation intensity in western regions, the higher the level of innovative human capital, the stronger the positive correlation between public environmental participation and industrial green and low-carbon transformation. After the turning point, the higher the level of innovative human capital, the stronger the negative correlation between public environmental participation and industrial green and low-carbon transformation. 5. Research conclusions and countermeasures Environmental regulation is a crucial institutional tool for promoting the green and low-carbon transformation of industry, while innovative human capital is a key factor in achieving this transformation. Based on endogenous growth theory, this paper empirically examines the impact of environmental regulation tools and innovative human capital on the green and low-carbon transformation of Chinese industry using provincial panel data from 1997 to 2021. The study finds: First, the impact of environmental regulatory tools on industrial green and low-carbon transformation varies. Command-Type environmental regulations, investment-type environmental regulations, and public participation in environmental protection show an inverted "U" shape, with effects initially increasing and then decreasing; expense-type environmental regulations and non-governmental environmental organizations exhibit a "U" shape, with effects initially decreasing and then increasing. Second, the green and low-carbon transformation of innovative human capital industry plays a promoting role, and plays a moderating role in the relationship between command environmental regulation, investment environmental regulation, cost environmental regulation, non-governmental environmental organizations, public participation in environmental protection and industrial green and low-carbon transformation. Third, the moderating role of innovative human capital exhibits regional heterogeneity. In the eastern region, innovative human capital only moderates the relationship between cost-based environmental regulations, non-governmental environmental organizations, and industrial green and low-carbon transformation; in the central region, it only moderates the relationship between Command-Type environmental regulations and industrial green and low-carbon transformation; in the western region, it only moderates the relationship between Command-Type environmental regulations, investment-type environmental regulations, public participation in environmental protection, and industrial green and low-carbon transformation. Based on the above conclusions, the following countermeasures are proposed: First, the central and local governments should formulate differentiated environmental policies and supervise and guide the public and non-governmental environmental organizations to participate rationally in environmental governance. Firstly, appropriate environmental emission standards, efficient pollution monitoring systems, stringent penalties for environmental damage, and comprehensive legal liability mechanisms should be established to form an environmental protection system that focuses on source prevention, process control, damage compensation, and accountability. Secondly, while implementing environmental infrastructure investment and construction or "three simultaneous" environmental investments, it is necessary to clearly define the energy consumption and pollution emissions of industrial enterprises to determine whether there is any "free-riding" behavior, and penalize enterprises that do not meet standards. Thirdly, environmental taxes levied by the government on enterprises should be increased within a reasonable range, and the emission trading market should continue to be improved, establishing a comprehensive trading network and system, and strengthening the supervision of the carbon emission trading market. Lastly, the public and non-governmental environmental organizations should be guided to protect the environment rationally, making informal environmental regulation a safeguard for the coordinated development of the environment and the economy. For example, enhancing environmental awareness through public education and establishing effective and smooth information feedback mechanisms and channels, government departments or regulatory bodies should actively respond to environmental pollution issues reported by the public. Encouraging government involvement in environmental protection can leverage their professional advantages to collaborate with enterprises in developing green and low-carbon products, actively promoting the green innovation achievements of enterprises, and shaping a group of representative enterprises that produce fewer pollutants and more green products. This approach aims to stimulate enterprises' willingness for green innovation from the perspective of market demand and promote voluntary green and low-carbon transformation of enterprises Second, the development of human capital should shift from a sole focus on quantitative expansion to qualitative improvement. On one hand, while continuing to popularize and implement compulsory education, emphasis should be placed on the development of higher education. This includes vigorously building higher education infrastructure, increasing support funds for higher education, optimizing higher education resources, breaking down institutional barriers to talent development, steadily enhancing the level and quality of talent, and focusing on promoting the industrialization of technological achievements to strengthen the integration between high-quality human capital, technological innovation, and industrial upgrading. On the other hand, the strategy of attracting overseas talent and intelligence should be utilized to improve the quality of human capital. Priority should be given to setting up "talent attraction stations" and "green innovation bases" in various levels of science associations, technology parks, enterprises, universities, and research institutions that have the necessary conditions. These serve as platforms for implementing the transfer of overseas technology, conversion of results, talent introduction, and project implementation. Building on this foundation, further efforts should be made to broaden channels for cooperation between schools and local areas, as well as between schools and enterprises, creating platforms for talent introduction, project collaboration, academic exchange, and policy consultation. This will provide a platform for expanding talent introduction channels and attracting high-end overseas talent, becoming a "strong magnet" for overseas talent. Third, when optimizing policies for cultivating innovative human capital, regions should integrate local environmental policies. Specifically, in the eastern region, on one hand, emphasis should be placed on the development of higher education, such as increasing support funds for higher education, optimizing educational resources, and building higher education infrastructure. On the other hand, strategies for attracting overseas talent and intelligence should be prioritized, such as establishing "talent attraction stations" and "green innovation bases" in qualified science parks, enterprises, universities, and research institutions, to attract international talent. This approach aims to enhance the level of innovative human capital through both "local cultivation" and "overseas introduction." At the same time, appropriate expense-type environmental regulations and enhanced management of non-governmental environmental organizations should be implemented to support the green and low-carbon transformation of industries. In the central region, efforts should focus on improving primary and secondary education infrastructure, strengthening the development of higher education, and fostering local innovative talent. Additionally, proactive policies for talent attraction should be formulated, such as providing "one-stop services" and "nanny-style services," to properly address key issues like housing, spousal placement, children's schooling, and healthcare, thereby creating a favorable service environment. At the same time, it is essential to focus on optimizing command-type environmental regulations, such as raising pollution emission standards and increasing penalties for violations, to impose strict constraints on corporate production activities, which will better facilitate the green and low-carbon transformation of industry. In western regions, local governments should improve basic educational facilities, while the central government continues to offer preferential policies in research funding allocation and fund support, helping to retain local high-quality talent and attract high-caliber talent from other regions. Local governments should also optimize command-type and investment-type environmental regulatory policies, facilitate public participation in environmental governance, and support the green and low-carbon transformation of industry. Declarations Ethics approval and consent to participate This study is not a clinical trial; it does not involve human participants or clinical interventions. Therefore, clinical trial registration details are not applicable. Consent for publication Not applicable Availability of data and material The data can be obtained from the corresponding author upon request. The calculated data used to support the findings of this study are included within the article. Competing interests No competing interests. Funding This study was supported by the National Social Science Fund of China(14AJL015) and Chongqing Social Science Foundation(2020ZDJJ01). Authors' contributions ZH was responsible for the logical reasoning of the research topic. DJ were responsible for experimental materials and data. ZX was responsible for collecting literature. All authors contributed to the article and approved the submitted version. Acknowledgements Thanks to the National Social Science Foundation of China (14AJL015) and Chongqing Social Science Foundation (2020ZDJJ01) for supporting this article. References J.-X. Jiang, J.-J. Wang, and Y.J.P.S. Cheng, The impact of industrial transformation on green economic efficiency: New evidence based on energy use. 21 (2024) 3644-3655. J. Hou, T.S. Teo, F. Zhou, M.K. Lim, and H.J.J.o.c.p. Chen, Does industrial green transformation successfully facilitate a decrease in carbon intensity in China? An environmental regulation perspective. 184 (2018) 1060-1071. K. Du, Y. Cheng, and X.J.E.E. Yao, Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. 98 (2021) 105247. H. Zheng, L. Zhang, X.J.O. Zhao, and C. 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Guo, Technological innovation on economic growth from the perspective of investment-oriented environmental regulations: considering the threshold effect of China human capital. 53 (2021) 4632-4645. D. Jingrong, Z. Haitao, Z. Wenqing, L. Jiahui, Y. Yi, and T.J.S.R. Yaqin, Analysis of the influence and coupling effect of environmental regulation policy tools on industrial green and low-carbon transformation. 14 (2024) 25873. Y. Liu, N. Luo, S.J.D.D.i.N. Wu, and Society, Nonlinear effects of environmental regulation on environmental pollution. 2019 (2019) 6065396. H.T.A. Bressers, and K.R. Lulofs, Industrial water pollution in the Netherlands: a expense-type approach, Choosing Environmental Policy, Routledge, 2010, pp. 91-116. S. Partelow, K.J. Winkler, and G.M.J.P.o. Thaler, Environmental non-governmental organizations and global environmental discourse. 15 (2020) e0232945. G. Li, Q. He, S. Shao, and J.J.J.o.e.m. Cao, Environmental non-governmental organizations and urban environmental governance: Evidence from China. 206 (2018) 1296-1307. J. Newig, and E. Kvarda, Participation in environmental governance: legitimate and effective?, Environmental governance, Edward Elgar Publishing, 2012. W. Lihua, M. Tianshu, B. Yuanchao, L. Sijia, and Y.J.S.o.t.T.E. Zhaoqiang, Improvement of regional environmental quality: Government environmental governance and public participation. 717 (2020) 137265. L. Cao, Y. Wang, J. Yu, Y. Zhang, and X.J.F.R.L. Yin, The impact of digital economy on low-carbon transition: What is the role of human capital? 69 (2024) 106246. F. Avvisati, G. Jacotin, and S.J.T.J.f.H.E. Vincent-Lancrin, Educating higher education students for innovative economies: What international data tell us. 1 (2013) 223-240. P.M.J.J.o.p.E. Romer, Endogenous technological change. 98 (1990) S71-S102. J. Zhang, S.J.I.J.o.E.R. Li, and P. Health, The impact of human capital on green technology innovation—moderating role of environmental regulations. 20 (2023) 4803. J. Abbas, H.J.E. Najam, Development, and Sustainability, Role of environmental decentralization, green human capital, and digital finance in firm green technological innovation for a sustainable society. (2024) 1-15. L. Ni, S.F. Ahmad, T.O. Alshammari, H. Liang, G. Alsanie, M. Irshad, R. Alyafi-AlZahri, R.H. BinSaeed, M.H.A. Al-Abyadh, and S.M.d.M.A.J.J.o.C.P. Bakir, The role of environmental regulation and green human capital towards sustainable development: The mediating role of green innovation and industry upgradation. 421 (2023) 138497. W. Song, L. Meng, D.J.E.S. Zang, and P. Research, Exploring the impact of human capital development and environmental regulations on green innovation efficiency. 30 (2023) 67525-67538. N.B. An, Y.-L. Kuo, F. Mabrouk, S. Sanyal, I. Muda, S.S. Hishan, and N.J.E.r.-E.i. Abdulrehman, Ecological innovation for environmental sustainability and human capital development: the role of environmental regulations and renewable energy in advanced economies. 36 (2023) 243-263. P. Liang, S. Xie, F. Qi, Y. Huang, and X.J.S. Wu, Environmental regulation and green technology innovation under the carbon neutrality goal: Dual regulation of human capital and industrial structure. 15 (2023) 2001. S. Munawar, H.Q. Yousaf, M. Ahmed, S.J.J.o.H. Rehman, and T. Management, Effects of green human resource management on green innovation through green human capital, environmental knowledge, and managerial environmental concern. 52 (2022) 141-150. F.A. Twum, X. Long, M. Salman, C.N. Mensah, W.A. Kankam, A.K.J.E.S. Tachie, and P. Research, The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific. 28 (2021) 17119-17131. W. Guo, H. Dai, X.J.E.S. Liu, and P. Research, Impact of different types of environmental regulation on employment scale: an analysis based on perspective of provincial heterogeneity. 27 (2020) 45699-45711. Y. Ou, Z. Bao, S.T. Ng, W.J.T.B. Song, and Society, Estimating the effect of air quality on bike-sharing usage in Shanghai, China: An instrumental variable approach. 33 (2023) 100626. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6826667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469352520,"identity":"968e6564-da18-421f-9a2d-84524f78aa0d","order_by":0,"name":"Zhang Haitao","email":"","orcid":"","institution":"Chongqing University of Education","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Haitao","suffix":""},{"id":469352521,"identity":"095408ce-2aba-457f-8b8d-71a7f327347f","order_by":1,"name":"Dong Jingrong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3RoQrCUBSA4XMZLB2x3iHoEwh3DISV+QI+xBHBtAewOVlYmf2KewvBfC1b0T4wWUwG10zqbGLZtQnev3+cczgAJtMP1u4Vxa26P+aLJFZ6xJFIjrQtFqc56RFRguhgTRI5EpqbHSNyZW1aEqvyAkG3HzUIlu0UXTnaznq58TOYeAPVQCwg2knB0c0O2w6CGm+biA3kxkiCD8vwrEeQk2ehIsFkaOsRjvspW0WKWJp7fiY0bhkWSQ7ViyTxqbzMgm4j+Rip+5o38q0wmUymv+gJSgxFQmzM4yEAAAAASUVORK5CYII=","orcid":"","institution":"Chongqing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Jingrong","suffix":""},{"id":469352522,"identity":"b68da0bc-6586-41fa-9901-d73a35a4e611","order_by":2,"name":"Zhang Xiyue","email":"","orcid":"","institution":"Chongqing University of Education","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Xiyue","suffix":""}],"badges":[],"createdAt":"2025-06-05 07:53:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6826667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6826667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84480112,"identity":"1d9e6302-42a5-41ee-99f8-e163a18c4ab6","added_by":"auto","created_at":"2025-06-12 12:26:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65832,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the proportion of innovative human capital by province\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6826667/v1/cf9994acbbbd4846989c409f.png"},{"id":86172827,"identity":"218f05c3-81cf-4ca5-9989-c55e65887f1b","added_by":"auto","created_at":"2025-07-07 14:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1787925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6826667/v1/34713dbe-94e7-4039-bec5-cbe17bbb917f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental regulation tools, innovative human capital and industrial green and low-carbon transformation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the reform and opening up, China has relied on an industrial-led development strategy to drive rapid economic expansion, successfully making a significant leap from being among the poorest countries to becoming one of the upper-middle-income nations. However, while China\u0026apos;s industrial sector has fueled rapid economic growth, its extensive development model has also led to substantial resource consumption and pollution emissions, increasingly highlighting the contradiction between economic development and environmental protection. This has become a bottleneck constraining China\u0026apos;s efforts to achieve its \u0026quot;dual carbon\u0026quot; goals and promote high-quality economic development[1]. In order to solve the increasingly severe environmental problems, the Chinese government has clearly pointed out that \u0026quot;we should accelerate the green transformation of development mode. We will make economic and social development green and low-carbon, and take \u0026quot;coordinated efforts to reduce carbon, reduce pollution, expand green and grow\u0026quot; as an important way to build an ecological civilization. It is necessary to \u0026quot;accelerate the green transformation of development mode\u0026quot; and \u0026quot;build a green, low-carbon and circular economic system\u0026quot;, which provides a clear policy guidance for industrial green transformation. As the primary sector responsible for energy and resource consumption and carbon emissions in China\u0026apos;s economic operations and development landscape, whether the industrial sector can achieve a green and low-carbon transformation in the new era is not only crucial for driving the green transformation of development methods and achieving high-quality economic development but also a matter of responsibility in addressing global climate change and building a community with a shared future for mankind. Therefore, how to promote the green and low-carbon transformation of industry has become a hot topic in the field of environmental economics both domestically and internationally, and an important issue in the process of China\u0026apos;s high-quality economic development.\u003c/p\u003e\n\u003cp\u003eOn the one hand, environmental regulation, as a core strategy and institutional support in the environmental governance system, is widely recognized as a key mechanism and effective tool for driving green technological innovation and promoting the transition to a green or low-carbon economy. This assertion has been fully validated in academic research, with studies by Hou et al.(2018)[2], Du et al. (2021)[3], and Zhang et al. (2022) [4]providing strong evidence. These studies collectively demonstrate that environmental regulation plays an irreplaceable role in guiding companies to adopt more environmentally friendly production methods and promote technological innovation. On the other hand, innovative human capital, as a higher form of human capital, not only helps optimize resource allocation and improve efficiency but also significantly promotes the innovation and development of green technology, thereby having a profound impact on the green transformation and high-quality development of the economy. The research findings by Jin et al. (2022)[5] provide an in-depth analysis, revealing the critical role of innovative human capital in driving green transformation.\u003c/p\u003e\n\u003cp\u003eBased on the above policy background and existing research foundation, this study proposes three core issues: First, do different types of environmental regulatory tools (command-type environmental regulation, investment-type environmental regulation, expense-type environmental regulation, non-governmental environmental organizations, and public environmental supervision) have differentiated characteristics and nonlinear relationships in their impact on industrial green transformation? Second, how does innovative human capital play a moderating role in the process of promoting industrial green transformation through environmental regulatory tools, and does its mechanism vary depending on the type of environmental regulatory tool? Third, considering the reality of uneven regional development in China, does the moderating effect of innovative human capital exhibit heterogeneous characteristics across eastern, central, and western regions? To address these questions, this study uses provincial panel data from Chinese industry from 1997 to 2021 as the research sample. After empirically testing the direct impacts of environmental regulatory tools and innovative human capital on industrial green and low-carbon transformation, we further delve into the moderating role of innovative human capital and conduct a detailed analysis of its heterogeneous effects on different environmental regulatory tools and in different regions, aiming to provide more precise theoretical support and empirical evidence for policy formulation and practice.\u003c/p\u003e"},{"header":"1. Theoretical analysis and research hypothesis","content":"\u003cp\u003e\u003cstrong\u003e1.1 Environmental regulation tools and industrial green and low-carbon transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental regulation refers to the regulatory power exercised by governments or the public for the purpose of environmental protection, using tangible systems or intangible awareness as means to constrain enterprises or individuals that cause environmental pollution. Depending on the implementing entity, environmental regulation can be divided into formal environmental regulation, which is primarily government-led, and informal environmental regulation, which is primarily led by the public [6]. Formal environmental regulation can be further categorized into three tools: Command-Type environmental regulation, investment-type environmental regulation, and cost-based environmental regulation; informal environmental regulation can be divided into two tools: non-governmental environmental organizations and public participation in environmental protection.\u003c/p\u003e\n\u003cp\u003eThe impact of command-type environmental regulations on corporate green and low-carbon transformation can either promote or inhibit it, depending on the intensity of these regulations. When the government first formulates environmental regulation policies, the intensity of command-type environmental regulations is relatively low. If companies improve their production and pollution emission indicators through green technological innovation and apply the results of such innovations to industrial processes, they can reduce pollution emissions while also gaining more environmental economic benefits, corporate competitiveness, and social benefits[7], creating \"compensatory gains\" that exceed the cost of environmental regulation, thus driving corporate green and low-carbon transformation. However, once the intensity of command-type environmental regulations exceeds a certain threshold, the strong constraints, high costs, and low efficiency associated with these regulations make production costs excessively high for companies, \"crowding out\" a significant amount of resources. The cost of environmental regulation becomes greater than the \"compensatory gains,\" making it unfavorable for companies to engage in green R\u0026amp;D activities [8], thereby inhibiting corporate green and low-carbon transformation.\u003c/p\u003e\n\u003cp\u003eThe impact of investment-type environmental regulation on corporate green and low-carbon transformation can vary depending on its intensity. When investment-type environmental regulation is first implemented, the negative environmental externalities of enterprises are addressed, leading them to shift part of their pollution control investments to the research and development of green technologies and products, thereby improving production efficiency and promoting green and low-carbon transformation[9]; as the intensity of investment-type environmental regulation exceeds a critical point, government involvement in environmental governance largely eliminates the negative environmental externalities generated by enterprises, which may lead them to avoid technological reforms and instead develop a dependency mindset that undervalues the research and development of green technologies and products[10], which is detrimental to industrial green and low-carbon transformation.\u003c/p\u003e\n\u003cp\u003eThe impact of expense-type environmental regulations on corporate green and low-carbon transformation varies depending on their intensity. When expense-type environmental regulations are first implemented, they act as a short-term measure directly affecting the production process of enterprises. Companies must allocate funds to pay pollution fees, taxes, or purchase emission rights, which diverts resources from research and development, leading to reduced innovation [11], thereby suppressing green and low-carbon transformation; as the intensity of expense-type environmental regulations gradually increases and passes a critical point, the profit margin formed by companies paying pollution fees decreases, even turning negative. This forces companies to make early strategic moves, adopting green technological innovations to enhance product competitiveness and production efficiency while reducing emissions, thus promoting corporate green and low-carbon transformation[12].\u003c/p\u003e\n\u003cp\u003eThe impact of non-governmental environmental organizations on the green and low-carbon transformation of industries varies depending on their level of development. In the early stages of their development, due to a lack of effective supervision and internal management mechanisms, these organizations often suffer from low efficiency, which can negatively affect companies' green technological innovation[13], hindering the transition to green and low-carbon practices; as they mature, under the backdrop of natural selection, the professionalism, impartiality, and non-profit nature of these organizations become evident. Measures such as publishing corporate pollution emission lists and analyzing government policies can promote the green and low-carbon transformation of enterprises[14].\u003c/p\u003e\n\u003cp\u003eWhen the public first began to participate in environmental governance, due to their small numbers, the government could provide precise feedback and regulate polluting enterprises, effectively curbing corporate emissions through supervision and reporting [15], promoting the green and low-carbon transformation of industry; however, when public participation in environmental governance becomes excessive, it may lead to false reports or even defamation, which increases the government's scrutiny of reported information, slows down feedback, and increases pressure on enterprises. This, in turn, can have negative impacts on production activities, dampening companies' enthusiasm for green technological innovation and diverting R\u0026amp;D funds [16], hindering the green and low-carbon transition of industry.\u003c/p\u003e\n\u003cp\u003eH1: Environmental regulation tools have a significant impact on the green and low-carbon transformation of industry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Innovative human capital and industrial green and low-carbon transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInnovative human capital, also known as advanced human capital, refers to the human resources that possess certain innovative capabilities and potential, bringing about innovation benefits for enterprises through increasing marginal returns and output multiplier effects [17]. Some empirical studies have found that innovative human capital serves as a crucial driving force for technological progress and economic growth, playing an important role in the green development of the economy. Pargal et al. (1996) [6]found that the level of regional pollution emissions is related to the local human capital level. Avvisati et al. (2013)[18] discovered that companies with more highly educated labor tend to implement environmental standards and increase their efforts in environmental protection, and whether the \"pollution haven hypothesis\" exists is also associated with the level of local human capital.\u003c/p\u003e\n\u003cp\u003eThe theoretical foundation for the impact of human capital on green development mainly stems from Romer's (1990) endogenous growth model[19]: human capital in the R\u0026amp;D sector is a key factor in achieving technological progress and efficiency. As a more advanced form of human capital, R\u0026amp;D human capital not only possesses the general attributes of human capital but also has the unique capability of innovative scarcity, characterized by increasing marginal returns and output multiplier effects. This not only creates conditions for green technology research and development but also facilitates the introduction, use, and absorption of advanced clean production technologies from abroad. Therefore, innovative human capital can influence the industrial transition to green and low-carbon through these two pathways of technological innovation.\u003c/p\u003e\n\u003cp\u003eFirst,\u0026nbsp;The level of innovative human capital directly affects the success rate of technological innovation in enterprises, which in turn impacts the green and low-carbon transformation of industries. Due to the uncertainty associated with new technology development, there is a certain probability of failure for companies engaging in green technological innovation; it does not necessarily lead to green innovation outcomes. An increase in the level of innovative human capital can enhance the likelihood of green technological innovation, increase both the quantity and variety of such innovations, and provide a greater advantage and higher success rate when developing new technologies. Conversely, if a company has a lower level of innovative human capital, this indicates a lower efficiency in utilizing knowledge and technology, making it difficult to convert knowledge and technical equipment into technological progress [20]. This results in increased investment in green innovation without a noticeable improvement in innovation output, leading to slow progress in the green and low-carbon transformation of industries.\u003c/p\u003e\n\u003cp\u003eSecond, the level of innovative human capital directly affects a company's ability to imitate and absorb technology, which in turn impacts the green and low-carbon transformation of industries. When companies choose to introduce green production technologies or purchase equipment to reduce pollution emissions, the efficiency of using these green technologies and equipment does not immediately reach its optimal state. It requires innovative human capital to learn and absorb the use of technology and equipment, fully mastering their usage methods. Companies with high levels of innovative human capital can effectively absorb and master introduced equipment and technologies, quickly achieving green production and reducing pollution emissions. However, if the level of innovative human capital fails to meet the requirements for absorbing and assimilating introduced technologies, it will lead to low utilization rates of green technologies, preventing their advantages from being fully realized. The extent to which new production models reduce pollution emissions and improve production efficiency may be very low, even lower than those of traditional production models [21].\u003c/p\u003e\n\u003cp\u003eH2: Innovative human capital will promote the green and low-carbon transformation of industry\u003c/p\u003e\n\u003cp id=\"_Toc13323\"\u003e\u003cstrong\u003e1.3 The moderating effect of innovative human capital\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe green and low-carbon transformation of industry requires substantial resource investment, often accompanied by certain risks. This makes companies tend to prioritize pollution control over green innovation when facing environmental regulations. Therefore, achieving the green and low-carbon transformation of industry is crucial for both resources and risk resistance. Innovative human capital can help reduce R\u0026amp;D risks and improve resource utilizatio[5], potentially moderating the relationship between environmental regulatory tools and the green and low-carbon transformation.\u003c/p\u003e\n\u003cp\u003eFirst, innovative human capital moderates the relationship between Command-Type environmental regulation and industrial green and low-carbon transformation. When the intensity of Command-Type environmental regulation is low, the \"innovation compensation effect\" generated by environmental regulatory constraints outweighs the \"resource crowding-out effect,\" thus promoting green technological innovation in industrial enterprises. If the level of innovative human capital in these enterprises increases at this time, it will lead to a higher success rate for green technological innovation and an accelerated pace of green and low-carbon transformation. However, when the intensity of Command-Type environmental regulation exceeds a certain threshold, the regulatory constraints exert a \"resource crowding-out effect\" on normal production inputs and R\u0026amp;D investments, making the additional production costs higher than the \"innovation compensation effect\" brought about by green technological innovation. If the level of innovative human capital in enterprises is increased, it provides more room for buffering, thereby mitigating the inhibitory effects of Command-Type environmental regulation on industrial green and low-carbon transformation [22].\u003c/p\u003e\n\u003cp\u003eSecond, innovative human capital moderates the relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. When the intensity of investment-oriented environmental regulation is low, companies can address their environmental negative externalities, shifting part of their pollution control investments to research and development of green technologies and products, thereby improving production efficiency and promoting green and low-carbon transformation. If the level of innovative human capital increases at this time, it will help companies improve R\u0026amp;D success rates, accelerating the pace of industrial green and low-carbon transformation; as the intensity of investment-oriented environmental regulation gradually increases, the effectiveness of government environmental governance improves, and most of the environmental negative externalities generated by companies are eliminated, they may not reform their existing technologies but instead develop a dependency mindset that undervalues the research and development of green technologies and products, which is detrimental to industrial green and low-carbon transformation. If the level of innovative human capital increases at this time, companies will engage in new rounds of technological innovation based on their existing technologies, thus reducing the negative impact of investment-oriented regulation on industrial green and low-carbon transformation[23].\u003c/p\u003e\n\u003cp\u003eThird, innovative human capital moderates the relationship between cost-based environmental regulation and industrial green and low-carbon transformation. When the intensity of cost-based environmental regulation is low, it acts as a short-term measure directly impacting the production process of enterprises. As a result, companies tend to allocate funds primarily for paying pollution fees, taxes, or purchasing emission rights, which diverts research and development funds, leading to reduced innovation and suppressed green and low-carbon transformation. If the level of innovative human capital increases at this time, it can help companies secure more R\u0026amp;D funding for innovation, thereby mitigating the negative effects of cost-based environmental regulation. As the intensity of cost-based environmental regulation gradually increases and passes a critical point, the profit margin formed by companies paying pollution fees decreases or even turns negative. This forces companies to make early strategic moves, shifting towards green technological innovations that enhance product competitiveness and reduce pollution emissions, thus promoting their green and low-carbon transformation. If the level of innovative human capital increases at this time, it will increase the success rate of green technological innovations and reduce R\u0026amp;D risks, further facilitating industrial green and low-carbon transformation[24].\u003c/p\u003e\n\u003cp\u003eFourth, innovative human capital moderates the relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. When the number of non-governmental environmental organizations is small, due to the lack of effective supervision and internal management mechanisms, work efficiency is low, which in turn negatively impacts companies' green technological innovation behaviors, hindering the transition to green and low-carbon [25]. If at this point the level of innovative human capital increases, companies, having had their credibility and income depleted, need to engage in certain green production activities, thus mitigating the negative impact of non-governmental environmental organizations on industrial green and low-carbon transformation; when the number of non-governmental environmental organizations increases, under the backdrop of survival of the fittest, the professionalism, fairness, and non-profit nature of these organizations become evident. Measures such as publishing corporate pollution emission lists and analyzing government policies can promote corporate green and low-carbon transformation. If at this point the level of innovative human capital rises, it will be even more conducive to corporate green technological innovation and the promotion of industrial green and low-carbon transformation[26].\u003c/p\u003e\n\u003cp\u003eFifth, innovative human capital moderates the relationship between public environmental participation and industrial green and low-carbon transformation. When the public first begins to participate in environmental governance, due to their small numbers, the government can provide precise feedback and regulate polluting enterprises, effectively curbing corporate pollution emissions through supervision and reporting, thus promoting industrial green and low-carbon transformation. If at this point the level of innovative human capital in enterprises increases, they will place greater emphasis on the research and development of green technologies, with higher success rates and faster green and low-carbon transformation. However, when public participation in environmental governance becomes excessive, it may lead to false reports or even defamation, which increases the government's scrutiny of reported information, slows down feedback, and increases pressure on enterprises, thereby negatively impacting their production activities, suppressing their enthusiasm for green technology innovation, and diverting R\u0026amp;D funds, which is detrimental to industrial green and low-carbon transformation. If the level of innovative human capital in enterprises rises, it will give them more confidence to continue green technology research and development, thus reducing the negative impact of public environmental participation on industrial green and low-carbon transformation[27].\u003c/p\u003e\n\u003cp\u003eBased on this, the following research hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003eH3: Innovative human capital moderates the relationship between environmental regulation tools and industrial green and low-carbon transformation\u003c/p\u003e"},{"header":"2. Model design, variable selection and descriptive statistics","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Model design\u003c/h2\u003e \u003cp\u003eFirstly, in order to test the impact of environmental regulation tools on industrial green and low-carbon transformation, the quadratic term of environmental regulation tools is introduced. The model is constructed as follows:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"439\" height=\"27\"\u003e\u003c/p\u003e\u003cp\u003eSecondly, according to the theoretical analysis above, in order to test the impact of innovative human capital on industrial green and low-carbon transformation, the benchmark model is constructed as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"424\" height=\"20\"\u003e\u003c/p\u003e \u003cp\u003eSubsequently, combining Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the interaction term of innovative human capital and environmental regulation tools and the interaction term of square of innovative human capital and environmental regulation tools are introduced to explore the moderating effect of innovative human capital:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"523\" height=\"48\"\u003e\u003c/p\u003e\u003cp\u003eWherere LGL\u003csub\u003eit\u003c/sub\u003e presents industrial green and low-carbon transformation, and represents environmental regulation tools, including command-type environmental regulation (cer\u003csub\u003eit\u003c/sub\u003e), investment-type environmental regulation (ier\u003csub\u003eit\u003c/sub\u003e), expense-type environmental regulation (cber\u003csub\u003eit\u003c/sub\u003e), non-governmental environmental organizations (ngo\u003csub\u003eit\u003c/sub\u003e), and public participation in environmental protection (pu\u003csub\u003eit\u003c/sub\u003es); thr\u003csub\u003eit\u003c/sub\u003e Represents innovative human capital and control\u003csub\u003eit\u003c/sub\u003e represents the control variable; i is the province, t is the time; a\u003csub\u003e0\u003c/sub\u003e is the constant term, u\u003csub\u003et\u003c/sub\u003e which represents the unobservable provincial individual effect, v\u003csub\u003et\u003c/sub\u003e is the time effect, and ε\u003csub\u003eit\u003c/sub\u003e is the random disturbance term. Due to the lag in the impact of formal and informal environmental regulation on industrial green and low-carbon transformation, LGL\u003csub\u003eit\u003c/sub\u003e starts from the leading first-period item of Y.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variable selection\u003c/h2\u003e \u003cp\u003e(1) Dependent variable\u003c/p\u003e \u003cp\u003eIndustrial green and low-carbon transformation (LGL\u003csub\u003eit\u003c/sub\u003e). The industrial green and low-carbon transformation index is measured by the green and low-carbon total factor productivity measured by the super-efficiency EBM model and GML model based on the common frontier and considering the undesirable output, and accumulated. The larger the value is, the better the effect of industrial green and low-carbon transformation is.\u003c/p\u003e \u003cp\u003e(2) Core explanatory variables\u003c/p\u003e \u003cp\u003eEnvironmental Regulation Tools:① Command-Type Environmental Regulation (cer): This paper combines the measurement methods of Du et al.(2021)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], using the entropy value method to calculate command-type environmental regulation based on the comprehensive utilization rate of \"three wastes.\" ② Expense-type Environmental Regulation (cber). Drawing on Guo et al. (2020) 's research[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the ratio of pollution discharge fee revenue to GDP is used as a measure. Specifically, from 1997 to 2006, it represents the pollution discharge fee revenue; from 2007 to 2017 and thereafter, it represents the amount collected into the treasury; from 2018 to 2021, it represents the environmental protection tax amount. The original data comes from the \"China Tax Yearbook.\" ③ Investment-Type Environmental Regulation (ier). Drawing on Dong et al. (2024) ' s research[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the ratio of investment in environmental pollution control to GDP is used as a measure. The original data comes from the \"China Environmental Statistics Yearbook.\" ④ Non-Governmental Environmental Organizations (engo). The number of non-governmental organizations in each province, the total of actual social organizations at year-end, actual funds at year-end, and actual private non-enterprise units at year-end is used to measure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].⑤ Public Environmental Participation (sup). This paper draws on Dong et al. (2024) 's approach[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], using the number of letters related to environmental issues to measure public environmental participation. It is a direct indicator reflecting citizens' environmental participation, where residents can directly report environmental problems to local environmental protection agencies through letters. The more letters there are, the higher the level of public environmental participation and the stronger the willingness to supervise.\u003c/p\u003e \u003cp\u003eInnovative Human Capital (thr). Innovative human capital refers to human resources with certain innovation capabilities and potential that can bring innovative benefits to enterprises. Existing research has not established a unified standard for measuring innovative human capital. Some studies measure it from an educational perspective by selecting the proportion of people employed in higher education or the proportion of those with a bachelor's degree or above. Some scholars measure it from an investment perspective by using actual R\u0026amp;D expenditure or multiplying the number of graduates with a bachelor's degree or above and professional technicians by the average monetary wage of employees. Other scholars focus on the quality of output from innovative human capital, choosing indicators such as the number of faculty members, educational expenditure, research funding, and fixed assets of universities to measure its input. This chapter draws on the research of Jin et al.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], using the ratio of R\u0026amp;D personnel to total staff to measure the stock of innovative human capital.\u003c/p\u003e \u003cp\u003eAt the same time, in order to intuitively reflect the changing trend of innovative human capital, the time change map of innovative human capital by province is drawn.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, on the whole, the proportion of innovative human capital in each province shows an upward trend, but there are obvious differences in the changes of innovative human capital among different regions. This indicates that with the improvement of education level and the popularization of compulsory education, China's stock of innovative human capital is gradually increasing, but the imbalance between regions still exists.\u003c/p\u003e \u003cp\u003e(3) Control variables\u003c/p\u003e \u003cp\u003e① R\u0026amp;D investment (ede) uses the perpetual inventory method to convert internal R\u0026amp;D expenditures into R\u0026amp;D capital stock, then measures R\u0026amp;D investment as the ratio of R\u0026amp;D capital stock to industrial output value. ② Foreign direct investment (fdi) is measured by the proportion of foreign direct investment in each province's actual GDP. ③ Urbanization level (ul) reflects urbanization through the ratio of urban population to the total regional population. ④ Industrial structure (is) reflects the level of industrial structure by the proportion of the tertiary industry's output value to local GDP in each province.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data sources and variable descriptive statistics\u003c/h2\u003e \u003cp\u003eBased on the possibility and completeness of data, this paper selects panel data of China's industrial provinces from 1997 to 2021, covering 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan). The relevant data for the dependent and independent variables mentioned above are all sourced from the \"China Statistical Yearbook,\" \"China Industrial Statistical Yearbook,\" \"China Science and Technology Statistical Yearbook,\" \"China Environmental Yearbook,\" \"China Tax Yearbook,\" \"China Environmental Statistics Yearbook,\" and relevant national and provincial statistical bureaus. Missing values in the data consolidation process were uniformly filled using the mean method.\u003c/p\u003e \u003cp\u003eIn order to make the data more stable, weaken the heteroscedasticity and introduce the concept of elasticity, we log-transformed the above variable data. The descriptive statistical results after processing are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\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=\"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 \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\u003eobserved value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaverage value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003estandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eleast value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecrest value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnLGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnsup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnede\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnfdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.176\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"},{"header":"3. Empirical analysis","content":"\u003cp\u003eFirstly, the impact of environmental regulation tools on industrial green and low-carbon transformation is analyzed; secondly, the impact of innovative human capital on industrial green and low-carbon transformation is studied; finally, the moderating effect of innovative human capital on environmental regulation tools and industrial green and low-carbon transformation and regional heterogeneity are further analyzed.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The impact of environmental regulation tools on the green and low-carbon transformation of industry\u003c/h2\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\u003eImpact of environmental regulation tools on industrial green and low-carbon transformation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.265***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.344***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.015\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(4.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-11.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.060***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.032***\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(-5.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.910)\u003c/p\u003e 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colname=\"c7\"\u003e \u003cp\u003e0.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.127***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.171***\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(9.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(9.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(9.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(11.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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\u003cp\u003e-0.665***\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(-11.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-11.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-10.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-11.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-13.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-13.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-12.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-12.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-11.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-11.304)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.796***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.833***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.902***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.520***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.637***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.388***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-10.769***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.711***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4.514***\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(-15.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-15.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-14.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-16.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-17.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-13.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-14.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-12.184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e393.90***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e346.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e525.90***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e445.78***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e449.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e420.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e405.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e338.31***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.74***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.94***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.84***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89.00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e77.49***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first term of the command-type environmental regulation in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is positive and significant, while the coefficient of the second term of the command-type environmental regulation in column (2) is negative at the 1% significance level. This indicates a significant inverted \"U\" relationship between command-type environmental regulation and industrial green and low-carbon transformation. Command-type environmental regulation initially promotes industrial green and low-carbon transformation, but when its intensity exceeds a certain threshold, it begins to inhibit this transformation. The results in column (3) show that the first term of investment-type environmental regulation is positive and significant, while the coefficient of the second term of investment-type environmental regulation in column (4) is negative and passes the 1% statistical significance test. This suggests an significant inverted \"U\" relationship between investment-type environmental regulation and industrial green and low-carbon transformation. The results in column (5) indicate that the first term of expense-type environmental regulation is negative and significant, while the coefficient of the second term of expense-type environmental regulation in column (6) is positive and passes the 1% statistical significance test. This suggests a significant \"U\" relationship between expense-type environmental regulation and industrial green and low-carbon transformation. The results in column (7) show that the first term of non-governmental environmental organizations is negative and significant, while the coefficient of the second term of non-governmental environmental organizations in column (8) is positive and passes the 1% statistical significance test. This suggests a significant \"U\" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. The results in column (9) show that the first-order term of public environmental participation is positive and significant, while the second-order term coefficient of public environmental participation in column (10) is negative. Both have passed the 1% statistical significance test, indicating a possible inverted \"U\" relationship between public environmental participation and industrial green and low-carbon transformation. The above results also confirm H1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Impact effect of innovative human capital on industrial green and low-carbon transformation\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the impact of innovative human capital on industrial green and low-carbon transformation under the fixed effect model, where models (1), (2), (3) and (4) are the regression results of national data, eastern region, central region and western region respectively.\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\u003eThe impact of innovative human capital on industrial green and low-carbon transformation\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enationwide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecentral section\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.570**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.394*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.974***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708**\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(2.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.209)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnede\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.190*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177**\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(3.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.436)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnfdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143***\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(4.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.506)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.662*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.151\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(1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.422)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.720***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.412**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.505***\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(-6.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-9.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.172)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.298***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.317*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.931***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.721***\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(-6.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.956)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.85***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.18***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.29***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the coefficient of the core explanatory variable, innovative human capital, is significant at least at the 10% level in all models, confirming H2, which states that innovative human capital has a significant and robust positive impact on the green and low-carbon transformation of industry. This means that innovative human capital has a significant positive effect on the green and low-carbon transformation of industry both nationally and regionally, indicating that following the strategy of \"innovation-driven development and talent-oriented\" helps achieve this transformation. Other control variables, combined with the results from regression using national data in Model (2), show that R\u0026amp;D investment is significantly positive at the 1% level, suggesting that increasing R\u0026amp;D investment has a positive impact on the green and low-carbon transformation of industry, which is consistent with the previous regression results; foreign direct investment is also positive and significant at the 1% level, indicating that from a national perspective, foreign direct investment has a technology spillover effect that effectively promotes the green and low-carbon transformation of industry; the coefficient for urbanization level is not significant; the coefficient for industrial structure is significantly negative at the 1% level, indicating that an increase in the output value of the tertiary sector is detrimental to the green and low-carbon transformation of industry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Test of the moderating effect of innovative human capital\u003c/h2\u003e \u003cp\u003eTo study the moderating effect of innovative human capital on the relationship between environmental regulation tools and industrial green and low-carbon transformation, the model introduces interaction terms between environmental regulation tools and innovative human capital, as well as the quadratic term of innovative human capital. Models (1) to (5) use Command-Type environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, with specific regression results shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e:\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\u003eThe moderating effect of innovative human capital\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.311***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271***\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(4.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.461***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130***\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(3.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.966)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.808***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019***\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(-8.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-10.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-6.059)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.054\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(1.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.926)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.013**\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(-3.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.581)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.306***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.836***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.204***\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(-16.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-9.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-11.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-12.210)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e381.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e313.66***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.99***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227.60***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor command-type environmental regulations, the coefficient of lnthr \u0026times; lncer2 in Model (1) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is negative and passes the 1% significance test (-0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that innovative human capital moderates the inverted \"U\" relationship between command-type environmental regulations and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make this inverted \"U\" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between command-type environmental regulations and industrial green and low-carbon transformation within the extreme value range, while beyond this extreme value, the negative correlation becomes more evident.\u003c/p\u003e \u003cp\u003eFor investment-oriented environmental regulation, the coefficient of lnthr \u0026times; lnier2 in model (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is negative and significant (-0.025, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that innovative human capital moderates the inverted \"U\" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted \"U\" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation within the extreme value range. Beyond this extreme value, the negative correlation becomes more evident.\u003c/p\u003e \u003cp\u003eFor cost-based environmental regulation, the coefficient of lnthr \u0026times; lncber2 in model (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is positive at the 5% significance level (0.024, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that innovative human capital moderates the \"U\" -shaped relationship between cost-based environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this \"U\" -shaped relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the negative correlation between cost-based environmental regulation and industrial green and low-carbon transformation within the extreme value range, and the more pronounced the positive correlation beyond the extreme value range.\u003c/p\u003e \u003cp\u003eFor non-governmental environmental organizations, the coefficient of lnthr \u0026times; lnengo2 in model (4) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is negative at the 1% significance level (-0.018, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that innovative human capital moderates the inverted \"U\" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make the \"U\" relationship between non-governmental environmental organizations and industrial green and low-carbon transformation more gradual. Specifically, the higher the level of innovative human capital, the stronger the negative effect of non-governmental environmental organizations on industrial green and low-carbon transformation will be within the extreme value range, and after exceeding this extreme value, the promoting effect of non-governmental environmental organizations on industrial green and low-carbon transformation will weaken.\u003c/p\u003e \u003cp\u003eFor public environmental participation, the coefficient of lnthr \u0026times; lnsup2 in Model (5) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is negative at the 5% significance level (-0.013, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that innovative human capital moderates the inverted \"U\" relationship between public environmental participation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted \"U\" relationship steeper. Specifically, the higher the level of innovative human capital, the more pronounced the positive correlation between public environmental participation and industrial green and low-carbon transformation within the extreme value range. Beyond this extreme value, the negative correlation becomes more evident. These results also confirm Hypothesis H3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Robustness test\u003c/h2\u003e \u003cp\u003e(1) Replace the dependent variable. The industrial green and low-carbon transition is derived from the cumulative total factor productivity of industrial green practices. Therefore, using the total factor productivity of industrial green and low-carbon practices as a proxy for the industrial green and low-carbon transition to test the robustness of the regression results. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. Specific regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e:\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 test of substitution of dependent variable\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.397***\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\u003e0.369***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.303***\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(5.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.871)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.443***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099***\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(5.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(9.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.014***\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(-3.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-5.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-3.776)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.009\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(1.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.059***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.056***\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(-4.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-3.447)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.306***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.836***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.204***\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(-16.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-9.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-11.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-12.210)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.99***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.88***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.99***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227.60***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it can be seen that the coefficients of innovative human capital are all positive and significant at the 1% level. The coefficient of environmental regulation instrumental variable is also significant at the 1% level and consistent with the sign of the previous regression results. Moreover, the interaction term coefficient between innovative human capital and environmental regulation instrument is significant, which is also consistent with the sign of the previous regression results, indicating that the regression results are robust.\u003c/p\u003e \u003cp\u003e(2) Sample Reduction. Generally, municipalities have a higher level of economic development and receive more favorable policies from the government. Therefore, municipalities can attract innovative talents independently, leading to a higher level of innovative human capital. To verify that municipalities do not substantially affect the estimation results, this paper excludes the municipalities (Beijing, Shanghai, Tianjin, Chongqing) from the sample and re-estimates the model. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. The specific results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. From the regression results, it is evident that the coefficients of innovative human capital and environmental regulatory tools, as well as the interaction terms, show no significant changes in sign or significance, indicating that the baseline estimation results remain robust.\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\u003eRobustness test of sample deletion\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.288***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.231***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.334***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.258***\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(2.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.964)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.502***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.395***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149***\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(3.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.398)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.059***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.756***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.023***\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(-5.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-5.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-7.412)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\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(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.437)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.025***\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(-4.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.626)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.463***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.845***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.370***\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(-17.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-9.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-12.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-12.687)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.97***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.76***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e664.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219.67***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Endogeneity test\u003c/h2\u003e \u003cp\u003e(1) Two-way causality\u003c/p\u003e \u003cp\u003eThe previous empirical results show that the effect of formal and informal environmental regulation on China's industrial green and low-carbon transformation is nonlinear. Therefore, when using the instrumental variable method, the two-stage residual intervention method (2SRI) which is more suitable for the estimation of nonlinear model should be adopted.\u003c/p\u003e \u003cp\u003eDrawing on the research of Ou et al. (2023) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], air circulation coefficient is chosen as an instrumental variable for environmental regulation. The air circulation coefficient is measured by the product of wind speed and boundary layer height. Therefore, the magnitude of the regional air circulation coefficient mainly depends on natural phenomena such as climate conditions, meeting the exogeneity requirement of an instrumental variable. This approach can, to some extent, overcome the endogeneity issues caused by the existence of reverse causality. Models (1) to (5) use command-type environmental regulation, investment-type environmental regulation, expense-type environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables. Under the assumption of endogeneity, according to the empirical judgment rule of \"weak instrument variables,\" if the F-statistic in the first stage is greater than 10, there is no need to worry about the weak instrument variable issue. In this paper, the F-statistics are all greater than 10 and significant at the 0.01 level. The Sargan-statistic further indicates that the instrument variable is exogenous (p\u0026thinsp;\u0026gt;\u0026thinsp;10%), thus satisfying the conditions of relevance and exogeneity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the regression results are highly consistent with those discussed earlier. Therefore, it further demonstrates the robustness of the empirical conclusions of this paper.\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\u003eEndogeneity test of bidirectional causality\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.223***\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(4.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.341)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.353***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106***\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(5.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-6.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.114)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.642***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015***\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(-3.774)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.303)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.009\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(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018***\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(-5.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-9.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.159***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137***\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(9.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(10.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.210)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.75***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.88***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSagan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e(2) Missing variables\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo test the impact of omitted variables on endogeneity issues and regression results, trade openness was added as a control variable. Models (1) to (5) use command environmental regulation, investment environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as explanatory variables, respectively. The specific correlation regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. From the regression results, it can be seen that the coefficients of innovative human capital and environmental regulation tools, as well as the interaction terms, show no significant changes in sign or significance. This indicates that the model fits well and the regression results are robust.\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\u003eEndogeneity test for the addition of control variables\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.260***\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(3.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.858)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.499***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.390***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141***\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(4.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.717***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.025***\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(-2.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-5.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-3.900)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\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-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010\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(0.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.215)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.023***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.038***\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(-3.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-9.197)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.788***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.409***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.284***\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(-16.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-9.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-11.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-12.308)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.72***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.10***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145.58***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.97***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148.14***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Heterogeneity test","content":"\u003cp\u003eTo analyze whether the moderating effect of innovative human capital varies across different regions, national data were divided into eastern, central, and western regions for re-verification. Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, and \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e present the regression results for the eastern, central, and western regions, respectively. Models (1) to (5) use command environmental regulation, investment-type environmental regulation, cost-based environmental regulation, non-governmental environmental organizations, and public participation in environmental protection as core explanatory variables.\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\u003eThe moderating effect of innovative human capital in eastern China\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200***\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\u003e0.291***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.306***\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(3.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.442)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.071***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.260**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179***\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(1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.809)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.017***\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(1.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\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(0.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.438)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.076***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016**\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(-1.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.778***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.054***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.794***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.388***\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(-5.512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.591)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.75***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.38***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.89***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165.02***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe coefficient of the squared term (lnthr\u0026times;lner2) between innovative human capital and command environmental regulation in model (1) in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e is not significant, indicating that innovative human capital in eastern China does not play a moderating role in the inverted \"U\" relationship between command environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThe coefficient of the squared term (lnthr\u0026times;lner2) of innovative human capital and investment-oriented environmental regulation in model (2) in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e is not significant, indicating that innovative human capital in eastern China does not play a moderating role in the inverted \"U\" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (3) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the coefficient of the squared term (lnthr \u0026times; lner2) for innovative human capital and cost-based environmental regulation is positive at the 1% significance level, indicating that innovative human capital moderates the \"U\" -shaped relationship between cost-based environmental regulation and industrial green and low-carbon transformation in the eastern region. An increase in the level of innovative human capital makes this \"U\" -shaped relationship steeper. In other words, before the intensity of cost-based environmental regulation in the eastern region exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between cost-based environmental regulation and industrial green and low-carbon transformation. After the intensity of cost-based environmental regulation in the eastern region exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between cost-based environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (4) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the coefficient of the squared term for innovative human capital and non-governmental environmental organizations (lnthr \u0026times; lner\u0026sup2;) is negative at the 1% significance level, indicating that innovative human capital moderates the \"U\" -shaped relationship between non-governmental environmental organizations and industrial green and low-carbon transformation in the eastern region. An increase in the level of innovative human capital can make this \"U\" -shaped relationship more gradual. Specifically, before the number of non-governmental environmental organizations in the eastern region exceeds the turning point, the higher the level of innovative human capital, the less pronounced the negative correlation between non-governmental environmental organizations and industrial green and low-carbon transformation; after the number of non-governmental environmental organizations in the eastern region exceeds the turning point, the higher the level of innovative human capital, the less pronounced the positive correlation between non-governmental environmental organizations and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThe coefficient of the square term (lnthr\u0026times;lner2) between innovative human capital and public environmental protection participation in model (5) in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e is not significant, indicating that in eastern China, innovative human capital does not play a moderating role in the inverted \"U\" relationship between public environmental protection participation and industrial green and low-carbon transformation.\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\u003eThe moderating effect of innovative human capital in central China\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.191**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.226***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.288***\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(1.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.664)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.319***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.176**\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(5.778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\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(-3.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.757)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.063\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(0.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.774)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.032\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(5.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.810)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.803)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.487***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.902***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.889***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.807***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.512***\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(-9.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.391)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121.57***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e191.68***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe coefficient of the squared term (lnthr \u0026times; lner\u0026sup2;) for innovative human capital in Model (1) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e is negative and passes the 1% significance test (-0.074, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that in central China, innovative human capital moderates the inverted \"U\" relationship between command environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted \"U\" relationship steeper. Specifically, before the intensity of command environmental regulation in central China exceeds the turning point, the higher the level of innovative human capital, the stronger the positive correlation between command environmental regulation and industrial green and low-carbon transformation. After the intensity of command environmental regulation in central China exceeds the turning point, the higher the level of innovative human capital, the stronger the negative correlation between command environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThe coefficient of the squared term (lnthr\u0026times;lner2) between innovative human capital and investment-oriented environmental regulation in model (2) in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e is not significant, indicating that innovative human capital in central China does not play a moderating role in the inverted \"U\" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThe coefficient of the squared term (lnthr\u0026times;lner2) between innovative human capital and cost-based environmental regulation in model (3) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e is not significant, indicating that innovative human capital in central China does not play a moderating role in the \"U\" relationship between cost-based environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (4) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the coefficient of the square term of innovative human capital and non-governmental environmental organizations (lnthr\u0026times;lner2) is not significant, indicating that the \"U\" -shaped relationship between innovative human capital and non-governmental environmental organizations and industrial green and low-carbon transformation in central China does not play a moderating role.\u003c/p\u003e \u003cp\u003eIn the model (5) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the coefficient of the square term of innovative human capital and public environmental protection participation (lnthr\u0026times;lner2) is not significant, indicating that the inverted \"U\" relationship between innovative human capital and public environmental protection participation and industrial green and low-carbon transformation in central China does not play a moderating role.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe moderating effect of innovative human capital in western China\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=\"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 \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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elncber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnengo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.405***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.369***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.433***\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(4.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.257)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.312***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.340***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.126*\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(2.681)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.946)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.051***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.005***\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(10.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.383)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.920)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\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(0.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnthr*lner2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.023**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015*\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(-2.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-9.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.883)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolled variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.369***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.395***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.775***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.942***\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(-8.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.622)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-7.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-5.306)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-7.205)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172.73***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202.81***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.82***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** represent the significance levels of 10%,5%, and 1%, respectively; the Z values are reported in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the model (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the coefficient of the squared term for innovative human capital and command-type environmental regulation (lnthr \u0026times; lner\u0026sup2;) is negative and passes the 5% significance test (-0.023, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that in western regions, innovative human capital moderates the inverted \"U\" relationship between command-type environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital can make this inverted \"U\" relationship steeper. Specifically, before the intensity of command-type environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between command-type environmental regulation and industrial green and low-carbon transformation. After the intensity of command-type environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between command-type environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (2) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the coefficient of the squared term for innovative human capital and investment-oriented environmental regulation (lnthr \u0026times; lner\u0026sup2;) is negative and significant (-0.167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that in western regions, innovative human capital moderates the inverted \"U\" relationship between investment-oriented environmental regulation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted \"U\" relationship steeper. Specifically, before the intensity of investment-oriented environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the positive correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation. After the intensity of investment-oriented environmental regulation in western regions exceeds the turning point, the higher the level of innovative human capital, the more pronounced the negative correlation between investment-oriented environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThe coefficient of the square term (lnthr\u0026times;lner2) of innovative human capital and cost-based environmental regulation in model (3) in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e is not significant, indicating that in western China, innovative human capital does not play a moderating role in the \"U\" relationship between cost-based environmental regulation and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (4) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the coefficient of the square term of innovative human capital and non-governmental environmental organizations (lnthr\u0026times;lner2) is not significant, indicating that in western China, innovative human capital does not play a moderating role in the \"U\" -shaped relationship between non-governmental environmental organizations and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eIn the model (5) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the coefficient of the squared term for innovative human capital and public environmental participation (lnthr \u0026times; lner\u0026sup2;) is negative at the 5% significance level (-0.015, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). This indicates that in western regions, innovative human capital moderates the inverted \"U\" relationship between public environmental participation and industrial green and low-carbon transformation. An increase in the level of innovative human capital makes this inverted \"U\" relationship steeper. Specifically, before the turning point of public environmental participation intensity in western regions, the higher the level of innovative human capital, the stronger the positive correlation between public environmental participation and industrial green and low-carbon transformation. After the turning point, the higher the level of innovative human capital, the stronger the negative correlation between public environmental participation and industrial green and low-carbon transformation.\u003c/p\u003e"},{"header":"5. Research conclusions and countermeasures","content":"\u003cp\u003eEnvironmental regulation is a crucial institutional tool for promoting the green and low-carbon transformation of industry, while innovative human capital is a key factor in achieving this transformation. Based on endogenous growth theory, this paper empirically examines the impact of environmental regulation tools and innovative human capital on the green and low-carbon transformation of Chinese industry using provincial panel data from 1997 to 2021. The study finds:\u003c/p\u003e \u003cp\u003eFirst, the impact of environmental regulatory tools on industrial green and low-carbon transformation varies. Command-Type environmental regulations, investment-type environmental regulations, and public participation in environmental protection show an inverted \"U\" shape, with effects initially increasing and then decreasing; expense-type environmental regulations and non-governmental environmental organizations exhibit a \"U\" shape, with effects initially decreasing and then increasing.\u003c/p\u003e \u003cp\u003eSecond, the green and low-carbon transformation of innovative human capital industry plays a promoting role, and plays a moderating role in the relationship between command environmental regulation, investment environmental regulation, cost environmental regulation, non-governmental environmental organizations, public participation in environmental protection and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eThird, the moderating role of innovative human capital exhibits regional heterogeneity. In the eastern region, innovative human capital only moderates the relationship between cost-based environmental regulations, non-governmental environmental organizations, and industrial green and low-carbon transformation; in the central region, it only moderates the relationship between Command-Type environmental regulations and industrial green and low-carbon transformation; in the western region, it only moderates the relationship between Command-Type environmental regulations, investment-type environmental regulations, public participation in environmental protection, and industrial green and low-carbon transformation.\u003c/p\u003e \u003cp\u003eBased on the above conclusions, the following countermeasures are proposed:\u003c/p\u003e \u003cp\u003eFirst, the central and local governments should formulate differentiated environmental policies and supervise and guide the public and non-governmental environmental organizations to participate rationally in environmental governance. Firstly, appropriate environmental emission standards, efficient pollution monitoring systems, stringent penalties for environmental damage, and comprehensive legal liability mechanisms should be established to form an environmental protection system that focuses on source prevention, process control, damage compensation, and accountability. Secondly, while implementing environmental infrastructure investment and construction or \"three simultaneous\" environmental investments, it is necessary to clearly define the energy consumption and pollution emissions of industrial enterprises to determine whether there is any \"free-riding\" behavior, and penalize enterprises that do not meet standards. Thirdly, environmental taxes levied by the government on enterprises should be increased within a reasonable range, and the emission trading market should continue to be improved, establishing a comprehensive trading network and system, and strengthening the supervision of the carbon emission trading market. Lastly, the public and non-governmental environmental organizations should be guided to protect the environment rationally, making informal environmental regulation a safeguard for the coordinated development of the environment and the economy. For example, enhancing environmental awareness through public education and establishing effective and smooth information feedback mechanisms and channels, government departments or regulatory bodies should actively respond to environmental pollution issues reported by the public. Encouraging government involvement in environmental protection can leverage their professional advantages to collaborate with enterprises in developing green and low-carbon products, actively promoting the green innovation achievements of enterprises, and shaping a group of representative enterprises that produce fewer pollutants and more green products. This approach aims to stimulate enterprises' willingness for green innovation from the perspective of market demand and promote voluntary green and low-carbon transformation of enterprises\u003c/p\u003e \u003cp\u003eSecond, the development of human capital should shift from a sole focus on quantitative expansion to qualitative improvement. On one hand, while continuing to popularize and implement compulsory education, emphasis should be placed on the development of higher education. This includes vigorously building higher education infrastructure, increasing support funds for higher education, optimizing higher education resources, breaking down institutional barriers to talent development, steadily enhancing the level and quality of talent, and focusing on promoting the industrialization of technological achievements to strengthen the integration between high-quality human capital, technological innovation, and industrial upgrading. On the other hand, the strategy of attracting overseas talent and intelligence should be utilized to improve the quality of human capital. Priority should be given to setting up \"talent attraction stations\" and \"green innovation bases\" in various levels of science associations, technology parks, enterprises, universities, and research institutions that have the necessary conditions. These serve as platforms for implementing the transfer of overseas technology, conversion of results, talent introduction, and project implementation. Building on this foundation, further efforts should be made to broaden channels for cooperation between schools and local areas, as well as between schools and enterprises, creating platforms for talent introduction, project collaboration, academic exchange, and policy consultation. This will provide a platform for expanding talent introduction channels and attracting high-end overseas talent, becoming a \"strong magnet\" for overseas talent.\u003c/p\u003e \u003cp\u003eThird, when optimizing policies for cultivating innovative human capital, regions should integrate local environmental policies. Specifically, in the eastern region, on one hand, emphasis should be placed on the development of higher education, such as increasing support funds for higher education, optimizing educational resources, and building higher education infrastructure. On the other hand, strategies for attracting overseas talent and intelligence should be prioritized, such as establishing \"talent attraction stations\" and \"green innovation bases\" in qualified science parks, enterprises, universities, and research institutions, to attract international talent. This approach aims to enhance the level of innovative human capital through both \"local cultivation\" and \"overseas introduction.\" At the same time, appropriate expense-type environmental regulations and enhanced management of non-governmental environmental organizations should be implemented to support the green and low-carbon transformation of industries. In the central region, efforts should focus on improving primary and secondary education infrastructure, strengthening the development of higher education, and fostering local innovative talent. Additionally, proactive policies for talent attraction should be formulated, such as providing \"one-stop services\" and \"nanny-style services,\" to properly address key issues like housing, spousal placement, children's schooling, and healthcare, thereby creating a favorable service environment. At the same time, it is essential to focus on optimizing command-type environmental regulations, such as raising pollution emission standards and increasing penalties for violations, to impose strict constraints on corporate production activities, which will better facilitate the green and low-carbon transformation of industry. In western regions, local governments should improve basic educational facilities, while the central government continues to offer preferential policies in research funding allocation and fund support, helping to retain local high-quality talent and attract high-caliber talent from other regions. Local governments should also optimize command-type and investment-type environmental regulatory policies, facilitate public participation in environmental governance, and support the green and low-carbon transformation of industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial; it does not involve human participants or clinical interventions. Therefore, clinical trial registration details are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be obtained from the corresponding author upon request. The calculated data used to support the findings of this study are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Social Science Fund of China(14AJL015) and Chongqing Social Science Foundation(2020ZDJJ01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZH was responsible for the logical reasoning of the research topic. DJ \u0026nbsp;were responsible for experimental materials and data. ZX was responsible for collecting literature. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the National Social Science Foundation of China (14AJL015) and Chongqing Social Science Foundation (2020ZDJJ01) for supporting this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ.-X. Jiang, J.-J. Wang, and Y.J.P.S. Cheng, The impact of industrial transformation on green economic efficiency: New evidence based on energy use. 21 (2024) 3644-3655.\u003c/li\u003e\n\u003cli\u003eJ. Hou, T.S. Teo, F. Zhou, M.K. Lim, and H.J.J.o.c.p. Chen, Does industrial green transformation successfully facilitate a decrease in carbon intensity in China? An environmental regulation perspective. 184 (2018) 1060-1071.\u003c/li\u003e\n\u003cli\u003eK. Du, Y. Cheng, and X.J.E.E. 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Song, and Society, Estimating the effect of air quality on bike-sharing usage in Shanghai, China: An instrumental variable approach. 33 (2023) 100626.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"environmental regulation tools, innovative human capital, industrial green and low-carbon transformation, regulatory effect, regional differences","lastPublishedDoi":"10.21203/rs.3.rs-6826667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6826667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInnovative human capital is a key element in achieving the green and low-carbon transformation of industry. Systematically clarifying the role of innovative human capital in promoting industrial green and low-carbon transformation through environmental regulation tools is crucial for achieving the \"dual carbon\" goals. This paper, based on endogenous growth theory and relevant theories of environmental economics, uses provincial panel data from China's industry from 1997 to 2021 as a sample to empirically examine the impact of environmental regulation tools and innovative human capital on industrial green and low-carbon transformation. The study finds that different types of environmental regulation tools exhibit an inverted \"U\" -shaped characteristic, with effects initially increasing and then decreasing, or a \"U\" -shaped characteristic with effects initially decreasing and then increasing; innovative human capital can promote industrial green and low-carbon transformation and also plays a moderating role in the relationship between environmental regulation tools and industrial green and low-carbon transformation, although this moderating effect varies across different regions.\u003c/p\u003e","manuscriptTitle":"Environmental regulation tools, innovative human capital and industrial green and low-carbon transformation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 12:26:32","doi":"10.21203/rs.3.rs-6826667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1200aab1-fea5-4621-a1da-b9330415e17a","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-10T15:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 12:26:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6826667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6826667","identity":"rs-6826667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}