Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6707; https://doi.org/10.3390/su16156707
Submission received: 17 June 2024
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Revised: 16 July 2024
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Accepted: 2 August 2024
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Published: 5 August 2024
Abstract
:To explore the impact of government fiscal intervention on regional carbon emissions, this paper employs a two-way fixed-effects model to analyze data from 30 provinces in China, spanning the period from 2008 to 2017. This study investigates the effects of local government environmental expenditure and government budget on the per capita volume, intensity, and performance of regional carbon emissions. The results show that government environmental expenditure is beneficial to reducing regional carbon emissions and improving regional carbon emission performance. Second, the smaller the deviation between the government budget and final accounts, the more conducive it is to reducing carbon emissions. Third, we found that government environmental expenditure has the strongest inhibitory effect on regional carbon emissions in the eastern region, followed by the central region, and the weakest in the western region. Finally, government financial transparency positively moderates the inhibitory effect of government budget management on regional carbon emissions, that is, when fiscal transparency is high, the amplification effect of budget deviation on regional carbon emissions is weakened.
1. Introduction
Climate change is the most intractable global problem facing humanity. In an effort to curb the release of carbon dioxide, the principal greenhouse gas responsible for exacerbating global warming, nations have endorsed and ratified several climate agreements since the 1990s. Significantly, China made a momentous proclamation to the international community in 2020, aiming to reach the target of reaching peak carbon dioxide emissions by 2030, aptly termed as “carbon peak”, and subsequently attaining carbon neutrality by 2060, termed as “carbon neutrality”. These intended accomplishments underscore the gravity of climate change and the imperative for concerted action to mitigate its harmful effects. The dual carbon goal for China, which is to reduce carbon, has put forward a clear timetable. To date, the resources and energy-driven and extensive economic growth model must be transformed and upgraded, driven by innovation. Paying attention to the quality of economic development will be an essential way for China to achieve dual carbon goals successfully [1].
While market- and policy-based CO2 emission control measures, such as carbon taxes and carbon trading markets [2,3,4], have been extensively discussed, limited attention has been paid to the impact of government-based fiscal measures on regional carbon emissions. As the implementors of central policies and regulations, local governments undertake multiple tasks, such as developing the economy, improving people’s livelihoods, and innovating development [5]. As an information system reflecting its economic activities, government accounting is a systematic embodiment of policy resources and tools, and plays different roles according to different functions.
There has been a preliminary discussion on how local financial systems affect environmental governance and carbon emissions. Basoglu’s and Uzar’s exploration, based on European environmental data, revealed an important finding: Although the expansion of the overall public financial expenditure has not shown significant positive effects on the ecological environment, and even aggravated the environmental burden in some cases, the increase in the special expenditure on environmental protection has significantly promoted the reduction in the ecological deficit and the improvement of environmental quality [6]. This finding provides strong evidence for the environmental orientation of fiscal policy. Furthermore, through an in-depth analysis of international panel data, Halkos and Paizanos made clear the direct contribution of government expenditure to the improvement of air quality. In particular, for key air pollutants, such as sulfur dioxide, government public expenditure showed a significant inhibitory effect. It is particularly noteworthy that economic growth plays a positive regulating role in this process, which enhances the improvement effect of government expenditure on air quality [7]. These findings not only enrich our understanding of the relationship between government spending and environmental governance, but also point to synergies between economic growth and environmental quality that need to be considered in policy making. Niu [8] found that government environmental expenditures can improve national ESG performance by promoting green innovation. Fan et al. [9] pointed out that local environmental protection expenditure has contributed to reduce industrial pollution emissions, with varying results across different regions. Xu et al. [10] analyzed the effect of environmental expenditure on carbon emission reduction and found that efficient environmental protection expenditure inhibits carbon emissions. Wei et al. [11] found that fiscal environmental expenditure has a long-term promoting effect on regional development and a short-term restraining effect on agricultural carbon emissions. Therefore, the further exploration of the impact of government environmental protection expenditure on regional carbon emissions is crucial. However, it should not be ignored that there are still research gaps in the current literature on how local environmental fiscal expenditure specifically promotes carbon emission reduction, and there are few in-depth analyses on how the scientific nature of government financial budget formulation affects the effectiveness of environmental governance. In view of this, this paper focuses on the unique background of China as a developing country, aiming to fill this research gap and explore the actual effect of government financial intervention in carbon emission control and its applicability and uniqueness in China. Through this research, we hope to provide theoretical support and practical guidance for building a more efficient and scientific environmental protection financial system. Moreover, although government environmental protection expenditures significantly impact regional carbon emissions, the allocation of government financial funds is constrained by the government budget, and only a reasonable financial budget can facilitate the efficient use of financial funds [12], thus promoting regional green development. Therefore, this paper innovatively incorporates government budget management into the governance effect system of fiscal expenditure on regional carbon emissions.
Our main research contributions are as follows. First, from the perspective of government financial intervention, the research on factors affecting regional carbon emissions has been enriched. Existing literature has shown that government environmental expenditure can affect provincial carbon emission intensity [13]. Furthermore, this study conducts a more comprehensive analysis of the influence of government budget management on regional carbon emissions, taking into account the government’s strategic planning and allocation of financial resources. Third, this study explores various aspects of regional carbon emission levels. We have chosen regional carbon emission indicators to examine the relationship between carbon emission and various factors. Firstly, the regional per capita carbon emissions indicator focuses on the relationship between carbon emissions and population density. Secondly, the regional carbon emission intensity indicator examines the relationship between carbon emissions and economic development. Lastly, we calculate the regional carbon emissions performance using the DEA model, which considers undesirable output. This allows us to comprehensively investigate the relationship between carbon emissions and energy, capital, and labor. By using these measurement indicators as dependent variables, we can analyze the impact of government accounting function on regional carbon emissions. This research, conducted in the context of China, offers valuable insights for the governments of emerging developing countries to engage in environmental governance.
The remaining sections of this paper are structured as follows. In Section 2, we theoretically analyze the relationships between government environmental protection expenditure, budget management and regional carbon emissions, and propose hypotheses. In Section 3, this paper’s data and methods are expounded. In Section 4, we conducted descriptive statistics and regression analysis. Finally, there is a section on conclusions and policy recommendations.
2. Hypothesis Development
2.1. The Impact of Government Environmental Expenditure (GEE) on Regional Carbon Emissions
Government environmental expenditures serve as a critical tool for environmental regulation, constituting the economic foundation, material guarantee, and direct manifestation of the government’s responsibility of environmental protection and governance. They play an immensely crucial role in balancing the often-conflicting interests of local economic development and environmental conservation. The influence of GEE on regional carbon emissions can be categorized into the following three paths.
First, at the macrolevel, GEE is component of public finance. It can be used for environmental infrastructure or part of the application of construction projects [14], laying a good foundation for subsequent environmental governance and pollution source clean-up and rectification, and also providing basic financial security for regulating and guiding enterprises and the public to carry out social behaviors, such as environmental protection, which is conducive to lowering the risk of the market [10]. At the same time, carbon dioxide is a pure public good, and the government can use public expenditure to intervene and correct market failure in the carbon emissions market, thus reducing regional carbon emissions.
Secondly, at the mesolevel, GEE harnesses the multiplier effect, demonstrating the government’s commitment to ecological protection. It stimulates green awareness among various economic entities, guides the rational allocation of social resources toward low-carbon green industries, enhances investment in carbon management by non-governmental entities, and facilitates the green transformation and upgrading of regional industrial structures [9]. In addition, the increase in GEE will open up the development of emerging industries related to green environmental protection, promote green technological innovation, reduce the cost of enterprise environmental protection, and encourage local enterprises to establish a green industrial chain, thus enhancing the environmental competitiveness of the whole industry and realizing the green upgrading of the industry, thus promoting the development of energy saving and emission reduction.
Thirdly, at the microlevel, in China, GEE, as an important environmental management tool, has the ability to influence the direction of corporate social investment and environmental behavior, thus producing significant positive externalities for economic development and environmental protection [15]. GEE can support local enterprises’ emission reduction behaviors through targeted subsidies and incentives, reduce the business risks of enterprises in carrying out green technological innovation, and enhance the stability of enterprises’ expected benefits from the application of green technology to developing related products. That is to say, local financial environmental expenditure will stabilize the future expectations and R&D confidence of local enterprises in carrying out green technological innovations, thereby promoting the increase in local green technological innovation output, which is conducive to reducing regional carbon emissions [8].
Therefore, Hypothesis 1 is proposed.
Hypothesis 1:
Government environmental expenditures can significantly reduce regional carbon emissions.
2.2. The Impact of Government Budget Management (GBM) on Regional Carbon Emissions
As an important way for the government to participate in the management of social and economic operations and to intervene in the allocation of macroeconomic resources, the government budget is a kind of constraint on the government’s administrative behavior, and its scientificity reflects the government’s ability to govern and its ability to provide public services. Generally speaking, an essential criterion for measuring the scientificity of government budget management is the degree of deviation from the government’s budget; a lower degree of deviation from the budget indicates that the government’s budget management is of a higher level and that the preparation of the budget for financial revenues and expenditures is more scientific, and the execution process is more standardized, with a certain supervisory role. A higher degree of deviation indicates that the government budget does not play its role in guiding and restraining governmental behaviors.
At present, China’s local fiscal revenue budget deviation shows “over-collection”, which indicates that regarding the local government in terms of tax revenue, there is the collection of “over-taxation”, to a certain extent, undermining the principle of tax smoothing and the principle of tax neutrality [16]. This shows that the local government has collected “excessive tax” in tax revenue, which to some extent undermines the principles of tax smoothing and tax neutrality, and directly increases the tax burden of market players, reduces the willingness of enterprises to save energy and reduce emissions, and thus increases regional carbon emissions. In terms of non-tax revenues, the situation of “over-recovery” shows the weakness and confusion of budget management and financial supervision of local governments, reflecting their low level of governance, and that government budgets cannot play the role of stabilizing market expectations, which makes it difficult for market players to make decisions on the effective allocation of resources [17].
At the same time, China’s local financial expenditure deviation shows the situation of “expenditure saving”, and the deviation of “expenditure saving” is higher than the deviation of “over-recovery”, that is to say, local governments have less incentive to carry out financial expenditure. The deviation of “expenditure saving” is higher than the deviation of “over-recovery”, i.e., local governments have less incentive to make fiscal expenditures. Fiscal decentralization makes the central government’s selection and evaluation of local officials change from political performance to economic development performance, which leads to the formation of a promotion tournament for local officials based on GDP growth [18]. Because the government has greater discretion in budget execution and the relevant supervision mechanism is not in place, local governments reduce their willingness to execute the expenditure budget by the law to realize GDP growth beyond the plan, resulting in a higher degree of fiscal expenditure deviation. In addition, when financial expenditure is invested in different areas, the GDP growth rate is different, and the growth rate of capital expenditure is higher than that of public service expenditure, which makes local governments tend to increase capital expenditure, thus reducing the scientificity of the structure and scale of local government’s financial expenditure, and making it difficult to satisfy the public needs of the local public and local enterprises, which is not conducive to the carbon reductions and emission reductions for enterprises.
Therefore, Hypothesis 2 is proposed.
Hypothesis 2:
Reasonable government budget management is significantly and negatively related to regional carbon emissions, i.e., an increase in the deviation of government budget expenditures will increase regional carbon emissions.
3. Materials and Methods
3.1. Variable Definition
3.1.1. Explained Variable
This paper selects regional per capita carbon emissions, carbon emission intensity, and carbon emission performance as explained variables to measure regional carbon emissions comprehensively. Regional per capita carbon emissions (Average) are calculated by dividing the total carbon emissions of each region by its population. This method reduces the impact of population density differences on total regional carbon emissions and enhances the comparability of regional carbon emissions data.
Carbon emission intensity (Intensity) quantifies the magnitude of the environmental ramifications resulting from the regional economic development, particularly in terms of total carbon dioxide (CO2) emissions. This parameter is derived by dividing the cumulative carbon emissions of a given province by its corresponding gross domestic product (GDP) [13,19]. This metric indicates the degree to which economic growth is dependent on high-energy-consuming industries. A higher carbon emission intensity suggests greater energy dependence on regional economic development, a slower transformation of economic structure, and the challenges of achieving the decoupling of economic growth from energy consumption.
Carbon emission performance (MCPI) has a prominent “total factor” characteristic, which must fully reflect the joint role of energy consumption, economic development, and other factors. The DEA method is widely recognized for efficiency evaluation, particularly in accommodating multi-output production activities with non-desirable outputs. Drawing on Miao et al. [20], this paper adopts the DEA model with undesired outputs to construct the Malmquist index that can be used for the dynamic change in CO2 emissions performance and measure the carbon emissions performance of each region from the integrated perspective of production and inputs, taking energy, capital, and labor as inputs, and carbon emissions as a kind of negative output, and fully analyze the dynamic change in its performance.
3.1.2. Explanatory Variable
For explanatory variable government environmental expenditure (GEE), we draw on Wei et al. [11] and adopt the proportion of local governments’ energy-saving and environmental protection expenditures to general public budget expenditures as a measure that can reflect the level of local government investment in carbon and emission reductions.
Government budget management is commonly used to measure the deviation of local fiscal revenue and expenditure from the pre-budget, reflecting the implementation and supervision of national fiscal budgeting. Wang [21] defined the formula for calculating the degree of deviation from the final account, i.e., the degree of deviation from the final account = (government final account expenditure − government budget expenditure) / government budget expenditure. If it is positive, it means that the final account exceeds the budget, which belongs to “overspending”, and on the contrary, it is “saving”. Therefore, this paper takes the degree of deviation of local government budget expenditure (GBM) to measure government budget management.
3.1.3. Control Variable
The factors that influence regional carbon emissions are complicated, and there are still many variables that impact regional carbon emissions besides government accounting. Therefore, according to the existing literature [20,22], this paper chooses the following control variables, including economic development (Eco), energy structure (Ener), industrial structure (Ind), technological level (Tech), ownership structure (Ownership), and opening up to the outside world (Open). The calculation of specific indicators is shown in Table 1.
3.2. Data Sources
The data from 30 provinces in China (Hong Kong, Macao, and Taiwan were excluded due to incomplete data) were selected as the research sample, with the data spanning from 2008 to 2017 (due to reasons such as late disclosure of government budget data, the data for the government budget expenditure deviation indicator spans from 2009 to 2016). The report on government fiscal transparency compiled by the Shanghai University of Finance and Economics is authoritative data widely used to measure the fiscal transparency of provincial governments in China. However, the report was updated to 2018, so updated data cannot be obtained. Based on the availability of data, the data range selected in this paper was 2008–2017. The raw data for each indicator were sourced from the National Bureau of Statistics website, the China Statistical Yearbook, the China Energy Statistical Yearbook, the China Stock Market & Accounting Research (CSMAR) database, the Wind database, and the regional statistical yearbooks, and interpolation was used to make up for the missing individual data in some provinces.
3.3. Model Setting
To test Hypotheses 1, the models are constructed as shown in Equations (1)–(3) to test the impact of government environmental expenditure on regional carbon emissions, where Averageit is the regional per capita carbon emissions, Intensityit is the regional carbon emission intensity, MCPIit is the regional carbon emission performance, GEEit is the government environmental protection expenditures, Control_variablesit is the listed control variables, and μ and λ represent the province fixed effects and year fixed effects, respectively. ε is the error term and β0 is the intercept term.
Averageit = β0 + β1 GEEit + β2 Control_variablesit + μi + λt + εit
Intensityit = β0 + β1 GEEit + β2 Control_variablesit + μi + λt + εit
MCPIit = β0 + β1 GEEit + β2 Control_variablesit + μi + λt + εit
If the coefficient, β1, is negative and β2 is positive, it means that there is a negative correlation between GEE and regional carbon emissions, i.e., the higher the GEE, the lower the regional carbon emissions, and Hypothesis 1 is established.
To test Hypothesis 2, models are constructed as shown in Equations (4)–(6) to empirically test the influence of the deviation of government budget expenditure on regional carbon emissions, where GBMit is the deviation of government budget expenditure. The remaining variables are the same as previously stated.
Averageit = β0 + β1 GBMit + β2 Control_variablesit + μi + λt + εit
Intensityit = β0 + β1 GBMit + β2 Control_variablesit + μi + λt + εit
MCPIit = β0 + β1 GBMit + β2 Control_variablesit + μi + λt + εit
Because GBMit is a negative indicator, it means that a higher deviation in government budget expenditure signifies lower government budget management efficiency. So, if the coefficient of β1 is positive and β2 is negative, it indicates a negative correlation between GBM and regional carbon emissions. In this scenario, a greater deviation in government budget expenditure corresponds to higher regional carbon emissions, thereby validating Hypothesis 2.
4. Results and Discussions
4.1. Descriptive Statistics
Table 2 reports the results of the descriptive statistics for all variables. The mean values of Average, Intensity, and MCPI are 10.090, 2.658, and 0.491, respectively, which exceed their respective median values of 7.940, 2.123, and 0.421. This right-skewed distribution suggests that a few provinces in China exhibit exceptionally high levels of carbon emissions, while the majority has regional carbon emissions below the mean, concentrated at lower levels. The standard deviations of Average, Intensity, and MCPI are 6.573, 1.751, and 0.232, respectively, indicating substantial variability in carbon emission levels across Chinese provinces.
The maximum value of GEE is 0.067, the minimum value is 0.010, and the standard deviation is 0.010, indicating a significant variation in the level of environmental expenditure across different provinces in China. At the same time, the mean value of GEE is 0.030, which is larger than the median of 0.028. This is a right-skewed distribution, indicating that most of the current financial environmental protection expenditures of local governments are smaller than the mean, and a few provinces have higher financial environmental protection expenditures.
The maximum value of deviation of GBM is −0.011, and the minimum value is −0.249, indicating a big difference in the budget expenditure situation of provinces in China, and all of them are “cost-saving”. The average value is −0.088, which is lower than the median value of −0.078. In terms of the absolute value, the budget implementation situation of most provinces is maintained at a more reasonable level, and a small number of provinces has a greater deviation of budgets. From the perspective of the absolute value, the budget execution of most provinces is maintained at a more reasonable level, while the deviation of the budgets of a few provinces is larger. At the same time, it can be found that the control variables still maintain good dispersion, indicating that the selection of control variables is more reasonable, which is helpful for further regression analysis.
4.2. Analysis of Baseline Regression Results
4.2.1. Regressions Results of Government Environmental Expenditures
Table 3 presents the regression results of the impact of government environmental expenditures on regional carbon emissions. Firstly, in column (1), the negative and significant coefficient of Average at the 1% level underscores the consistent finding in the extant research that regional per capita carbon emissions are inversely related to government environmental protection expenditures [23]. This corroborates the notion that increased investments in environmental protection measures by governments can effectively mitigate carbon emissions at the regional level. Our study adds to this body of knowledge by demonstrating this effect in the specific context examined.
In column (2), the coefficient of Intensity is 0.782, which is not significant, indicates that government environmental expenditures do not significantly affect regional carbon emission intensity. Regional carbon emission intensity is the ratio of total carbon emissions to GDP, reflecting the dependency level of unit economic output and carbon emissions. The possible reason lies in the fact that the macro-economy of developing China still relies heavily on energy consumption and carbon emissions, and the factors affecting regional carbon emissions are very complex [24], so it is difficult to fundamentally improve the dependence of economic development on carbon emissions by government fiscal regulations alone.
In column (3), the coefficient of MCPI is 0.688 and significant at the 5% level, which indicates a significant positive correlation between regional carbon emission performance and government environmental protection expenditures. This suggests that an increase in government environmental expenditures contributes to improve regional carbon emission performance. It can be seen that the promotion effect of government environmental expenditure on carbon emission performance can be accurately tested after a comprehensive consideration of input and output factors.
In summary, the increase in government environmental expenditures will significantly inhibit regional carbon emissions, thus promoting the development of carbon reduction and emission reduction, and Hypothesis 1 is supported.
4.2.2. Regression Results of Government Budget Management
Table 4 displays the regression results regarding the influence of government budget management on regional carbon emissions. In column (1), the coefficient of Average is 12.286 and significant at a 10% level, indicating there is a promotion effect of government budgetary expenditure deviation on regional per capita carbon emissions. Specifically, a greater deviation in government budgetary expenditure implies a weaker budgetary management capacity, ultimately leading to higher regional per capita carbon emissions. Therefore, our findings lend support to Hypothesis 2.
In column (2), the coefficient of Intensity is 1.861 and is significant at a 1% level, suggesting a robust positive correlation between budgetary expenditure deviation and regional carbon emission intensity. In other words, a lower budgetary expenditure deviation corresponds to more a disciplined budget management, leading to a reduction in regional carbon emission intensity.
In column (3), the coefficient of MCPI is −0.149 and insignificant at the 10% level, indicating there is no significant negative correlation between regional carbon emission performance and government budget expenditure deviation, and that the effect of the government’s budgetary management on enhancing regional carbon emission performance is not significant.
In summary, our analysis reveals a negative correlation between regional carbon emissions and government budget management. There are few studies on the influence of government budget rationality on regional carbon emissions. The findings suggest that regions with stronger budget management exhibit lower deviations in budget expenditure, which in turn leads to decreased levels of carbon emissions in these regions. This relationship can be attributed to the fact that a lower deviation in budget expenditure signifies a higher level of government administration. Such efficient management is beneficial for creating and maintaining a conducive local business environment and promoting the expectation of environmentally sustainable development among local enterprises. As a result, the reduction in regional carbon emissions is facilitated. In line with these observations, Hypothesis 2 is validated.
4.3. Robustness Test
4.3.1. Robustness Tests of Government Environmental Expenditures
Table 5 presents the results of robustness tests examining the impact of the government environmental expenditures on regional carbon emissions. In column (1), we replace the core explanatory variables, with the natural logarithm of government environmental expenditures (Env) being the main variable of interest to be regressed again. The regression coefficient of Average and Env is −2.475, and it is statistically significant at the 1% level, which indicates that government environmental protection expenditures have an inhibitory effect on regional per capita carbon emissions, thus supporting Hypothesis 1.
In column (2), the regression results are obtained after substituting the explanatory variables. The natural logarithm of the total amount of regional carbon emissions (Total) is chosen as the explanatory variable. The coefficient of Total and GEE is −5.114, and it is significant at the 5% level, indicating that the increase in government environmental protection expenditures will effectively restrain the growth of regional carbon emissions.
Column (3) shows the regression results, considering the lag effect of the influence of regional carbon emission performance and government environmental protection expenditures, and alleviating the possible endogeneity problem by lagging one period. The regression coefficient of L.MCPI and GEE is 1.367 and significant at the 5% level. This suggests that an increase in government environmental protection expenditures has a significant and positive impact on enhancing regional carbon emission performance. Therefore, the finding supports the robustness of Hypothesis 1.
4.3.2. Robustness Tests of Government Budget Management
The robustness test of the impact of government budget management on regional carbon emissions is conducted by replacing the explanatory variable of the deviation of budget expenditure (GBM) with the deviation of budget income (GBM_in) and then regressing. The results are presented in Table 6.
In column (1), the regression coefficient of Average and GBM_in is 10.754, and it is significant at the level of 10%. This result implies a positive relationship between the deviation in government budget revenue and regional per capita carbon emissions. Specifically, it suggests that as the deviation in government budget revenue increases, there is a corresponding increase in regional per capita carbon emissions.
In column (2), the coefficient of Intensity and GBM_in is 3.104 and significant at the 5% level, indicating that after replacing the explanatory variables, the government’s budgetary management function still significantly negatively affects regional carbon emission intensity.
In column (3), the coefficient of MCPI and GBM_in is 0.023 and non-significant at the 10% level, a result identical to that of budgetary expenditure deviation, suggesting that the conclusions of Hypothesis 2 are robust.
4.4. Further Analysis
4.4.1. Analysis of Regional Heterogeneity of Government Environmental Expenditures
Given the disparities in economic development and policy implementation across regions in China, it is imperative to delve deeper into the variation in the impact of government expenditures on environmental protection on regional carbon emissions. This study aims to analyze the heterogeneous nature of this relationship across regions, specifically focusing on the division of the National Development and Reform Commission into eastern, central, and western regions. The regression results following this regional division are presented in Table 7.
Columns (1)–(3) present the empirical findings regarding the influence of government environmental expenditures on regional per capita carbon emissions in different regions of China. It can be seen that, after regression by region, the regression coefficients of Average and GEE are −46.180, −80.317, and −115.047, and they are significant at the levels of 1%, 5%, and 5%, respectively, which verifies the conclusion of the baseline regression above, once again, i.e., government environmental expenditures are significantly negatively correlated with regional per capita carbon emissions. The results above once again show that government environmental expenditure can play a significant environmental governance effect when economic factors are not taken into account, and this paper proves that government environmental expenditure can significantly reduce total carbon emissions in any region.
Columns (4)–(6) show the results of the regression of government environmental expenditures on regional carbon emission intensity by region. Notably, the regression coefficients of Intensity and GEE remain insignificant, even after conducting the analysis at a regional level. Given the dependence of economic development on carbon emissions, there is still no significant relationship between government environmental spending and carbon intensity in any region of China.
Columns (7)–(9) show the regression results of the influence between government environmental expenditures and carbon emission performance across different regions. It is observed that the western region demonstrates the highest coefficient of 2.439, but it is not statistically significant. This suggests that government environmental protection expenditures in the western region do not significantly influence regional carbon emission performance. Similarly, the eastern region exhibits the second highest coefficient of 1.116, which surpasses the nationwide influence level, but the significance decreases from 5% to 10%. On the other hand, the central region displays the lowest regression coefficient of 0.301, but remains statistically significant at the 5% level. This implies that the central region, by increasing government environmental protection expenditures, can effectively enhance the local regional carbon emission performance, hence reducing the overall level of regional carbon emissions. Carbon emission performance can be defined as the economic, social, and ecological benefits derived from the utilization of natural carbon ecological capacity in human society’s production and everyday activities. Its primary objective is to optimize the management of resource elements, aiming to achieve optimal economic, social, and ecological outcomes by minimizing resource inputs and mitigating carbon emissions. Consequently, it provides a more comprehensive assessment of carbon emissions. Government investments in environmental initiatives are advantageous in promoting the advancement of low-carbon industries. However, due to the great differences in the economic development level, legal environment, and policy implementations in different regions of China, the eastern and central regions tend to have richer financial resources and better market environments than the western regions, and many heavy industries tend to gather in the central and western regions. Therefore, government environmental expenditure in the eastern region has a more significant effect on carbon emission control.
Overall, it can be summarized that there are significant regional disparities in carbon emissions in China, which are influenced by the government’s environmental expenditure policies. Notably, enhancing government expenditures on environmental protection has been found to have the most substantial inhibitory impact on carbon emissions, particularly in the western region.
4.4.2. The Regulating Effect of Government Financial Transparency on Budget Management
Transparency in government finances can lead to budget deviations [25]. Ríos et al. [25] have shown that local fiscal budgets with high transparency will be more prudent, thus affecting the deviation of government budgets. Therefore, we aim to conduct a more in-depth analysis of the moderating effect that the government’s financial transparency has on this influencing mechanism. To measure the fiscal transparency exhibited by local governments, we utilized the highly regarded China Fiscal Transparency Report, which was released by the prestigious Shanghai University of Finance and Economics. The report is currently the only authoritative measure of provincial fiscal transparency, covering the government’s general budget funds, government-managed funds, state-owned enterprise funds, and other public information. According to the principal-agent theory, the information asymmetry between the local government and enterprises can be alleviated when the government’s financial transparency is improved. The higher degree of financial information disclosure and transparency will ensure that the budget deviation will be kept within a reasonable range, and at the same time, ensure that the funds of “over-recovery” and “cost-saving” are utilized in the proper place instead of being misappropriated and abused. Therefore, it is assumed that government financial transparency plays a regulatory role in the management of government budgets, thereby affecting regional carbon emissions. The samples are grouped by the annual median of government fiscal transparency, and when the fiscal transparency of the province is higher than the annual median, it is classified as the group with higher fiscal transparency, otherwise it is classified as the group with lower fiscal transparency. The regression results for groups are presented in Table 8.
Column (1), compared with column (4), reflects the difference in the impact of government budget management on regional per capita carbon emissions under different levels of government financial accounting functions. When the financial transparency is lower, the regression coefficient of Average and GBM is 22.661, which is significant at 1% level. At the same time, its significance level and regression coefficient are significantly improved compared with the baseline regression results, indicating that the positive relationship between the deviation of the government’s budgetary expenditures and the regional per capita carbon emissions is more significant and stronger under the lower government’s financial transparency. When government financial transparency is higher, the coefficient of GBM is −0.3 and is not significant, which indicates that the government financial transparency negatively moderates the promotion effect of the deviation of the government’s budget expenditures on regional per capita carbon emissions.
Comparing columns (2) and (5), it can be seen that when government financial transparency is lower, the regression results of Intensity with GBM are higher than those when the government financial transparency is higher in terms of regression coefficients and significance, which confirms once again that there is a moderating effect of the government financial transparency on the process of the government’s budgetary management affecting the regional carbon emission intensity. Comparing columns (3) and (6), it can be seen that, when government financial transparency is higher, the inhibitory effect of government budgetary expenditure deviation on regional carbon emission performance is weakened, and the significance of this influence can be improved when government financial transparency is higher.
It can be seen that the higher the government financial transparency, the weaker the promotion effect of government budget deviation on the regional carbon emission level, and the stronger the inhibition effect of government budget management on regional carbon emissions. In other words, government financial transparency plays a vital role in positively regulating the inhibition effect of government budget management on regional carbon emission levels.
5. Conclusions
The purpose of this study was to explore the impact of government environmental expenditure on total regional carbon emissions, carbon intensity, and carbon emission performance in 30 provinces of China from 2008 to 2017. The results show that there is a significant negative correlation between government environmental expenditure and regional carbon emissions. Specifically, increased government spending on environmental protection led to a reduction in per capita carbon emissions, while also improving regional carbon performance. However, the impact of environmental expenditure on regional carbon intensity is not significant. It is worth noting that the impact of government environmental protection spending on carbon emissions varies by region. The eastern region has the most significant carbon emission reduction effect, followed by the central region, and the western region has the weakest effect.
In addition, we found that government budget management has a significant negative correlation with regional carbon emissions. Strengthening the scientific management of government budget and reducing the deviation of budget and final accounts can effectively reduce regional per capita carbon emissions and carbon emission intensity. However, the impact of government budget management on regional carbon emission performance is not significant. In addition, government financial transparency positively moderates the inhibitory effect of government budget management on regional carbon emissions. In other words, higher levels of fiscal transparency enhance the effectiveness of government budget management in reducing carbon emissions at the regional level. In summary, this study reveals the important role of government environmental spending and budget management in reducing carbon emissions at the regional level in China, which is of great significance for the formulation and implementation of regional carbon emission reduction policies. Therefore, this paper puts forward the following policy recommendations.
First, the government should play a guiding role in government spending on environmental protection. The government should scientifically plan its investment scale, ensure that relevant investment projects meet the needs of green development, and improve the efficiency of government investment. At the same time, it should give full play to its guiding role, provide relevant concessional loans or subsidies, and encourage and guide private capital to make green investments.
Second, it should establish a standardized and scientific budget system, increase fiscal investment in environmental protection, and effectively improve the governance capacity of the government. In terms of budget management, we should continue to promote the reform of the budget management system, fully reduce the budget deviation of local governments, and achieve “stable expectations and promote development”. It is imperative to tailor strategies to local conditions and implement targeted initiatives, considering regional disparities in economic development and energy composition. Additionally, efforts should be made to enhance the effectiveness of local financial resources allocated for environmental preservation.
Third, local governments should actively respond to the government’s public data opening policy, establish a fair and transparent mechanism for government openness, and improve government transparency by releasing financial data and policy implementation, so as to enhance the public’s sense of participation and trust in government governance and strengthen the guiding power of government environmental policies to the public and enterprises.
However, there are some limitations in this study. First, due to the limitation of data availability, this study only uses data from 2008 to 2017, which may affect the applicability of our conclusions to China’s recent development; therefore, it is necessary to expand the scope of the study to explore whether the conclusions are applicable to China’s recent development. If future scholars have access to more open government public data, they should build a more comprehensive calculation method to calculate the latest provincial government financial transparency and budget management data. Secondly, although this paper studies government environmental expenditure on the basis of existing literature and expands the scientific impact of government budget management on regional carbon emissions, it still cannot deeply analyze the relationship between government budget management and environmental expenditure. Future studies can further explore the mechanism and internal relationship between environmental expenditure and budget management on carbon emissions. Third, compared with a single carbon emission intensity index, carbon emission performance can more comprehensively measure the economic benefits of carbon emissions. Although this paper preliminarily discusses the differences in different carbon emission measurement methods, it is still unable to comprehensively analyze other important factors affecting the relationship between carbon emission and economic development. Future research could further explore the mechanisms of action between carbon emissions and economic performance. In addition, future research could further explore the long-term effects of specific measures of government spending and budget management on carbon emissions, as well as the moderating effects of other factors on this relationship.
Author Contributions
Z.T.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, and Writing—review and editing. Z.Z.: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, and Writing—review and editing. W.D.: Conceptualization, Formal analysis, Supervision, and Writing—review and editing. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Beijing Education Science “14th Five-Year Plan” 2022 Annual Key Project, grant number AGAA22053; and the National Accounting Key Research Project “Research on the Construction of Financial and Accounting Supervision System and High-quality Economic Development”, grant number 2023KJA3-12.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to privacy or ethical restrictions.
Acknowledgments
All authors declare that there is no moral problem in this article, and all authors agree to participate in and publish it, and the data and materials are available.
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Variable definitions and measurements.
| Type | Variables | Name | Measurement |
|---|---|---|---|
| Explained variables | Regional per capita carbon emissions | Average | Total carbon emissions by province/population by province |
| Regional carbon emission intensity | Intensity | Total carbon emissions by province/GDP by province | |
| Regional carbon emission performance | MCPI | Based on the DEA method | |
| Explanatory variables | Government spending on environmental protection | GEE | The ratio of local government spending on energy conservation and environmental protection to the overall public budget expenditure |
| Deviation degree of government expenditure in budget and final accounts | GBM | (Government expenditure − Government budget expenditure)/Government budget expenditure | |
| Control variables | Economic development | Eco | The natural logarithm of GDP per capita |
| Energy structure | Ener | Total energy consumption by region/total GDP by region | |
| Industrial structure | Ind | Value added of tertiary industry by region/value added of industry by region | |
| Technical level | Tech | R&D expenditure by region/total GDP by region | |
| Level of opening up | Open | Total trade by region/total GDP by region | |
| Ownership structure | Owner | Number of employees of state-owned enterprises by region/number of employed persons by region |
Table 2.
Descriptive statistics of variables.
| Variables | N | Mean | Median | SD | Min | Max |
|---|---|---|---|---|---|---|
| Average | 300 | 10.090 | 7.940 | 6.573 | 2.649 | 37.600 |
| Intensity | 300 | 2.658 | 2.123 | 1.751 | 0.403 | 8.629 |
| MCPI | 300 | 0.491 | 0.421 | 0.232 | 0.209 | 1.217 |
| GEE | 300 | 0.030 | 0.028 | 0.010 | 0.010 | 0.067 |
| GBM | 240 | −0.088 | −0.078 | −0.051 | −0.011 | −0.249 |
| Eco | 300 | 10.510 | 10.490 | 0.501 | 9.180 | 11.820 |
| Ener | 300 | 0.991 | 0.818 | 0.538 | 0.237 | 2.835 |
| Ind | 300 | 1.426 | 1.213 | 0.892 | 0.564 | 6.359 |
| Tech | 300 | 0.010 | 0.009 | 0.006 | 0.001 | 0.032 |
| Open | 300 | 0.303 | 0.145 | 0.337 | 0.018 | 1.575 |
| owner | 300 | 0.097 | 0.082 | 0.035 | 0.042 | 0.220 |
Table 3.
Baseline regression results of government environmental protection expenditures.
| Variables | Average | Intensity | MCPI |
|---|---|---|---|
| (1) | (2) | (3) | |
| GEE | −108.625 *** | 0.782 | 0.688 ** |
| (−4.65) | (0.27) | (2.85) | |
| Eco | −2.043 | −0.737 ** | 0.028 |
| (−1.80) | (−2.14) | (0.99) | |
| Ener | 1.375 | 2.073 *** | −0.079 *** |
| (0.69) | (5.17) | (−6.09) | |
| Ind | −1.145 *** | −0.310 *** | −0.002 |
| (−4.58) | (−3.12) | (−0.16) | |
| Tech | −118.746 * | −23.461 | 4.585 *** |
| (−2.20) | (−1.69) | (3.41) | |
| Open | 0.536 | −0.178 | −0.012 |
| (0.47) | (−0.77) | (−0.47) | |
| Owner | −15.115 | 0.066 | −0.054 |
| (−1.24) | (0.03) | (−0.51) | |
| Constant | 33.834 ** | 8.711 ** | 0.274 |
| (2.62) | (2.49) | (0.98) | |
| Year FE | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
| N | 300 | 300 | 300 |
| R2 | 0.411 | 0.692 | 0.075 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 4.
Baseline regression results of government budget management.
| Variables | Average | Intensity | MCPI |
|---|---|---|---|
| (1) | (2) | (3) | |
| GBM | 12.286 * | 1.861 *** | −0.149 |
| (1.99) | (5.90) | (−1.85) | |
| Eco | −2.730 | −1.927 *** | 0.096 |
| (−1.58) | (−6.75) | (1.54) | |
| Ind | −0.742 | 0.130 *** | 0.002 |
| (−1.04) | (3.66) | (0.10) | |
| Tech | −240.667 *** | 9.796 | 7.193 *** |
| (−4.46) | (0.55) | (3.87) | |
| Open | 1.336 | −0.344 *** | 0.011 |
| (0.65) | (−4.27) | (0.71) | |
| Owner | 4.432 | 0.821 | −0.039 |
| (0.35) | (1.26) | (−0.39) | |
| Constant | 39.699 ** | 22.790 *** | −0.555 |
| (2.70) | (7.61) | (−0.83) | |
| Year FE | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
| N | 240 | 240 | 240 |
| R2 | 0.342 | 0.783 | 0.066 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 5.
Robustness tests of government environmental expenditures.
| Variables | Average | Total | L.MCPI |
|---|---|---|---|
| (1) | (2) | (3) | |
| Env | −2.475 *** | ||
| (−5.79) | |||
| GEE | −5.114 ** | 1.367 ** | |
| (−2.58) | (2.70) | ||
| Eco | −0.572 | 0.252 | 0.041 |
| (−0.58) | (1.26) | (0.44) | |
| Ener | 1.131 | 0.424 ** | −0.087 ** |
| (0.49) | (2.39) | (−2.60) | |
| Ind | −1.344 *** | −0.108 * | 0.005 |
| (−4.28) | (−1.91) | (0.08) | |
| Tech | −77.978 | −10.354 | 3.579 |
| (−1.51) | (−1.18) | (0.96) | |
| Open | 1.038 | 0.058 | 0.005 |
| (1.08) | (0.51) | (0.09) | |
| Owner | −19.104 | −1.248 | 0.070 |
| (−1.32) | (−1.59) | (0.19) | |
| Constant | 25.292 * | 7.514 *** | 0.105 |
| (2.22) | (3.65) | (0.10) | |
| Year FE | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
| N | 300 | 300 | 300 |
| R2 | 0.378 | 0.678 | 0.081 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 6.
Robustness test of government budget management.
| Variables | Average | Intensity | MCPI |
|---|---|---|---|
| (1) | (2) | (3) | |
| GBM_in | 10.754 * | 3.104 ** | 0.023 |
| (1.77) | (2.14) | (0.23) | |
| Eco | 7.419 *** | 0.019 | |
| (5.57) | (0.84) | ||
| Ind | −1.940 *** | −0.503 *** | 0.041 *** |
| (−3.59) | (−4.10) | (4.57) | |
| Tech | 119.266 | −24.660 | −0.162 |
| (1.18) | (−1.20) | (−0.10) | |
| Open | −9.837 *** | −1.732 *** | 0.597 *** |
| (−6.09) | (−4.47) | (22.07) | |
| Owner | 110.573 *** | 24.558 *** | −0.737 *** |
| (9.89) | (8.86) | (−3.94) | |
| Constant | −74.734 *** | 1.561 *** | 0.126 |
| (−5.71) | (3.92) | (0.58) | |
| Year FE | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
| N | 240 | 240 | 240 |
| R2 | 0.396 | 0.456 | 0.869 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 7.
Analysis of regional heterogeneity.
| Variables | Average | Intensity | MCPI | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Eastern | Central | Western | Eastern | Central | Western | Eastern | Central | Western | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| GEE | −46.180 *** | −80.317 ** | −115.047 ** | −1.079 | −0.093 | 1.102 | 1.116 * | 0.301 ** | 2.439 |
| (−4.92) | (−2.79) | (−2.88) | (−0.25) | (−0.02) | (0.11) | (2.21) | (2.32) | (1.42) | |
| Ind | 0.688 *** | −1.620 | −3.478 | −0.133 *** | 0.275 | 0.248 | −0.038 *** | −0.032 ** | 0.119 * |
| (4.85) | (−1.15) | (−1.67) | (−4.03) | (0.72) | (0.73) | (−3.75) | (−2.51) | (2.19) | |
| Tech | 170.002 *** | −111.529 | 19.125 | −7.444 | −63.076 ** | 109.648 * | 3.385 *** | 1.555 * | 9.584 |
| (3.40) | (−0.66) | (0.09) | (−1.13) | (−2.92) | (2.21) | (5.10) | (2.24) | (1.53) | |
| Open | 1.518 | 9.448 | −16.027 *** | −0.654 *** | 3.604 ** | −1.548 *** | −0.035 | −0.116 *** | 0.202 *** |
| (1.49) | (1.48) | (−5.82) | (−10.11) | (3.10) | (−5.51) | (−0.91) | (−3.25) | (5.44) | |
| Owner | 22.405 *** | −106.355 *** | −70.860 *** | 1.745 ** | 8.355 * | −19.187 ** | −0.582 *** | 0.324 *** | 0.700 * |
| (4.90) | (−4.40) | (−3.43) | (2.66) | (2.01) | (−2.90) | (−6.10) | (3.61) | (2.02) | |
| Constant | 2.994 ** | 23.969 *** | 25.540 *** | 2.936 *** | 2.621 *** | 5.860 *** | 0.756 *** | 0.402 *** | −0.040 |
| (2.82) | (5.81) | (6.12) | (21.49) | (10.61) | (4.46) | (17.59) | (17.33) | (−0.22) | |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 120 | 90 | 90 | 120 | 90 | 90 | 120 | 90 | 90 |
| R2 | 0.621 | 0.571 | 0.540 | 0.835 | 0.864 | 0.819 | 0.359 | 0.395 | 0.154 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 8.
The regulating effect of government financial transparency.
| Variables | Low Government Financial Transparency | High Government Financial Transparency | ||||
|---|---|---|---|---|---|---|
| Average | Intensity | MCPI | Average | Intensity | MCPI | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
| GBM | 22.661 *** | 2.148 *** | −0.162 | −0.300 | 1.425 * | −0.147 * |
| (4.66) | (5.86) | (−1.01) | (−0.05) | (1.91) | (−2.04) | |
| Eco | 1.220 | −2.580 *** | 0.285 | −2.283 | −1.703 ** | −0.019 |
| (0.75) | (−4.98) | (1.28) | (−0.67) | (−3.11) | (−0.66) | |
| Ind | −0.890 | −0.557 * | 0.337 | 0.549 | 0.149 | −0.055 *** |
| (−0.65) | (−2.06) | (1.12) | (0.69) | (1.64) | (−6.78) | |
| Tech | −161.969 | 17.747 | 7.352 | −38.798 | −56.627 ** | −0.318 |
| (−1.00) | (0.72) | (1.23) | (−0.26) | (−2.95) | (−0.26) | |
| Open | −6.497 *** | −0.165 | 0.230 ** | 0.990 | −0.036 | −0.125 *** |
| (−8.66) | (−0.59) | (2.38) | (1.01) | (−0.19) | (−6.14) | |
| Owner | −2.982 | 0.213 | 0.318 | 25.376 | 2.696 | 0.322 |
| (−0.56) | (0.19) | (0.69) | (0.88) | (0.62) | (1.02) | |
| Constant | 3.369 | 30.060 *** | −2.978 | 24.733 | 20.805 *** | 0.758 ** |
| (0.19) | (5.41) | (−1.10) | (0.79) | (3.82) | (2.76) | |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 118 | 118 | 118 | 122 | 122 | 122 |
| R2 | 0.523 | 0.795 | 0.471 | 0.507 | 0.679 | 0.588 |
Note: t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
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Tang, Z.; Zhang, Z.; Deng, W. Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China. Sustainability 2024, 16, 6707. https://doi.org/10.3390/su16156707
AMA Style
Tang Z, Zhang Z, Deng W. Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China. Sustainability. 2024; 16(15):6707. https://doi.org/10.3390/su16156707
Chicago/Turabian StyleTang, Ziru, Zenglian Zhang, and Wenyueyang Deng. 2024. "Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China" Sustainability 16, no. 15: 6707. https://doi.org/10.3390/su16156707
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