Analysis of the Impact of Ecological Innovation and Green Investment on China's CO2 Emissions.

In order to effectively address or eliminate the impact of CO2 emissions, it is crucial to conduct a CO2 emissions evolution analysis using a green investment model. Ecological innovation helps to limit carbon dioxide emissions, which is crucial to resource distribution and effectively summarizes the regularity and innovation of the process of limiting carbon dioxide emissions. Under the condition of fully grasping the principles of low-carbon city development and related policy protection, find a suitable low-carbon city development model. This paper analyzes the impact of ecological innovation and green investment on carbon dioxide emission limitations by building a data analysis model. The results of the case analysis show that the impact of the green investment scale on Chinese carbon dioxide emission restrictions is an inverted U-shaped relationship. The scale of green investment, economic competition, and marketization of capital allocation has a negative impact on Chinese carbon dioxide emissions, while green investment and ecological innovation have a positive effect on the green and low-carbon development of the Chinese economy.

1. Introduction

Green investment plays a very important role in promoting the green economy in China. The implementation of green investment can effectively reduce the consumption of carbon dioxide per unit in promoting ecological innovation. The development of the economy has made the investment of enterprises intensified, resulting in an increase in carbon dioxide emissions and the increase in consumer demand for household appliances, which has increased carbon dioxide emissions [1, 2]. But what is the impact of green investment on the limitation of China's carbon dioxide emission? How to achieve green and low-carbon development of the Chinese economy through green investment.

Real-time, rational, and precise monitoring of carbon dioxide emissions can also provide a theoretical basis for the decision-making analysis of relevant environmental protection testing departments, which is of great significance to the emergence of more ecological innovation projects [3, 4]. A detailed analysis of ecological innovation is carried out to explore the problems and shortcomings of the carbon dioxide emission process so as to formulate training and improvement plans in the later stage. In the traditional CO2 emission evolution analysis process, CO2 emissions and real-time risk alerts are also effective data collection and analysis processes for CO2 emission devices. To collect indicator data and feedback from CO2 emission process, the obtained data is used to analyze the performance of carbon dioxide emission, analyze the operation state of carbon dioxide emission, and diagnose the cause of failure, and can also be used to monitor the cause of failure. Carbon dioxide emissions and real-time risk alerts are an important manifestation of carbon dioxide emissions management and system integration, providing detailed data information for the operation and maintenance of carbon dioxide emissions. The ecological innovation, green investment, and development of carbon emissions have attracted more attention from researchers and scholars [5, 6]. Facts have proved that there are large gaps in carbon emissions changes under different systems in different environments. The man focusing on the earliest research on this issue was Panayotou. The research can prove that in the initial stage of regional economic growth, the per capita growth of green investment will lead to a rapid increase in carbon dioxide emissions. After green investment exceeds a certain value, with the growth of green investment, Carbon dioxide, on the other hand, showed a downward trend. Yu Yi verified the situation in China. The results of the analysis were different from those in the past. He concluded that there is a nonlinear relationship between carbon dioxide emissions, economic development, and capacity loss. There is a certain value in the development of the green investment. After the green investment reaches a certain value, the environmental pollution will double. The areas exceeding this value are distributed in the eastern area, followed by the middle area, and the western area is the least. Bin He invented a set of threshold models for per capita green investment in each region of China and the impact of the state-owned economy on carbon dioxide emissions. The data proves that the development of the state-owned economy has a threshold value. When the development reaches a fixed threshold value, carbon dioxide emissions and green investment show a “U”-shaped relationship, but in other intervals, it shows other shapes of curves. Based on the regional differences in carbon emissions and green investment development in the current ecological environment, measures are put forward on the factors affecting carbon emissions of economic growth, and the estimation and detection are carried out according to the revised STIRPAT module. By studying the situation of CO2 emissions in economically developing regions, the findings are shown to be nonlinear. In an environment with a low level of development through green investment, there is a direct link between faster economic development and carbon emissions. China has expressed its commitment to climate development, forced the development of low-carbon life, and made real improvements in the way of carbon reduction [7, 8]. This improvement determines the qualitative change in the living environment and proves that it has shouldered the burden of social responsibility. At the same time, it is a long development process to rectify and develop the structure of the industry. In the implementation stage, the emission reduction needs to be maximized. The Chinese environment study has entered a new course. The differences in economic development and economic situation in each region have a decisive impact on local carbon emissions [9, 10]. Change and role require lower carbon, greener, and less energy loss to generate maximum green investment. Due to the large differences in economic growth within regions, according to the level of economic development and carbon dioxide emissions within each country and the actual situation, formulating reasonable and scientific carbon emission reduction policy standards has become the first choice for the development of a low-carbon economy in China [11, 12]. When formulating low-carbon emission reduction policies, various countries need to effectively take into account their own basic economic development and social conditions, energy availability, and green-related policies, actively encourage the country to actively explore carbon dioxide emissions and renewable energy quotas suitable for its own region, and guide the whole society to save energy and improve energy efficiency to reduce environmental pollution.

There are obvious gaps in the relationship between domestic economic growth and carbon dioxide emissions. Some data show that after calculating the total carbon emissions, artificially distinguish regions and conduct research through factors in different regions. Questions are raised about the arrangement of such predistinguished regions, which should be distinguished according to regional carbon emissions. Because of the interference of the economy, it is necessary to make regular distinctions and then make distinctions according to the results and categories so that the carbon emission factors can be divided into regions. To study the relationship between economic growth and carbon emission growth changes, it is recommended to carry out effective carbon reduction strategies.

2. Theoretical Model Analysis

This paper confirms that there is a nonlinear relationship between the growth of green investment and carbon dioxide emissions. This is mainly because the growth of green investment in the Chinese economy varies across regions, and the impact on carbon dioxide emissions varies. Secondly, since economic growth is still at different levels, which will have different consequences, it is crucial to identify economic tipping points for CO2 impacts.

The sample group {y, x, q} selected in this paper is calculated as follows:

In the expression: q represents the threshold variable, κ represents the threshold value, and the constructed linear model y=θ1′x+e can be divided into two different divisions: high and low. It should be noted that the threshold variable q can not only be used as an explanatory variable but also as an exogenous variable related to the model economy.

The nonlinear impact analysis of ecological innovation and green investment on carbon dioxide emissions is a linear model with finite parameters. On the basis of satisfying the finite parameter linear model, the finite parameter linear model can be used for optimization according to the steady-state ecological environment of the system [13]. In the process of dynamic monitoring of the ecological environment, the nonlinear impact of ecological innovation and green investment on carbon dioxide emissions can be analyzed. According to the problems analyzed above, the green investment demand function used in this paper is expressed as follows:

As the basis of ecological civilization construction, ecological environmental protection can realize real-time, rational, and precise monitoring of ecological, environmental protection and can also provide a theoretical basis for the decision-making analysis of relevant environmental protection testing departments, which is of great significance to the emergence of more emerging economic ecological transformation projects [14]. This paper conducts a detailed analysis of the ecological transformation of emerging economies to explore the problems and shortcomings in the process of ecological environmental protection so as to formulate training and improvement plans in the later stage. In the process of analyzing the evolution of ecological environment protection in the traditional emerging economy's ecological transformation, ecological environment protection and real-time risk alerts are also effective data collection and analysis processes for ecological environment protection. Collect indicator data from ecological environment protection and give feedback. According to the obtained data, it is used to analyze the performance of ecological environment protection, analyze the operation state of ecological environment protection, and diagnose the cause of failure, and can also be used to monitor the cause of failure.

Suppose the operation of group k(k=1,2,…, K) in the CO2 emission limit analysis can be expressed as follows:

In the formula, represents the entire computing time, represents the fast time, and tect(u) represents the corresponding rectangular window.

If there are multiple feature points in the CO2 emission limitation analysis, then the coefficient of the features after the p(p=1,2,…, P) feature point can be expressed as σ, and if the corresponding time delay τ(t) value of the characteristic point p in the transition assistance in the carbon dioxide emission limitation analysis is not changed, then there will be τ(t) ≈ τ(t), t=mT+kNT. At the same time, for the characteristic points of the carbon dioxide emission limit analysis, the mth subtransition boost under the k-th group of ecological innovation boosting high-performance computing can be expressed as follows:

In the formula, τ(t)=2R(t)/c and R(t) are successively expressed as the instantaneous slope distance between the p-th characteristic point and the emission limit analysis of the carbon dioxide emission limit analysis, c is the speed of light, and is the additive limit [15]. Then, for the transition-assisted frequency modulation processing, can be set, then the carbon dioxide emission limit analysis process is as follows:

In the formula, ΔR=xsin  θ+y, Φ, Φ, respectively, represent the phase error caused by the translation between the transition boosters and the phase error caused by the translation between the transition booster strings in the carbon dioxide emission limitation analysis.

The definition of life welfare is the effective combination of consumption and carbon dioxide emissions and the negative impact of emissions on the happy life of the family [16]. Suppose the expression of family life welfare is as follows:

In the expression, C represents user consumption, P represents the stock of carbon dioxide, if β > 0 occurs, it represents the impact strength index of carbon dioxide emissions on family life, and if ρ > 0 occurs, it represents the family patience index. The family utilizes the selected optimal consumption combination to maximize the family life, and the carbon dioxide emission is externally given to the family [17]. The dynamic expression that can realize the accumulation of household consumption after dealing with the problem of household consumption is as follows:

In recent years, researchers of Jiang Guogang have studied China's emissions and regional economic differences through a nonlinear level. Through experiments, four dimensions were finally selected to establish a competitive index system. Through experiments, it is proved that each type of carbon reduction policy in each region directly affects the local low-carbon competitiveness, and it is proved that the incentive market carbon reduction policy can significantly improve the local competitiveness. They advocate that the government needs to enrich market strategies and adopt market policies to advocate low-cost enterprises to achieve the goal of reducing emissions and improve low-carbon competitiveness; based on the above analysis, it can be seen that the expression of carbon dioxide emissions given in this paper is as follows:

In the expression, represents the carbon dioxide emissions after green investment, Ω represents the growth rate of the green investment economy, if F(Y, A)=YA−, where ω > 0, φ > 0. This paper mainly discusses the carbon dioxide emission limit. It can be obtained by dividing both sides of expression (9) by Y, respectively, as follows:

In order to ensure that the constraint parameters used in this paper satisfy the constraint ω=1+φ. It is worth noting that, according to the above analysis and analysis from a technical point of view, it can be seen that the carbon dioxide emissions of green investment on the scale of green investment above can be regarded as a constant necessary condition.

With the continuous development of the green economy, the relationship between the amount of household capital consumption and the amount of carbon dioxide emissions consumed by daily use is expressed as follows:

According to the principle of symmetry, if the green economy is in a state of equilibrium, it is necessary to conduct demand analysis for products with carbon dioxide emissions [18]. According to the carbon dioxide emission restriction conditions, the emissions of various products should also be the same, which needs to be satisfied: x(i)=x, i ∈ [0, A]. Substituting x(i)=x into expression (11), it can be yielded as follows:

Substituting expression (12) into expression (1) in turn, the total carbon dioxide emissions can be expressed as follows:

According to the above analysis, if the convergence of green investment is on the path of balanced growth, and the growth rates of K, A, P and Y are the same, set Ω; L, L, P, and x are all constants.

According to expression (13) and the growth of the green economy, r=ρ+Ω can be calculated, and the time t can be derived from both sides of expression (12); in turn, and combined with the scale of green investment, the expression can be obtained as follows:

Substituting expression (14) into expression (17) while satisfying the optimal problem, expressions (12), (13), (14), and the green investment scale condition can be obtained as follows:

Divide both sides of expression (15) by A, under the condition that Ω=δL is satisfied, combined with the economic equilibrium condition L+L=L, and substitute this condition into expression (15); we can get the following:

According to expression (16), the economic growth rate of green investment can be obtained as follows:

According to expression (17), dΩ/dθ > 0 can be obtained. If the green investment is higher, then the local economic growth rate is higher. At the same time, combining the optimality of carbon dioxide emissions and household consumption choices, we can get the following:

According to expression (18), we can get the following:

Combining the conditions of ecological innovation and the production function of green investment, the following expressions need to be satisfied:

Substituting the total carbon dioxide emission function of expression (20) into expression (19), the carbon dioxide emission under the average green investment can be obtained as follows:

Substituting expressions (19) and (20) into expression (21), the expression that can use the economic growth rate as the limiting condition of carbon dioxide emissions is as follows:

Substituting expression (17) into expression (22), the condition of carbon dioxide emission limitation is expressed by the green investment function. According to expression (17), the degree of influence of green investment on the economic growth rate can be realized. According to expression (22), the carbon dioxide emission limit is changed according to the economic growth rate. Therefore, according to the qualitative analysis, green investment can realize the assessment of the impact of carbon dioxide emission limitation.

3. Empirical Model Setting and Variable Selection

In this paper, in the process of using the economic growth rate to evaluate the carbon dioxide emission limit, the expression (22) can be used to derive Ω, and we can get the following:

According to expression (23), if ((1 − α/α) − 1)φ − 1 < 0. is satisfied, then the relationship between the growth rate of the green economy and the carbon dioxide emission limit can be represented by an inverted U shape. Using Q to represent the horizontal axis and to represent the vertical axis, then the corresponding intercept of expression (23) on the vertical axis is not less than zero. If (((1 − α)/α) − 1)φ − 1 < 0 is satisfied, then the relationship between δL (1 − (1 − α) φ/α) − ρ (1 + φ) and 0 has a certain value on the horizontal axis corresponding to the extreme point of expression (23) impact, according to three different scenarios in Figure 1. If δL(1 − (1 − α/α)φ) − ρ(1+φ) < 0, 0 is satisfied, then it corresponds to CASE1 in Figure 1; if δL(1 − (1 − α/α)φ) − ρ(1+φ)=0 is satisfied, then it corresponds to CASE2; if δL(1 − (1 − α/α)φ) − ρ(1+φ) > 0 is satisfied, then it corresponds to CASE3 in Figure 1. In the three cases in Figure 1, if the economic growth rate Ω > 0 is satisfied. In this paper, the intersection point with the horizontal axis in the interval not less than 0 for expression (23) is Ω. It can be concluded that if 0 < Ω < Ω, , the growth rate of the green economy keeps rising and the carbon dioxide emissions also increase; If Ω > Ω, is satisfied, then the growth rate of the green economy will also rise, and carbon dioxide emissions will continue to decline. If Ω=Ω, , then the carbon dioxide emission will reach a maximum value at this time. According to the constructed model variables, ((1 − α/α) − 1)φ − 1 < 0 meets the criteria for green investment economic development [19-21]. This is because in the expression (13) used, α represents the share of economic income in the total income. According to the green economy income share, it generally needs to be maintained between 60% and 70%, and (1 − α)/α can be obtained <1, the condition of ((1 − α/α) − 1)φ − 1 < 0 needs to be met.

Figure 1

Visual analysis of the impact of economic growth rate on carbon dioxide emissions.

According to the above-detailed analysis, this paper can obtain the impact of green investment and ecological innovation on Chinese carbon dioxide emissions through quantitative analysis. In the process of given model data and variable selection, the impact of green investment and ecological innovation on carbon dioxide emissions is obtained according to the constructed regression equation as follows:

Among them, in the constructed expression, the explanatory variable is the effect of limiting carbon dioxide emissions, and the core explanatory variables mainly include the level of green investment and ecological innovation. Combined with the model, the basic impact of green investment on carbon dioxide emissions can be effectively calculated. Therefore, this paper can analyze the factors affecting carbon dioxide emissions in detail. The calculation of carbon dioxide emissions is a dynamic continuity, and the constructed dynamic panel data model can be expressed as follows:

In the expression, L. (CO2/green investment) is used to denote the emission limit of lagging CO2. In this paper, the estimated expression of carbon dioxide produced by fossil fuels is: CO2=∑αβE, where α represents the conversion rate of the ith energy, β is the carbon dioxide emission coefficient of the ith energy, and E is the energy consumption of its energy. On this basis, divide the total CO2 emissions by the actual green investment to obtain the CO2 emissions from the average green investment. It is of great significance for the distribution of resources related to carbon dioxide emissions, the service level of flow planning, and safety monitoring. According to the obtained data, it is used to analyze the performance of carbon dioxide emission, analyze the operation state of carbon dioxide emission and diagnose the cause of failure, and can also be used to monitor the cause of failure. Carbon dioxide emission monitoring and real-time risk alerts are important manifestations of carbon dioxide emission management and systematic integration, providing detailed data information for the operation and maintenance of carbon dioxide emissions. CO2 emission performance analysis, abnormal monitoring, link status monitoring, and capacity planning all play an important role together. As the focus of current research in this field, carbon dioxide emission monitoring can be perfectly combined with different industries so that data from different industries can be fully used in the actual carbon dioxide emission monitoring environment.

4. Analysis of Demonstration and Results

This paper adopts the Chinese interprovincial panel model to express the relationship between ecological innovation and green investment to limit China's carbon dioxide emissions as follows:

In (26), the explanatory variable is the ecological innovation Tech in the Chinese region, the explanatory variable is the green investment Fin, and Control represents the control variable of the model. Since Chinese productivity is compared against a comprehensive measure of ecological innovation, total factor productivity (TFP) is used as an ecological innovation variable.

The impact of domestic total factor productivity using the Solow residual method: If the conditional restriction function is Yit=AitKitLit, Yit in the expression represents the output of region i in year t, and A, K, and L correspond to ecological innovation, capital stock, and labor input in turn. If the constant return to scale is satisfied (α+β=1), the model expressions ln(Y/L)=ln  A+α  ln(K/L)+ε obtained above are obtained, respectively. According to the panel data of Chinese provinces, α=0.65 can be estimated, and then β=1 − β=0.36. Then, using the formula TFPit=Yit/(Kit0.6465Lit0.3635), the green investment rate of return of each province (autonomous region, city) in different years can be obtained according to the calculation of each province in China.

The test results according to the constructed model are shown in Table 1. In Table 1, it can be seen that Fin is calculated by using expressions (10) and (11) to be the comparison between the traditional economy and green investment in turn. It can be concluded that both the traditional economic investment and the green investment proposed in this paper are all positively correlated with the return on green investment; that is, green investment is also conducive to the progress and development of ecological innovation. In Table 1, the calculated results using expression (12) and expression (13), respectively, show that it is of great significance for limiting carbon dioxide emissions. According to the test results, it can be concluded that the corresponding Fin in expression (12) is the ratio of direct investment to indirect investment, and it can be concluded that the ratio obtained is positively correlated with the return on green investment, indicating that with the continuous development of green economy, the proportion of direct investment can be effectively increased, which in turn helps to improve ecological innovation. Through the investment proportion of Fin in expression (13), the research results show that green investment has certain advantages, which shows that the development of green investment can effectively increase the share of the traditional economy. The same amount of growth in green investment boosts TFP almost twice as much as in conventional economies. Fin in expression (15) is the proportion of ecological innovation, and this ratio is also significantly positively correlated with total factor productivity.

Table 1

Relationship between green investment and total factor productivity.

Variable	Expression (10)	Expression (11)	Expression (12)	Expression (13)	Expression (14)
	Traditional innovation. economy	Green investment	Economic structure	Proportion of green investment	Proportion of ecological innovation
LTech	0.34∗∗∗ (16.81)	0.32∗∗∗ (14.91)	0.33∗∗∗ (24.19)	0.33∗∗∗ (10.95)	0.33∗∗∗ (14.34)
Fin	0.023∗∗∗ (25.44)	0.01∗∗∗ (14.69)	0.02∗∗∗ (9.22)	0.03∗∗∗ (7.55)	0.26∗∗∗ (8.13)
Edu	0.05∗∗∗ (9.55)	0.03∗∗∗ (9.96)	0.03∗∗∗ (11.34)	0.03∗∗∗ (10.32)	0.01∗ (1.94)
FDI	0.45∗∗∗ (6.34)	0.56∗∗∗ (3.42)	0.73∗∗∗ (5.45)	0.65∗∗∗ (3.55)	0.79∗∗∗ (5.94)
Open	0.28∗∗∗ (15.56)	0.27∗∗∗ (20.42)	0.26∗∗∗ (13.44)	0.26∗∗∗ (18.55)	0.26∗∗∗ (22.33)
Fiscal	0.03∗∗∗ (7.66)	0.03∗∗∗ (5.92)	0.02∗∗∗ (2.56)	0.03∗∗∗ (6.65)	0.04∗∗∗ (3.73)
Abond P value	0.22	0.19	0.23	0.43	0.18
Sargan test	25.78	25.02	22.11	25.33	25.71

According to the above analysis, this paper also uses interprovincial panel data to analyze the relationship between green investment and economic structure. The model constructed is as follows:

The explanatory variable Struct in the constructed model is used as the industrial structure index in this area, and LStruct represents the Struct with a lag of one period. The explanatory variable is the green investment Fin, and Control represents the control variable. The industrial structure is reflected by two indicators: (1) The proportion of the traditional economy. This paper first calculates the ratio of the difference to the regional green investment by subtracting the output value of the high-tech industry from the output value of the secondary industry, and uses this ratio to measure the proportion of the traditional economy. (2) The proportion of green investment. The proportion of the total output value of the high-tech industry and the tertiary industry in the green investment in the region is used as an indicator to measure the proportion of green investment.

The results available according to the constructed model are reported in Tables 2 and 3. According to Table 2, we can see the relationship between green investment and economic industries. The explained variables in expressions (15) and (16) are mainly the proportion of the traditional economy, and the proportion of green investment and traditional economy can be found; according to the explained variables in expression (17) and expression (18) in the proportion of green investment, these two expressions can effectively study the relationship between the proportion of green investment and green investment. According to expression (15) and expression (17), the corresponding Fin is taken as the ratio of the traditional economy to green investment. From expression (15), we can see that there is a negative correlation between the proportion of the traditional investment and the traditional economy. It can be seen from expression (17) that the proportion of traditional investment and green investment is positively correlated. According to expression (16) and expression (18), Fin is the ratio of green investment to the traditional economy. According to the test results of expression (16), it shows that the proportion of green investment and traditional economy presents a U-shaped relationship. Green investment has not yet increased the proportion of the traditional economy. Green investment can have an inverted U-shaped relationship with the proportion of the traditional economy and a U-shaped relationship with the proportion of green investment, which is because green investment based on venture capital and private equity funds is in the initial stage. Investments in traditional economic sectors are often required. The analysis of the above results shows that both traditional economy and green investment, and after developing to a certain extent, can effectively reduce the proportion of the traditional economy and greatly increase the proportion of green investment; that is, green investment is conducive to the transformation and upgrading of industries.

Table 2

Relationship between green investment and industrial structure.

Variable	The proportion of explained variables as the traditional economy		The proportion of explained variables as green investment
	Expression (15)	Expression (16)	Expression (17)	Expression (18)
	Traditional (17 economy)	Green investment	Traditional (17 economy)	Green investment
LStruct	0.33∗∗∗ (47.23)	0.96∗∗∗ (42.12)	0.95∗∗∗ (35.11)	0.94∗∗∗ (90.84)
Fin	−0.01∗∗∗ (−4.78)	0.01∗∗∗ (6.90)	0.003∗∗ (1.94)	−0.01∗∗∗ (−9.25)
Fin2		−0.001∗∗∗ (−5.42)		0.001∗∗∗ (6.91)
Consum	−0.13∗∗∗ (−19.44)	−0.11∗∗∗ (−11.03)	0.12∗∗∗ (19.44)	0.11∗∗∗ (21.44)
Labor	0.00 (0.04)	0.00 (0.12)	−0.002∗∗∗ (−4.54)	−0.001∗∗ (−2.45)
Fiscal	−0.02%∗ (−2.29)	−0.03∗∗∗ (−5.34)	−0.01 (−1.36)	−0.002 (−0.33)
Edu	−0.20∗∗∗ (−23.33)	−0.22∗∗∗ (−16.90)	0.12∗∗∗ (11.69)	0.13∗∗∗ (13.32)
Abond P value	0.29	0.55	0.14	0.19
Sargan test	28.56	27.39	28.33	28.45

Table 3

Relationship between green investment structure and industrial structure.

Variable	The explanatory variable is the economic structure		Explanatory variable is the proportion of green investment		Explanatory variable is the proportion of ecological innovation
	Expression (19)	Expression (20)	Expression (21)	Expression (22)	Expression (23)	Expression (24)
	Proportion of the traditional economy	Proportion of green investment	Proportion of the traditional economy	Proportion of green investment	Proportion of the traditional economy	Proportion of green investment
LStruct	0.94∗∗∗ (32.12)	0.99∗∗∗ (28.16)	0.94∗∗∗ (49.23)	0.91∗∗∗ (116.93)	0.91∗∗∗ (35.34)	0.94∗∗∗ (49.66)
Fin	0.01∗ (1.74)	−0.01∗∗∗ (−5.67)	0.03∗∗∗ (6.45)	−0.04∗∗∗ (−11.84)	−0.30∗∗∗ (−7.55)	0.20∗∗∗ (11.44)
Fin1	−0.02∗∗ (−2.13)	0.02∗∗∗ (2.73)	−0.01∗∗∗ (−6.35)	0.01∗∗∗ (12.86)
Control	Adjust and control	Adjust and control	Adjust and control	Adjust and control	Adjust and control	Adjust and control
Abond P value	0.35	0.1071	0.44	0.39	0.36	0.25
Sargan test	27.22	27.21	29.12	28.86	27.18	28.34

Table 3 reflects the relationship between green investment structure and industrial structure. According to the explanatory variables, Fin of expression (19) and expression (20) are all economic structures, the explained variable of expression (19) is the proportion corresponding to the traditional economy, and the explained variable of expression (20) is the proportion of green investment. According to the analysis of the above two expressions, there is an inverted U-shaped relationship between the economic structure and the proportion of the traditional economy and a U-shaped relationship with the proportion of green investment. The analysis results of this example show that the proportion of green investment has an inverted U-shaped relationship with the proportion of the traditional economy and a U-shaped relationship with the proportion of green investment, indicating that the initial stage of green investment promoted the increase of the proportion of the traditional economy and the decrease of the proportion of green investment, but after it developed to a certain extent, it showed the inhibition effect on the proportion of the traditional economy and the promotion effect on the proportion of green investment. These two expressions show that the increase in the proportion of ecological innovation will help reduce the proportion of the traditional economy and increase the proportion of green investment, thereby promoting industrial transformation and upgrading.

5. Conclusion

This paper constructs a model of green investment, ecological innovation, and carbon dioxide emissions, studies the impact of green investment and ecological innovation on carbon dioxide emissions through data, and finds that there is an inverted U-shaped relationship between green investment and carbon dioxide emissions. In this study, it is found that the impact of green investment in different dimensions on carbon dioxide emission limitation cannot be determined. The relationship between the scale of green investment and carbon dioxide emission limits is similar to the relationship between ecological innovation and carbon dioxide emissions. In the proportion of green investment, economic competition, and ecological innovation invested in large amounts, compared with traditional policies and regulation, it has greatly reduced carbon dioxide emissions and has a positive role in promoting the long-term development of the domestic economy. Growth can also realize the optimization of the economic structure of the green investment. Ecological innovation and green investment have a certain restrictive effect on carbon dioxide emissions. At the same time, it is also necessary to vigorously develop the proportion of investment in the green economy, which also has a certain impact on ecological innovation.