Impact of Green Infrastructure Investment on Urban Carbon Emissions in China

By inergency On Mar 25, 2024

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1. Introduction

With global climate warming and the increasingly deteriorating state of resources and the environment, the development idea of a low-carbon, sustainable, and green economy has been implemented. In the process of low-carbon and sustainable development, the international community has successively signed international agreements to address climate change. The local governments of various countries have also formulated policies and measures to reduce emissions, put forward targets for the reduction in greenhouse gas emissions at all stages, and formulated strategic plans and specific arrangements at the national, industrial, and enterprise levels. The Proposal of the CPC Central Committee on formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-term goals for 2035 clearly indicates that we should hasten the promotion of green and low-carbon development. The total carbon emissions should peak by 2030 and then show a downward trend, and the ecological environment should manifest fundamental improvements. Chinese President Xi Jinping, in his speech at the 75th session of the United Nations General Assembly, pledged that China would increase its autonomous contribution and adopt more effective policies and measures, and emphasized that China will strive to reach its peak carbon dioxide (CO₂) emissions before 2030 and achieve carbon neutrality before 2060.

China is the largest developing country in the world and has the highest primary energy consumption [1]. In a global rigid carbon constraint environment, the reduction in CO₂ emissions has become the core focus for China to achieve its set goals in a timely manner and achieve sustainable economic development. China’s CO₂ emission reduction policy can be traced back to the 11th Five-Year Plan proposed in 2006, which states that energy consumption per unit of gross domestic product (GDP) and total emissions of major pollutants will be reduced by approximately 20% and 10%, respectively, during the 11th Five-Year Plan period. As shown in Figure 1, China’s total CO₂ emissions from 2006 to 2019 fluctuated; that is, these initially increased, decreased, and then increased again. From the source of CO₂ production, the proportion of raw coal in the total CO₂ emissions has decreased but still accounts for a large proportion. Therefore, China’s dependence on traditional high-carbon energy remains very high, and the task of CO₂ emission reduction is still arduous.

Urban carbon emissions refer to the emissions of greenhouse gases, such as CO₂, produced by urban activities. As the core of climate change mitigation and the sustainability of human development [2], cities represent two-thirds of global energy consumption and account for more than 70% of greenhouse gas emissions [3]. The green industry is a key means to achieve green, low-carbon, and sustainable development. In the urban development process, the green upgrading of infrastructure plays an important role. Green infrastructure investment (GII) refers to a category of investment in sustainable and environmentally friendly infrastructure projects. It aims to address the shortcomings of existing infrastructure and promote economic growth and social wellbeing while reducing the negative impact on the environment. From 2006 to 2020, China’s GII total showed an overall upward trend, with an average annual growth rate of 13.71% (Figure 2).

In this section, the total GII in Chinese cities in 2006–2019 was grouped into more than RMB 50 billion, RMB 10–50 billion, RMB 5–10 billion, and less than RMB 5 billion, and its geographical distribution characteristics were observed. The results showed the unbalanced distribution of GII in Chinese cities. Cities with the highest investment levels are mainly concentrated in municipalities such as Beijing, Tianjin, and Chongqing. Cities with relatively high investment levels are mainly located in eastern coastal regions. Meanwhile, cities with lower investment levels are primarily distributed in central, western, and northeastern regions (Figure 3). Such an imbalance may be influenced by a combination of factors, including the level of economic development, urban size and population, government policies, geographical and climatic conditions, and urban sustainable development needs.

To sum up, GII and urban carbon emissions are complex systems. It is important to deeply explore the relationship between GII and urban carbon emission for sustainable urban development and the timely achievement of carbon emission reduction targets in China. This paper is organized into five sections. The first part of this article is the introduction. This section presents the background and significance of this study. The second part explores the research status of the domestic and foreign literature and presents a brief literature review. The third section begins with a detailed description of how GII affects urban carbon emissions and presents the research hypotheses of this paper. In addition, the study model is introduced, including data and variables. The fourth part conducts a scientific empirical analysis and explains the results. The fifth part first summarizes the main conclusions, and then puts forward the corresponding suggestions according to the actual situation.

The main goal of this paper was, first, to explore the complex influence mechanism of GII on urban carbon emissions, because the role of GII on urban carbon emissions may have two sides. Green infrastructure was aimed at carbon reduction, but the green infrastructure construction process may lead to increased energy consumption and increased carbon emissions. On this basis, another main goal of the study was to verify whether there is a time-lag effect and a threshold effect for the impact of GII on carbon emissions.

The unique contributions of this paper are as follows: (1) Lots of studies have empirically analyzed the influencing factors of carbon emissions, such as economic level, population size, and fiscal expenditure. However, these explanatory variables are relatively macro. This paper refines the research perspective to a certain type of infrastructure investment and deeply explores the impact of GII on urban carbon emissions to better fill the gap of previous studies. (2) Most of the existing literature focused on the accounting methods, influencing factors, industry differences and potentials of carbon emissions, but few studies have gone deeper into the mechanisms that affect carbon emissions. This paper provides a comprehensive analysis of the mechanism of GII on urban carbon emissions. According to the scale, technological, and structural effect, this paper gives a general analysis of the relationship between GII and urban carbon emissions, and combines a specific impact of GII on urban carbon emissions. (3) This paper considers the time-lag effect and the threshold effect, and sets up the corresponding models for empirical studies. Compared with the existing linear studies which can only reflect the overall trend and relationship, the threshold-effect study in this paper can find the neglected turning points, identify the key thresholds for technological progress affecting carbon emissions, and deepen the understanding of the relationship between variables.

The conclusions drawn from this study are useful for all parties in society to make more reasonable investment decisions. This study can be applied to carbon emission reduction initiatives, providing lessons for reducing greenhouse gas emissions and helping global climate governance.

4. Results and Discussion

4.1. Benchmark Regression Results Based on the Fixed-Effects Model

Table 5 presents the regression results based on the fixed-effects model. The influence coefficient of the core explanatory variable GII on the urban CEI was −0.032, and it passed the significance test of 1% level. This finding suggests that GII can inhibit the CEI in local cities.

Next, this paper divided the samples into four groups for regression (the same grouping method in Section 1). As shown in Table 6, heterogeneity was observed in the effects of cities with different GII levels on their CEI. In the group with the highest level of GII, that is, the cities with a total investment of more than RMB 50 billion from 2006 to 2019, the impact of the core explanatory variable on the explained variable changed into a significant positive effect. This change may be due to the diseconomies of scale of GII, which led to a short-term increase in carbon emissions. For example, in municipalities like Beijing, Tianjin and Chongqing, land use was relatively saturated. However, green infrastructure projects require large areas of land for construction, which may lead to the destruction of or reduction in existing vegetation, thus reducing the carbon absorption capacity. And in the process, activities such as excavation or landfill will also produce certain carbon emissions.

In the second, third, and fourth group cities with lower levels of GII, the influence of the core explanatory variable on the explained variable remained negative. Among these groups, in the cities with a total GII between RMB 5 billion and 10 billion, GII had the largest and most significant inhibitory effect on CEI, and its inhibitory effect was higher than that of the whole sample. Such cities include Qinhuangdao, Chifeng, Guilin and Haikou. They are often known for their rich natural landscapes and ecological environment, and are at a critical stage of urban scale and development. In order to achieve sustainable development, they need to increase GII, improve environmental quality, and improve the quality of life of residents while reducing carbon emissions. Moreover, these cities may have suitable climate conditions, such as being sunny, warm and humid, which provides better conditions for the use of renewable energy (such as solar and wind energy). In addition, these cities may have received the attention and support of the government and society.

4.2. Robustness Discussion

The results of the panel metrology model may be influenced by the data-processing and estimation methods. For example, the choice of a fixed-effects model or a random-effects model may change the outcome. As shown in Table 7, the choice of fixed-effects and random-effects models did not significantly differ on the regression results, and it can be tentatively considered that the selected models are robust. This means that individual or group effects have less influence on the results, and choosing either of the model yields reliable estimates of the results.

The explained variable was replaced with carbon emissions per unit of industrial production to further verify the robustness of the model. Table 8 shows the whole-sample regression results, which showed good robustness. The role of GII in urban carbon emissions remained significantly negative, but the impact coefficient changed. Among the other control variables, only the individual effects changed.

Thus far, it has been demonstrated that the model estimates are reliably robust and that H1 holds true.

4.3. Analysis of the Empirical Results Based on the Time-Lag Effect

Referring to the study by Zhong and Sun [43], temporal differences exist in the impact of infrastructure investment on carbon emissions. Considering the long construction cycle of green infrastructure, this paper set the sample data to a lag of five periods to further verify the lag in the impact of GII on urban CEI. Each period step mentioned in this article is one year. According to the construction of Equation (1), the time-lag model of GII lagging by 1, 2, 3, 4 and 5 years can be expressed as follows:

${l n C E I}_{i t} = α_{0} + α_{1} l n {G I I}_{i t - τ} + α_{k} \sum_{k = 2}^{7} X_{c o n t r o l} {+ ε}_{i t} (τ = 1, 2, 3, 4, 5)$

(2)

where $α_{1}$ is the coefficient of the independent variable lagging behind $τ$ period, reflecting the effect of the independent variable value of the past period on the value of the dependent variable in the current period. $ε_{i t}$ is the error term, representing the random error term, the total influence term of the explanatory variable not included in the model and some other random factors on the explained variable. The rest of the letters mean the same as in Equation (1).

By regression, the coefficients of the core explanatory variable at one lags, two lags, three lags, four lags and five lags were −0.046, −0.054, −0.058, −0.053 and −0.049, respectively, and all were significant at the 1% level (Table 9). It can be seen that the inhibitory effect of GII on CEI is significantly present in all the five lag periods, among which the inhibition of three lags is the strongest, and then shows a decreasing trend. Thus, the impact of GII on urban carbon emissions has a time-lag effect, which proves that H2 holds true.

The time lag in GII on urban CEI was mainly caused by three factors. The first factor is the building cycle. The construction of green infrastructure usually requires a long time and includes multiple stages, such as planning, design, bidding, construction, and checking. These stages require some time to complete, especially for large infrastructure projects. During the construction period, some carbon emissions may occur. Thus, the emission reduction benefits of green infrastructure cannot be reflected immediately before it has been fully built and put into use. The second factor is the accumulation of technology and experience. In the field of green infrastructure, the related TP and accumulation of experience are improved and optimized gradually to enhance the effect of emission reduction. The third reason is the change in civic behavior patterns. The successful application of green infrastructure requires the active participation and adaptation of citizens. For example, if a city invests in intelligent recycling bins for garbage classification, but citizens refuse to cooperate with the active classification, then the effect of emission reduction may not be immediately evident.

However, the inhibitory effect of GII on CEI being strongest at three lags and then starting to weaken could also be explained. The marginal inhibition of carbon emissions in green infrastructure projects will decline over time. Without a new round of GII, the use of completed projects and the aging of the equipment will reduce the suppression effect.

4.4. Analysis of the Empirical Results Based on the Threshold Effect

4.4.1. Static Panel Threshold Model

Based on the analysis of the impact mechanism in Section 3.1, this paper assumed that a nonlinear relationship possibly exists between GII and urban carbon emissions, and TP has a threshold effect. To test this hypothesis, we first employed the non-dynamic panel regression model proposed by Hansen [67]. The threshold regression model can be expressed as follows:

$y_{i} = x_{i'} β_{1} + μ_{i}, q_{i} \leq γ y_{i} = x_{i'} β_{2} + μ_{i}, q_{i} > γ$

(3)

where $y_{i}$ is the explanatory variable; $x_{i'}$ is the explanatory variable vector of order $P \times 1$ ; $q_{i}$ is the threshold variable, which may or may not be part of the $x_{i'}$ ; and $γ$ is the threshold value.

Using Hansen’s ideas, this paper constructs the following static panel threshold model with TP as the threshold variable:

${l n C E I}_{i t} = β_{0} + β_{1} l n {G I I}_{i t} \cdot I ({l n T P}_{i t} \leq ω_{1}) + β_{2} {l n G I I}_{i t} \cdot I (ω_{1} < {l n T P}_{i t} \leq ω_{2}) + \dots + β_{n + 1} l n {G I I}_{i t} \cdot I ({l n T P}_{i t} > ω_{n}) + γ_{1} {E C S}_{i t} + γ_{2} l n O_{i t} + γ_{3} {l n P D}_{i t} + γ_{4} {U L}_{i t} + γ_{5} l n {G E G}_{i t} {+ ε}_{i t}$

(4)

Here, $β_{0}$ is a constant term, and $β_{1}$ to $β_{n + 1}$ are the elasticity coefficients of the core explanatory variable lnGII at different threshold values. TP was selected for the threshold variables. $I (\cdot)$ is an indicator function with a value of 0 or 1. $ω_{1}$ to $ω_{n}$ are the threshold values to be estimated.

The threshold existence test was conducted using Stata16, and the test results under different thresholds are shown in Table 10. The single- and double-threshold models had p-values of less than 0.05 and passed the significance test, but the triple-threshold model failed. Therefore, this paper can consider that GII had a double-threshold effect on urban carbon emission based on TP, which verifies H3.

In the double-threshold model regression, the first threshold value for TP was 3.9120 with a 95% confidence interval of [3.8286, 3.9703]. The second threshold value for TP was 6.8035, with a 95% confidence interval of [6.5624, 6.9791]. Figure 4 shows the corresponding threshold map. The red dashed line indicates the critical value at the 5% significance level. The part of the LR image below the dashed line is the confidence interval of the threshold value. At different levels of TP, GII had a significant negative impact on urban carbon emissions, but the degrees of impact varied (Table 11). The inhibitory effect of GII on urban CEI was strengthened with the level of TP. Specifically, when the level of TP did not exceed the first threshold value, the effect coefficient of GII on urban CEI was −0.057. When the level of TP was between the first and second threshold values, the effect coefficient of GII on urban CEI was −0.088. In addition, when the level of TP was greater than the second threshold, the effect coefficient of GII on urban CEI was −0.126. This result indicates that when lnTP exceeds the second threshold value 6.8035 (number of green utility model patent applications greater than 901), the inhibitory effect of TP on CEI increases by 121.0526% relative to lnTP, which is less than the first threshold. At this time, the technical content of green infrastructure has greatly increased, which improves investment efficiency and significantly reduces urban carbon emissions.

4.4.2. Re-Examination of the Dynamic Panel Threshold Model

Considering that the current urban carbon emissions may have an impact on the next phase through greenhouse gas accumulation, carbon cycle and climate feedback, and the continuity of infrastructure investment, we introduced the lag phase of the dependent variable CEI to build a dynamic panel model in this section. Caner and Hansen [68] proposed two-stage least squares (2SLS) estimation and Generalized Method of Moments (GMM) estimation for threshold parameters for cross-section data containing endogenous explanatory variables and exogenous threshold variables. Kremer et al. [69] further applied the above methods to dynamic panel data to solve the inherent endogenous problems of dynamic panel data. In this paper, using the method of Kremer et al.,

{l n C E I}_{i t - 1}

was added to Equation (4) to obtain the following dynamic panel threshold model:

${l n C E I}_{i t} = β_{0} + β_{1} l n {G I I}_{i t} \cdot I ({l n T P}_{i t} \leq ω_{1}) + β_{2} {l n G I I}_{i t} \cdot I (ω_{1} < {l n T P}_{i t} \leq ω_{2}) + \dots + β_{n + 1} l n {G I I}_{i t} \cdot I ({l n T P}_{i t} > ω_{n}) + φ {l n C E I}_{i t - 1} + γ_{1} {E C S}_{i t} + γ_{2} l n O_{i t} + γ_{3} {l n P D}_{i t} + γ_{4} {U L}_{i t} + γ_{5} l n {G E G}_{i t} {+ ε}_{i t}$

(5)

where $φ$ is the influence coefficient of the current CEI on the next CEI. The rest of the letters mean the same as in Equation (4).

For the dynamic panel data model, to exclude the model setting error, the autocorrelation of the residual terms and the validity of the instrumental variables must be statistically tested after the GMM estimation. The results of systematic GMM regression are shown in Table 12.

The p-value corresponding to AR (1) was of less than 0.1 and that corresponding to AR (2) was greater than 0.1, indicating that the residual terms had first-order but not second-order autocorrelation, which meets the requirements of the autocorrelation test. The p-value of the Hansen test was 0.387, greater than 0.1, indicating that the instrumental variable was jointly valid and there was no over-identification. The above tests showed that the model setting in this paper was appropriate. In addition, the current CEI was indeed positively affected by the previous period and the coefficient was 1.029. The regression coefficient for the core explanatory variable GII was −0.038, which was significantly negative at the 1% level.

Referring to the research of Wang and Peng [70], the effect of GII on CEI was further analyzed by the dynamic panel threshold regression model using TP as the threshold variable. According to the dynamic panel threshold regression model test, GII and CEI had a nonlinear relationship under the TP threshold (Table 13). The threshold was 3.666 and was significant at the 95% confidence interval [3.575, 3.756]. Next, the samples were divided into two groups for dynamic panel regression based on the threshold value 3.666 (Table 14). It can be seen that when lnTP is below 3.666, GII cannot yet inhibit CEI. However, when lnTP is greater than 3.666, GII can significantly inhibit CEI.

We log-converted the threshold value (3.666) mentioned above and found that the corresponding TP was 39.095. Two examples of real cities were considered for further verification, as shown in Figure 5 and Figure 6. The TP, represented by the number of green utility model patent applications, in Qinhuangdao City gradually exceeded the threshold value after 2008. During the period when Qinhuangdao City’s TP had not reached the threshold value, its CEI exhibited significant fluctuations, indicating that the carbon reduction effect had not yet been realized. However, from 2009 onwards, its CEI started to decline. In addition, in the case of Haikou City, its TP exceeded the threshold value after 2010, and the turning point of CEI was also in 2010: the CEI of Haikou remained relatively stable between 2006 and 2010, and began to decline significantly from 2010. Such a reality is consistent with the conclusion drawn by the above threshold regression, indicating that TP has a threshold effect on the process of carbon emission reduction.

In conclusion, the static panel exhibited double thresholds. At different TP levels, GII had an inhibitory effect on CEI, but the coefficients were different. The higher the level of TP, the more significant the inhibitory effect of GII on CEI. In the dynamic panel, however, the threshold changed over time and the effect of GII on CEI shifted from facilitation to inhibition. This means that GII suppressed CEI only after the TP reached a specific threshold. The change in the threshold number of the static and dynamic panels may be due to the different treatment of the time dimension between the two panel data models. In the static panel data model, the cross-sectional data at a specific time point were considered. In this case, multiple thresholds may be present as different thresholds may have effects on the relationship between the explanatory variable and the explained variable at different time points. However, the dynamic panel data model considered the changing time series. In this case, the evolution of TP, the market uptake process, or other external factors may lead to threshold changes over time.

4.5. Conclusions and Policy Suggestions

Using the panel data from 235 cities in China from 2006 to 2019, this paper studied the impact of GII on urban carbon emissions from the perspective of time lag and threshold effects. The main conclusions are as follows: (1) GII has a negative inhibitory effect on urban carbon emission; that is, increasing GII can reduce urban carbon emissions. (2) A time-lag effect exists on the impact of GII on urban carbon emissions, and the greatest reduction in carbon emissions occurs in the third lag period. (3) The impact of GII on urban carbon emissions has a non-linear relationship based on the threshold variable TP.

However, when we try to reduce CEI by increasing GII, the trade-offs, limitations, or potential negative impacts of increasing GII should be critically considered. First, green infrastructure projects usually require a high capital investment. This could lead to a higher cost of capital, which reduces the return on investment. Second, some green infrastructure projects may have adverse effects on local communities in the early stage of construction, such as land acquisition, relocation, and social conflicts. Therefore, a comprehensive assessment of the economic benefits and social impact of the GII is needed to ensure its feasibility and sustainability.

Based on the above conclusions and considerations, we can draw the following policy suggestions: (1) The increase in GII plays an important role in promoting sustainable development and reducing carbon emissions. Governments should formulate relevant incentive policies and regulations, including incentive tax policies, preferential financing, and loan conditions, to attract various forces and funds as well as to encourage and support GII. (2) From the perspective of energy structure, we should reduce the dependence on high-carbon energy and develop and promote renewable energy. In the process, the environmental preferences of urban residents are more important than the content of their climate plans [71]. Individual and family awareness and actions play an important role in carbon reduction in cities. We should advocate for a low-carbon lifestyle and encourage environmental behaviors such as green traffic and garbage sorting to aid in the low-carbon transformation of urban energy consumption. For example, measures to incentivize include using electric cars, electric buses or shared bikes instead of traditional cars to reduce carbon emissions and traffic congestion. (3) TP is an important internal driving force for the reduction in urban carbon emissions. We should encourage cooperation between scientific research institutions, universities, and enterprises to realize the sharing of research results and technical resources for the continuous improvement of the overall green technology innovation capability. Meanwhile, we should strengthen international exchanges and actively participate in international green technology innovation projects. (4) Based on the resource endowments and geographical conditions of different regions, we must formulate appropriate carbon emission reduction targets and paths. In addition, we should fortify exchanges and cooperation between cities and rationally allocate capital, labor force, information, and other resources. For example, we can establish an intercity information disclosure platform to provide comprehensive and accurate data on GII opportunities and project information to help investors further gain insights into investment opportunities, reduce the risk of information asymmetry, and promote the market to achieve a healthy flow of factors.

4.6. Study Limitations and Recommendations for Future

There are still some shortcomings in this study. First, this paper uses panel data from 235 cities at the prefecture level and above in China from 2006 to 2019 to study the impact of GII on urban carbon emissions. However, due to the limitation of data access channels, this paper fails to include all prefecture-level cities in China. Secondly, due to the different calculation caliber of CO₂ emissions, this paper chooses to use China Carbon Emission Accounting and Database for research and analysis, but it was only updated up to 2019 during the writing period, which limits the time span of this study and does not rule out the possibility that different conclusions will be drawn after extending the length of the study period. Third, one of the control variables in this study failed to show the expected results in the model regression. Specifically, the variable of UL was not significant, which may be because of the error or incompleteness of the measurement and representative indicator for UL. Future studies could focus on these issues. We will also use the updated database as much as possible to improve data collection and measurement to make the results more reliable and valid.

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