Research Article: The impact of local government investment on the carbon emissions reduction effect: An empirical analysis of panel data from 30 provinces and municipalities in China

Date Published: July 20, 2017

Publisher: Public Library of Science

Author(s): Lingyun He, Fang Yin, Zhangqi Zhong, Zhihua Ding, Yongtang Shi.


Among studies of the factors that influence carbon emissions and related regulations, economic aggregates, industrial structures, energy structures, population levels, and energy prices have been extensively explored, whereas studies from the perspective of fiscal leverage, particularly of local government investment (LGI), are rare. Of the limited number of studies on the effect of LGI on carbon emissions, most focus on its direct effect. Few studies consider regulatory effects, and there is a lack of emphasis on local areas. Using a cointegration test, a panel data model and clustering analysis based on Chinese data between 2000 and 2013, this study measures the direct role of LGI in carbon dioxide (CO2) emissions reduction. First, overall, within the sample time period, a 1% increase in LGI inhibits carbon emissions by 0.8906% and 0.5851% through its influence on the industrial structure and energy efficiency, respectively, with the industrial structure path playing a greater role than the efficiency path. Second, carbon emissions to some extent exhibit inertia. The previous year’s carbon emissions impact the following year’s carbon emissions by 0.5375%. Thus, if a reduction in carbon emissions in the previous year has a positive effect, then the carbon emissions reduction effect generated by LGI in the following year will be magnified. Third, LGI can effectively reduce carbon emissions, but there are significant regional differences in its impact. For example, in some provinces, such as Sichuan and Anhui, economic growth has not been decoupled from carbon emissions. Fourth, the carbon emissions reduction effect in the 30 provinces and municipalities sampled in this study can be classified into five categories—strong, relatively strong, medium, relatively weak and weak—based on the degree of local governments’ regulation of carbon emissions. The carbon emissions reduction effect of LGI is significant in the western and central regions of China but not in the eastern and northeast regions. This study helps overcome the limitations of previous studies on the regulatory effects of LGI on carbon emissions, and the constructed model may more closely reflect actual economic conditions. Moreover, the current study can benefit countries similar to China that aim to objectively identify the impacts of their LGI on carbon emissions, and such countries can use it as a reference in the formulation of investment policies based on their economic and industrial characteristics.

Partial Text

Since the Intergovernmental Panel on Climate Change (IPCC) released its third climate evaluation report, climate warming has gradually become an increasingly important agenda item. Countries must take on the important tasks of developing low-carbon economies and addressing climate warming. The Paris agreement, which was approved on December 12, 2015, sets the following goal: “To achieve a balance between anthropogenic emissions and removals of greenhouse gases in the second half of this century”.

From a research perspective, the key to reducing carbon emissions is identifying the major factors that influence them, including both productive and consumptive factors. The research methods used include both factor decomposition and other empirical methods. The former mainly refers to the log mean Divisia index (LMDI) method and the Kaya identity in order to decompose the influencing factors, while the latter measure the relationships between various factors and carbon emissions based on theory. Research shows that the level of economic development, the economic structure, energy intensity, technological progress, the urbanization level, the degree of trade openness, population and other factors all impact carbon dioxide (CO2) emissions [1–14]. From the perspective of this study, two of these factors merit attention. First, we consider the relationship between the level of investment and carbon emissions. Many scholars in China and elsewhere acknowledge this linkage and contend that investment has a pulling effect on carbon emissions. Most of these studies focus on foreign direct investment (FDI) and fixed asset investment (FAI) [15, 16]. Some scholars have noted a decoupling of the relationship between economic growth induced by investment and carbon emissions [17]. Others argue that China can potentially mitigate its carbon emissions through domestic investment [18, 19]. Second, we consider the relation between structural and efficiency (technical) factors and carbon emissions. Many studies have confirmed the impact of these two factors [20–23]. However, for the structural and efficiency paths to function, they require the advancement of a comprehensive set of policy tools. Therefore, many scholars also examine the relationships between policy tools and carbon emissions; the majority of them focus on prices and taxation. Most of the research from a financial perspective affirms the relations among energy prices, energy consumption and carbon emissions, arguing that reasonable energy prices can effectively lower carbon emissions [24–27]. From the perspective of finance and taxation, most of the research focuses on taxation, with few studies considering fiscal expenditures. Drezner notes that tax preferences influence the direction in which future energy policy develops [28]. Glomm et al. and Hubler argue that government taxation is instrumental in motivating enterprises and society to implement energy conservation measures and reduce emissions [29, 30]. An increase in taxation can reduce carbon emissions to a certain extent. Meanwhile, fiscal expenditures comprise fiscal investments, fiscal subsidies, revenues and expenditures of extra-budgetary funds. This study focuses on fiscal investment policy.

When estimating model (6), this study considers two cases in which the first-order lag of CO2 is either considered or not and then compares the two sets of results. The LS method is adopted for the estimation, and the results are presented in Table 3.

Differing economic development levels, investment structures and policy orientations result in differences in the carbon emissions reduction effects of government investment. To further explore the corresponding patterns, the 30 sampled provinces are divided into groups based on the estimation results of the dynamic model. This study adopts the K-means clustering method to classify them into five categories. The results are shown in Fig 2.

This study measures the direct and regulating role of LGI on carbon emissions in 30 sampled provinces in China and categorizes these provinces using a dynamic regulating model. First, the empirical study concluded that within the sample time period, LGI can inhibit carbon emissions by guiding the industrial structure and energy efficiency. The role of the structural path is stronger than that of the efficiency path. Second, when carbon emissions inertia is considered, the carbon emissions reduction effects of LGI are improved to some extent through these two paths, which indicates that good carbon emissions reduction effects magnify the carbon inhibition generated by LGI. Meanwhile, in places with sizeable carbon emissions, carbon inhibition through LGI might be offset by carbon emissions inertia; therefore, its role is hardly evident. Third, there are regional differences in the impact of LGI on carbon emissions. For most provinces in China, LGI can generate an effective carbon inhibition effect. However, for some places, such as Jiangsu, Shanghai and Hunan, LGI does not generate effective carbon inhibition through the related paths. Instead, it generates a carbon pulling effect, which indicates that economic growth in these places has not yet achieved carbon decoupling. Fourth, based on the dynamic comprehensive regulating effect of LGI on carbon emissions, the 30 sampled provinces are classified into five categories. LGI’s role in carbon emissions reduction is classified into the categories of strong, relatively strong, medium, relatively weak and weak. On the whole, the carbon emissions reduction effect of LGI has no apparent correlation with the local economic development level. Instead, it is influenced by industrial structure characteristics, efficient energy use, historical carbon emissions levels and policy directions. Relatively speaking, the carbon emissions reduction effects in the western and central areas of China are more evident. It is difficult for LGI in most provinces in the eastern and northeast areas to effectively inhibit carbon emissions. Furthermore, as noted in Section 2, there is a theoretical inverted U-shaped relationship between LGI and carbon emissions. The results of this empirical study reveal that for some provinces, the carbon pulling effect of increases in economic aggregates as a result of LGI exceeds the carbon inhibition generated through the structural and efficiency paths and therefore does not generate emissions reduction effects. For other provinces, the carbon inhibition generated by LGI through these two paths effectively offsets the carbon pulling effect induced by investment to promote economic growth. This difference also reflects the linkage between the investment structure and carbon emissions to some degree. In actual economic operations, reducing carbon emissions is a goal that every local government pursues while attempting to realize economic growth. According to this study, this goal can be achieved through structural optimization, improvements to efficiency and policy regulation and guidance, which is consistent with the core idea emphasized in China’s “new normal” economy.




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