# Fiscal Decentralization, Local Government Behavior, and Macroeconomic Effects of Environmental Policy

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## Abstract

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## 1. Introduction

_{2}emissions as well as restrain economic growth. Zhang [2] believes that the carbon tax is seen as an essential policy tool to influence the sustainability. Cao et al. [3] find that the 2030 carbon peaking target for China are easily met, but the 2060 carbon neutrality goal cannot be achieved even with our highest carbon tax rates. For environmental policy, existing studies have expanded from economic reasons to institutional factors and government behavior. At the same time, fiscal decentralization is a necessary institutional arrangement that affects the supply of public goods such as the environment. It is of great theoretical and practical significance to explore what effect fiscal decentralization has on environmental pollution and how to stimulate the subjective initiative of local governments in ecological governance under the fiscal decentralization system.

## 2. Literature Review

_{2}emissions by using a balanced panel dataset of seven OECD countries, and show that fiscal decentralization improves environmental quality. Chen, Xu, and Qi [11] investigate the causal effects of environmental decentralization on local governments’ environmental governance. They present strong evidence that establishing the Supervision Centers for Environmental Protection significantly prompt firms to reduce emission pollution. Meng et al. [12] examine the asymmetric link between fiscal decentralization, environmental innovation, and carbon emissions in highly decentralized countries. They show that fiscal decentralization significantly mitigates carbon emissions only at lower to medium emissions quantiles. Other studies believe that fiscal decentralization reduces the quality of the environment. Under the fiscal decentralization system, local governments compete to develop the local economy, attract investment, increase employment opportunities or tax revenue, and relax environmental supervision. Under low-standard environmental constraints, pollution cannot be effectively controlled, leading to the deterioration of environmental quality [13,14,15,16,17]. Zeng et al. [18] find that fiscal decentralization in China provides local governments with incentives for the development of high pollution industries and of large state-owned enterprises, which is not conducive to the improvement of environmental quality. Xia et al. [19] use the Spatial Durbin Model analyzing the spatial impact of fiscal decentralization on regional carbon emissions. They find that fiscal decentralization, both within the region and in its neighborhood, will contribute to carbon emissions in the region. Environmental decentralization will help reduce carbon emissions, while environmental decentralization in neighboring regions will increase carbon emissions in the region.

## 3. Multi-Level-Government E-DSGE Model

#### 3.1. Household

#### 3.2. Firms

_{2}concentration, and $\chi $ denotes a positive parameter that measures the output loss caused by an additional unit of pollutant.

#### 3.3. The Multi-Level Governments

#### 3.4. The Central Bank

## 4. Parameter Estimation

#### 4.1. Calibration of Basic Parameters

#### 4.2. Bayesian Estimation

## 5. Impulse Response Analysis

#### 5.1. Impulse Response Analysis of Fiscal Expenditure Decentralization

#### 5.1.1. The Impact of Fiscal Expenditure Decentralization on Output

#### 5.1.2. The Impact of Fiscal Expenditure Decentralization on Environmental Pollution

#### 5.2. Impulse Response Analysis of Fiscal Revenue Decentralization

#### 5.2.1. The Impact of Fiscal Revenue Decentralization on Output

#### 5.2.2. The Impact of Fiscal Revenue Decentralization on Environmental Pollution

#### 5.3. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. List of the First-Order Conditions under E-DSGE Model

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Parameter | Description | Value | Data Sources |
---|---|---|---|

$\beta $ | Quarterly discount factor | 0.995 | Sample mean |

$\pi $ | Steady-state value of inflation | 1.0071 | Sample mean |

$R$ | Steady-state value of nominal interest rate | 1.0122 | Euler equation |

$\delta $ | Depreciation rate of capital | 0.025 | Heutel [20] |

$\theta $ | Elasticity of substitution for intermediate goods | 6 | Gali [28] |

${\alpha}_{K}$ | Output share of capital | 0.45 | Xiao et al. [29] |

${\alpha}_{G}$ | Output Share of NEGE | 0.06 | Sample mean |

$1-{\delta}_{M}$ | Pollution decay | 0.992 | Heutel [20] |

$N$ | Steady-state value of labor | 0.25 | Gali [28] |

$I/Y$ | Steady-state value of investment–output ratio | 0.168 | Sample mean |

${\theta}_{1}$ | Decentralization of NEGE | 0.5 | Sample mean |

${\theta}_{2}$ | Decentralization of EGE | 0.5 | Sample mean |

$\psi $ | Conversion coefficient of EGE to improve pollution degree | 0.35 | Angelopouloset et al. [30] |

$\phi $ | Carbon emission scale parameters | 0.1235 | Annicchiaricoand Di Dio [26] |

${G}_{1}^{C}/Y$ | Steady-state value of central government NEGE–output ratio | 0.0369 | Sample mean |

${G}_{1}^{L}/Y$ | Steady-state value of local government NEGE–output ratio | 0.1874 | Sample mean |

${G}_{2}^{C}/Y$ | Steady-state value of central government EGE–output ratio | 0.0003 | Sample mean |

${G}_{2}^{L}/Y$ | Steady-state value of local government EGE–output ratio | 0.0056 | Sample mean |

${\tau}^{C}/Y$ | Steady-state value of consumption tax–output ratio | 0.0452 | Sample mean |

${\tau}^{K}/Y$ | Steady-state value of capital tax–output ratio | 0.0965 | Sample mean |

${\tau}^{N}/Y$ | Steady-state value of labor tax–output ratio | 0.0551 | Sample mean |

${\omega}^{C}$ | Decentralization of consumption tax | 0.36 | Sample mean |

${\omega}^{K}$ | Decentralization of capital tax | 0.4 | Sample mean |

${\omega}^{N}$ | Decentralization of labor tax | 0.4 | Sample mean |

$\gamma $ | Local government fiscal expenditure bias parameter | 0.03 | Steady-state calculation |

$\eta $ | The inverse elasticity of labor supply | 1.5 | Gali [28] |

$\mathsf{\Lambda}$ | The negative externality of pollution on output | 0.9970 | Annicchiaricoand Di Dio [31] |

$CA/Y$ | Abatement cost–output ratio | 0.00013 | Annicchiaricoand Di Dio [26] |

$\tilde{Z}/Z$ | Steady-state value of pre-industrial emissions–emissions ratio | 1.280/8.475 | Annicchiaricoand Di Dio [26] |

$\tilde{M}/M$ | Steady-state value of preindustrial emissions stock–emissions stock ratio | 600/829 | Annicchiaricoand Di Dio [26] |

${\varphi}_{2}$ | Elasticity parameters for abatement costs | 2.8 | Nordhaus [32] |

Parameter | Prior Distribution | Post Mean | 90% Confidence Interval | |
---|---|---|---|---|

${s}^{\u2033}$ | Gamma distribution [5,1.5] | 4.0850 | 2.4041 | 5.4247 |

${\gamma}_{p}$ | Gamma distribution [5,1.5] | 20.5948 | 19.8339 | 21.0497 |

${\rho}_{R}$ | Beta distribution [0.8,0.1] | 0.7704 | 0.7555 | 0.7838 |

${\rho}_{pZ}$ | Beta distribution [0.8,0.1] | 0.9993 | 0.9987 | 0.9999 |

${\rho}_{G1C}$ | Beta distribution [0.8,0.1] | 0.2196 | 0.1463 | 0.2761 |

${\rho}_{G1L}$ | Beta distribution [0.8,0.1] | 0.9043 | 0.8394 | 0.9824 |

${\rho}_{G2C}$ | Beta distribution [0.8,0.1] | 0.8976 | 0.8135 | 0.9773 |

${\rho}_{G2L}$ | Beta distribution [0.8,0.1] | 0.6012 | 0.5006 | 0.7353 |

${\rho}_{\tau C}$ | Beta distribution [0.8,0.1] | 0.2349 | 0.1750 | 0.2959 |

${\rho}_{\tau K}$ | Beta distribution [0.8,0.1] | 0.8955 | 0.8084 | 0.9790 |

${\rho}_{\tau N}$ | Beta distribution [0.8,0.1] | 0.8076 | 0.6829 | 0.9294 |

${\rho}_{A}$ | Beta distribution [0.8,0.1] | 0.9555 | 0.9464 | 0.9635 |

${\sigma}_{R}$ | Inverse-gamma distribution [0.01,2] | 0.0798 | 0.0694 | 0.0905 |

${\sigma}_{pZ}$ | Inverse-gamma distribution [0.01,2] | 0.0130 | 0.0107 | 0.0152 |

${\sigma}_{G1C}$ | Inverse-gamma distribution [0.01,2] | 0.7215 | 0.6346 | 0.7930 |

${\sigma}_{G1L}$ | Inverse-gamma distribution [0.01,2] | 0.0107 | 0.0024 | 0.0181 |

${\sigma}_{G2C}$ | Inverse-gamma distribution [0.01,2] | 0.0096 | 0.0027 | 0.0223 |

${\sigma}_{G2L}$ | Inverse-gamma distribution [0.01,2] | 0.0080 | 0.0023 | 0.0142 |

${\sigma}_{\tau C}$ | Inverse-gamma distribution [0.01,2] | 0.2589 | 0.1149 | 0.4118 |

${\sigma}_{\tau K}$ | Inverse-gamma distribution [0.01,2] | 0.0085 | 0.0022 | 0.0161 |

${\sigma}_{\tau N}$ | Inverse-gamma distribution [0.01,2] | 0.0083 | 0.0021 | 0.0159 |

${\sigma}_{A}$ | Inverse-gamma distribution [0.01,2] | 0.6927 | 0.5425 | 0.8391 |

Model | Degree of Decentralization | Local Government Behavior | Social Welfare | Environmental Pollution | Consumption Compensation |
---|---|---|---|---|---|

Benchmark model | ${\theta}_{1}=0.5,\text{}{\theta}_{2}=0.5$ | 0.01 | −3.3920 | 17.7264 | 0.0997 |

0.03 | 3.2960 | 17.7027 | 0 | ||

0.05 | −3.2663 | 17.6842 | −0.0301 | ||

Fiscal expenditure decentralization | ${\theta}_{1}=0.4,\text{}{\theta}_{2}=0.4$ | 0.01 | −3.3372 | 17.7320 | 0.0411 |

0.03 | −3.1926 | 17.7171 | −0.0991 | ||

0.05 | −3.2770 | 17.7063 | −0.0198 | ||

Fiscal expenditure decentralization | ${\theta}_{1}=0.6,\text{}{\theta}_{2}=0.6$ | 0.01 | −4.4685 | 17.7198 | 0.1871 |

0.03 | −3.2190 | 17.6834 | 0.0749 | ||

0.05 | −3.2762 | 17.6527 | −0.0205 |

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**MDPI and ACS Style**

Chen, S.; Liu, X.; Lu, C.
Fiscal Decentralization, Local Government Behavior, and Macroeconomic Effects of Environmental Policy. *Sustainability* **2022**, *14*, 11069.
https://doi.org/10.3390/su141711069

**AMA Style**

Chen S, Liu X, Lu C.
Fiscal Decentralization, Local Government Behavior, and Macroeconomic Effects of Environmental Policy. *Sustainability*. 2022; 14(17):11069.
https://doi.org/10.3390/su141711069

**Chicago/Turabian Style**

Chen, Shi, Xun Liu, and Chong Lu.
2022. "Fiscal Decentralization, Local Government Behavior, and Macroeconomic Effects of Environmental Policy" *Sustainability* 14, no. 17: 11069.
https://doi.org/10.3390/su141711069