Research on the Impact of Green Investment on Low-Carbon Economic Development: Based on the Test of Spatial Spillover Effect

Abstract

To address the challenge of achieving coordinated development between the economy and the environment in the context of a green economy, this study utilized the SBM-GML model to assess the total factor carbon productivity across 30 provinces in China from 2012 to 2021. This assessment aimed to quantify the development index of the low-carbon economy and elucidate its spatial characteristics. The findings indicate the following: (1) The transition and development of China’s low-carbon economy exhibit spatial agglomeration characteristics; however, there are notable disparities in the degree of agglomeration across different regions. (2) The influence of green investments on the low-carbon economic advancement of both local and adjacent regions exhibits distinct nonlinear attributes, with the local impact being more pronounced than the neighboring effect. (3) Innovation in low-carbon technologies serves as a partial intermediary in the relationship between green investment and low-carbon economic advancement. (4) Different types of green investment have heterogeneous impacts on low-carbon economic development.

1. Introduction

Global greenhouse gas emissions are intricately linked to the overarching climate crisis and have emerged as a pressing global concern for the international community. The “Carbon Dioxide Emissions 2023” report published by the International Energy Agency indicates that global carbon emissions have slowed significantly in the past decade, and GDP energy consumption has also decreased significantly. It can be seen that the development trend of a low-carbon economy is very obvious, and economic transformation is very necessary, which needs us to further maintain [1]. Currently, as the world’s second-largest economy, China stands as a pivotal energy producer and consumer. The substantial demand for fossil fuels positions China as the largest carbon emitter globally. In response, China articulated its “dual carbon” objectives at the United Nations General Assembly in 2020, aiming for carbon peaking and carbon neutrality. This entails a commitment to reducing greenhouse gas emissions while fostering economic growth, enhancing resource utilization efficiency, and advancing a green, low-carbon transformation. Since then, China has developed and disseminated numerous strategic initiatives aimed at establishing and refining a green, low-carbon, and circular economic framework. The China Securities Investment Fund Association has issued Green Investment Guidelines, advocating for the financial sector to enhance green investment, elevate the environmental performance of investments, and facilitate green and low-carbon economic advancement. In 2021, the Ministry of Industry and Information Technology of China released the “14th Five-Year Plan for Green Industrial Development”, emphasizing the necessity of constructing a green and low-carbon technological framework and expediting breakthroughs in critical common technologies to achieve the “dual carbon” goals. The State Council’s 2024 document, “Opinions on Accelerating the Comprehensive Green Transformation of Economic and Social Development Mode”, underscores the importance of collaboratively promoting carbon reduction, pollution mitigation, and green growth while deepening reforms in the ecological civilization system, enhancing mechanisms for green and low-carbon development, and establishing a spatial framework for high-quality, green, and low-carbon development. It is evident that green investment and low-carbon technological innovation are crucial for advancing low-carbon economic development; they not only provide a direct financial foundation for advancements in green and low-carbon technologies but also play a critical role in strengthening the framework for environmental governance and protection. They can effectively stimulate the innovation vitality of green and low-carbon technologies, reduce the problem of resource mismatch, make up the gap of environmental governance, improve the total factor carbon productivity, and thus promote the green and low-carbon development of the economy. But at the same time, we should take into account the objective of green investment has a certain risk; whether it can continue to promote the development of a low-carbon economy is worthy of further exploration.

The United Nations Sustainable Development Goals underscore the critical need for investment in a green economy. Green investment encompasses the allocation of resources to enterprises or projects that yield environmental benefits while mitigating ecological costs and risks. This is achieved through the implementation of systematic green investment strategies aimed at enhancing corporate environmental performance, fostering the development of green industries, and minimizing environmental hazards. It is also viewed as a strategic approach to addressing environmental challenges alongside various economic issues [2]. This paradigm serves as a novel mechanism for advancing green economic transformation and sustainable development through ecological preservation and environmental governance.

Research on green investment predominantly concentrates on three key dimensions: the ecological environment, green technology, and sustainable economic development. One area of inquiry examines the influence of green investment on the ecological environment. Zhe discovered that green investment significantly contributes to the reduction of carbon emissions in China at a regional level, with a more pronounced effect observed in the central and western regions, albeit constrained by the provincial economic development levels [3]. From an enterprise perspective, Sisi highlighted that an increase in green investment correlates with a decrease in corporate carbon emissions, noting that the ownership structure of firms influences the relationship between green investment and carbon emission intensity [4]. Chen and Zhang identified an M-shaped nonlinear effect and certain spatial spillover effects of green investment on the optimization of energy structures [5]. Dong et al. found that green investment enhances energy efficiency through technological innovation and R&D intensity, with the advancement of the digital economy further amplifying this effect [6]. Junming et al. established that investments in renewable energy and green finance can lower carbon emissions and foster sustainable environmental development by facilitating industrial structure upgrades [7]. Ullah et al. employed a spatial panel autoregressive distributed lag model to investigate the interplay between green investment and energy intensity in reconciling economic growth with environmental degradation. Their findings indicate that during the initial phases of economic growth, carbon emissions rise in tandem with energy demand and economic development; however, as economic growth progresses, green investment proves effective in curbing carbon emissions [8]. The second aspect pertains to the influence of green investment on the innovation of green technologies. At the enterprise level, Tang et al. identified that green investment serves as a significant catalyst for fostering green innovation within enterprises, with large state-owned enterprises exhibiting a more pronounced impact on green technology innovation [9]. Nuryanto et al. highlighted the critical role of green investment in advancing ecological control technologies and eco-friendly innovations [10]. Xie and Yu demonstrated that investments in intelligent technologies can effectively enhance the sustainable innovation capabilities of enterprises [11,12]. Rauf et al. utilized corporate ESG reports to reveal that green R&D investments can play a compensatory and supportive role by emitting green signals that enhance corporate green innovation performance [13]. At the regional level, Muhammad et al. employed a spatial econometric model to establish that green investment and environmental regulations can stimulate green innovation in localities across China and its adjacent areas, thereby creating a positive feedback loop among the three elements [14]. Sun et al. found that the degree of green technology innovation within a region is positively influenced by R&D investments, with digital inclusive finance helping to bridge the innovation disparities between regions [15]. Thirdly, the implications of green investment on sustainable economic development are examined from a macro perspective. Xu and Zhu discovered that green financial investments can facilitate the execution of environmental protection initiatives, drive green technology innovation, and support the development of green infrastructure, thereby advancing the green transformation of the economy [16]. Li and Wang noted significant regional disparities in the effectiveness of green investment in promoting sustainable development across China, suggesting that leveraging regional advantages could enhance the spillover effects of sustainable development [17]. Shen et al. applied the autoregressive distributed lag technique and found that green financial investments positively influence both economic and environmental performance [18]. Deng et al. indicated that China’s fintech sector, in conjunction with green investments, can achieve environmental sustainability and stimulate the growth of the green economy through the DYNARDL method. From a microeconomic standpoint [19], Yuyu and Li revealed that green financial investments in non-state-owned enterprises can foster sustainable economic development via renewable energy and technological advancements [20]. Jingyu and Ying, utilizing data from Chinese listed companies, found that green investments significantly enhance the sustainable development of highly competitive enterprises by alleviating internal financing constraints [21].

Furthermore, numerous researchers contend that the impact of green investment is not exclusively positive or linear. Xie posits that green investment, as a novel investment strategy capable of fostering sustainable economic growth, is susceptible to market risks, technological constraints, and capital deficiencies during its evolution [22]. Liu, Pham, and Mai discovered that China’s severe pollution and the inadequate governance capacity of enterprises could hinder the advancement of the green economy, primarily due to the prolonged return cycles associated with green investments and various environmental risks [23]. Yali et al. identified that government expenditures on environmental protection may exert a crowding-out effect on corporate capital investments, suggesting that the structure of green investment needs optimization to facilitate the green and low-carbon progression of the economy [24]. Shubing and Chong advocated for the promotion of renewable energy project financing, taking into account the risk dynamics associated with green investments to ensure the sustainable development of the economy [25]. Lu et al. employed a dynamic panel threshold model to reveal that China’s environmental regulations and R&D investments significantly influence the development of enterprises’ green economy in a nonlinear manner [26].

The low-carbon economy represents a sustainable development paradigm defined by minimal energy consumption, reduced emissions, and limited pollution. This model facilitates a synergistic relationship between economic and social advancement and the preservation of ecological and environmental integrity. Advancing green and low-carbon economic and social development is fundamental to addressing China’s resource, environmental, and ecological challenges, and it serves as a crucial element in fostering high-quality development within the country. Research on low-carbon economic development predominantly concentrates on three key areas: the digital economy, technological innovation, and governmental environmental protection policies. Qiang et al. identified that China’s digital economy exerts a “U”-shaped influence and a notable siphoning effect on the low-carbon transformation of urban economies [27]. Jing discovered that the convergence of the digital economy with the energy sector can enhance the establishment of digital infrastructure within the energy industry, bolster energy technology innovation, and facilitate the green and low-carbon evolution of China’s energy sector [28]. Ma and Zhang utilized county-level panel data from China to reveal that the digital economy is a viable pathway toward achieving low-carbon sustainable development, albeit with a nonlinear relationship between the two variables [29]. Bin and Bingjun found that the innovation ecosystem significantly drives low-carbon economic transitions and exhibits certain spatial spillover effects, characterized by “U”-shaped nonlinear traits [30]. Liu et al. highlighted that China’s low-carbon economy exhibits significant spatiotemporal evolution characteristics and regional disparities, with green technology innovation playing a pivotal role in advancing the development of China’s low-carbon economy [31]. The third is the impact of green environmental protection investment on low-carbon economic development: Sang and Pan posited that investments in green infrastructure can lead to a reduction in urban carbon emissions [32]; however, the protracted construction timelines associated with such infrastructure result in a lagged effect on both carbon emissions and the progression of low-carbon economic development. Wu et al. discovered that in the energy-intensive shipping sector, government fiscal policies exhibit an inverted U-shaped relationship with the transition to a low-carbon economy [33]. Yuling and Feng asserted that renewable energy subsidies can significantly enhance the energy consumption framework and facilitate low-carbon economic growth [34]. Furthermore, Wenqi et al. identified that China’s green finance initiatives can drive the low-carbon economic transformation through innovations in low-carbon technologies, although the effectiveness of this impact diminishes with the introduction of low-carbon technological innovations [35]. Lastly, Jiajia et al., in light of China’s carbon neutrality objectives, found that green investments and innovations within the energy sector can effectively lower carbon dioxide emissions and enhance environmental quality [36].

In addition, the current academic research predominantly examines the effects of green investment on the ecological environment, green technology innovation, and sustainable development from regional and enterprise perspectives. However, the impact of green investment has been assessed in varying ways. There is a pressing need to further investigate the dynamic mechanisms through which green investment influences these areas. Existing studies on low-carbon economic development primarily concentrate on the roles of the digital economy and technological innovation, with the latter being significantly interconnected with both. Consequently, this paper will first analyze the spatial spillover effects of green investment. It will also consider low-carbon technology innovation as a mediating variable, providing a nuanced examination of how green investment drives low-carbon economic development through technological innovation, thereby enriching the current body of research. Additionally, the literature review reveals a strong correlation between digital inclusive finance, green investment, and the low-carbon economy. This relationship will be utilized to explore the threshold effects of green investment on the development of a low-carbon economy, offering theoretical insights that could facilitate the advancement of low-carbon economic initiatives and support the achievement of “dual carbon” goals.

The contributions of this research are as follows: (1) it provides a comprehensive assessment of green investment indicators across various dimensions, including government, market, and enterprise sectors, employing the SBM-GML model that integrates stochastic frontier analysis with the global ML index to evaluate total factor carbon productivity, while accounting for the effects of undesirable carbon emissions; (2) while existing literature predominantly emphasizes green technology innovation, this study recognizes the broad spectrum of green technology and utilizes low-carbon technology innovation as a mediating factor to investigate the intricate mechanisms through which green investment influences low-carbon economic development; (3) by employing a spatial econometric model to illustrate the spatial differentiation characteristics, this paper elucidates the dynamic trends associated with the influence of green investment on low-carbon economic development; (4) a heterogeneity analysis is conducted to examine the varying impacts of different categories of green investment on low-carbon economic development.

The remainder of the manuscript is organized as follows: Section 2 undertakes a theoretical examination of the interplay between green investment, low-carbon technological innovation, and low-carbon economic development while proposing pertinent research hypotheses; Section 3 focuses on the design of the research model and the construction of variable indices; Section 4 primarily presents and analyzes the results of the empirical tests; Section 5 offers the paper’s conclusions and associated recommendations.

2. Theoretical Analysis and Research Hypothesis

A low-carbon economy is characterized as a model of economic development that, guided by the principles of sustainable development, aims to minimize greenhouse gas emissions through various means such as technological innovation, institutional reform, industrial transformation, and the advancement of new energy sources. This approach seeks to achieve a synergistic relationship between economic and social progress and the protection of the ecological environment. In this study, total factor carbon productivity is employed as the key metric for assessing low-carbon economic development, representing the optimal economic efficiency attainable while maintaining minimal carbon emissions. Enhancing total factor carbon productivity is crucial for realizing the dual benefits of economic growth and low-carbon outcomes. Green investment involves the synergistic financial contributions from government entities, private enterprises, and market participants, acting as a crucial resource that harmonizes the tripartite advantages of social progress, environmental improvement, and economic efficiency. It serves as a fundamental funding mechanism for initiatives aimed at environmental protection, governance strategies, and the research and development of low-carbon technologies, thus delivering the financial drive essential for enhancing total factor carbon productivity and realizing the dual objectives of economic expansion and low-carbon development. On one hand, the reasonable investment of green capital and technology, the increase in environmental protection expenditure, and the increase in governance intensity can promote the new mode of green production, urge the green transformation and upgrading of industries, so as to effectively reduce energy consumption and carbon emission intensity, improve the efficiency of the allocation of social resources, and realize the improvement of total factor carbon productivity. On the other hand, green investment can elevate total factor carbon productivity primarily through technological innovation, facilitating the transition and development of a low-carbon economy. Strategic allocation of research and development funds for technology is essential for stimulating low-carbon technological advancements and increasing the conversion rate of technological innovations. Advancements in clean energy technology can mitigate the resource crowding-out effects associated with technological innovation and enhance energy utilization efficiency, thereby contributing to the improvement of total factor carbon productivity and the establishment of a low-carbon economic cycle. However, the new classical theory and pertinent literature indicate that overall social capital and resources are relatively finite. As green investment escalates, the marginal benefits associated with enhancements in total factor carbon productivity are likely to diminish, potentially hindering the low-carbon transformation of the economy.

Hypothesis 1.

The relationship between green investment and low-carbon economic development exhibits an inverted U-shaped pattern.

Green investment is pivotal for fostering sustained innovation in low-carbon technologies and enhancing technology-driven competitiveness. A portion of green investment is allocated to macro-level technology research and micro-level green technology innovation within enterprises. Another segment is dedicated to market oversight and enterprise management, compelling businesses to transition towards greener practices and facilitating the advancement of a low-carbon economy [37]. The theoretical framework is as follows: Initially, the accumulation of knowledge and technology brought by green investment is the main driving force for technological innovation, which can create a free-flowing, safe, and efficient financing environment for the innovative R&D of low-carbon technology and clean energy technology, provide financing power, and guide more resources to be invested in the innovation and R&D of green technology. Furthermore, augmenting environmental governance expenditures within green investment and enhancing oversight of corporate carbon emissions can compel enterprises to evolve and upgrade to low-carbon and clean operations, thereby improving resource allocation efficiency and expanding the demand for low-carbon technologies. This, in turn, stimulates the research and development of green innovation technologies and enhances the conversion rate of technological achievements. The innovation in low-carbon technologies, characterized by reduced carbon emissions and energy consumption, is a significant driver of total factor productivity, facilitating the green transformation of the economy. Green technological innovation yields positive externalities for the environment, capable of reshaping economic development models, accelerating the transition to clean energy structures, and diminishing carbon emissions. The dual economic and environmental benefits generated can establish a virtuous ecological cycle, effectively enhancing total factor carbon productivity and promoting the low-carbon trajectory of economic development. However, it is essential to consider the risks associated with green investment. As green investment increases beyond a certain threshold, it may displace capital allocation in other areas, such as production, leading to an imbalanced investment structure that could hinder the research and development of green and low-carbon technologies and the translation of technological advancements. Consequently, the marginal improvement effect of increased green investment on low-carbon technology innovation and total factor carbon productivity may gradually diminish.

Hypothesis 2.

Green and low-carbon technology innovation serves as an intermediary in the relationship between green investment and low-carbon economic development.

The impact of green investment is influenced by three key dimensions: market demand, governmental guidance, and the enterprises themselves. With the swift advancement of the digital economy, the proliferation of internet-based big data platforms, and the subsequent sharing of information across regions, despite the geographical dispersion of market investment and financing entities across the nation, their capital exhibits characteristics of substantial scale, extensive flow, and rapid circulation. Furthermore, the investment and financing activities among various regions are increasingly frequent and interconnected. Additionally, the trade dynamics and demand expansion among enterprises, coupled with technology spillover effects, have deepened the connections between different areas. Environmental protection investment policies enacted by the government are likely to be replicated and influence neighboring regions. Consequently, the increased frequency of market fund circulation and trade, along with learning effects and technology spillover phenomena—including the pollution haven effect and the spillover effects of green investment—may create certain dynamics that impact the low-carbon economic development of surrounding areas. Firstly, the green investment policies and practices of local governments may be blindly imitated and referenced by adjacent regions. Simultaneously, polluting industries affected by green policies may relocate to areas with less stringent environmental governance, thereby adversely affecting the total factor carbon productivity of those regions and hindering their low-carbon development. Secondly, green investment activities within a region may exert a spatial spillover effect on the low-carbon economic development of adjacent areas through the evolution of the market economy [38]. Thirdly, the low-carbon technological innovations resulting from green investments by enterprises in this region will likely influence the low-carbon transformation and development of surrounding areas through technology spillover effects.

Hypothesis 3.

The influence of green investment on low-carbon economic development exhibits a spatial spillover effect.

3. Material and Method

The dependent variable of this paper is the development of a low-carbon economy. The relevant indicators of economic development are primarily assessed through total factor productivity, which can be categorized into two main methodologies. These include the LP method, the MML index method, and the SBM directional distance function approach, among others. To minimize measurement errors and avoid biases stemming from selection direction and angle, Tone introduced the GML index, specifically the SBM-GML model, which allows for the disaggregation of technical efficiency and technological progress changes, building upon the traditional super-efficiency SBM framework [39]. The principal measurement variables encompass both input and output indices. Input indicators consist of labor input, capital input, and energy input, quantified by the total number of employees, fixed capital stock, and total energy consumption across various regions over time. The output index primarily reflects the desired output, while the assessment of low-carbon economic development incorporates carbon dioxide emissions as part of the undesirable output index, referred to as total factor carbon productivity. Detailed measurement indicators are presented in

Table 1. Total indicators of low-carbon economic development.

Indicators	Variable Name	Indicator Description
Variable of input	Labor input	Regional year-end employment numbers
	Capital input	Fixed capital stock
	Energy input	Total regional energy consumption
Expected output	Gross regional product	Gross product at constant prices
Undesirable output	Carbon dioxide emissions	Direct and indirect carbon emissions by province

.

In reference to the perpetual inventory method, utilizing the year 2000 as the base period and a capital depreciation rate of 9.6% over the years, the fixed capital stock is assessed. For the missing price indices of individual provinces, the arithmetic mean of the price indices is employed as a substitute. The formula for calculating the social capital is presented in Equation (1), which considers the investments from the previous year and the current ones.

$$K_t=(1-{\mathrm{ɸ}}_t ) K_{t-1}+I_t$$

(1)

where K is the capital stock; t is the year t; ɸ is the depreciation rate; and I is the amount of investment [40,41].

From the standpoint of output metrics, regional Total Factor Productivity is analyzed at two tiers: desirable output and undesirable output. The desirable output encompasses the overall output value generated within the region, while the undesirable output pertains to the carbon dioxide emissions produced by the region. The aggregate carbon emissions for each province consist of the total direct carbon emissions within the province, indirect carbon emissions that occur outside the province’s jurisdiction but are associated with the province’s energy consumption, and carbon emissions resulting from the province’s economic activities that fall outside its jurisdiction. The formula for the SBM-GML model used to calculate total factor carbon productivity is as follows:

$$E_c^G(x^t,y^t,z^t)=\rho =min{\displaystyle \frac{1-\frac{1}{M}{\sum }_{i=1}^m\frac{s_m^x}{x_{i0}}}{1-\frac{1}{s_1+s_2}({\sum }_{k=1}^{s_1}\frac{s_k^y}{y_{k0}}+{\sum }_{i=1}^{s_2}\frac{s_i^z}{z_{i0}})}}$$

(2)

$$s.t. x_{m0}\ge {\sum }_{j=1,\ne 0}^n{\lambda }_j{x}_j+s_m^x,\forall m;$$

(3)

$$y_{k0}\le {\sum }_{j=1,\ne 0}^n{\lambda }_j{y}_j-s_k^y,\forall k;$$

(4)

$$z_{i0}\ge {\sum }_{j=1,\ne 0}^n{\lambda }_j{z}_j+s_i^z,\forall i;$$

(5)

$$s_m^x\ge 0,s_k^y\ge 0,s_i^z\ge 0, {\lambda }_j\ge 0,\forall m,j,k,i$$

(6)

where the $\rho$ represents the efficiency value derived from the super-efficiency SBM model, represented by Equation (2); Equations (3)–(5) represent the conditional expressions satisfied by input and output, respectively. It is assumed that the research subject comprises a collective of j decision-making units; m, k, and I are input index, desired output index, and undesired output index, respectively; $\mathrm{M}, {\mathrm{s}}_1, \mathrm{and} {\mathrm{s}}_2$ represent the number of variables of input, desired output, and undesired output; $\mathrm{\rho }=$ (${\mathrm{\rho }}_{\mathrm{m}}^{\mathrm{x}}, {\mathrm{\rho }}_{\mathrm{k}}^{\mathrm{y}}, {\mathrm{\rho }}_{\mathrm{i}}^{\mathrm{z}}$) is for the standardization of input output weighted vector; and x, y, and z are input, desired output, and undesired output, respectively. When ${\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{\lambda }}_{\mathrm{j}}=1$, it indicates that the formula meets the condition of variable returns to scale.

The Malmquist index, introduced and refined by economist Sten Malmquist in 1953, serves as a foundation for various derivative indices. While the Malmquist index itself enjoys broad acceptance within the academic community, GML index can be used for intertemporal comparison in the study of total factor carbon productivity, which overcomes the non-transitivity problem existing in traditional ML index and the defect that there is no feasible solution in linear programming. In the case of variable returns to scale, it can be decomposed into low-carbon technical efficiency change EC and low-carbon technical progress change BPC. Therefore, this paper refers to the research ideas of relevant scholars and uses the efficiency value calculated by the unexpected output SBM $\mathrm{\rho }$ to apply the GML index formula to measure the total factor carbon productivity so as to measure the development of a low-carbon economy [42,43,44]. The details are as follows:

$${GML}_c^G\left(x^t,y^t,z^t{x}^{t+1},y^{t+1},z^{t+1}\right)={\displaystyle \frac{E_c^t(x^{t+1},y^{t+1},z^{t+1})}{E_c^t(x^t,y^t,z^t)}}$$

(7)

$${GMLEC}_c={\displaystyle \frac{E_c^{t+1}\left(x^{t+1},y^{t+1},z^{t+1}\right)}{E_c^t\left(x^t,y^t,z^t\right)}}$$

(8)

$$G{MLBPC}_C=\left[{\displaystyle \frac{E_c^G(x^{t+1},y^{t+1},z^{t+1})/E_c^{t+1}(x^{t+1},y^{t+1},z^{t+1})}{E_c^G(x^t,y^t,z^t)/E_c^t(x^t,y^t,z^t)}}\right]$$

(9)

$$G{ML}_c^G={GMLEC}_c\times {GMLBPC}_C$$

(10)

The formula for technical efficiency is shown in Equation (8), and the formula for change in technological progress is shown in Equation (9). The GML index formula is calculated by applying the M index to the two measured technical efficiency values, as shown in Equations (7) and (10).

$$\overrightarrow{D}\left(x,y,z;g\right)=\max \left({w\rho }_m^x{\delta }_m^x+{w\rho }_k^y{\delta }_k^y+{w\rho }_i^z{\delta }_i^z\right){ \delta }_m^x, {\delta }_k^y, {\delta }_i^z\ge 0$$

(11)

where $\overrightarrow{\mathrm{D}}\left(\mathrm{x},\mathrm{y},\mathrm{z};\mathrm{g}\right)$ is the directional distance function that depends on the global production possibility set; when it is equal to 0, the MATLAB (R2020a) software is utilized to implement the SBM-GML model in order to derive the optimal solution ${\mathrm{\delta }}_{\mathrm{x}}^{*},{\mathrm{\delta }}_{\mathrm{y}}^{*},{\mathrm{\delta }}_{\mathrm{z}}^{*},$ which can subsequently be integrated into Equation (12) to calculate the total factor carbon productivity for each province.

$$TCP=1-{\displaystyle \frac{1}{J}}({\sum }_{t=1}^T{\delta }_{bj}^{\ast })$$

(12)

Equation (12) represents the conditions satisfied by the variables in Equation (11), where $\mathrm{\rho }=({\mathrm{\rho }}_{\mathrm{m}}^{\mathrm{x}}, {\mathrm{\rho }}_{\mathrm{k}}^{\mathrm{y}}, {\mathrm{\rho }}_{\mathrm{i}}^{\mathrm{z}}$) is the standardized weighting vector of input–output; M, K, and I are the numbers of input indicators, desired output indicators, and undesired output indicators; ${\mathrm{\delta }}_{\mathrm{m}}^{\mathrm{x}}, {\mathrm{\delta }}_{\mathrm{k}}^{\mathrm{y}}, {\mathrm{\delta }}_{\mathrm{i}}^{\mathrm{z}}$ is the scale factor; and ${\mathrm{g}}_{\mathrm{xm}},{\mathrm{g}}_{\mathrm{yk}},{\mathrm{g}}_{\mathrm{zi}}$ is the direction vector.

The independent variable of this paper is green investment. The academic community has yet to establish a standardized approach for quantifying green investment; scholars believe that half of green investment comes from private and business institutions, and the other half comes from public resources [45]. At the enterprise level, metrics such as investments in environmental protection equipment, advancements in environmental technology research and development, and pollution management costs are utilized for evaluation. Conversely, at the governmental level, scholars typically employ indicators like public expenditure on pollution mitigation and clean energy initiatives. Given that green investment is influenced by capital market demand, this paper aims to provide a more holistic assessment of regional green investment by incorporating market demand as an additional dimension alongside the enterprise and government perspectives. This market demand is represented by the environmental protection investment figures reported by the Ministry of Finance for each province. At the governmental level, the focus remains on pollution control expenditures, while at the enterprise level, due to the scarcity of comprehensive provincial-level corporate green investment data, it is represented by the ratio of environmental technology R&D investment for enterprises operating above the provincial scale. Additionally, the entropy method is employed to determine the weights of these three indicators (all of which are positive), facilitating the measurement of green investment levels across different regions.

Table 2. Total indicators of green investment.

Primary Indicators	Secondary Indicators	Weighting
Green investment	Proportion of investment in environmental protection Proportion of investment in pollution control Investment ratio of environmental protection technology R&D of scale enterprises	0.342 0.446 0.212

presents the comprehensive evaluation index system for green investment; its weight is calculated by the entropy method. On the basis of constructing the evaluation matrix, the three index data are standardized and normalized, and then the information entropy and information utility value of each index are calculated, so as to calculate the weight of each index. Indicators with significant weight account for a larger proportion in the comprehensive evaluation and have a greater impact on the comprehensive score. According to the weight results in the table, the input of pollution control and environmental protection accounts for a large proportion of green investment, which has a large impact on green investment.

The mediating variable in this study is the quantity of granted patents for green and low-carbon technologies. According to the Patent Classification System for Green and Low-carbon Technologies promulgated by the State Intellectual Property Office in December 2022, these technologies are categorized into four distinct levels using a line classification method, with their structure and coding aligned with the International Patent Classification Table (IPC2022). Utilizing the patent authorization code, title, and additional details, we correlate the year of authorization with the province and application timeline. Given that new low-carbon technology patents are authorized immediately upon application, the total count of green low-carbon technology invention patents comprises both the number of new low-carbon technology patent applications and the existing low-carbon technology non-new patent inventions. The data on the number of granted green and low-carbon technology invention patents across various provinces were manually compiled to serve as the metric for assessing innovation in green and low-carbon technologies.

To mitigate significant inaccuracies resulting from the exclusion of critical variables, reference the existing literature research, and combined with the adopted model, the following six control variables are selected: (1) Level of economic development. It can reflect the economic scale, growth rate, and living standards of a country or region, measured by per capita GDP. (2) Industrial structure. It refers to the proportional relationship and interrelation between different industrial sectors, measured by the ratio of each industry. (3) Energy consumption structure. Refers to the proportion of different energy sources in total energy consumption, expressed by electricity consumption and total energy consumption. (4) Structure of fiscal expenditure. The government’s intervention in the economy will affect the development efficiency of the low-carbon economy. In this paper, the proportion of provincial government’s fiscal expenditure to GDP is measured. (5) Level of outbound investment. Reflecting the capital flow between a country or region and other countries or regions, this paper uses the proportion of foreign investment in GDP to measure. (6) Level of urbanization. It refers to the proportion of urban population in the total population of a country or region. It is an important indicator to measure the urbanization process of a country or region.

Table 3. Description of each variable.

Variables	Variable Abbreviated Name	Variable Categories	Method of Calculation
Low-carbon economic development	TCP	Explained variable	SBM-GML
Green investment	GI	Explanatory variables	Entropy method
Low-carbon technological innovation	GPAT	Mediating variable	Number of green and low-carbon patented technologies granted
Level of economic development	pgdp		GDP to total population
Industrial structure	inds		Ratio of the output value of the tertiary industry to the output value of the secondary industry
Energy consumption structure	es	Control variables	Electricity consumption as a
Structure of fiscal expenditure	cz		percentage of total consumption
Level of outbound investment	invs		Total fiscal spending as a percentage of GDP
Level of urbanization	czh		Foreign investment as a percentage of GDP

provides a detailed description of each variable along with its corresponding measurement methodology.

In accordance with Hypothesis 3 outlined in Section 2 above, it is plausible that spatial autocorrelation exists between green investment and the low-carbon economy. Consequently, the following spatial econometric model has been developed to empirically investigate the spatial spillover effects of green investment on the advancement of the low-carbon economy. The specific formula is shown in Equation (13), where ${\mathrm{TCP}}_{\mathrm{it}}$ is the development of a low-carbon economy, ${\mathrm{GI}}_{\mathrm{it}}$ is the level of green investment, ${\mathrm{Z}}_{\mathrm{it}}$ is the control variable, ${\mathrm{w}}_{\mathrm{ij}}{\mathrm{TCP}}_{\mathrm{jt}},{\mathrm{w}}_{\mathrm{ij}}{\mathrm{GI}}_{\mathrm{it}},{\mathrm{w}}_{\mathrm{ij}}{\mathrm{GI}}_{\mathrm{it}}^2,{\mathrm{w}}_{\mathrm{ij}}{\mathrm{Z}}_{\mathrm{it}}$ is the spatial lag term of the variable, ${\mathrm{u}}_{\mathrm{it}}$ and ${\mathrm{T}}_{\mathrm{it}}$ are the individual effect and time effect, and ${\epsilon }_{it}$ is the random error term. The matrix w represents the spatial weight matrix, constructed based on the geographical locations of the provinces and their levels of economic development, resulting in a spatial geographic distance matrix (w1), a contiguous spatial weight matrix (w2), and an economic distance weighting matrix (w3). Moreover, ${\mathrm{a}}_0$ is the constant term; ${\mathrm{\rho }}_1$ represents the spatial effect of low-carbon economic development; ${\mathrm{\beta }}_1$ represents the impact of green investment on the low-carbon economy of the region; ${\mathrm{\beta }}_2$ represents the nonlinear impact of green investment on regional low-carbon economic development; ${\gamma }_0$ indicates the influence of the control variables on the low-carbon economic development of the region; ${\mathrm{\rho }}_2$ represents the impact of green investment on the low-carbon economy of the neighboring region; ${\mathrm{\rho }}_3$ represents the nonlinear impact of green investment on low-carbon economic development in neighboring areas; and ${\mathrm{\gamma }}_1$ represents the influence of the control variables on the low-carbon economic development of the neighboring areas.

$$\begin{gathered} {TCP}_{it}=a_0+{\rho }_1{\sum }_{j=1}^N{w}_{ij}{TCP}_{jt}+{\beta }_1{GI}_{it}+{\beta }_2{GI}_{it}^2+{\gamma }_0{Z}_{it}+{\rho }_2{\sum }_{j=1}^N{w}_{ij}{GI}_{it}+{\rho }_3{\sum }_{j=1}^N{w}_{ij}{GI}_{it}^2+ \\ {\gamma }_1{\sum }_{j=1}^N{w}_{ij}{Z}_{it}+u_{it}+T_{it}+{\epsilon }_{it} \end{gathered}$$

(13)

To validate Hypothesis 2 outlined in the preceding theoretical framework, the relationship between green investment levels and total factor productivity is examined through the mediating role of green low-carbon technology innovation, which is posited to exhibit an inverted “U” shape. Existing literature frequently addresses the mediation effect mechanism; thus, the proposed model incorporates green low-carbon technologies as an intermediary variable. The subsequent analysis aims to test the existence of this mediation mechanism, leading to the formulation of specific hypotheses and model construction as follows: where ${\mathrm{GPAT}}_{\mathrm{jt}}$ is low-carbon technology innovation;${\mathrm{w}}_{\mathrm{ij}}{\mathrm{GPAT}}_{\mathrm{jt}}, {\mathrm{w}}_{\mathrm{ij}}{\mathrm{GI}}_{\mathrm{it}},{ \mathrm{w}}_{\mathrm{ij}}{\mathrm{GI}}_{\mathrm{it}}^2, {\mathrm{w}}_{\mathrm{ij}}{\mathrm{Z}}_{\mathrm{it}}$ is the spatial lag term of the variable; ${\mathrm{\rho }}_4$ represents the spatial effect of low-carbon innovation; ${\mathrm{\beta }}_3$ represents the impact of green investment on low-carbon technological innovation in the region; ${\mathrm{\beta }}_4$ represents the nonlinear impact of green investment on low-carbon technology innovation in the region; $\mathrm{\eta }$ represents the influence of control variables on low-carbon technological innovation in the region; ${\mathrm{\rho }}_5$ represents the impact of green investment on low-carbon technology innovation in neighboring areas; ${\mathrm{\rho }}_6$ represents the nonlinear impact of green investment on low-carbon technology innovation in neighboring areas; and ${\mathrm{\gamma }}_2$ represents the influence of control variables on low-carbon technological innovation in the region. Equation (15) adds the intermediary variable low-carbon technological innovation on the basis of Equation (13), where ${\mathrm{\beta }}_7$ represents the impact of low-carbon technological innovation on low-carbon economic development; ${\mathrm{\beta }}_5$ and ${\mathrm{\rho }}_8$ represent the impact of green investment on the low-carbon economic development of the region and neighboring areas through low-carbon technology innovation; ${\mathrm{\beta }}_6$ and ${\mathrm{\rho }}_9$ represent the nonlinear impact of green investment on the low-carbon economic development of the region and neighboring areas through low-carbon technology innovation; and $\mathrm{\phi }$ and ${\mathrm{\gamma }}_4$ represent the influence of the control variables on the low-carbon economic development of the region and neighboring regions, and the meaning of the remaining variables is similar to that in Equation (13).

$${GPAT}_{it}=a_1+{\rho }_4\sum _{j=1}^N{w}_{ij}{GPAT}_{jt}+{\beta }_{3}G{I}_{it}+{\beta }_4{GI}_{it}^2+\eta Z_{it}+{\rho }_5\sum _{j=1}^N{w}_{ij}{GI}_{it}+{\rho }_6\sum _{j=1}^N{w}_{ij}{GI}_{it}^2+{\gamma }_2\sum _{j=1}^N{w}_{ij}{Z}_{it}+u_{it}+{\epsilon }_{it}$$

(14)

$${TCP}_{it}=a_2+{\rho }_7\sum _{j=1}^N{w}_{ij}{TCP}_{jt}+{\beta }_5{GI}_{it}+{\beta }_6{GI}_{it}^2+\phi Z_{it}+{\beta }_7{GPAT}_{it}+{\rho }_8\sum _{j=1}^N{w}_{ij}{GI}_{it}+{\rho }_9\sum _{j=1}^N{w}_{ij}{GI}_{it}^2+{\gamma }_4\sum _{j=1}^N{w}_{ij}{Z}_{it}+{\rho }_{10}\sum _{j=1}^N{w}_{ij}{GI}_{it}^2+u_{it}+{\epsilon }_{it}$$

(15)

$${\epsilon }_{it}=\sigma \sum _{j=1}^N{w}_{ij}{\epsilon }_{it}+u_{it}$$

(16)

The Moran’s I index serves as a statistical measure for evaluating spatial autocorrelation, predominantly utilized within the domains of geography and spatial data analysis to examine the autocorrelation of spatial datasets and to elucidate spatial distribution patterns. The methodology encompasses the following several steps: Initially, a spatial weight matrix is established based on the spatial relationships among the provinces under investigation, which is subsequently categorized into an adjacency matrix and a distance weight matrix. The adjacency matrix employs binary values (0 and 1) to denote the presence or absence of border relationships between entities, whereas the distance weight matrix utilizes specific numerical values to indicate the distances separating these entities. Following this, the spatial weight matrix is integrated with the attribute values to compute the global Moran’s I index, with the calculation formula delineated in Equation (9), where N represents the sample size, ${\mathrm{x}}_{\mathrm{i}}$ signifies the ith observation value, $\overline{\mathrm{x}}$ denotes the mean of the samples, and ${\mathrm{w}}_{\mathrm{ij}}$ corresponds to the spatial weight matrix. Ultimately, the p-value is derived to assess the statistical significance of the Moran’s I index. When the p-value is less than 0.05, it indicates the existence of spatial autocorrelation.

$$I={\displaystyle \frac{N{\sum }_{i=1}^N{\sum }_{j=1}^N{w}_{ij}(x_i-\overline{x})(x_j-\overline{x})}{{\sum }_{i=1}^N{(x_i-\overline{x})}^2}}$$

(17)

In this paper, the LM test and Hausman test are used to select the appropriate spatial econometric model for this study. The LM test is to detect spatial autocorrelation by calculating Lagrange multiplier statistics, which include two main statistics: LM-lag and LM-error, which are used to detect spatial lag effect and spatial error effect, respectively. The specific inspection process is as follows: First, OLS is used to estimate the residual error of the model, and then LM-lag and LM-error statistics are calculated (the specific calculation formulas are as follows: (10) and (11), as well as their robust versions, Robust LM-lag and Robust LM-error. Finally, according to the significance of the statistics, the following judgments are made: (1) If LM-lag is significant and greater than LM-error, and Robust LM-lag is significant and greater than Robust LM-error, the spatial lag model (SLM) is selected. (2) If LM-error is significant and greater than LM-lag, and Robust LM-error is significant and greater than Robust LM-lag, the spatial error model (SEM) is selected. (3) If both statistics of LM are not significant, the spatial econometric model cannot be used. (4) If both statistics of LM are significant, the spatial Durbin model (SDM) should be used. The Hausman test is used to compare the applicability of the fixed-effect model and the random effect model and to help determine which effect model to use in the spatial panel data model. If the calculated p-value is less than 0.05, it indicates that the Hausman statistic is significant and there is a significant difference, so the fixed-effect model should be used. If the Hausman statistic is not significant, the random effects model can be used. Where N represents the sample size; ${\mathrm{w}}_{\mathrm{ij}}$ corresponds to the spatial weight matrix; ${\widehat{\mathrm{\beta }}}_{\mathrm{FE}}$ is the residual error; ${\hat{\mathrm{\beta }}}_{\mathrm{RE}}$ is the parameter estimation of the fixed-effect model; and $\mathrm{Var}\left({\hat{\mathrm{\beta }}}_{\mathrm{FE}}\right)$ and $\mathrm{Var}\left({\hat{\mathrm{\beta }}}_{\mathrm{RE}}\right)$ are the variances of the parameter estimates of the fixed and random effects models, respectively.

$$LM-Lag=N({\displaystyle \frac{1}{N}}\sum _{I=1}^N\sum _{j=1}^N{w}_{ij}\widehat{u_i }\widehat{u_j })$$

(18)

$$LM-Error=N({\displaystyle \frac{1}{N}}\sum _{I=1}^N\sum _{j=1}^N{w}_{ij}\widehat{u_i }\widehat{u_j })$$

(19)

$$H={\left({\hat{\beta }}_{FE}-{\hat{\beta }}_{RE}\right)}^{\prime }{\left[Var\left({\hat{\beta }}_{FE}\right)-Var\left({\hat{\beta }}_{RE}\right)\right]}^{-1}({\hat{\beta }}_{FE}-{\hat{\beta }}_{RE})$$

(20)

4. Empirical Results and Analysis

4.1. Explanation and Processing of Data Sources

This study conducts empirical research utilizing panel data from 30 provinces in China spanning the years 2012 to 2021, given the availability of data. The primary sources of the research data include the China Statistical Yearbook, the China Statistical Yearbook on Environment, the State Intellectual Property Office, provincial statistical yearbooks, and the EPS database. For the majority of provinces during the period from 2012 to 2021, the variable data are relatively comprehensive; however, several provinces exhibited significant gaps in data. To enhance the accuracy of the dataset, Stata (18) software was used to eliminate the data of missing provinces. To solve the problem of missing data of some years in a few provinces, this paper uses moving average processing of similar years. The moving average method can smooth the short-term fluctuation of the data by using the average of the years before and after the missing value of the data as a substitute. To mitigate excessive variance that could lead to substantial errors and to enhance the stationarity of the data, the levels of economic development, industrial structure, and fiscal expenditure underwent logarithmic transformation. Subsequently, descriptive statistical analysis was performed, with the variables presented in

Table 4. Descriptive statistics results.

Variable Name	Obs.	Mean	Med.	Sd.	Min.	Max.
TCP	300	0.527	0.269	0.181	1.19	1.19
GI	300	0.162	0.064	0.062	0.539	0.539
cz	300	0.263	0.113	0.105	0.758	0.758
inds	300	1.374	0.738	0.611	5.244	5.244
invs	300	10.771	49.118	0.769	696.053	696.053
es	300	0.033	0.023	0.004	0.094	0.094
pgdp	300	10.871	0.435	9.849	12.142	12.142
czh	300	60.231	11.814	36.3	89.6	89.6
GPAT	300	1160.387	1404.916	11	9434	9434

, columns 2 to 6 are the observed values, mean values, standard deviations, and minimum and maximum values of the variables, respectively. The standard deviation of the explained variable and explanatory variable is small, indicating that the data fluctuation is small. The standard deviation of the level of foreign investment and mediating variables is relatively large, and the fluctuation of the data is relatively obvious. Some of the data used in this paper are shown in the Supplementary Materials.

4.2. Results of the Moran Index Test

According to the Moran index test method described in Section 3 (the results are shown in

Table 5. Results of the Moran index test.

Year	GI Global Moran’s I	GI p-Value	TCP Global Moran’s I	TCP p-Value
2012	0.093	0.011	0.129	0.001
2013	0.167	0.000	0.127	0.001
2014	0.170	0.000	0.123	0.002
2015	0.103	0.004	0.112	0.003
2016	0.124	0.001	0.111	0.003
2017	0.202	0.000	0.117	0.002
2018	0.144	0.000	0.123	0.002
2019	0.109	0.005	0.123	0.002
2020	0.078	0.026	0.109	0.004

), there is a significantly positive correlation between green investment from 2012 to 2021 and the global Moran’s I index for low-carbon economy development.

Specifically, a positive spatial correlation exists between China’s green investment and its low-carbon economic growth. In terms of regional distribution, carbon total factor productivity is observed to be higher in the eastern coastal region compared to the western region, suggesting that the eastern area demonstrates superior low-carbon economic development, with more pronounced spatial agglomeration and regional characteristics. The distribution of green investment is notably imbalanced, with the eastern region exhibiting greater investment levels than the western region, where agglomeration characteristics are less evident.

4.3. Test Results of Spatial Econometric Model

The test results of the spatial econometric model are shown in

Table 6. Results of the spatial econometric model test.

Text Name	p-Value of w1	p-Value of w2	p-Value of w3
LM-error text	0.000	0.000	0.000
LM-lag text	0.000	0.000	0.000
Robust LM-error text	0.650	0.260	0.298
Robust LM-lag text	0.000	0.000	0.000
Hausman test	0.003	0.002	0.003
LR text	0.000	0.000	0.000

.

The LM test indicates that at a 1% significance level, both LM-error and LM-lag are significant, while Robust LM-lag is insignificant. Conversely, suggesting that OLS regression is not suitable; instead, a spatial lag measurement model should be employed. The Hausman test yields significant p-values, indicating the necessity of utilizing fixed-effect regression. Furthermore, based on the likelihood ratio inspection and goodness-of-fit assessments of dual statistical models, fixed-effect models, individual-fixed effects, and time-fixed effects, the results advocate for the application of a double-fixed spatial lag model to estimate the impact of green investment on the development of a low-carbon economy.

4.4. The Spatial Lag Model Regression Results Analysis

In this study, a spatial lag model is employed to investigate the influence of green investment on the development of a low-carbon economy. Utilizing the entire sample set under the spatial distance weighting matrix w1, the decomposition regression results presented in

Table 7. Results of the spatial lag model regression.

TCP	Direct Effects	Direct Effects	Total Effect
$GI$	0.254 ** (0.110)	−0.080 * (0.046)	0.174 ** (0.082)
${GI}^2$	−0.422 ** (0.188)	0.133 * (0.079)	−0.288 ** (0.140)
$cz$	0.107 (0.075)	−0.034 (0.027)	0.072 (0.052)
$inds$	−0.040 *** (0.011)	0.012 ** (0.005)	−0.027 *** (0.008)
$invs$	−0.000 (0.000)	0.000 (0.000)	−0.000 (0.000)
$es$	−1.836 *** (0.524)	0.582 ** (0.270)	−1.254 *** (0.410)
$pgdp$	0.404 *** (0.034)	−0.129 *** (0.047)	0.275 *** (0.043)
$czh$	−0.005 *** (0.000)	0.001 *** (0.001)	−0.003 *** (0.000)
$sigma2\text{\_}e$	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$rho$	−0.497 ** (0.207)	−0.497 ** (0.207)	−0.497 ** (0.207)
$R^2$	0.243	0.243	0.243
$N$	300	300	300

Notes: ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

reveal both direct and total effects; it is different from the results of Shen and Deng’s research that green investment can effectively promote sustainable economic development [18,19]. The regression coefficients indicate a positive relationship, while the negative quadratic term coefficient for green investment, significant at the 5% level, suggests an inverted “U”-shaped characteristic between green investment and low-carbon economy development.

In the analysis of control variables, both the direct and total effect results indicate that the industrial structure, energy composition, and urbanization rate significantly negatively impact the development of a low-carbon economy. Specifically, a higher proportion of local secondary industry correlates with increased energy consumption, and elevated urbanization rates are detrimental to the province’s low-carbon economic advancement. Conversely, the level of economic development exhibits a positive correlation with the province’s transition towards a low-carbon economy, suggesting that economic growth facilitates this transformation. Regarding indirect effects, the findings reveal that the industrial structure, energy composition, and urbanization rates of neighboring provinces positively influence the development of a low-carbon economy. This suggests that a higher local secondary industry share and greater energy consumption may lead to a pollution haven effect, thereby promoting low-carbon economic development in adjacent provinces. Furthermore, regional economic development appears to exert an inhibitory effect on the low-carbon economic progress of surrounding provinces, potentially due to siphoning effects that hinder improvements in carbon total factor productivity in these areas. This is consistent with the findings of Li and Wang [17].

As shown in Figure 1, the inflection point of the impact of green investment on the regional low-carbon economic development is between 0.3 and 0.4. This implies that while green investment initially promotes the low-carbon economy by enhancing resource utilization, reducing carbon emissions, and improving total factor productivity, excessive green investment may eventually hinder development; this is consistent with Yali’s research [24].

Furthermore, as shown in Figure 2, the indirect effects indicate a “U”-shaped relationship between green investment in a province and the development of the low-carbon economy in neighboring provinces. Figure 2 shows that the inflection point of the impact of green investment on low-carbon economic development in neighboring areas is close to 0.4. A turning point exists, suggesting that increased green investment may initially suppress total factor productivity in neighboring areas; this is consistent with the results of Liu’s study [23]. This phenomenon may be attributed to early-stage environmental protection investments and heightened environmental regulation, which can lead to the relocation of heavily polluting industries to less regulated neighboring provinces, thereby inhibiting their total factor productivity—a scenario consistent with the pollution haven effect. Additionally, the spatial spillover effects of green investment in adjacent provinces demonstrate a notable lag. Nevertheless, as the level of green investment within the province escalates, the investments in environmental protection and the regulatory rigor in neighboring provinces also intensify. This synergy results in an improvement in total factor productivity and fosters the advancement of a low-carbon economy in those regions.

4.5. Spatial Spillover Effect Test Based on Mediating Variables

In contrast to the mediating variable of technological innovation examined by Yuyu and Li [17,20], this paper explores the influence of green investment on low-carbon economic development from a nuanced perspective, utilizing low-carbon technological innovation as the mediating variable. The mediation analysis presented in

Table 8. Results of the spatial spillover effect test based on mediating variables.

TCP	Direct Effects	Direct Effects	Total Effect
$GI$	0.182 * (0.108)	−0.066 * (0.044)	0.115 * (0.072)
${GI}^2$	−0.326 * (0.186)	0.118 * (0.076)	−0.207 ** (0.125)
$GPAT$	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$cz$	0.067 (0.074)	−0.025 (0.030)	0.041 (0.046)
$inds$	−0.023 ** (0.011)	0.000 * (0.000)	−0.015 ** (0.007)
$invs$	−0.000 (0.000)	−0.144 (0.041)	−0.000 (0.000)
$es$	−1.716 *** (0.505)	0.625 ** (0.244)	−1.091 *** (0.360)
$pgdp$	0.394 *** (0.028)	−0.144 *** (0.041)	0.250 *** (0.036)
$czh$	−0.004 *** (0.001)	0.001 *** (0.000)	−0.002 *** (0.000)
$sigma2\text{\_}e$	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$rho$	−0.595 *** (0.208)	−0.595 *** (0.208)	−0.595 *** (0.208)
$R^2$	0.439	0.439	0.439
$N$	300	300	300

Notes: ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

reveals that both the direct and total effect results indicate a significantly positive intermediary role of low-carbon technology innovation investment in green initiatives on total factor productivity. The Sobel test and Bootstrap test were performed, revealing a significant mediating effect of low-carbon innovation, quantified at 0.271. This suggests that a stronger capacity for low-carbon technology innovation correlates with an increased potential for green investments to foster the development of a low-carbon economy. Furthermore, from the perspective of indirect effects, low-carbon technology innovation plays a crucial role in promoting economic development in our region and neighboring provinces. Additionally, the relationship between green investment and economic development in the surrounding provinces exhibits a “U”-shaped pattern, which aligns with the aforementioned regression findings.

4.6. Threshold Effect Test

Digital inclusive finance refers to the delivery of financial services via digital technologies, such as internet-based information systems, with the objective of advancing inclusive finance. In China, the rapid evolution of digital finance has been characterized by a system centered around banking institutions, supplemented by internet companies and non-bank financial entities. On one hand, digital finance leverages technology to lower the costs associated with risk assessment and reduce barriers to financing, thereby influencing the funding processes for green investment initiatives. On the other hand, it assists governmental entities and regulatory agencies in implementing green financial investment strategies by offering data support and conducting risk evaluations, thereby facilitating informed decision-making and enhancing the stability of green financial markets.

Consequently, this paper employs digital finance to investigate the threshold effects of green investment within the context of low-carbon economic development. Digital finance is analyzed through three secondary indices: coverage, depth of use, and digitization, with data sourced from Peking University. The results, presented in

Table 9. Results of the threshold regression.

	Digital Inclusive Finance
The threshold value n1	92.98
The threshold value n2	164.05
$MK\times I ({Thr}_{it}\le n)$	0.203 *** (0.046)
$MK\times I \left({n_1<Thr}_{it}{<n}_2\right)$	0.421 *** (0.072)
$MK\times I ({Thr}_{it}\ge n_2)$	0.172 *** (0.047)
Fixed effects	control
Control variables	control
$R^2$	0.782
$N$	300

Notes: *** represents significance at the 1% levels.

, indicate that as the level of digital inclusive finance rises, the positive impact of green investment on low-carbon economic development also intensifies. However, beyond a certain threshold, the beneficial effect of green investment on low-carbon economic growth begins to diminish. This decline may be attributed to the fact that once digital finance reaches a higher level and the threshold decreases significantly, the financing costs associated with green investments may rise, thereby reducing their effectiveness in promoting low-carbon economic development. This is similar to the findings of Qiang and Ma [27,29].

4.7. The Impact of Different Types of Green Investment on the Development of Low-Carbon Economy

Green investment can be categorized into the following three distinct types: environmental protection investment (EI), pollution control investment (PI), and environmental protection technology research and development investment of scale enterprises (EPTI). To further investigate the effects of these various forms of green investment, a spatial lag model is employed to analyze their influence on the development of a low-carbon economy, the results are shown in

Table 10. Results of heterogeneity analysis.

TCP	EI	PI	EPTI
$GI$	0.254 ** (0.110)	−0.080 * (0.046)	0.174 ** (0.082)
${GI}^2$	−0.422 ** (0.188)	0.133 * (0.079)	−0.288 ** (0.140)
$cz$	0.107 (0.075)	−0.034 (0.027)	0.072 (0.052)
$inds$	−0.040 *** (0.011)	0.012 ** (0.005)	−0.027 *** (0.008)
$invs$	−0.000 (0.000)	0.000 (0.000)	−0.000 (0.000)
$es$	−1.836 *** (0.524)	0.582 ** (0.270)	−1.254 *** (0.410)
$pgdp$	0.404 *** (0.034)	−0.129 *** (0.047)	0.275 *** (0.043)
$czh$	−0.005 *** (0.000)	0.001 *** (0.001)	−0.003 *** (0.000)
$sigma2\text{\_}e$	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$rho$	−0.497 ** (0.207)	−0.497 ** (0.207)	−0.497 ** (0.207)
$R^2$	0.243	0.243	0.243
$N$	300	300	300

Notes: ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

. The findings reveal significant heterogeneity; overall, the role of green investment in fostering low-carbon economic development diminishes from strong to weak. Specifically, environmental protection investment exhibits an inverted “U”-shaped nonlinear effect on low-carbon economic development. The model’s calculations of marginal effects indicate that as environmental protection investment increases, its marginal effect on total factor productivity remains positive and gradually intensifies until it reaches a threshold of 4.4%. Beyond this point, the marginal effect shifts from positive to negative, suggesting that excessive environmental protection investment may hinder further advancements in the low-carbon economy.

Similarly, pollution control investment mirrors the trends observed in environmental protection investment, with its marginally positive impact on low-carbon economic development transitioning to a negative effect at a threshold of 6.3%. This phenomenon may be attributed to the necessity for stable and continuous financial support to facilitate the transformation of production modes and the consistent enhancement of production efficiency. While both environmental protection and pollution control investments can effectively mitigate enterprise pollution emissions in the initial stages and provide some financial backing for production mode transformation, these investments are ultimately consumptive in nature. The substantial financial resources required for the long-term development of enterprises can lead to diminishing marginal benefits, particularly when investment costs surpass economic returns. Lu’s research believes that R&D investment has a nonlinear impact on the development of the green economy [26], but the results of this paper show that the R&D investment of environmental protection technology of large-scale enterprises has a positive impact on TFP, and there is no inverted U-shaped relationship, indicating that the R&D investment of green technology is conducive to the sustainable development of a low-carbon economy.

4.8. Robustness Test

This study evaluates the robustness of the aforementioned regression findings by substituting the spatial weight matrix. The results are shown in

Table 11. Results of robustness test.

TCP	Direct Effect	Indirect Effect	Total Effect	Direct Effect	Indirect Effect	Total Effect
	W2	W2	W2	W3	W3	W3
$GI$	0.253 ** (0.110)	−0.028 * (0.038)	0.224 ** (0.107)	0.258 ** (0.110)	−0.016 * (0.018)	0.274 ** (0.120)
${GI}^2$	−0.420 ** (0.188)	0.004 (0.066)	−0.372 ** (0.181)	−0.423 ** (0.189)	0.026 * (0.312)	−0.450 ** (0.181)
$cz$	0.009 (0.074)	−0.011 (0.017)	0.084 (0.065)	0.084 (0.075)	0.004 (0.008)	0.089 (0.080)
$inds$	−0.037 *** (0.011)	0.004 * (0.005)	−0.032 *** (0.011)	−0.036 *** (0.011)	−0.000 (0.002)	−0.038 *** (0.012)
$invs$	−0.000 (0.000)	0.000 (0.000)	−0.000 (0.000)	−0.000 (0.000)	0.000 (0.000)	−0.000 (0.000)
$es$	−1.822 ** (0.524)	0.209 * (0.253)	−1.612 *** (0.524)	−1.829 ** (0.525)	0.113 * (0.114)	−1.943 *** (0.563)
$pgdp$	0.391 *** (0.033)	−0.046 (0.051)	0.345 *** (0.032)	0.378 *** (0.033)	0.023 (0.051)	0.401 *** (0.037)
$czh$	−0.005 *** (0.000)	0.000 *** (0.001)	−0.004 *** (0.001)	−0.005 *** (0.000)	0.000 (0.001)	−0.005 *** (0.001)
$rho$	−0.169 * (0.159)	−0.169 * (0.159)	−0.169 * (0.159)	0.054 (0.053)	0.054 (0.053)	0.054 (0.053)
$sigma2\text{\_}e$	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$R^2$	0.062	0.062	0.062	0.185	0.185	0.185
$N$	300	300	300	300	300	300

Notes: ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

, it employs the adjacency spatial weight matrix (w2) and the economic spatial weight matrix (w3) to re-examine the spatial lag model regression. The findings indicate that an inverted U-shaped relationship persists between green investment and low-carbon economic development, aligning with prior results. Consequently, the research outcomes presented in this article demonstrate a degree of robustness.

Based on the above research results, a horizontal comparative analysis is conducted with other related studies. In terms of measurement methods, relevant studies use carbon emission rates or combine them with economic, social, and other comprehensive indicators to measure the development of a low-carbon economy. In terms of the mediating mechanism, other relevant studies focus more on the overall technological innovation, energy structure, environmental regulation, and other mediating factors [20,34], while this study focuses on exploring the influencing mechanism of green investment on the low-carbon economy from the subtle perspective of low-carbon technology. In terms of research conclusions, some studies believe that green investment has a continuous positive promotion effect on the development of a low-carbon economy [36], while others believe that green investment has a lagged effect on the development of a low-carbon economy due to its long investment cycle [32]. The conclusion of this paper is similar to that of Wu and Wen [33,35], which holds that green investment has an inverted U-shaped characteristic for the low-carbon economic development of the local region and has a U-shaped nonlinear characteristic for the neighboring regions. In addition, this paper explores the threshold effect of green investment on the development of low-carbon economy, and further analyzes the heterogeneity of different types of green investment, enriching and expanding the existing research.

5. Conclusions and Recommendations

This paper employs the SBM-GML model alongside a spatial econometric framework to empirically assess the influence of green investment on low-carbon economic development while also examining the mediating role of green and low-carbon technological innovation within this transmission pathway and the threshold effect of digital inclusive finance in it. Finally, the robustness analysis is carried out using other spatial weight matrices. The results reveal that the following: (1) There exists spatial agglomeration in the development of low-carbon economy transformation across China’s provinces, characterized by high-high concentration along the eastern coast, while the western regions exhibit low concentration. (2) Both Hypothesis 1 and Hypothesis 2 proposed above are valid, green investment exerts an inverted U-shaped effect on local low-carbon economic development, contrasted with a U-shaped effect on surrounding areas, with local impacts surpassing neighboring effects. (3) Low-carbon technological innovation serves as a partial intermediary in the relationship between green investment and low-carbon economic development, so Hypothesis 3 proposed above is also valid. (4) As the level of digital inclusive finance improves, the positive influence of green investment on low-carbon economic development initially increases before subsequently declining. (5) Investments in environmental protection and pollution control demonstrate nonlinear effects on low-carbon economic development, whereas investments in environmental protection technology by large-scale enterprises consistently promote the advancement of the low-carbon economy without exhibiting nonlinear effects.

In light of the aforementioned research findings, to establish a green, low-carbon, and circular economic development framework and to facilitate the attainment of the “dual carbon” objectives, the following policy recommendations are proposed based on the empirical analysis conducted:

Firstly, it is essential to enhance the awareness surrounding green technology innovation and to bolster the capacity for technological advancement. The findings indicate that investment in environmental protection technologies has a sustained impact on enhancing total factor carbon productivity and fostering low-carbon economic growth. It is imperative to fully leverage the support provided by green investment policies to stimulate research and development in new energy technologies. On one hand, enterprises should refine their technology innovation incentive systems and optimize the allocation of low-carbon technology resources, thereby promoting collaborative sharing among enterprises to effectively advance green low-carbon technologies and facilitate the low-carbon transformation and upgrading of businesses. On the other hand, the government should increase financial support for low-carbon technology innovation and set up special funds to encourage enterprises, universities and research institutions to increase R&D investment, encouraging independent development of new technologies, and fostering new drivers for technology research and development, ultimately steering enterprises towards green and high-quality growth. Simultaneously, China ought to enhance the development of high-tech interdisciplinary expertise and foster the collaboration among industry, academia, and research institutions, thereby facilitating deeper partnerships between enterprises, universities, and research organizations, thereby further enriching the talent pool necessary for advancing low-carbon transformation initiatives.

Secondly, to enhance the role of green investment in steering and facilitating low-carbon economic development, it is imperative to amplify funding for research and development in green technologies. The government should utilize the “dual carbon target” as a guiding principle, strategically planning long-term green investments and development through market-oriented approaches [46]. This entails fostering a low-carbon economy via the dual transformation of economic and energy structures, augmenting support for environmental protection technologies, and striving for a harmonious balance between economic growth and environmental sustainability. At the institutional level, it is essential to continuously refine policies related to green financing and to bolster environmental regulations. Practically, there is a need to expedite the development of diverse low-carbon economic infrastructure, aiming for nearly zero-energy buildings, minimal carbon emissions, and the establishment of carbon capture and sequestration technology demonstration projects. At the financing level, financial institutions should further optimize digital inclusive financial services, lower service thresholds and costs, provide more financial support for green investment, and promote the development of low-carbon economy; financial departments should also implement preferential tax incentives for enterprises with high pollution and emissions to facilitate their transition towards green, low-carbon practices.

Furthermore, it is vital to harness the synergy among multiple stakeholders to maximize the utility of green investments. Research indicates that the impact of green investment on total factor productivity in the region is not sustained over the long term; thus, all parties must focus on optimizing the short-term benefits of green investments. Individuals should actively adopt low-carbon lifestyles and consumption patterns, promoting demand-side transformations from the supply side. Enterprises must judiciously allocate green investments and enhance funding for scientific research, while also fostering communication across all production stages to improve efficiency and advance their own low-carbon transitions [47]. The government should expand the scale of green investments, refine the precision of green investment policies, and ensure alignment with industrial development to maximize the effectiveness of limited green investments within a constrained timeframe, establish and improve the supervision mechanism of environmental protection investment and pollution control investment, and strengthen the evaluation and supervision of green investment projects. Through effective supervision and evaluation, the government can ensure the rational use of investment funds and improve the efficiency of green investment, thereby enhancing their sustainability. Additionally, strengthening regional cooperation and information sharing is crucial for establishing a spatial framework for high-quality green and low-carbon development.

Finally, research findings reveal that the effects of green investment on total factor productivity exhibit spatial spillover effects, characterized by spatial imbalances; the eastern coastal regions demonstrate high concentrations of investment, while the western regions show lower concentrations. Consequently, the eastern coastal regions ought to enhance their pivotal role in high–high agglomeration, facilitating the advancement of the central and western areas through industrial relocation and technological collaboration, thereby reducing the disparities in low-carbon economic development across regions. The government should formulate corresponding green investment policies according to local conditions, geographical conditions, resource advantages, industrial structure, and other characteristics; promote information sharing and resource flow among regions; and achieve regional coordinated, green, and balanced development. Moreover, the integration of carbon-energy synergy hubs and dual-level low-carbon economic planning should be leveraged to mitigate carbon emissions and foster coordinated green and low-carbon development across all regions [48].

6. Practical Implications and Shortcomings and Future Research Directions

In a practical sense, first, the research results on the impact of green investment and low-carbon technology innovation on the development of a low-carbon economy can provide a scientific basis for policymakers to help formulate and adjust green investment and low-carbon technology innovation policies to achieve the coordinated development of the economy and the environment. Second, by revealing the positive impact of green investment and low-carbon technology innovation on the low-carbon economy, enterprises and governments can be encouraged to increase research and development and investment in green technology and promote the economic transformation into a green and low-carbon direction. Third, this research is helpful to optimize resource allocation, guide capital to low-carbon technological innovation through financial instruments such as green credit, improve capital allocation efficiency, and then enhance total factor carbon productivity to promote low-carbon economic development. Fourth, this study reveals the spatial spillover effect of green investment and low-carbon economic development, which can promote inter-regional cooperation in technology demonstration and industry correlation and provide certain strategies for inter-regional collaborative development.

The shortcomings of this study are as follows: (1) Although the SBM-GML model and spatial lag model are adopted, the model does not cover all factors that affect the development of a low-carbon economy, such as policy changes, social culture, and trade exchanges. (2) Although this paper points out that there are differences in the agglomeration levels of green investment and low-carbon economic development among regions in China, in-depth analysis of the characteristics of low-carbon economic development in different regions (such as the eastern, central, and western regions) may be insufficient. Therefore, the future can be further explored from the following aspects: (1) Use the model that can consider more factors affecting the development of a low-carbon economy to evaluate its development level more comprehensively. (2) Conduct a more in-depth analysis of the characteristics of low-carbon economic development in different regions and explore the influencing factors and path differences of low-carbon economic development in different regions. (3) Further analyze the long-term impact of green investment on low-carbon economic development, and pay attention to the sustainability and simultaneous risks of green investment.