4.1. Empirical Model Set
As mentioned in
Section 2.1, in China, the central government’s work reports and their successive Five-Year Plans are the basis for the resources allocation and energy devotion across the whole country, and the provincial governments would demonstrate consistent intention allocation by publishing similar reports in accordance with the central government under the political centralization and GDP tournament chairmanship.
Cognitive motivation theory holds that human behavior depends on the attention attribution of related events [
55]; management is only decision-making, the key to which is how decision-makers effectively allocate their limited attention [
22]. Subsequently, Jones introduced attention study into the field of government management and proposed an attention-driven policy choice model, which articulates that the choice of government behavior mainly depends on the factors that most attract government decision-makers’ attention to specific matters. As analyzed in
Section 2.1, language is a reflection of mental processes [
23]; frequently used words that are in the center of people’s cognition can reflect people’s cognitive tendencies and the most active part of their thinking [
56]. Correspondingly, the AI and RI derived from environment-related keyword occurrence can reflect Chinese governments’ attention allocation, motivation tendencies, and their intention or will regarding environmental governance. We observed a correlation between governments’ absolute intention or relative intention and the provincial environmental quality (EQ) which acted as the dependent variable in our empirical specification. Due to the usually regular transfer of officials, the policy is not necessarily continuous, so there was no lag.
Based on the above data, results, and analyses, we present the empirical equations as follows. All data were processed logarithmically.
where EQ
it is used as described in
Section 3.5; AI and RI represent the absolute index and relative index of provincial governments’ intention to govern the environment, respectively; CAI and RI represent the absolute index and relative index of the central government’s intention to govern the environment, respectively; and the absolute value and direction of the coefficients β
1 and β
2 were observed in relation to the provincial environmental quality. RPGDP represents the real GDP per capital of provinces or municipalities. We used the GDP per capita in 2004 as the base to deal with inflation. IS is for the provincial industrial structure; EF is for the provincial energy efficiency; ES is for the provincial energy structure; EGP denotes the proportion of provincial environmental proposals among 10,000 people, reflecting people’s environment appeal; and PD is the provincial population density. All other variables were used to control the equations. α is the constant term, and ε is the error term.
4.2. Results
We first used pooled regression.
Table 3 shows the results of the pooled regression. In
Table 3, Models (1) and (3) show the results of general pooled regression and Models (2) and (4) show the results of regression using the province as the clustering variable. Model (1) shows that the coefficient of AI is significant, which means that provincial governments’ AI positively influences the provincial environmental quality. Other columns show that RPGDP, EF, ES, and PD also play important roles. However, the general result is not as satisfactory as expected; in particular, some key variables, such as CAI and CRI, are not significant. Comparing the general standard error with the cluster-robust standard error, the former is about half of the latter. Therefore, the pooled regression seems to be unideal, though several results can be used.
Next, we considered the existence of fixed effects or random effects in the model. Then, the statistical software package STATA was employed for testing by using the command xtoverid. We took the Hausman test to compare the random effect model with the fixed effect model, and included the constant terms when comparing coefficient estimates. The regression results were as follows. For CAI and AI, testing of over identifying restrictions: fixed vs. random effects, cross-section time-series model: xtreg re robust cluster (province code), Sargan–Hansen statistic 26.99 χ2 (8), p-value = 0.0007. For CRI and RI, testing of over identifying restrictions: fixed vs. random effects, cross-section time-series model: xtreg re robust cluster (province code), Sargan–Hansen statistic 26.45 χ2 (8), p-value = 0.0009. Here, (8) refers to 8 independent variables which are AI, CAI, IS, RPGDP, EGP, EF, ES, PD for testing AI or CAI, and RI, CRI, IS, RPGDP, EGP, EF, ES, PD for testing RI or CRI, respectively.
The
p-values were 0.0007 and 0.0009, respectively, so the random effect is strongly rejected and the fixed effect model should be used. The situations of each province are different and some variables that do not change with time may be missing, so the fixed effect (within estimator) and the least square dummy variable (LSDV) methods were used for regression. The results are shown in
Table 4.
In
Table 4, the regression results are still unsatisfactory, regarding significant results. We think that an unobservable relationship to error terms in CAI and CRI may exist. So, we tried to find the instrument variable that is not related to the error term but affects the CAI and CRI variables. Then, we tried to use provincial governments’ fiscal investment (FI) in the environmental governance, which can somewhat reflect their intention and effort regarding environmental governance. Due to the political centralization system, provincial governments’ FI can also reflect the central government’s CAI and CRI somewhat, but are not related to the error term in the environmental governance. Therefore, FI was used as the instrument variable. The specific operation process used was as follows. First, the deviation transformation of the fixed effect model was completed, then the instrument variable FI was used to estimate CAI and CRI. Next, the results, which are displayed in
Table 5 and
Table 6, were acquired by the two-step least square (2SLS) regression. The results of the two tables were derived using different control variables, and the coefficients were found to be relatively significant.
4.3. Discussion
Table 5 and
Table 6 show that the absolute index (AI) and relative index (RI) of the central government’s intention to govern the environment were negatively correlated with the provincial environmental quality, at least at an 5% significant level, indicating that the intensification of the central government’s intention negatively impacts the environment. Specifically, the central government’s intentionality has a destructive effect on the provincial environment, and a 1% increase in intentionality will cause the environmental index to decline by about 0.8%. This is in contrast to our H1 that the AI and RI of the central government’s intention to govern the environment are positively correlated with the provincial environmental quality. The possible causes of this are as follows. First, China’s political centralization has resulted in a GDP-oriented political tournament, which ensures the central government’s strong control of the local administration. As GDP is the key indicator used by the upper-level government to appraise officials, and residents’ mobility across jurisdictions is very low due to China’s Hukou system [
57], provincial officials are not controlled like in other countries and can be indulgent in their pursuit of economic growth and career promotion. In developed countries, local residents who have higher mobility across states can indicate their preferences in elections, so officials must strike a balance between a resident’s environmental utility and economic growth to achieve their political objectives. This can force officials to implement higher environmental standards to win more elections [
58], so local government officials seeking economic development to the detriment of the environment can be curbed. However, in China, provincial governments with no constraint from the bottom inevitably race to the bottom in environmental regulation [
59], which means that officials usually maintain economic growth during their tenure by easing environmental regulations.
Second, in China, the power of economic development is decentralized to provincial governments through fiscal decentralization. With its strong political control, the growth rate of the provincial economy [
60] or revenue collection [
61] are used by the central government to evaluate and promote the local officials. Fiscal decentralization provides provincial governments more independence to address and manage economic interests and behavior targets [
43]. As a result, provincial governments, as interested individuals, pursue economic growth at the expense of the environment and even secretly protect some high-pollution and high-energy-consuming enterprises who may be major taxpayers in their jurisdiction. As more stringent environmental measures increase the costs of firms and deflect capital elsewhere, this naturally motivates provincial governments to choose excessively lax standards for local environmental quality [
62].
Environmental performance and economic performance play extremely different roles in the evaluation (promotion) system, which generates dually asymmetric incentives to the conduct of provincial government officials [
63]. Usually, officials are blamed or punished for environmental incidents, but they are seldom promoted for improving the environment, and the default outcome of this is that GDP is the only key indicator of the career promotion championships. This can be designated as a mismatch of power structure or incentives of China’s institution system. As provincial officials are perversely motivated [
32], provincial governments may indulge in the GDP championship again when environmental inspectors leave.
Third, environmental regulation in China has been centralized. This is also reflected in the statement that environmental centralization has been a significant and long-term by-product of China’s political centralization [
64,
65]. However, decisions at higher and more distant levels of government undermine local governments’ self-determination and reflect a lower diversity than local decisions [
30]. Additionally, environmental centralization increases the cost of inspection; the higher the cost, the more difficult or the less detailed the inspection. This also contributes to the negative influence of the central government.
Fourth, from the perspective of economics, China, while undergoing its middle or later industrialization, is faced with the environmental Kuznets curve issue, which implies that a positive correlation exists between economic growth and pollution emissions. This also partly explains why local government officials strive for faster growth at the expense of environmental quality and protect, instinctively or secretly, emission-intensive industries and enterprises.
As for the second hypothesis,
Table 5 and
Table 6 show that the AI and RI of the provincial governments’ intention were positively correlated with the environmental quality at a significant level, indicating that the intensification of the provincial governments’ intention to govern the environment positively impacts the environment. Column 5 of
Table 5 and
Table 6 shows that after controlling other variables, the coefficients of the AI and RI of the provincial governments’ intention are both approximately 0.3. In other words, when the provincial governments’ intention increases by 1%, the environmental quality improves by 0.3%. This is consistent with H2, which stated that the AI and RI of the provincial governments’ intention to govern the environment are positively correlated with the provincial environmental quality. The possible reasons for this are as follows.
First, from the perspective of environmental federalism, China’s central authority has fought to reverse China’s environmental Kuznets curve by sending environmental governance inspection groups to the provincial jurisdictions and encouraging high-quality and green development. This ongoing transformation necessitates a painful industry structure adjustment and updating, which may undermine some official careers since their tenure is usually short in one jurisdiction. So, a conflict exists between the central and provincial governments. Models (4) in the above last two tables show that the coefficients of lnRPGDP are about 0.2, which shows that, though not significant, the real GDP per capita is positively correlated with the environmental quality. This may indicate that the central government has authority in this conflict in which the provincial governments have somewhat progressed in their efforts to reverse their environmental Kuznets curve.
Second, as economics teaches, when environmental background conditions, emissions levels, climate, weather, risk preferences, policy priorities, and income levels diverge, only regulations tailored to localized circumstances improve social welfare [
66]. The central government’ one-size-fits-all policies may not be appropriate for all provinces. The process of environmental governance should be free flowing with openness and positive interactions from the public, and should exploit all kinds of knowledge and information. In present China, ecological civilization has been upheld by a central authority, which has been involved in all kinds of propaganda; accordingly, the public’s ecological awareness is increasing. In combination, the provincial governments and their chief officials have been tasked to continuously provide ecological public goods. As a result, the environmental governance of provincial governments has been somewhat effective. Therefore, the provincial governments’ AI and RI have had positive impacts on the provincial environmental quality.
When the control variables are considered, the industrial structure has a positive effect on the provincial environment. The higher the proportion of the tertiary industry, the better the environmental quality. This implies that optimizing the provincial industrial structure is crucial for environmental governance. The energy structure (here, the proportion of coal consumption is the main reference object) and energy use efficiency have a relatively positive effect on the provincial environment. As expected, the population density significantly negatively influences the environment. Notably, the coefficient of people’s environmental appeal is approximately 0.05, which, though negative, indicates that people’s appeal has a small impact on the environment governance and that the governments have not paid much attention to people’s appeal. China’s top-down governance has resulted in a lack of being accountable for the bottom. However, the public’s environmental appeal should be carefully considered, since, in November 2017, the 19th National Congress of the Communist Party of China declared that China is now mainly facing a contradiction between China’s unbalanced and inadequate development and the people’s ever-growing needs for a better and high-quality life. That is, the Chinese people are not just fighting for food and clothing but for high-quality lives, including their need for high-quality environmental public goods.