Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry

Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry Shanhong Li, Yanqin Lv, Yang Ping This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3306547/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To explore the impact of digital economy on the low-carbon development of manufacturing industry, this paper constructs theoretical and empirical models, and studies from both theoretical and empirical perspectives. The results show that there is a U-shaped relationship between digital economy and low-carbon development of manufacturing industry. In terms of theoretical model analysis, we adopt the Green Solow model as the analytical framework, and improve and expand the CES production function to introduce digital economy into it. By deducing the theoretical model, we draw the conclusion that there is a possibility of an inverted U-shaped curve between the development of digital economy and carbon emissions. In terms of empirical verification, this paper applies fixed-effect and intermediate-effect empirical models, and relies on panel data of 30 provinces and cities in China from 2011 to 2020 to conduct an empirical study on the relationship between digital economy and low-carbon development of manufacturing industry. The results show that the impact of digital economy on the low-carbon development of manufacturing industry is not linear, but exhibits a U-shaped relationship. In regions with high and medium levels of marketization, digital economy has a significant impact on the low-carbon development of manufacturing industry, while it has no obvious impact in regions with low levels of marketization. Furthermore, this study finds that digital economy can influence the low-carbon development of manufacturing industry through various ways, such as innovative green technology, reducing capital mismatch, and promoting industrial upgrading of manufacturing industry, based on the analysis of influencing mechanism. Digital economy Green Solo model Manufacturing industry Low-carbon development 1. Introduction China's manufacturing industry, as one of the largest manufacturing countries in the world, has become an urgent issue for low-carbon development under the heightened concern over global climate change and the commitment of all countries to reduce carbon emissions. Although the Chinese government has made low-carbon development a top priority and started to formulate a series of policies and measures, there are still many challenges and dilemmas to realize low-carbon development in the manufacturing sector. According to China Statistical Yearbook 2021, in 2020, China's manufacturing industry will account for 54.80% of the country's energy consumption and 51.64% of the country's carbon emissions, but the value added of manufacturing industry will only account for 27.84% of the country's value added. This indicates that in the new round of rapid manufacturing and heavy manufacturing, the rough development of the manufacturing industry, which is characterized by high energy consumption, high emissions, high input and low efficiency, is very obvious (Yan and Fang,2015). In this context, measures are needed to improve the carbon emission efficiency of the manufacturing sector to achieve the goals of "carbon peak" and "carbon neutral" actions and to promote China's green and high-quality development. Therefore, an effective response to the problems of low-carbon development is crucial for the transformation and upgrading of China's manufacturing sector, sustainable growth and environmental protection. Globally, the development of digital economy is gradually becoming a new driving force for economic and social development. Developed countries that developed digital industries and informationization construction more maturely in the early days, such as the United States, Japan, and European countries, the development of digital economy has entered the peak period (Kshetri, 2014). And emerging economies and developing countries, such as India, Brazil, China and Southeast Asia, along with the wide application of big data technology in areas such as the Internet and social media, the data economy has been able to rise rapidly. As an important player in the digital economy, the Chinese government attaches great importance to the development of data factors and regards them as strategic resources.In 2015, a big data exchange was established in Guiyang City, Guizhou Province, followed by a large number of data exchanges across the country, which provided platforms and opportunities for the development of data factors.In 2020, the Chinese government further issued a document explicitly placing the data factor, along with land, labor, capital and technology as one of the five major factors of production, and called for the construction of a better institutional mechanism for market-based allocation in order to further enhance the role of data factors in supporting the economy and society. Although the digital economy, especially the data element, has become one of the basic production factors, its positive or negative effects on the low-carbon development of the manufacturing industry are not yet clear. Specifically, the digital economy can help manufacturing firms to optimize production processes and reduce carbon emissions, for example, by optimizing key information such as process flow, product design and energy consumption (Lange et al., 2020). However, there may also be environmental issues involved in utilizing data, such as energy consumption for data storage (Ma et al., 2022; Koot and Wijnhoven, 2021). Therefore, we need to think about a very relevant and practical question: whether and how the digital economy can help manufacturing industries to achieve a low-carbon transition? However, few studies have explored this important question in depth. Research on the digital economy involves two main aspects: measurement and indicators, as well as its economic effects. However, due to the lack of a unified consensus on the connotation of the digital economy, the participants involved, and the statistical scope, there are significant differences in the measurement, content, and results of the digital economy.At present, there are various measurement methods for the digital economy, including the commonly used national economic accounting method, value-added measurement method, competitiveness indicator system, and digital economy satellite account method. The national economic accounting method mainly measures the size of the digital economy based on GDP and the national accounts system (Ahmad et al., 2017). The value-added method mainly calculates the actual value-added of the digital economy based on the reduction method and Fisher ideal index (KNICKREHM et al., 2016; DEAN et al., 2017; Barefoot et al., 2018). The competitiveness evaluation indicator system of the digital economy is formed based on the comprehensive consideration and collation of the results of extensive survey research and statistical analysis. Cockayne’s (2016) research further explores the issue of evaluating the competitiveness of the digital economy. The digital economy satellite account method has been established in countries such as Chile, Australia, and South Africa, aiming to more comprehensively and accurately measure the development of the digital economy (Shadibekova, 2019; OECD, 2017a; OECD, 2017b). With the continuous deepening of the information technology revolution, the rapid development of the digital economy has had unprecedented and far-reaching impacts, bringing important changes at the macro, meso, and micro levels. The digital economy not only promotes high-quality economic development through optimizing factors allocation and improving productivity (Qian et al., 2022), but also plays an irreplaceable role in eliminating social poverty (Qian et al., 2022), encouraging entrepreneurship (Jiang et al., 2022), and promoting employment (Nodirovna et al., 2022). At the same time, the digital economy also has green value and environmental effects, improving environmental governance performance through promoting public participation and strengthening government environmental regulation (Luo et al., 2022). According to the research of Su et al. (2021), the development of the digital economy at a moderate level can promote high-quality economic development and effectively promote industrial structure upgrading. At the micro-level, the development of the digital economy can significantly improve enterprise productivity (Li R et al.,2022), accelerate the process of enterprise management transformation (Lee and Yang, 2016), reduce financial risks, stimulate consumer spending, lower social costs, and increase micro welfare (Cennamo and Santalo, 2013;). Research has shown that the digital economy has become a key driving force for profound changes in production and lifestyle, and has had a wide and far-reaching impact on various sectors. The literature on low-carbon development in the manufacturing industry mainly focuses on three aspects: the connotation of low-carbon transformation in the manufacturing industry, measurement, and influencing factors. In terms of connotation, economic growth theories have proposed growth models that take environmental variables as one of the production factors (Bovenberg and Smulders, 1995), thus more reasonably considering environmental issues such as carbon emissions and energy consumption in the process of economic growth. If we want to consider the impact and cost of carbon emission reduction targets on the economy, we need to base the analysis on a growth accounting framework that includes environmental elements. The literature mainly uses total factor productivity and index system methods for the measurement of low-carbon transformation in the industry. In the measurement of low-carbon green transformation in the industry, many studies have adopted the total factor productivity method. This approach uses various parametric or non-parametric efficiency measurement methods to evaluate the industrial low-carbon green transformation, taking energy consumption as input factors and non-expected output indicators such as carbon emissions or "three wastes" emissions for measurement.(Fare et al., 2007). The latter is measured by a comprehensive index system, such as the comprehensive evaluation index system for China's low-carbon economic development and the industrial green development performance index. In summary, these studies have deeply studied the low-carbon development of the manufacturing industry, providing important reference for promoting low-carbon transformation in the industry. Kaya identity (Kaya, 1989) believes that carbon emissions are affected by population, economy, energy intensity, carbon intensity and other factors. Afterwards, scholars conducted in-depth analysis of the factors affecting carbon emissions on this basis. This includes factors such as human capital (Tian et al., 2019), industrial structure (Sun et al., 2020), energy structure (Akram et al., 2020), and technological progress (Yang et al., 2021). These research results provide useful reference basis for further studying the factors affecting carbon emissions in the manufacturing industry. Currently, research on the impact of the digital economy on the manufacturing industry mainly focuses on green development. Scholars have studied the impact of the digital economy on the green development of the manufacturing industry from three perspectives: resource allocation, technological innovation, and external supervision. The digital economy optimizes resource allocation by directing capital and other production factors towards high-efficiency sectors, reducing society's excessive demand for energy and promoting the digitalization, rationalization, and greening of industrial structures, thereby promoting green development (Kohli and Melville, 2019). In terms of technological innovation, the development of the digital economy facilitates resource sharing and flow of production factors, promotes energy technology progress and interconnection, improves energy utilization efficiency, and affects the efficiency of green production (Nambisan et al., 2017). In terms of external supervision, the universality and penetration of the digital economy can be applied to various industries, and regulatory agencies can build ecological information governance to achieve external supervision and promote green development (Zhang et al., 2018). In addition, energy big data analysis also helps improve enterprise production efficiency and reduce energy consumption in production, providing important support for the digital economy's green development in the manufacturing industry (Hu et al., 2020). After analyzing existing literature, it was found that although some studies have focused on the impact of digital economy on the green development of manufacturing industry, the exploration of the impact of digital economy on the low-carbon development of manufacturing industry is not yet sufficient. In terms of whether the digital economy can promote carbon reduction, some literature has deducted the relationship between digital economy and carbon emissions from a theoretical modeling perspective (such as Li and Wang, 2022). However, due to the Cobb-Douglas production function assuming that the contributions of each factor are independent and separable in the production process, the introduction of digital economy as a production factor into the production function is not consistent with the actual integration of digital economy and other factors. In addition, existing literature often refers to the green total factor productivity of manufacturing industry as a reference for measuring the low-carbon development of manufacturing industry, but this index cannot well reflect the differences in industrial structure of manufacturing industry in different regions, which can lead to result biases. Therefore, it is necessary to further explore the mechanism and measurement methods of the impact of digital economy on the low-carbon development of manufacturing industry in order to improve the accuracy and practicality of research. Therefore, the innovation of this paper mainly lies in three aspects: firstly, the innovation in theoretical models. Based on the green Solow model, this paper constructs a theoretical model of the relationship between the digital economy and carbon emissions, using the CES production function, which proves the impact of digital economy development on the carbon emissions of the manufacturing industry from a theoretical model perspective and enriches the application of the green Solow model. Secondly, the innovation in research perspective is to study the impact of the digital economy on the low-carbon development of the manufacturing industry from the perspective of the digital economy, and explore its influencing mechanism. Thirdly, the innovation in research methods is the introduction of a new measurement method for the energy consumption and carbon emissions of the manufacturing industry, which calculates the proportion of the output value of the manufacturing industry in the sample region to the industrial output value, and is more scientific than traditional methods, thus improving the accuracy of research results. The following is the structure of this article: the second part proposes theoretical mechanisms and hypotheses, the third part discusses research methods and data sources, the fourth part conducts basic empirical analysis, the fifth part examines the expansion analysis of regional marketization level, and finally concludes in the sixth part. 2. Theoretical models and mechanisms. 2.1Theoretical models Some studies have combined the Solow model with pollution emissions and proposed the Green Solow model (Brock and Taylor, 2010), which provides a theoretical basis for the inverse U-shaped relationship between pollution emissions and per capita income. This paper refers to the basic framework of the Green Solow model and constructs a Solow model of the relationship between digital economic development and carbon emissions, providing a theoretical basis for further exploring the impact of digital economic development on carbon emission reduction in the manufacturing industry. In April 2020, the "Opinions of the Central Committee of the Communist Party of China and the State Council on Building a More Perfect System for Market-oriented Allocation of Factors of Production" for the first time included data as a factor of production. Compared with the industrial age, the significant feature of the digital economy age is that data becomes a new factor of production. The specific form of the traditional CES function is as follows: $$\text{Y}=\text{A}\left[\right({\text{a}}_{1}\ast {\text{K}}^{{\rho }}+{\text{a}}_{2}\ast {\text{L}}^{{\rho }}+{\text{a}}_{3}\ast {\text{M}}^{{\rho }}{)}^{1/{\rho }}]$$ 1 Y represents output, A represents total factor productivity, K represents capital input, L represents labor input, M represents technological progress or other input,are constants, and ρ represents the elasticity coefficient. The elasticity coefficient ρ reflects the degree of elasticity of substitution between the different input factors. In the CES function, different input factors are substitutable, and the degree of substitution elasticity may vary significantly at different variable values. According to the assumption made in the article, data capital can be viewed as a factor of production and added to the production function. We divide the capital input in the CES function into two parts, physical capital input K and data capital input D, so the above expression can be simplified as: $$\text{Y}=\text{A}\left[\right({\text{a}}_{1}\ast {\text{K}}^{{\rho }}+{\text{a}}_{2}\ast {\text{L}}^{{\rho }}+{\text{a}}_{3}\ast {\text{D}}^{{\rho }}{)}^{1/{\rho }}]$$ 2 D represents data capital input. Based on the Inada conditions and the constant returns to scale property of the production function, we can derive the following relationship: Y = F(K, D, L) ( 3 ) F represents the production function. Next, we followed Copeland and Taylor's approach in modeling the impact of carbon emission reduction and treating it as a by-product of production (Copeland and Taylor, 1994). We assume that the carbon emissions E are a co-product of production, and that every increase in one unit of output will produce pollution of Ω, i.e.: E = ΩY ( 4 ) Ω reflects the pollution emissions per unit of output. We assume that Ω is the carbon emissions per unit of output, also known as carbon intensity of production. According to the hypothesis of the green Solow model, we introduce the impact of emission reduction into the model and assume that the emission reduction function r(.) is increasing and strictly concave with respect to the economic output Y that is invested in emission reduction efforts. Based on these hypothesis, we can rewrite the model as: $$\text{Y}=\text{A}\left[\right( {\text{a}}_{1}\ast {\text{K}}^{{\rho }}+{\text{a}}_{2}\ast {\text{L}}^{{\rho }}+{\text{a}}_{3}\ast {\text{D}}^{{\rho }}{)}^{1/{\rho }}]$$ 5 E = ΩY(1 - r(Y/ \({\text{Y}}^{\text{R}}\) )) ( 6 ) By solving the above model, we can obtain the following: \({\text{k}}^{\ast }\) , \({\text{d}}^{\ast }\) : the level of capital stock at steady state. \({\text{e}}^{\ast }\) : the level of carbon emissions at steady state (per unit of effective labor). \({\text{g}}_{\text{Y}}\) : the total output growth rate on the balanced growth path. \({\text{g}}_{\text{E}}\) : the carbon emission growth rate on the balanced growth path. Assuming a CES production function and introducing the co-effects of carbon emissions, the steady-state levels of capital and emissions are as follows: $${\text{k}}^{\ast }={［（\text{s}\ast {\text{a}}_{1}\ast {\text{a}}_{3}\ast {\rho })/（\text{n}+\text{g}+{{\delta }}_{\text{D}}］}^{\frac{1}{1-{\rho }}}\ast {［（\text{n}+\text{g}+{{\delta }}_{\text{D}})/（\text{n}+\text{g}+{{\delta }}_{\text{K}}］}^{\frac{1-{\rho }{\omega }}{1-{\rho }}}$$ 7 $${\text{d}}^{\ast }={［（\text{s}\ast {\text{a}}_{2}\ast {\text{a}}_{3}\ast {\rho })/（\text{n}+\text{g}+{{\delta }}_{\text{K}}］}^{\frac{1}{1-{\gamma }}}\ast {［（\text{n}+\text{g}+{{\delta }}_{\text{K}})/（\text{n}+\text{g}+{{\delta }}_{\text{D}}］}^{\frac{1-{\rho }}{1-{\rho }{\omega }}}$$ 8 \({\text{e}}^{\ast }\) = \(\frac{{\Omega }}{\text{A}}\ast ［（\text{s}\ast {\text{a}}_{1}\ast {\text{a}}_{3}\ast {\text{k}}^{\ast }\) ( \({\rho }\) -1) \(\ast {\text{d}}^{\ast }\ast {\rho }\) + \(\frac{\text{s}\ast {\text{a}}_{2}\ast {\text{a}}_{3}\ast {\text{k}}^{\ast }{\rho }\ast {\text{d}}^{\ast }({\rho }-1)}{\text{n}+\text{g}-{\rho }\ast ({{\delta }}_{\text{K}}+{{\delta }}_{\text{D}})}］\) ( 9 ) Here, s represents the saving rate, n represents the population growth rate, g represents the rate of technological progress, δK and δD represent the depreciation rate of physical capital and data capital respectively, ρ and σ are parameters in the CES function, Ω is the carbon emissions per unit of output, and A is the total factor productivity. In this model, both the level of capital stock and the level of carbon emissions at steady state exhibit a inverted-U relationship. That is, when the level of capital is too low or too high, the level of carbon emissions is relatively low, while when the level of capital is moderate, the level of carbon emissions is relatively high. 2.2 Theoretical Mechanisms Greening embodies a production approach that emphasizes environmental protection with "energy-saving and emission reduction" as its core. It not only improves production efficiency, but more importantly, emphasizes the protection of the natural environment. Traditional production techniques that use machines and standardized production result in resource consumption and environmental pollution, while digital technology has advantages of high technological content and low environmental costs (Li et al., 2018). If we focus on the coordination between production and the environment while increasing production efficiency, the consumption of energy and resources will be relatively small, and the impact on the environment will also be very small (Gobbo et al., 2018; Jabbour et al., 2018; Dubey et al., 2019). The digital economy can promote low-carbon development in manufacturing from design, production, supply chain management and other aspects. Digital twin technology can simulate and test the sustainability, degradability, energy-saving and emission-reducing properties, health and safety, and quality performance of various design schemes to achieve energy-saving and emission-reducing during the production and use process. For example, BMW and other automotive companies collaborate with Dassault to use CAD and CAE platform 3D Experience for aerodynamic and fluid acoustic simulation verification, and reduce air resistance by optimizing the streamline of product design to achieve environmental goals such as energy-saving and emission reduction. Dassault's assistance and technical support have contributed significantly to environmental protection achievements of these car companies. However, in the early stages of the digital economy, manufacturing companies face the problem of high costs for digital transformation due to relying on modern information technology and advanced equipment. Therefore, manufacturing companies that undergo low-carbon transformation in the early stages may not be able to bear the additional cost burden. Based on this premise, this paper proposes the following hypothesis: H1: The impact of the digital economy on low-carbon development in manufacturing has a U-shaped trend, that is, it will initially inhibit low-carbon development but gradually become a powerful factor promoting low-carbon development over time. Theoretical analysis suggests that the development of regional digital economy can provide more technical support and investment for the innovation of green technology. The digital economy provides technological platforms such as cloud computing and big data analytics, which can help local industries to better conduct research and application of green technology. The application of green technology can improve the energy utilization efficiency of the manufacturing industry, reduce carbon emission intensity, and drive the improvement of energy efficiency in the manufacturing industry to achieve the goal of low-carbon development, such as using energy-saving and environmentally friendly production equipment and renewable energy. However, in the early stages of digital economy development, there may be a crowding-out effect of the digital economy, leading to a reduction in investment in green technology innovation because limited resources are used for the development of the digital economy. Nevertheless, as the digital economy develops to a certain stage, it will have a positive promoting effect on the innovation of green technology, thereby promoting its development (Dou and Gao X,2022). Therefore, the following hypothesis is proposed: H2: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of green technology innovation. With the continuous popularization of digital technologies such as big data, cloud computing, blockchain, and mobile internet in the financial industry, China's digital finance industry has shown a rapid development trend, effectively breaking through the limitations of traditional finance and significantly reducing the operating costs of financial institutions. The extensive application of digital technologies has expanded the coverage of financial services. In particular, the application of these technologies has greatly improved the level of financial services for the manufacturing industry, especially the availability of capital (Agyapong, 2021). Digital finance can use digital technologies such as cloud computing, big data, and blockchain to quickly, conveniently, and efficiently mine, aggregate, and analyze customers' transaction and credit data, use intelligent matching algorithms to scientifically and comprehensively evaluate the operating risks and credit conditions of the manufacturing industry, and promote the availability of manufacturing industry capital (Qian et al., 2022; Pei et al., 2018). However, as the development of the digital economy enters a certain stage, the problem of capital misallocation also emerges. The rapid development of the digital economy has promoted the rise of emerging industries and attracted a large amount of investment capital. However, these capital tend to focus more on a few sectors and companies, resulting in uneven resource allocation and potentially exacerbating the problem of capital misallocation (Smith and Johnson, 2019). H3: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of capital misallocation. According to human capital theory, there is a property of human capital called knowledge spillover effects, which mainly manifest in promoting the continuous development and upgrading of the industry towards high technology, high knowledge, and high added value, improving the production efficiency of the manufacturing industry, and generating the effect of industrial upgrading within the manufacturing industry (Su et al., 2021). Skilled labor can handle more complex jobs, make appropriate responses according to different external environment changes, and reduce inefficient labor, learning costs, and resource waste, thereby further improving the production efficiency of the manufacturing industry. On the other hand, with the continuous improvement of human capital and the continuous improvement of labor quality, their career preferences will change, and they will prefer high-end industrial sectors with good salaries and high income levels. This preference will lead to the transfer of labor from labor-intensive manufacturing to capital intensive or technology intensive manufacturing, thereby promoting the upgrading of manufacturing structure(Yang et al., 2021). Therefore, the digital economy, through the human capital effect, can affect the production efficiency and high-level evolution of the manufacturing industry, generating the effect of industrial upgrading within the manufacturing industry. The industrial upgrading of the manufacturing industry can gradually transform from the low-end of the value chain to the high-end value chain, achieving the goal of low-carbon development such as energy-saving and reducing carbon emissions (Wu and Yang, 2022). H4: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of industrial upgrading of the manufacturing industry. 3. Research methodology and data sources 3.1 Model construction 3.1.1 Calculation method of green total factor productivity Green total factor productivity (GTFP) is developed on the basis of total factor productivity (TFP), and its calculation method involves incorporating unexpected outputs into the TFP calculation model. This paper refers to the calculation method proposed by Xia and Xu(2020),and uses the super-efficiency SBM model with unexpected outputs to evaluate the DMU (x0, y0, z0), as shown in the following formula: \(\rho =\hbox{min} \frac{{1+\frac{1}{m}\sum\nolimits_{{i=1}}^{m} {\frac{{s_{i}^{x}}}{{{x_{i0}}}}} }}{{1 - \frac{1}{{{s_1}+{s_2}}}\left( {\sum\nolimits_{{k=1}}^{{{s_1}}} {\frac{{s_{k}^{y}}}{{{y_{k0}}}}+} \sum\nolimits_{{l=1}}^{{{s_2}}} {\frac{{s_{l}^{z}}}{{{z_{l0}}}}} } \right)}}\) \(\begin{gathered} {x_{i0}} \geqslant \sum\limits_{{j=1, \ne 0}}^{n} {{\lambda _j}{x_j}+{\text{s}}_{i}^{x},} \forall i; \hfill \\ {y_{k0}} \leqslant \sum\limits_{{j=1, \ne 0}}^{n} {{\lambda _j}{y_j}{\text{- s}}_{k}^{y},} \forall k; \hfill \\ {z_{l0}} \geqslant \sum\limits_{{j=1, \ne 0}}^{n} {{\lambda _j}{z_j}+{\text{s}}_{l}^{z},} \forall l; \hfill \\ \end{gathered}\) \(1 - \frac{1}{{{s_1}+{s_2}}}\left( {\sum\nolimits_{{k=1}}^{{{s_1}}} {\frac{{s_{k}^{y}}}{{{y_{k0}}}}+} \sum\nolimits_{{l=1}}^{{{s_2}}} {\frac{{s_{l}^{z}}}{{{z_{l0}}}}} } \right)>0;\) \(s_{i}^{x} \geqslant 0,s_{k}^{y} \geqslant 0,s_{l}^{z} \geqslant 0,{\lambda _j} \geqslant 0,\forall i,j,k,l;\) 3.1.2 Fixed-effects panel model This paper first empirically tests the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry, and then further identifies the influencing factors between the digital economy and the low-carbon total factor productivity of the manufacturing industry. Specifically, this paper sets up the following empirical model: \({\text{G}\text{T}\text{F}\text{P}}_{\text{i}\text{t}}\) =α+ \({{\beta }}_{1}{\text{d}\text{i}\text{g}\text{i}\text{t}\text{a}\text{l}}_{\text{i}\text{t}}\) + \({{\beta }}_{2}{{(\text{d}\text{i}\text{g}\text{i}\text{t}\text{a}\text{l}}_{\text{i}\text{t}})}^{2}\) + \({\text{X}}^{{\prime }}{\phi }\) + \({{\gamma }}_{\text{i}}\) + \({{\lambda }}_{\text{t}}\) + \({{\epsilon }}_{\text{i}\text{t}}\) ( 1 ) In which, \({\text{G}\text{T}\text{F}\text{P}}_{\text{i}\text{t}}\) and \({\text{d}\text{i}\text{g}\text{i}\text{t}\text{a}\text{l}}_{\text{i}\text{t}}\) are the low-carbon total factor productivity and digital economy of the manufacturing industry in province i at time t, respectively. \({{\gamma }}_{\text{i}}\) is the fixed effect of province, \({{\lambda }}_{\text{t}}\) is the fixed effect of time, X is the fixed effect, and \({{\epsilon }}_{\text{i}\text{t}}\) is the random disturbance term.βis the regression coefficient between the digital economy and the low-carbon total factor productivity of the manufacturing industry. When the relationship between the two presents a U-shape, that is, the theoretical hypothesis is established, it is expected that \({{\beta }}_{1}\) 0. 3.2 Indicator selection Dependent variable: Low-carbon total factor productivity of the manufacturing industry. At present, the measurement of low-carbon total factor productivity of the manufacturing industry includes the establishment of an indicator system and the use of it by scholars.This paper calculates the low-carbon total factor productivity by considering the following indicators: capital stock, labor input, and energy consumption. When calculating the expected output, the output value of the manufacturing industry is selected and measured by the current year's main business income. At the same time, non-expected output, i.e. carbon dioxide emissions, also needs to be considered. As for the measurement of energy consumption in the manufacturing industry, the existing statistical yearbooks only have data on industrial energy consumption, and there are no statistics on the energy consumption of provincial manufacturing industries. Therefore, existing literature studies use industrial energy consumption data instead of manufacturing energy consumption data. However, this measurement method is not scientific because the proportion of manufacturing to industry varies greatly among provinces and cities in China, and using industrial energy consumption instead will produce a large error. Therefore, this paper indirectly measures the energy consumption of the manufacturing industry based on the proportion of manufacturing to total industrial output value combined with industrial energy consumption data, which improves the accuracy and scientificity of the measurement compared to existing literature studies. Core explanatory variable: Level of digital economic development. Referring to the research of Jiao et al(2020), a comprehensive evaluation index of digital development level is constructed, and the entropy weight method is used to calculate the comprehensive index value. The construction of the comprehensive evaluation index system for digital development level is shown in Table 1 . Control variables. Based on existing literature on land resource mismatches, this paper further controls the influence of other variables on land resource mismatches, the selected control variables are as follows: Financial level: Measured by the ratio of deposits and loans, i.e., the ratio of bank deposits to loans. Some studies have shown that Financial levelalso affect green total factor productivity (Lee,2022). RD intensity: Measured by the ratio of RD expenditure to GDP. This indicator mainly measures the level of technological innovation in a region, as technological innovation is an important driver of green total factor productivity in the region. Environmental regulation intensity: Measured by the ratio of the number of pollution-related words in the government work report of the sampled provinces to the total number of words in the government work report of the same year. Table 1 Introduction to Related Variables and Data Sources Primary Indicators Secondary Indicators Tertiary Indicators Units Digital Infrastructure Digital carrier Number of websites Millions Internet penetration rate % Digital circulation Mobile phone exchange capacity households households Number of mobile phone base stations Millions Mobile phone penetration rate % Long distance optical cable line length kilometre Digital industrialization Industrialization investment Employment in information transmission, computer services, and software industries ten thousand people Fixed investment in information transmission, computer services, and software industries Ten thousand yuan Industrialization output Total telecommunications business volume Ten thousand yuan Software business revenue Ten thousand yuan Digital TV penetration rate % Number of Top 100 Internet Comprehensive Enterprises Millions Industrial Digitization Digital Transactions E-commerce sales Ten thousand yuan proportion of enterprises with ecommerce transaction activities % number of websites per hundred enterprises Millions number of computers (per hundred people) Million units Digital Business Formats number of rural broadband access users, operating income of computer 10000 households communication and other electronic equipment manufacturing industry Ten thousand yuan digital inclusive finance index express delivery volume Ten thousand yuan 3.2 Data sources This paper takes 30 provinces and cities in China from 2011 to 2020 as the research objects, and Table 2 provides the data sources and descriptive statistical information of these related variables. Table 2 Descriptive Statistics of Main Variables Variable name Sample size Mean Standard deviation Minimum value Maximum value GTFP 300 0.30 0.14 0.11 1.02 Digital economy 300 0.71 0.33 0.04 1.58 Digital economy 300 0.61 0.43 0.00 2.50 Financial level 300 0.98 0.12 0.77 1.12 RD intensity 300 3.23 1.16 1.52 8.13 Environmental regulation intensity 300 0.02 0.01 0.00 0.06 4. Empirical Analysis 4.1 Basic results After conducting regression analysis using formula ( 1 ), the regression results are shown in Table 3 . The first column of Table 3 presents the regression results after controlling for time and province fixed effects. The regression coefficient of the first-order term of the digital economy is -0.8566, which has passed the statistical test at the significance level of 1%. The regression coefficient of the second-order term of the digital economy is 2.3304 and has highly significant results at the 1% significance level, indicating a first decline and then an increase relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry. When the digital economy is small, it will inhibit the low-carbon total factor productivity of the manufacturing industry, and only when the digital economy develops to a certain degree, it can promote the low-carbon total factor productivity of the manufacturing industry. This is consistent with the assumed theory. The second to sixth columns of Table 3 present the regression results with the gradual addition of control variables, including economic development level, industrial structure, the proportion of fiscal expenditure, financing constraints, RD intensity, etc. Compared with the first column, according to the regression results in columns ( 2 ) to ( 6 ), the regression coefficients of the first-order term of the digital economy are negatively correlated with the significance level of 1%, while the second-order term coefficients are positively correlated, indicating U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry. Table 3 Basic Regression Results (1) (2) (3) (4) (5) (6) Digital economy -0.86*** -1.19*** -1.27*** -1.28*** -1.29*** -1.44*** (-3.27) (-4.57) (-4.87) (-4.88) (-5.06) (-5.48) Square of digital economy 2.33*** 2.24*** 2.32*** 2.35*** 2.41*** 2.49*** (8.17) (8.16) (8.46) (8.35) (8.81) (9.09) Economic development 0.00*** 0.00*** 0.00*** 0.00* 0.00 (4.89) (4.64) (3.71) (1.82) (1.64) Industrial structure -0.05** -0.04** -0.04 -0.02 (-2.22) (-1.98) (-1.65) (-1.11) Proportion of fiscal expenditure -0.07 0.30 0.31* (-0.42) (1.62) (1.67) Financial level -0.07*** -0.07*** (-4.14) (-4.56) RD intensity 4.80** (2.15) Constant term 0.29*** 0.22*** 0.29*** 0.29*** 0.41*** 0.37*** (16.21) (9.48) (5.09) (5.09) (6.59) (5.65) Fixed effects of province Control Control Control Control Control Control Fixed effects of time Control Control Control Control Control Control N 300 300 300 300 300 300 \({\text{R}}^{2}\) 0.59 0.629 0.63 0.63 0.65 0.66 Note: ( 1 ) Robust standard errors are reported in parentheses; ( 2 ) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively; ( 3 ) N is the sample size, and R^2 is the goodness-of-fit. 4.2 Robustness Tests 4.2.1 Setting of Transformed Explanatory Variables To further enhance the credibility of the regression results, this paper will conduct robustness tests on the regression results of the previous section through various strategies. Table 2 measures the digital economy with comprehensive indicators of digital infrastructure, digital industrialization, and industrial digitalization. In this section, we will use alternative indicators to measure digital economic development and conduct regression tests. Table 4 presents the "Internet penetration rate" as an alternative variable for the digital economy, with the remaining control variables unchanged. The coefficient of the first regression of the digital economy is negatively correlated and is statistically significant at the 1% level. The coefficient of the quadratic term of the digital economy is positively correlated and is also significant at the 1% level. From the regression results of the transformed explanatory variables, it can be seen that regardless of which indicator is used to measure the digital economy, the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry always exhibits a U-shaped relationship. Table 4 Robustness Test Using Different Digital Economy Indicators Dependent variable: Low-carbon total factor productivity of the manufacturing industry Independent variable: Internet penetration rate Digital economy -0.01*** (-3.52) Square of digital economy 0.00*** (5.11) Other control variables Y Fixed effects of province Control Fixed effects of time Control N 300 \({\text{R}}^{2}\) 0.53 4.2.2 Setting of Transformed Dependent Variable To verify the credibility of the previous regression analysis, we will further conduct a test by substituting the explained variable. The core of low-carbon development in manufacturing is to reduce carbon emissions in the process of industrial development. Therefore, in Table 5 , "Main business income of manufacturing industry/carbon emissions" is used as a robustness test for the low-carbon development of the manufacturing industry as the explained variable. After regression analysis, we found that the coefficient of the first order of the digital economy is still negative and significant at the 1% level, while the coefficient of the quadratic term of the digital economy is positive and significant at the 5% level. This indicates that after replacing the explained variable, the U-shaped relationship between the digital economy and the low-carbon development of the manufacturing industry still holds. Table 5 Robustness Test with Transformed Dependent Variable. Dependent variable: Output value of the manufacturing industry/carbon emissions. Digital economy -0.34*** (-3.37) Square of digital economy 0.11** (2.00) Other control variables Y Fixed effects of province Control Fixed effects of time Control N 300 \({\text{R}}^{2}\) 0.40 4.2.3 Endogeneity Test Given that the difficulty of achieving economic growth targets may create endogeneity, this paper will use the commonly used two-stage least squares method in instrumental variables to conduct a regression test. We use "the number of Internet domain names in each province" as the instrumental variable for the digital economy in the two-stage least squares regression. A reliable instrumental variable must satisfy two conditions: first, there is a correlation with the explanatory variable (in this case, the digital economy); second, the instrumental variable cannot affect the dependent variable (here, the low-carbon development of the manufacturing industry) in other ways. Internet domain names can be used as the instrumental variable for the digital economy because Internet technology is one of the core elements of the digital economy, and the number of Internet domain names can reflect the level of application and popularity of Internet technology. When studying causal effects, if we use the digital economy as the independent variable, then the digital economy itself will also be affected by the low-carbon development of the manufacturing industry, making it difficult to accurately estimate the impact of the digital economy on the low-carbon development of the manufacturing industry. Using the number of Internet domain names as the instrumental variable can better satisfy the above two conditions, making the research results more reliable and accurate. After analyzing the panel data with the two-stage least squares regression, the regression results obtained are shown in columns ( 1 ) and ( 2 ) of Table 6 . In the second stage regression results of column ( 1 ), the coefficient of the digital economy is negative, and the coefficient of the quadratic term of the digital economy is positive, both statistically significant at the 1% level, once again confirming the U-shaped relationship between the digital economy and the low-carbon development of the manufacturing industry. Table 6 Using the Number of Internet Domain Names as the Instrumental Variable. Dependent variable GTFP Second-stage regression. Digital economy First-stage regression Square of the digital economy First-stage regression (1) (2) (3) Digital economy -2.39*** (-3.57) Square of digital economy 3.43*** (2.90) Ⅳ : Number of Internet domain names 0.44*** 0.25*** (-3.69) (2.34) Ⅳ : Square of the number of Internet domain names -0.51*** -0.15 (-4.22) (-1.27) Other control variables Y Y Y Fixed effects of provinces Control Control Control Fixed effects of time Control Control Control N 300 300 300 \({\text{R}}^{2}\) 0.24 0.42 0.36 4.3 Mediation Effects Testing 4.3.1 Mediation Effects Testing Based on Green Technology Innovation The U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry is somewhat related to the region's green technology innovation. The development of the regional digital economy can provide more technical support and investment for green technology innovation in theory. For example, the digital economy can provide technical platforms such as cloud computing and big data analysis to assist local industries in better carrying out green technology research and application. The application of green technology can improve the energy utilization efficiency of the manufacturing industry, reduce carbon emission intensity, and drive the improvement of energy efficiency in the manufacturing industry, thereby achieving low-carbon development. For example, using energy-saving and environmentally friendly production equipment, and using renewable energy. However, in the early stage of the development of the digital economy, there may be a crowding-out effect of the digital economy. For industries within the region, allocating limited resources to the development of the digital economy may be more attractive, resulting in decreased investment in green technology innovation. However, once the digital economy reaches a certain stage of development, its positive promotion effect on green technology innovation will be greater than the crowding-out effect, thereby promoting green technology innovation. To verify the above logic, in Table 3 , we added "green technology innovation" as a control variable in the regression whose explained variable is the low-carbon total factor productivity of the manufacturing industry, and, referring to existing research results, the green technology innovation indicator measures the sum of the number of regionally authorized green utility model patents and green invention patents in the current year. We examined the robustness of the regression results. Column ( 1 ) of Table 7 has the same regression results as Column ( 1 ) of Table 3 , indicating the regression relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry without other control variables. Column ( 2 ) of Table 6 is based on Column ( 1 ) of Table 1 , adding control variables, indicating the regression relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry. Column ( 3 ) is based on Column ( 2 ), adding "green technology innovation" as a control variable, and the regression coefficient of green technology innovation is 0.0001 and significant at a 10% confidence level. Table 6 Analysis Based on Green Technology Innovation (1) (2) (3) Digital economy -0.86*** -1.15 *** -1.02 *** (-3.27) (-4.13) (-3.56) Square of digital economy 2.33*** 1.51 *** 1.32 *** (8.17) (4.46) (3.74) Green Technology Innovation 0.00 * (1.91) Other control variables N Y Y Fixed effects of provinces Control Control Control Fixed effects of time Control Control Control N 300 300 300 \({\text{R}}^{2}\) 0.59 0.52 0.52 Next, we further examine whether green technology innovation is affected by the digital economy. Taking green technology innovation as the explained variable, with the explanatory variables and other control variables remaining unchanged, the regression results are shown in Table 7 . The regression analysis shows that the coefficient of the digital economy is negative and significant at a 1% level of significance. At the same time, the coefficient of the squared term of the digital economy is positive and significant at a 1% level of significance. This indicates that there is also a U-shaped relationship between the digital economy and green technology innovation. Therefore, green technology innovation is one of the intermediary variables of the digital economy’s impact on the low-carbon total factor productivity of the manufacturing industry. From a micro perspective, in the early stage of the digital economy's development, to seize the opportunities of the new change, industries will make certain digital investments. However, the initial cost of digital infrastructure is relatively high, which has a certain negative impact on the amount of personnel and funding invested in green technology innovation. However, with the decrease in the cost of digital infrastructure hardware equipment and the gradual maturity of enterprise industry digital applications, the cost of digital economy applications has greatly reduced, and the crowding-out effects on green technology innovation have gradually decreased. Moreover, enterprise digital applications have improved the overall level of green technology innovation by reducing the innovation time cycle and cost of technical personnel. As the overall level of green technology innovation is promoted, the promotion and application of green technology innovation in the manufacturing industry has pushed for energy conservation, emission reduction, and low-carbon development. Table 7 Regression Results of the Digital Economy on Green Technology Innovation Explained variable: Green technology innovation Digital economy -4.0e + 04*** (-4.01) Square of digital economy 5.9e + 04*** (4.81) Other control variables Y Fixed effects of provinces Control Fixed effects of time Control N 300 \({\text{R}}^{2}\) 0.60 4.3.2 Mediation Effect Test Based on Factor Resource Mismatch The development of the digital economy can help industries more accurately identify the optimal configuration of factor resources in production and management processes. Therefore, the development of the digital economy may affect the low-carbon total factor productivity of the manufacturing industry by reducing the degree of factor resource mismatch. In order to verify this hypothesis, this section used the degree of capital mismatch as a mediating variable for regression, and the regression results are shown in Table 9 . In Tables (1) and ( 2 ), the regression coefficients of the digital economy are all negative, and at a statistical significance level of 1%, the square regression coefficients of the digital economy are also positive and significant, and the regression coefficients of the square term of the digital economy are positive and significant at the 1% level as well. The U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry still holds. In the regression of column ( 1 ) of Table 9 , it can be seen that there is a U-shaped relationship between the digital economy and capital mismatch. That is, in the early stages of the development of the digital economy, the development of the digital economy will reduce the degree of capital mismatch, but when the digital economy develops to a certain stage, it may deepen capital mismatch. This is mainly because the rapid development of the digital economy has promoted the rise of emerging industries and attracted a large amount of investment capital. However, these capital tend to focus on a few areas and companies, resulting in uneven resource allocation. Table 8 Mediation effect analysis based on resource mismatch (1)Dependent variable: capital mismatch. (2)Dependent variable: GTFP Digital economy -1.69 *** -1.29 *** (-3.74) (-4.53) Square of digital economy 1.12 ** 1.60 *** (2.04) (4.72) Capital mismatch 4.85 * (1.77) Other control variables Y Y Fixed effects of provinces Control Control Fixed effects of time Control Control N 300 300 \({\text{R}}^{2}\) 0.47 0.53 4.3.3 Mediation Effect Test Based on Industrial Upgrading As a new production factor, the digital economy can be used by the manufacturing industry to transform traditional manufacturing, promote the intelligent and high-end transformation of traditional manufacturing, and thus promote the transformation and upgrading of the manufacturing industry. The process of upgrading the manufacturing industry can also promote the development of low-carbon manufacturing. In order to test the above hypothesis, in this section, we used industrial upgrading of manufacturing as a mediating variable for regression analysis and obtained the corresponding regression results, as shown in Table 10 . We found that the regression coefficients of the digital economy in columns ( 1 ) and ( 2 ) are negative and significant at the 1% level, and the regression coefficients of the square term of the digital economy are positive and significant at the 1% level. This indicates that the U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry still exists. In column ( 1 ) of Table 10 , it can be seen that there is a U-shaped relationship between the digital economy and industrial upgrading of manufacturing, that is, the impact of the digital economy on the industrial upgrading of manufacturing shows a trend of initial restraint and later promotion. This may be because in the initial development stage of the digital economy, it often attracts a large amount of investment and resources, such as the Internet industry, e-commerce, etc., and the rapid development of these fields may attract talents and resources from related areas of the manufacturing industry, thereby reducing the speed of industrial upgrading in the manufacturing industry. Table 9 Mediation effect analysis based on industrial upgrading (1)Dependent variable: Industrial upgrading (2)Dependent variable: GTFP Digital economy -4.41 *** -1.08 *** (-2.99) (-3.93) Square of digital economy 5.13 *** 1.42 *** (2.83) (4.28) Industrial upgrading 0.03 *** (3.42) Other control variables Y Y Fixed effects of provinces Control Control Fixed effects of time Control Control N 300 300 \({\text{R}}^{2}\) 0.05 0.54 5. Heterogeneity Analysis Based on Regional Market Environment So far, we have found through various tests that the hypothesis of a U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry is valid. Next, we further test whether the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry will change with the level of marketization. We grouped provinces according to the level of marketization and tested the relationship between the digital economy and the green total factor productivity of the manufacturing industry according to the grouping. Based on the scores of the marketization index of various provinces and cities in the "China Province Market Index Report (2018)", we grouped the provinces according to the ranking of the third quartile of their marketization index. We divided the level of marketization into three levels: high, medium, and low, and conducted regression analysis in the case of the three levels of marketization grouping. The corresponding regression results are shown in Table 11. The table displays the sample regression results for regions with high, medium, and low marketization levels, with the first, second, and third columns corresponding to regions with high, medium, and low marketization levels, respectively. The regression coefficients of the digital economy in columns ( 1 ) and ( 2 ) are significant, and we found that the regression coefficient of the digital economy in the third column of Table 11 is not significant. This means that the U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry only exists in regions with a higher or medium degree of marketization, and regions with a lower degree of marketization are not affected by this relationship. In regions with different levels of marketization, the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry shows heterogeneity, and the reasons for this may be as follows. First, low levels of marketization lead to a lack of environmental awareness among enterprises. In regions with lower levels of marketization, the level of attention paid by enterprises to environmental protection is often lower than that in areas with higher levels of marketization. Therefore, when the digital economy develops rapidly, the increase in carbon emissions from the manufacturing industry is not sufficiently paid attention to, and a U-shaped curve cannot be formed. Second, the production methods are outdated. Regions with lower levels of marketization tend to have more backward production methods, relatively lower technological content, and higher carbon emissions. The impact of the digital economy on the manufacturing industry in this situation is not obvious, and a U-shaped curve cannot be formed. Third, the resource endowment is different. Regions with lower levels of marketization often have different resource endowments, such as relatively insufficient human capital and natural resources. As a result, the impact of the development of the digital economy on the manufacturing industry is not as significant as in areas with higher levels of marketization, and the fluctuation of carbon emissions in the manufacturing industry is smaller, thus a U-shaped curve cannot be formed. Table 10 Sample regression based on the degree of marketization Marketization (high) Marketization (medium) Marketization (low) (1) (2) (3) Digital economy -2.26** -0.97** -1.48 (-2.51) (-2.05) (-0.82) Square of digital economy 2.97*** 2.09* 7.78 (4.14) (1.81) (1.05) Other control variables Y Y Y Fixed effects of provinces Control Control Control Fixed effects of time Control Control Control N 90 140 70 0.64 0.74 0.59 6. Conclusion and Recommendations This article chooses the panel data of 30 provinces and municipalities in China from 2016 to 2020 and uses panel fixed effects and mediation effects models to explore the impact of the digital economy on low-carbon development of manufacturing, the mechanism and heterogeneity. The main conclusions are as follows: First, there is a U-shaped relationship between regional digital economy and green low-carbon development of manufacturing, which still holds true after a series of robustness tests. Second, the digital economy has an impact on the green total factor productivity of manufacturing by affecting green innovation, industrial upgrading, and capital misallocation. Third, the impact of the digital economy on low-carbon development of manufacturing shows significant regional differences, with a more significant effect on provinces with high and moderate degrees of marketization and a less obvious effect on regions with low marketization. By verifying the hypothesis of the impact of the digital economy on low-carbon development of manufacturing, we can not only promote our better understanding of the dynamic process and mechanism of the impact of digital economic development on green low-carbon development of manufacturing, but also help us to understand the direction of the improvement of green total factor productivity of manufacturing. Based on the above conclusions, we propose the following policy recommendations: Based on the impact and mechanism of the digital economy on low-carbon development of manufacturing discovered in this study, countermeasures and suggestions can be proposed in the following aspects: 1. Enhance the innovation capability of the digital economy and promote the application of digital technology in the manufacturing industry. By establishing a digital technology industrial ecology and strengthening the cooperation between various research institutions, technology companies, and the government in the digital technology field, the innovation and application capabilities are enhanced. This promotes the popularization and application of digital technology in the manufacturing industry, and promotes the positive impact of the digital economy on the low-carbon development of manufacturing. 2.Promote the green upgrading and transformation of manufacturing. The goal is to reduce carbon emissions and reduce resource consumption by gradually promoting the transformation of traditional manufacturing to green manufacturing. Focus on promoting the research, application, and promotion of green and low-carbon technologies, and promote the transformation of the production mode of manufacturing towards green, low-carbon, circular and efficient direction. 3.Strengthen the guidance role of local governments. We should strengthen the support and policy guidance for provinces with low degrees of marketization, formulate more targeted policy measures, and promote the coordinated development of the digital economy and low-carbon manufacturing. At the same time, for regions with relatively high and moderate degrees of marketization, we should strengthen the integration and coordination of the digital economy and low-carbon manufacturing, and build a "dual-wheel drive" development pattern of the digital economy and low-carbon manufacturing industry. 4.Strengthen data monitoring and study the relationship between the digital economy and manufacturing in-depth. We should establish a relationship index system between the digital economy and manufacturing, strengthen the monitoring and analysis of relevant indicators, and continuously study the impact mechanism and regional differences of the digital economy on the low-carbon development of manufacturing, providing theoretical and practical support for further promoting the coordinated development of the digital economy and manufacturing. Declarations Acknowledgements We thank Iianzheng Fan for his help in data processing. Author contributions SL and YL wrote the main manuscript text.YP collected and analyzed the data. SL discussed the results. All authors reviewed the manuscript. Funding This study was supported by National Social Science Fund of China(22XJY036),Xinjiang University Excellent Doctoral Student Research Innovation Project(XJU2022BS014). Availability of data and materials The dataset supporting the conclusions of this article is included within the article. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests References Yan X, Fang Y. CO2 emissions and mitigation potential of the Chinese manufacturing industry[J]. Journal of Cleaner Production, 2015, 103: 759-773. Kshetri N. Big data׳ s impact on privacy, security and consumer welfare[J]. Telecommunications Policy, 2014, 38(11): 1134-1145. Lange S, Pohl J, Santarius T. Digitalization and energy consumption. 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World Surv.Res. 2021, 13–23. Xu, X.C.; Zhang, M.H. Research on the Scale Measurement of China’s Digital Economy Based on the Perspective of InternationalComparison. China Ind. Econ. 2020, 23–41. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3306547","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":230473680,"identity":"dd473ea0-87d4-43d7-b215-d87ce5c59248","order_by":0,"name":"Shanhong Li","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanhong","middleName":"","lastName":"Li","suffix":""},{"id":230473681,"identity":"2de5bfb1-2039-4bca-ba91-3cc6a78ae564","order_by":1,"name":"Yanqin Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RMQrCMBTG8ZTC6/LQNaVQTyCkFKqF0rO0Cpl6ADcNrhFXBcErFATnQsBJ946K4OSgm4ODq06Nm2D++w/exyPEZPrBwBHieHskftuZVnqkhUoFS8lDV+4yPeJTzj0ElZeLgmkeRoqIInKrJMW9vpLU704ayT7s035i98hhE6/IMIyqJmLJoGbIIRazrYekyreNxEZGM1DIFF40CcCAVqAo2yFoErRVICRnroQwXjGNLZ31SZyfj2Q875xP9XWU+o3kI4qar3kn3wqTyWT6i15FYUCFHbWP+wAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yanqin","middleName":"","lastName":"Lv","suffix":""},{"id":230473682,"identity":"79f9afb3-e279-47bf-af93-cc76ccb7c9c4","order_by":2,"name":"Yang Ping","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Ping","suffix":""}],"badges":[],"createdAt":"2023-08-29 11:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3306547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3306547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44704409,"identity":"8576eab9-70ad-41f3-a96e-dc99edeabbe7","added_by":"auto","created_at":"2023-10-16 16:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":626537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3306547/v1/acea91d6-5dad-4110-8919-a8919ac32663.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina's manufacturing industry, as one of the largest manufacturing countries in the world, has become an urgent issue for low-carbon development under the heightened concern over global climate change and the commitment of all countries to reduce carbon emissions. Although the Chinese government has made low-carbon development a top priority and started to formulate a series of policies and measures, there are still many challenges and dilemmas to realize low-carbon development in the manufacturing sector. According to China Statistical Yearbook 2021, in 2020, China's manufacturing industry will account for 54.80% of the country's energy consumption and 51.64% of the country's carbon emissions, but the value added of manufacturing industry will only account for 27.84% of the country's value added. This indicates that in the new round of rapid manufacturing and heavy manufacturing, the rough development of the manufacturing industry, which is characterized by high energy consumption, high emissions, high input and low efficiency, is very obvious (Yan and Fang,2015). In this context, measures are needed to improve the carbon emission efficiency of the manufacturing sector to achieve the goals of \"carbon peak\" and \"carbon neutral\" actions and to promote China's green and high-quality development. Therefore, an effective response to the problems of low-carbon development is crucial for the transformation and upgrading of China's manufacturing sector, sustainable growth and environmental protection.\u003c/p\u003e \u003cp\u003eGlobally, the development of digital economy is gradually becoming a new driving force for economic and social development. Developed countries that developed digital industries and informationization construction more maturely in the early days, such as the United States, Japan, and European countries, the development of digital economy has entered the peak period (Kshetri, 2014). And emerging economies and developing countries, such as India, Brazil, China and Southeast Asia, along with the wide application of big data technology in areas such as the Internet and social media, the data economy has been able to rise rapidly. As an important player in the digital economy, the Chinese government attaches great importance to the development of data factors and regards them as strategic resources.In 2015, a big data exchange was established in Guiyang City, Guizhou Province, followed by a large number of data exchanges across the country, which provided platforms and opportunities for the development of data factors.In 2020, the Chinese government further issued a document explicitly placing the data factor, along with land, labor, capital and technology as one of the five major factors of production, and called for the construction of a better institutional mechanism for market-based allocation in order to further enhance the role of data factors in supporting the economy and society.\u003c/p\u003e \u003cp\u003eAlthough the digital economy, especially the data element, has become one of the basic production factors, its positive or negative effects on the low-carbon development of the manufacturing industry are not yet clear. Specifically, the digital economy can help manufacturing firms to optimize production processes and reduce carbon emissions, for example, by optimizing key information such as process flow, product design and energy consumption (Lange et al., 2020). However, there may also be environmental issues involved in utilizing data, such as energy consumption for data storage (Ma et al., 2022; Koot and Wijnhoven, 2021). Therefore, we need to think about a very relevant and practical question: whether and how the digital economy can help manufacturing industries to achieve a low-carbon transition? However, few studies have explored this important question in depth.\u003c/p\u003e \u003cp\u003eResearch on the digital economy involves two main aspects: measurement and indicators, as well as its economic effects. However, due to the lack of a unified consensus on the connotation of the digital economy, the participants involved, and the statistical scope, there are significant differences in the measurement, content, and results of the digital economy.At present, there are various measurement methods for the digital economy, including the commonly used national economic accounting method, value-added measurement method, competitiveness indicator system, and digital economy satellite account method. The national economic accounting method mainly measures the size of the digital economy based on GDP and the national accounts system (Ahmad et al., 2017). The value-added method mainly calculates the actual value-added of the digital economy based on the reduction method and Fisher ideal index (KNICKREHM et al., 2016; DEAN et al., 2017; Barefoot et al., 2018). The competitiveness evaluation indicator system of the digital economy is formed based on the comprehensive consideration and collation of the results of extensive survey research and statistical analysis. Cockayne\u0026rsquo;s (2016) research further explores the issue of evaluating the competitiveness of the digital economy. The digital economy satellite account method has been established in countries such as Chile, Australia, and South Africa, aiming to more comprehensively and accurately measure the development of the digital economy (Shadibekova, 2019; OECD, 2017a; OECD, 2017b).\u003c/p\u003e \u003cp\u003eWith the continuous deepening of the information technology revolution, the rapid development of the digital economy has had unprecedented and far-reaching impacts, bringing important changes at the macro, meso, and micro levels. The digital economy not only promotes high-quality economic development through optimizing factors allocation and improving productivity (Qian et al., 2022), but also plays an irreplaceable role in eliminating social poverty (Qian et al., 2022), encouraging entrepreneurship (Jiang et al., 2022), and promoting employment (Nodirovna et al., 2022). At the same time, the digital economy also has green value and environmental effects, improving environmental governance performance through promoting public participation and strengthening government environmental regulation (Luo et al., 2022). According to the research of Su et al. (2021), the development of the digital economy at a moderate level can promote high-quality economic development and effectively promote industrial structure upgrading. At the micro-level, the development of the digital economy can significantly improve enterprise productivity (Li R et al.,2022), accelerate the process of enterprise management transformation (Lee and Yang, 2016), reduce financial risks, stimulate consumer spending, lower social costs, and increase micro welfare (Cennamo and Santalo, 2013;).\u003c/p\u003e \u003cp\u003eResearch has shown that the digital economy has become a key driving force for profound changes in production and lifestyle, and has had a wide and far-reaching impact on various sectors.\u003c/p\u003e \u003cp\u003eThe literature on low-carbon development in the manufacturing industry mainly focuses on three aspects: the connotation of low-carbon transformation in the manufacturing industry, measurement, and influencing factors. In terms of connotation, economic growth theories have proposed growth models that take environmental variables as one of the production factors (Bovenberg and Smulders, 1995), thus more reasonably considering environmental issues such as carbon emissions and energy consumption in the process of economic growth. If we want to consider the impact and cost of carbon emission reduction targets on the economy, we need to base the analysis on a growth accounting framework that includes environmental elements. The literature mainly uses total factor productivity and index system methods for the measurement of low-carbon transformation in the industry. In the measurement of low-carbon green transformation in the industry, many studies have adopted the total factor productivity method. This approach uses various parametric or non-parametric efficiency measurement methods to evaluate the industrial low-carbon green transformation, taking energy consumption as input factors and non-expected output indicators such as carbon emissions or \"three wastes\" emissions for measurement.(Fare et al., 2007). The latter is measured by a comprehensive index system, such as the comprehensive evaluation index system for China's low-carbon economic development and the industrial green development performance index. In summary, these studies have deeply studied the low-carbon development of the manufacturing industry, providing important reference for promoting low-carbon transformation in the industry.\u003c/p\u003e \u003cp\u003eKaya identity (Kaya, 1989) believes that carbon emissions are affected by population, economy, energy intensity, carbon intensity and other factors. Afterwards, scholars conducted in-depth analysis of the factors affecting carbon emissions on this basis. This includes factors such as human capital (Tian et al., 2019), industrial structure (Sun et al., 2020), energy structure (Akram et al., 2020), and technological progress (Yang et al., 2021). These research results provide useful reference basis for further studying the factors affecting carbon emissions in the manufacturing industry.\u003c/p\u003e \u003cp\u003eCurrently, research on the impact of the digital economy on the manufacturing industry mainly focuses on green development. Scholars have studied the impact of the digital economy on the green development of the manufacturing industry from three perspectives: resource allocation, technological innovation, and external supervision. The digital economy optimizes resource allocation by directing capital and other production factors towards high-efficiency sectors, reducing society's excessive demand for energy and promoting the digitalization, rationalization, and greening of industrial structures, thereby promoting green development (Kohli and Melville, 2019). In terms of technological innovation, the development of the digital economy facilitates resource sharing and flow of production factors, promotes energy technology progress and interconnection, improves energy utilization efficiency, and affects the efficiency of green production (Nambisan et al., 2017). In terms of external supervision, the universality and penetration of the digital economy can be applied to various industries, and regulatory agencies can build ecological information governance to achieve external supervision and promote green development (Zhang et al., 2018). In addition, energy big data analysis also helps improve enterprise production efficiency and reduce energy consumption in production, providing important support for the digital economy's green development in the manufacturing industry (Hu et al., 2020).\u003c/p\u003e \u003cp\u003eAfter analyzing existing literature, it was found that although some studies have focused on the impact of digital economy on the green development of manufacturing industry, the exploration of the impact of digital economy on the low-carbon development of manufacturing industry is not yet sufficient. In terms of whether the digital economy can promote carbon reduction, some literature has deducted the relationship between digital economy and carbon emissions from a theoretical modeling perspective (such as Li and Wang, 2022). However, due to the Cobb-Douglas production function assuming that the contributions of each factor are independent and separable in the production process, the introduction of digital economy as a production factor into the production function is not consistent with the actual integration of digital economy and other factors. In addition, existing literature often refers to the green total factor productivity of manufacturing industry as a reference for measuring the low-carbon development of manufacturing industry, but this index cannot well reflect the differences in industrial structure of manufacturing industry in different regions, which can lead to result biases. Therefore, it is necessary to further explore the mechanism and measurement methods of the impact of digital economy on the low-carbon development of manufacturing industry in order to improve the accuracy and practicality of research.\u003c/p\u003e \u003cp\u003eTherefore, the innovation of this paper mainly lies in three aspects: firstly, the innovation in theoretical models. Based on the green Solow model, this paper constructs a theoretical model of the relationship between the digital economy and carbon emissions, using the CES production function, which proves the impact of digital economy development on the carbon emissions of the manufacturing industry from a theoretical model perspective and enriches the application of the green Solow model. Secondly, the innovation in research perspective is to study the impact of the digital economy on the low-carbon development of the manufacturing industry from the perspective of the digital economy, and explore its influencing mechanism. Thirdly, the innovation in research methods is the introduction of a new measurement method for the energy consumption and carbon emissions of the manufacturing industry, which calculates the proportion of the output value of the manufacturing industry in the sample region to the industrial output value, and is more scientific than traditional methods, thus improving the accuracy of research results.\u003c/p\u003e \u003cp\u003eThe following is the structure of this article: the second part proposes theoretical mechanisms and hypotheses, the third part discusses research methods and data sources, the fourth part conducts basic empirical analysis, the fifth part examines the expansion analysis of regional marketization level, and finally concludes in the sixth part.\u003c/p\u003e"},{"header":"2. Theoretical models and mechanisms.","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Theoretical models\u003c/h2\u003e \u003cp\u003eSome studies have combined the Solow model with pollution emissions and proposed the Green Solow model (Brock and Taylor, 2010), which provides a theoretical basis for the inverse U-shaped relationship between pollution emissions and per capita income. This paper refers to the basic framework of the Green Solow model and constructs a Solow model of the relationship between digital economic development and carbon emissions, providing a theoretical basis for further exploring the impact of digital economic development on carbon emission reduction in the manufacturing industry.\u003c/p\u003e \u003cp\u003eIn April 2020, the \"Opinions of the Central Committee of the Communist Party of China and the State Council on Building a More Perfect System for Market-oriented Allocation of Factors of Production\" for the first time included data as a factor of production. Compared with the industrial age, the significant feature of the digital economy age is that data becomes a new factor of production. The specific form of the traditional CES function is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{Y}=\\text{A}\\left[\\right({\\text{a}}_{1}\\ast {\\text{K}}^{{\\rho }}+{\\text{a}}_{2}\\ast {\\text{L}}^{{\\rho }}+{\\text{a}}_{3}\\ast {\\text{M}}^{{\\rho }}{)}^{1/{\\rho }}]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eY represents output, A represents total factor productivity, K represents capital input, L represents labor input, M represents technological progress or other input,are constants, and ρ represents the elasticity coefficient. The elasticity coefficient ρ reflects the degree of elasticity of substitution between the different input factors. In the CES function, different input factors are substitutable, and the degree of substitution elasticity may vary significantly at different variable values.\u003c/p\u003e \u003cp\u003eAccording to the assumption made in the article, data capital can be viewed as a factor of production and added to the production function. We divide the capital input in the CES function into two parts, physical capital input K and data capital input D, so the above expression can be simplified as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{Y}=\\text{A}\\left[\\right({\\text{a}}_{1}\\ast {\\text{K}}^{{\\rho }}+{\\text{a}}_{2}\\ast {\\text{L}}^{{\\rho }}+{\\text{a}}_{3}\\ast {\\text{D}}^{{\\rho }}{)}^{1/{\\rho }}]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eD represents data capital input.\u003c/p\u003e \u003cp\u003eBased on the Inada conditions and the constant returns to scale property of the production function, we can derive the following relationship:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;F(K, D, L) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eF represents the production function.\u003c/p\u003e \u003cp\u003eNext, we followed Copeland and Taylor's approach in modeling the impact of carbon emission reduction and treating it as a by-product of production (Copeland and Taylor, 1994). We assume that the carbon emissions E are a co-product of production, and that every increase in one unit of output will produce pollution of Ω, i.e.:\u003c/p\u003e \u003cp\u003eE\u0026thinsp;=\u0026thinsp;ΩY (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eΩ reflects the pollution emissions per unit of output. We assume that Ω is the carbon emissions per unit of output, also known as carbon intensity of production.\u003c/p\u003e \u003cp\u003eAccording to the hypothesis of the green Solow model, we introduce the impact of emission reduction into the model and assume that the emission reduction function r(.) is increasing and strictly concave with respect to the economic output Y that is invested in emission reduction efforts.\u003c/p\u003e \u003cp\u003eBased on these hypothesis, we can rewrite the model as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{Y}=\\text{A}\\left[\\right( {\\text{a}}_{1}\\ast {\\text{K}}^{{\\rho }}+{\\text{a}}_{2}\\ast {\\text{L}}^{{\\rho }}+{\\text{a}}_{3}\\ast {\\text{D}}^{{\\rho }}{)}^{1/{\\rho }}]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eE\u0026thinsp;=\u0026thinsp;ΩY(1 - r(Y/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{Y}}^{\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e)) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBy solving the above model, we can obtain the following:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{k}}^{\\ast }\\)\u003c/span\u003e \u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{d}}^{\\ast }\\)\u003c/span\u003e\u003c/span\u003e: the level of capital stock at steady state.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{e}}^{\\ast }\\)\u003c/span\u003e \u003c/span\u003e: the level of carbon emissions at steady state (per unit of effective labor).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{g}}_{\\text{Y}}\\)\u003c/span\u003e \u003c/span\u003e: the total output growth rate on the balanced growth path.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{g}}_{\\text{E}}\\)\u003c/span\u003e \u003c/span\u003e: the carbon emission growth rate on the balanced growth path.\u003c/p\u003e \u003cp\u003eAssuming a CES production function and introducing the co-effects of carbon emissions, the steady-state levels of capital and emissions are as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${\\text{k}}^{\\ast }={［（\\text{s}\\ast {\\text{a}}_{1}\\ast {\\text{a}}_{3}\\ast {\\rho })/（\\text{n}+\\text{g}+{{\\delta }}_{\\text{D}}］}^{\\frac{1}{1-{\\rho }}}\\ast {［（\\text{n}+\\text{g}+{{\\delta }}_{\\text{D}})/（\\text{n}+\\text{g}+{{\\delta }}_{\\text{K}}］}^{\\frac{1-{\\rho }{\\omega }}{1-{\\rho }}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\text{d}}^{\\ast }={［（\\text{s}\\ast {\\text{a}}_{2}\\ast {\\text{a}}_{3}\\ast {\\rho })/（\\text{n}+\\text{g}+{{\\delta }}_{\\text{K}}］}^{\\frac{1}{1-{\\gamma }}}\\ast {［（\\text{n}+\\text{g}+{{\\delta }}_{\\text{K}})/（\\text{n}+\\text{g}+{{\\delta }}_{\\text{D}}］}^{\\frac{1-{\\rho }}{1-{\\rho }{\\omega }}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{e}}^{\\ast }\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{\\Omega }}{\\text{A}}\\ast ［（\\text{s}\\ast {\\text{a}}_{1}\\ast {\\text{a}}_{3}\\ast {\\text{k}}^{\\ast }\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }\\)\u003c/span\u003e\u003c/span\u003e-1)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ast {\\text{d}}^{\\ast }\\ast {\\rho }\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{s}\\ast {\\text{a}}_{2}\\ast {\\text{a}}_{3}\\ast {\\text{k}}^{\\ast }{\\rho }\\ast {\\text{d}}^{\\ast }({\\rho }-1)}{\\text{n}+\\text{g}-{\\rho }\\ast ({{\\delta }}_{\\text{K}}+{{\\delta }}_{\\text{D}})}］\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eHere, s represents the saving rate, n represents the population growth rate, g represents the rate of technological progress, δK and δD represent the depreciation rate of physical capital and data capital respectively, ρ and σ are parameters in the CES function, Ω is the carbon emissions per unit of output, and A is the total factor productivity.\u003c/p\u003e \u003cp\u003eIn this model, both the level of capital stock and the level of carbon emissions at steady state exhibit a inverted-U relationship. That is, when the level of capital is too low or too high, the level of carbon emissions is relatively low, while when the level of capital is moderate, the level of carbon emissions is relatively high.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical Mechanisms\u003c/h2\u003e \u003cp\u003eGreening embodies a production approach that emphasizes environmental protection with \"energy-saving and emission reduction\" as its core. It not only improves production efficiency, but more importantly, emphasizes the protection of the natural environment. Traditional production techniques that use machines and standardized production result in resource consumption and environmental pollution, while digital technology has advantages of high technological content and low environmental costs (Li et al., 2018). If we focus on the coordination between production and the environment while increasing production efficiency, the consumption of energy and resources will be relatively small, and the impact on the environment will also be very small (Gobbo et al., 2018; Jabbour et al., 2018; Dubey et al., 2019). The digital economy can promote low-carbon development in manufacturing from design, production, supply chain management and other aspects. Digital twin technology can simulate and test the sustainability, degradability, energy-saving and emission-reducing properties, health and safety, and quality performance of various design schemes to achieve energy-saving and emission-reducing during the production and use process. For example, BMW and other automotive companies collaborate with Dassault to use CAD and CAE platform 3D Experience for aerodynamic and fluid acoustic simulation verification, and reduce air resistance by optimizing the streamline of product design to achieve environmental goals such as energy-saving and emission reduction. Dassault's assistance and technical support have contributed significantly to environmental protection achievements of these car companies. However, in the early stages of the digital economy, manufacturing companies face the problem of high costs for digital transformation due to relying on modern information technology and advanced equipment. Therefore, manufacturing companies that undergo low-carbon transformation in the early stages may not be able to bear the additional cost burden. Based on this premise, this paper proposes the following hypothesis:\u003c/p\u003e \u003cp\u003eH1: The impact of the digital economy on low-carbon development in manufacturing has a U-shaped trend, that is, it will initially inhibit low-carbon development but gradually become a powerful factor promoting low-carbon development over time.\u003c/p\u003e \u003cp\u003eTheoretical analysis suggests that the development of regional digital economy can provide more technical support and investment for the innovation of green technology. The digital economy provides technological platforms such as cloud computing and big data analytics, which can help local industries to better conduct research and application of green technology. The application of green technology can improve the energy utilization efficiency of the manufacturing industry, reduce carbon emission intensity, and drive the improvement of energy efficiency in the manufacturing industry to achieve the goal of low-carbon development, such as using energy-saving and environmentally friendly production equipment and renewable energy. However, in the early stages of digital economy development, there may be a crowding-out effect of the digital economy, leading to a reduction in investment in green technology innovation because limited resources are used for the development of the digital economy. Nevertheless, as the digital economy develops to a certain stage, it will have a positive promoting effect on the innovation of green technology, thereby promoting its development (Dou and Gao X,2022). Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH2: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of green technology innovation.\u003c/p\u003e \u003cp\u003eWith the continuous popularization of digital technologies such as big data, cloud computing, blockchain, and mobile internet in the financial industry, China's digital finance industry has shown a rapid development trend, effectively breaking through the limitations of traditional finance and significantly reducing the operating costs of financial institutions. The extensive application of digital technologies has expanded the coverage of financial services. In particular, the application of these technologies has greatly improved the level of financial services for the manufacturing industry, especially the availability of capital (Agyapong, 2021). Digital finance can use digital technologies such as cloud computing, big data, and blockchain to quickly, conveniently, and efficiently mine, aggregate, and analyze customers' transaction and credit data, use intelligent matching algorithms to scientifically and comprehensively evaluate the operating risks and credit conditions of the manufacturing industry, and promote the availability of manufacturing industry capital (Qian et al., 2022; Pei et al., 2018). However, as the development of the digital economy enters a certain stage, the problem of capital misallocation also emerges. The rapid development of the digital economy has promoted the rise of emerging industries and attracted a large amount of investment capital. However, these capital tend to focus more on a few sectors and companies, resulting in uneven resource allocation and potentially exacerbating the problem of capital misallocation (Smith and Johnson, 2019).\u003c/p\u003e \u003cp\u003eH3: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of capital misallocation.\u003c/p\u003e \u003cp\u003eAccording to human capital theory, there is a property of human capital called knowledge spillover effects, which mainly manifest in promoting the continuous development and upgrading of the industry towards high technology, high knowledge, and high added value, improving the production efficiency of the manufacturing industry, and generating the effect of industrial upgrading within the manufacturing industry (Su et al., 2021). Skilled labor can handle more complex jobs, make appropriate responses according to different external environment changes, and reduce inefficient labor, learning costs, and resource waste, thereby further improving the production efficiency of the manufacturing industry. On the other hand, with the continuous improvement of human capital and the continuous improvement of labor quality, their career preferences will change, and they will prefer high-end industrial sectors with good salaries and high income levels. This preference will lead to the transfer of labor from labor-intensive manufacturing to capital intensive or technology intensive manufacturing, thereby promoting the upgrading of manufacturing structure(Yang et al., 2021). Therefore, the digital economy, through the human capital effect, can affect the production efficiency and high-level evolution of the manufacturing industry, generating the effect of industrial upgrading within the manufacturing industry. The industrial upgrading of the manufacturing industry can gradually transform from the low-end of the value chain to the high-end value chain, achieving the goal of low-carbon development such as energy-saving and reducing carbon emissions (Wu and Yang, 2022).\u003c/p\u003e \u003cp\u003eH4: The digital economy can affect the low-carbon development of the manufacturing industry through the pathway of industrial upgrading of the manufacturing industry.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research methodology and data sources","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model construction\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Calculation method of green total factor productivity\u003c/h2\u003e \u003cp\u003eGreen total factor productivity (GTFP) is developed on the basis of total factor productivity (TFP), and its calculation method involves incorporating unexpected outputs into the TFP calculation model. This paper refers to the calculation method proposed by Xia and Xu(2020),and uses the super-efficiency SBM model with unexpected outputs to evaluate the DMU (x0, y0, z0), as shown in the following formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\rho =\\hbox{min} \\frac{{1+\\frac{1}{m}\\sum\\nolimits_{{i=1}}^{m} {\\frac{{s_{i}^{x}}}{{{x_{i0}}}}} }}{{1 - \\frac{1}{{{s_1}+{s_2}}}\\left( {\\sum\\nolimits_{{k=1}}^{{{s_1}}} {\\frac{{s_{k}^{y}}}{{{y_{k0}}}}+} \\sum\\nolimits_{{l=1}}^{{{s_2}}} {\\frac{{s_{l}^{z}}}{{{z_{l0}}}}} } \\right)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\begin{gathered} {x_{i0}} \\geqslant \\sum\\limits_{{j=1, \\ne 0}}^{n} {{\\lambda _j}{x_j}+{\\text{s}}_{i}^{x},} \\forall i; \\hfill \\\\ {y_{k0}} \\leqslant \\sum\\limits_{{j=1, \\ne 0}}^{n} {{\\lambda _j}{y_j}{\\text{- s}}_{k}^{y},} \\forall k; \\hfill \\\\ {z_{l0}} \\geqslant \\sum\\limits_{{j=1, \\ne 0}}^{n} {{\\lambda _j}{z_j}+{\\text{s}}_{l}^{z},} \\forall l; \\hfill \\\\ \\end{gathered}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(1 - \\frac{1}{{{s_1}+{s_2}}}\\left( {\\sum\\nolimits_{{k=1}}^{{{s_1}}} {\\frac{{s_{k}^{y}}}{{{y_{k0}}}}+} \\sum\\nolimits_{{l=1}}^{{{s_2}}} {\\frac{{s_{l}^{z}}}{{{z_{l0}}}}} } \\right)\u0026gt;0;\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(s_{i}^{x} \\geqslant 0,s_{k}^{y} \\geqslant 0,s_{l}^{z} \\geqslant 0,{\\lambda _j} \\geqslant 0,\\forall i,j,k,l;\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Fixed-effects panel model\u003c/h2\u003e \u003cp\u003eThis paper first empirically tests the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry, and then further identifies the influencing factors between the digital economy and the low-carbon total factor productivity of the manufacturing industry. Specifically, this paper sets up the following empirical model:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{T}\\text{F}\\text{P}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e \u003c/span\u003e=α+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{1}{\\text{d}\\text{i}\\text{g}\\text{i}\\text{t}\\text{a}\\text{l}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{2}{{(\\text{d}\\text{i}\\text{g}\\text{i}\\text{t}\\text{a}\\text{l}}_{\\text{i}\\text{t}})}^{2}\\)\u003c/span\u003e\u003c/span\u003e +\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{X}}^{{\\prime }}{\\phi }\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\lambda }}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\epsilon }}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn which, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{T}\\text{F}\\text{P}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{d}\\text{i}\\text{g}\\text{i}\\text{t}\\text{a}\\text{l}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003eare the low-carbon total factor productivity and digital economy of the manufacturing industry in province i at time t, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003eis the fixed effect of province, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\lambda }}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the fixed effect of time, X is the fixed effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\epsilon }}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the random disturbance term.βis the regression coefficient between the digital economy and the low-carbon total factor productivity of the manufacturing industry. When the relationship between the two presents a U-shape, that is, the theoretical hypothesis is established, it is expected that\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;0 and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt;0.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Indicator selection\u003c/h2\u003e \u003cp\u003eDependent variable: Low-carbon total factor productivity of the manufacturing industry. At present, the measurement of low-carbon total factor productivity of the manufacturing industry includes the establishment of an indicator system and the use of it by scholars.This paper calculates the low-carbon total factor productivity by considering the following indicators: capital stock, labor input, and energy consumption. When calculating the expected output, the output value of the manufacturing industry is selected and measured by the current year's main business income. At the same time, non-expected output, i.e. carbon dioxide emissions, also needs to be considered. As for the measurement of energy consumption in the manufacturing industry, the existing statistical yearbooks only have data on industrial energy consumption, and there are no statistics on the energy consumption of provincial manufacturing industries. Therefore, existing literature studies use industrial energy consumption data instead of manufacturing energy consumption data. However, this measurement method is not scientific because the proportion of manufacturing to industry varies greatly among provinces and cities in China, and using industrial energy consumption instead will produce a large error. Therefore, this paper indirectly measures the energy consumption of the manufacturing industry based on the proportion of manufacturing to total industrial output value combined with industrial energy consumption data, which improves the accuracy and scientificity of the measurement compared to existing literature studies.\u003c/p\u003e \u003cp\u003eCore explanatory variable: Level of digital economic development. Referring to the research of Jiao et al(2020), a comprehensive evaluation index of digital development level is constructed, and the entropy weight method is used to calculate the comprehensive index value. The construction of the comprehensive evaluation index system for digital development level is shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eControl variables. Based on existing literature on land resource mismatches, this paper further controls the influence of other variables on land resource mismatches, the selected control variables are as follows:\u003c/p\u003e \u003cp\u003eFinancial level: Measured by the ratio of deposits and loans, i.e., the ratio of bank deposits to loans. Some studies have shown that Financial levelalso affect green total factor productivity (Lee,2022).\u003c/p\u003e \u003cp\u003eRD intensity: Measured by the ratio of RD expenditure to GDP. This indicator mainly measures the level of technological innovation in a region, as technological innovation is an important driver of green total factor productivity in the region.\u003c/p\u003e \u003cp\u003eEnvironmental regulation intensity: Measured by the ratio of the number of pollution-related words in the government work report of the sampled provinces to the total number of words in the government work report of the same year.\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\u003eIntroduction to Related Variables and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDigital Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of websites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet penetration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDigital circulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMobile phone exchange capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehouseholds\u003c/p\u003e \u003cp\u003ehouseholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of mobile phone base stations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMobile phone penetration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong distance optical cable line length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekilometre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDigital industrialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndustrialization investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployment in information transmission, computer services, and software industries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eten thousand people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed investment in information transmission, computer services, and software industries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIndustrialization output\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal telecommunications business volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoftware business revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital TV penetration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Top 100 Internet Comprehensive Enterprises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eIndustrial Digitization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDigital Transactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-commerce sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproportion of enterprises with ecommerce transaction activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enumber of websites per hundred enterprises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enumber of computers (per hundred people)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillion units\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDigital Business Formats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enumber of rural broadband access users, operating income of computer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10000 households\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecommunication and other electronic equipment manufacturing industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edigital inclusive finance index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eexpress delivery volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand yuan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data sources\u003c/h2\u003e \u003cp\u003eThis paper takes 30 provinces and cities in China from 2011 to 2020 as the research objects, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the data sources and descriptive statistical information of these related variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Main 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=\"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 name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental regulation intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\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":"4. Empirical Analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Basic results\u003c/h2\u003e \u003cp\u003eAfter conducting regression analysis using formula (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), the regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The first column of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the regression results after controlling for time and province fixed effects. The regression coefficient of the first-order term of the digital economy is -0.8566, which has passed the statistical test at the significance level of 1%. The regression coefficient of the second-order term of the digital economy is 2.3304 and has highly significant results at the 1% significance level, indicating a first decline and then an increase relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry. When the digital economy is small, it will inhibit the low-carbon total factor productivity of the manufacturing industry, and only when the digital economy develops to a certain degree, it can promote the low-carbon total factor productivity of the manufacturing industry. This is consistent with the assumed theory. The second to sixth columns of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the regression results with the gradual addition of control variables, including economic development level, industrial structure, the proportion of fiscal expenditure, financing constraints, RD intensity, etc. Compared with the first column, according to the regression results in columns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), the regression coefficients of the first-order term of the digital economy are negatively correlated with the significance level of 1%, while the second-order term coefficients are positively correlated, indicating U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry.\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\u003eBasic Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.44***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-5.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.49***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(8.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEconomic development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndustrial structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProportion of fiscal expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFinancial level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRD intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.80**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(16.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(6.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of province\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Robust standard errors are reported in parentheses; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) N is the sample size, and R^2 is the goodness-of-fit.\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\u003e4.2 Robustness Tests\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Setting of Transformed Explanatory Variables\u003c/h2\u003e \u003cp\u003eTo further enhance the credibility of the regression results, this paper will conduct robustness tests on the regression results of the previous section through various strategies. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e measures the digital economy with comprehensive indicators of digital infrastructure, digital industrialization, and industrial digitalization. In this section, we will use alternative indicators to measure digital economic development and conduct regression tests. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the \"Internet penetration rate\" as an alternative variable for the digital economy, with the remaining control variables unchanged. The coefficient of the first regression of the digital economy is negatively correlated and is statistically significant at the 1% level. The coefficient of the quadratic term of the digital economy is positively correlated and is also significant at the 1% level. From the regression results of the transformed explanatory variables, it can be seen that regardless of which indicator is used to measure the digital economy, the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry always exhibits a U-shaped relationship.\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\u003eRobustness Test Using Different Digital Economy Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent variable: Low-carbon total factor productivity of the manufacturing industry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent variable: Internet penetration rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Setting of Transformed Dependent Variable\u003c/h2\u003e \u003cp\u003eTo verify the credibility of the previous regression analysis, we will further conduct a test by substituting the explained variable. The core of low-carbon development in manufacturing is to reduce carbon emissions in the process of industrial development. Therefore, in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \"Main business income of manufacturing industry/carbon emissions\" is used as a robustness test for the low-carbon development of the manufacturing industry as the explained variable. After regression analysis, we found that the coefficient of the first order of the digital economy is still negative and significant at the 1% level, while the coefficient of the quadratic term of the digital economy is positive and significant at the 5% level. This indicates that after replacing the explained variable, the U-shaped relationship between the digital economy and the low-carbon development of the manufacturing industry still holds.\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 with Transformed Dependent Variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent variable: Output value of the manufacturing industry/carbon emissions.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.34***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Endogeneity Test\u003c/h2\u003e \u003cp\u003eGiven that the difficulty of achieving economic growth targets may create endogeneity, this paper will use the commonly used two-stage least squares method in instrumental variables to conduct a regression test.\u003c/p\u003e \u003cp\u003eWe use \"the number of Internet domain names in each province\" as the instrumental variable for the digital economy in the two-stage least squares regression. A reliable instrumental variable must satisfy two conditions: first, there is a correlation with the explanatory variable (in this case, the digital economy); second, the instrumental variable cannot affect the dependent variable (here, the low-carbon development of the manufacturing industry) in other ways. Internet domain names can be used as the instrumental variable for the digital economy because Internet technology is one of the core elements of the digital economy, and the number of Internet domain names can reflect the level of application and popularity of Internet technology. When studying causal effects, if we use the digital economy as the independent variable, then the digital economy itself will also be affected by the low-carbon development of the manufacturing industry, making it difficult to accurately estimate the impact of the digital economy on the low-carbon development of the manufacturing industry. Using the number of Internet domain names as the instrumental variable can better satisfy the above two conditions, making the research results more reliable and accurate. After analyzing the panel data with the two-stage least squares regression, the regression results obtained are shown in columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In the second stage regression results of column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), the coefficient of the digital economy is negative, and the coefficient of the quadratic term of the digital economy is positive, both statistically significant at the 1% level, once again confirming the U-shaped relationship between the digital economy and the low-carbon development of the manufacturing industry.\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\u003eUsing the Number of Internet Domain Names as the Instrumental Variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003cp\u003eSecond-stage regression.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003cp\u003eFirst-stage regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquare of the digital economy\u003c/p\u003e \u003cp\u003eFirst-stage regression\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eⅣ : Number of Internet domain names\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eⅣ : Square of the number of Internet domain names\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\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 \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Mediation Effects Testing\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Mediation Effects Testing Based on Green Technology Innovation\u003c/h2\u003e \u003cp\u003eThe U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry is somewhat related to the region's green technology innovation. The development of the regional digital economy can provide more technical support and investment for green technology innovation in theory. For example, the digital economy can provide technical platforms such as cloud computing and big data analysis to assist local industries in better carrying out green technology research and application. The application of green technology can improve the energy utilization efficiency of the manufacturing industry, reduce carbon emission intensity, and drive the improvement of energy efficiency in the manufacturing industry, thereby achieving low-carbon development. For example, using energy-saving and environmentally friendly production equipment, and using renewable energy. However, in the early stage of the development of the digital economy, there may be a crowding-out effect of the digital economy. For industries within the region, allocating limited resources to the development of the digital economy may be more attractive, resulting in decreased investment in green technology innovation. However, once the digital economy reaches a certain stage of development, its positive promotion effect on green technology innovation will be greater than the crowding-out effect, thereby promoting green technology innovation. To verify the above logic, in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we added \"green technology innovation\" as a control variable in the regression whose explained variable is the low-carbon total factor productivity of the manufacturing industry, and, referring to existing research results, the green technology innovation indicator measures the sum of the number of regionally authorized green utility model patents and green invention patents in the current year. We examined the robustness of the regression results. Column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e has the same regression results as Column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicating the regression relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry without other control variables. Column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e is based on Column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, adding control variables, indicating the regression relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry. Column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) is based on Column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), adding \"green technology innovation\" as a control variable, and the regression coefficient of green technology innovation is 0.0001 and significant at a 10% confidence level.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e Analysis Based on Green Technology Innovation\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGreen Technology Innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, we further examine whether green technology innovation is affected by the digital economy. Taking green technology innovation as the explained variable, with the explanatory variables and other control variables remaining unchanged, the regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The regression analysis shows that the coefficient of the digital economy is negative and significant at a 1% level of significance. At the same time, the coefficient of the squared term of the digital economy is positive and significant at a 1% level of significance. This indicates that there is also a U-shaped relationship between the digital economy and green technology innovation. Therefore, green technology innovation is one of the intermediary variables of the digital economy\u0026rsquo;s impact on the low-carbon total factor productivity of the manufacturing industry. From a micro perspective, in the early stage of the digital economy's development, to seize the opportunities of the new change, industries will make certain digital investments. However, the initial cost of digital infrastructure is relatively high, which has a certain negative impact on the amount of personnel and funding invested in green technology innovation. However, with the decrease in the cost of digital infrastructure hardware equipment and the gradual maturity of enterprise industry digital applications, the cost of digital economy applications has greatly reduced, and the crowding-out effects on green technology innovation have gradually decreased. Moreover, enterprise digital applications have improved the overall level of green technology innovation by reducing the innovation time cycle and cost of technical personnel. As the overall level of green technology innovation is promoted, the promotion and application of green technology innovation in the manufacturing industry has pushed for energy conservation, emission reduction, and low-carbon development.\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\u003eRegression Results of the Digital Economy on Green Technology Innovation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplained variable: Green technology innovation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.0e\u0026thinsp;+\u0026thinsp;04***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-4.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9e\u0026thinsp;+\u0026thinsp;04***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Mediation Effect Test Based on Factor Resource Mismatch\u003c/h2\u003e \u003cp\u003eThe development of the digital economy can help industries more accurately identify the optimal configuration of factor resources in production and management processes. Therefore, the development of the digital economy may affect the low-carbon total factor productivity of the manufacturing industry by reducing the degree of factor resource mismatch. In order to verify this hypothesis, this section used the degree of capital mismatch as a mediating variable for regression, and the regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. In Tables\u0026nbsp;(1) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), the regression coefficients of the digital economy are all negative, and at a statistical significance level of 1%, the square regression coefficients of the digital economy are also positive and significant, and the regression coefficients of the square term of the digital economy are positive and significant at the 1% level as well. The U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry still holds.\u003c/p\u003e \u003cp\u003eIn the regression of column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, it can be seen that there is a U-shaped relationship between the digital economy and capital mismatch. That is, in the early stages of the development of the digital economy, the development of the digital economy will reduce the degree of capital mismatch, but when the digital economy develops to a certain stage, it may deepen capital mismatch. This is mainly because the rapid development of the digital economy has promoted the rise of emerging industries and attracted a large amount of investment capital. However, these capital tend to focus on a few areas and companies, resulting in uneven resource allocation.\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\u003eMediation effect analysis based on resource mismatch\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)Dependent variable: capital mismatch.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)Dependent variable: GTFP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCapital mismatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Mediation Effect Test Based on Industrial Upgrading\u003c/h2\u003e \u003cp\u003eAs a new production factor, the digital economy can be used by the manufacturing industry to transform traditional manufacturing, promote the intelligent and high-end transformation of traditional manufacturing, and thus promote the transformation and upgrading of the manufacturing industry. The process of upgrading the manufacturing industry can also promote the development of low-carbon manufacturing. In order to test the above hypothesis, in this section, we used industrial upgrading of manufacturing as a mediating variable for regression analysis and obtained the corresponding regression results, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. We found that the regression coefficients of the digital economy in columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) are negative and significant at the 1% level, and the regression coefficients of the square term of the digital economy are positive and significant at the 1% level. This indicates that the U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry still exists.\u003c/p\u003e \u003cp\u003eIn column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, it can be seen that there is a U-shaped relationship between the digital economy and industrial upgrading of manufacturing, that is, the impact of the digital economy on the industrial upgrading of manufacturing shows a trend of initial restraint and later promotion. This may be because in the initial development stage of the digital economy, it often attracts a large amount of investment and resources, such as the Internet industry, e-commerce, etc., and the rapid development of these fields may attract talents and resources from related areas of the manufacturing industry, thereby reducing the speed of industrial upgrading in the manufacturing industry.\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\u003eMediation effect analysis based on industrial upgrading\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)Dependent variable: Industrial upgrading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)Dependent variable: GTFP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndustrial upgrading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\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 \u003c/div\u003e"},{"header":"5. Heterogeneity Analysis Based on Regional Market Environment","content":"\u003cp\u003eSo far, we have found through various tests that the hypothesis of a U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry is valid. Next, we further test whether the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry will change with the level of marketization.\u003c/p\u003e \u003cp\u003eWe grouped provinces according to the level of marketization and tested the relationship between the digital economy and the green total factor productivity of the manufacturing industry according to the grouping. Based on the scores of the marketization index of various provinces and cities in the \"China Province Market Index Report (2018)\", we grouped the provinces according to the ranking of the third quartile of their marketization index. We divided the level of marketization into three levels: high, medium, and low, and conducted regression analysis in the case of the three levels of marketization grouping. The corresponding regression results are shown in Table\u0026nbsp;11. The table displays the sample regression results for regions with high, medium, and low marketization levels, with the first, second, and third columns corresponding to regions with high, medium, and low marketization levels, respectively. The regression coefficients of the digital economy in columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) are significant, and we found that the regression coefficient of the digital economy in the third column of Table\u0026nbsp;11 is not significant. This means that the U-shaped relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry only exists in regions with a higher or medium degree of marketization, and regions with a lower degree of marketization are not affected by this relationship.\u003c/p\u003e \u003cp\u003eIn regions with different levels of marketization, the relationship between the digital economy and the low-carbon total factor productivity of the manufacturing industry shows heterogeneity, and the reasons for this may be as follows. First, low levels of marketization lead to a lack of environmental awareness among enterprises. In regions with lower levels of marketization, the level of attention paid by enterprises to environmental protection is often lower than that in areas with higher levels of marketization. Therefore, when the digital economy develops rapidly, the increase in carbon emissions from the manufacturing industry is not sufficiently paid attention to, and a U-shaped curve cannot be formed. Second, the production methods are outdated. Regions with lower levels of marketization tend to have more backward production methods, relatively lower technological content, and higher carbon emissions. The impact of the digital economy on the manufacturing industry in this situation is not obvious, and a U-shaped curve cannot be formed. Third, the resource endowment is different. Regions with lower levels of marketization often have different resource endowments, such as relatively insufficient human capital and natural resources. As a result, the impact of the development of the digital economy on the manufacturing industry is not as significant as in areas with higher levels of marketization, and the fluctuation of carbon emissions in the manufacturing industry is smaller, thus a U-shaped curve cannot be formed.\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\u003eSample regression based on the degree of marketization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarketization (high)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarketization (medium)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarketization (low)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.97**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare of digital economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of provinces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects of time\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Conclusion and Recommendations","content":"\u003cp\u003eThis article chooses the panel data of 30 provinces and municipalities in China from 2016 to 2020 and uses panel fixed effects and mediation effects models to explore the impact of the digital economy on low-carbon development of manufacturing, the mechanism and heterogeneity. The main conclusions are as follows: First, there is a U-shaped relationship between regional digital economy and green low-carbon development of manufacturing, which still holds true after a series of robustness tests. Second, the digital economy has an impact on the green total factor productivity of manufacturing by affecting green innovation, industrial upgrading, and capital misallocation. Third, the impact of the digital economy on low-carbon development of manufacturing shows significant regional differences, with a more significant effect on provinces with high and moderate degrees of marketization and a less obvious effect on regions with low marketization. By verifying the hypothesis of the impact of the digital economy on low-carbon development of manufacturing, we can not only promote our better understanding of the dynamic process and mechanism of the impact of digital economic development on green low-carbon development of manufacturing, but also help us to understand the direction of the improvement of green total factor productivity of manufacturing. Based on the above conclusions, we propose the following policy recommendations:\u003c/p\u003e \u003cp\u003eBased on the impact and mechanism of the digital economy on low-carbon development of manufacturing discovered in this study, countermeasures and suggestions can be proposed in the following aspects:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1. Enhance the innovation capability of the digital economy and promote the application of digital technology in the manufacturing industry. By establishing a digital technology industrial ecology and strengthening the cooperation between various research institutions, technology companies, and the government in the digital technology field, the innovation and application capabilities are enhanced. This promotes the popularization and application of digital technology in the manufacturing industry, and promotes the positive impact of the digital economy on the low-carbon development of manufacturing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e2.Promote the green upgrading and transformation of manufacturing. The goal is to reduce carbon emissions and reduce resource consumption by gradually promoting the transformation of traditional manufacturing to green manufacturing. Focus on promoting the research, application, and promotion of green and low-carbon technologies, and promote the transformation of the production mode of manufacturing towards green, low-carbon, circular and efficient direction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e3.Strengthen the guidance role of local governments. We should strengthen the support and policy guidance for provinces with low degrees of marketization, formulate more targeted policy measures, and promote the coordinated development of the digital economy and low-carbon manufacturing. At the same time, for regions with relatively high and moderate degrees of marketization, we should strengthen the integration and coordination of the digital economy and low-carbon manufacturing, and build a \"dual-wheel drive\" development pattern of the digital economy and low-carbon manufacturing industry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e4.Strengthen data monitoring and study the relationship between the digital economy and manufacturing in-depth. We should establish a relationship index system between the digital economy and manufacturing, strengthen the monitoring and analysis of relevant indicators, and continuously study the impact mechanism and regional differences of the digital economy on the low-carbon development of manufacturing, providing theoretical and practical support for further promoting the coordinated development of the digital economy and manufacturing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Iianzheng Fan for his help in data processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eSL and YL wrote the main manuscript text.YP collected and analyzed the data. SL discussed the results. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Social Science Fund of China(22XJY036),Xinjiang University Excellent Doctoral Student Research Innovation Project(XJU2022BS014).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYan X, Fang Y. 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Econ. 2020, 23\u0026ndash;41.\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":"Digital economy, Green Solo model, Manufacturing industry, Low-carbon development","lastPublishedDoi":"10.21203/rs.3.rs-3306547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3306547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo explore the impact of digital economy on the low-carbon development of manufacturing industry, this paper constructs theoretical and empirical models, and studies from both theoretical and empirical perspectives. The results show that there is a U-shaped relationship between digital economy and low-carbon development of manufacturing industry. In terms of theoretical model analysis, we adopt the Green Solow model as the analytical framework, and improve and expand the CES production function to introduce digital economy into it. By deducing the theoretical model, we draw the conclusion that there is a possibility of an inverted U-shaped curve between the development of digital economy and carbon emissions. In terms of empirical verification, this paper applies fixed-effect and intermediate-effect empirical models, and relies on panel data of 30 provinces and cities in China from 2011 to 2020 to conduct an empirical study on the relationship between digital economy and low-carbon development of manufacturing industry. The results show that the impact of digital economy on the low-carbon development of manufacturing industry is not linear, but exhibits a U-shaped relationship. In regions with high and medium levels of marketization, digital economy has a significant impact on the low-carbon development of manufacturing industry, while it has no obvious impact in regions with low levels of marketization. Furthermore, this study finds that digital economy can influence the low-carbon development of manufacturing industry through various ways, such as innovative green technology, reducing capital mismatch, and promoting industrial upgrading of manufacturing industry, based on the analysis of influencing mechanism.\u003c/p\u003e","manuscriptTitle":"Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-06 04:40:45","doi":"10.21203/rs.3.rs-3306547/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":"1ebada00-d9f6-408c-8f05-07e62ae67d8a","owner":[],"postedDate":"September 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T15:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-06 04:40:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3306547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3306547","identity":"rs-3306547","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}