To What Extent Does Environmental Regulation Influence Emission Reduction? Evidence from Local and Neighboring Locations in China
Abstract
:1. Introduction
2. Literature Review and Hypothesis Development
- (1)
- Technological Progress. Grossman and Kruegert (1995) argue that the reduction of emissions mainly results from technological progress [13]. They developed the environmental Kuznets curve to reveal the inverted U-shaped curve relationship between economic development and environmental pollution. Based on the pollution shelter theory, Oates and Portney (2003) document that local governments would attract external investment by deregulation to achieve economic development [14]. In addition, Gorg and Greenaway (2004) believe that foreign technology spillover plays a positive role in carbon reduction [15].
- (2)
- Industrial Structure. Using China’s interprovincial panel data, Zhang et al. (2014) empirically analyze the relationship between industrial structure and carbon emissions and point out that secondary industry is the most important industry affecting carbon emissions [16]. They conclude that industrial upgrading would reduce the carbon emissions of cities. Moreover, Zhang et al. (2018) focus on the impact of industrial agglomeration on urban carbon emissions [17]. The empirical results show a significantly negative relationship between manufacturing agglomeration and urban carbon emissions. As a result, one of the crucial ways to reduce industrial carbon emissions is the improvement of enterprise agglomeration.
- (3)
- Environmental Regulation. Hao et al. (2021) find that environmental regulation and FDI spillover effects have complementary effects on carbon emission technology [18]. Similarly, Chen et al. (2020) indicate a negative U-shaped relationship between environmental regulation and carbon emissions [19]. In addition, Whitmarsh et al. (2011) claim that public participation in environmental regulation and voluntary environmental regulation could effectively reduce carbon emissions [20].
3. Data and Methodology
3.1. Dependent Variable
3.2. Explanation Variable
3.3. Controlling Variables
3.4. Models
- is the amount of carbon dioxide emissions
- is the environmental regulation of city i in year t
- Controlit is the series of controlling variables
- is the province (individual) fixed effect
- is the time fixed effect.
- is the spatial lag coefficient
- is the spatial exchange term coefficient
- W is the spatial weight matrix
4. Empirical Findings
4.1. Main Effects Regression Analysis
4.2. Spatial Spillover Effect Analysis
4.3. Robust Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Conflicts of Interest
References
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CO2 Emission Sources | Coal | Coke | Crude Oil | Gasoline | Diesel Oil | Fuel Oil | Natural Gas | Kerosene |
---|---|---|---|---|---|---|---|---|
emission factor (tC/t) | 0.4925 | 0.7705 | 0.8187 | 0.7977 | 0.8461 | 0.8691 | 0.5896 | 0.8281 |
Variable Name | Variable Definitions | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|---|
perco2 | carbon dioxide emissions per capita (t) | 330 | 6.460 | 3.840 | 1.330 | 24.96 |
CO2E | carbon dioxide emissions (million t) | 330 | 255.78 | 176.35 | 16.50 | 842.20 |
ER | environmental regulation intensity | 330 | 0.470 | 0.86 | 0 | 7.20 |
ENS | energy utilization efficiency (kg/yuan) | 330 | 1.060 | 0.49 | 0.23 | 3.12 |
URBAN | urbanization level | 330 | 50.59 | 14.34 | 26.28 | 89.60 |
TP | year-end total population (thousands of people) | 330 | 4400 | 2646 | 539 | 10,724 |
PIFI | the proportion of fixed-asset investment in GDP | 330 | 0.62 | 0.19 | 0.25 | 1.24 |
TRADE | share of total imports and exports in GDP | 330 | 0.05 | 0.06 | 0.01 | 0.24 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
FE | RE | FE | RE | |
lnperco2 | lnperco2 | lnCO2E | lnCO2E | |
ER | −0.0356 *** | −0.0368 *** | −0.0429 *** | −0.0457 *** |
(−4.3231) | (−4.2966) | (−4.3690) | (−4.4276) | |
ENS | −0.4008 *** | −0.4450 *** | −0.3895 *** | −0.4546 *** |
(−9.7829) | (−10.6702) | (−7.9736) | (−9.0493) | |
URBAN | 0.0109 *** | 0.0170 *** | 0.0132 *** | 0.0199 *** |
(3.6296) | (7.3135) | (3.7107) | (7.1626) | |
PIFI | 0.1861 *** | 0.2320 *** | 0.2835 *** | 0.3616 *** |
(2.8708) | (3.6683) | (3.6681) | (4.7466) | |
TRADE | −1.1829 *** | −0.7822 * | −1.1291 ** | −0.6977 |
(−2.7291) | (−1.8723) | (−2.1847) | (−1.3881) | |
lnTP | −0.6458 *** | −0.1256 ** | 0.0935 | 0.8265 *** |
(−5.0425) | (−2.3612) | (0.6121) | (13.1297) | |
Constant | 6.6062 *** | 2.1019 *** | 3.7136 *** | −2.5439 *** |
(6.0390) | (4.4889) | (2.8470) | (−4.5886) | |
N | 330 | 330 | 330 | 330 |
individual effect | yes | yes | individual effect | yes |
year effect | yes | yes | year effect | yes |
R2 | 0.9017 | 0.9141 | ||
Hausman-Test | chi2(7) = 38.35 prob > chi2 = 0.000 | chi2(7) = 44.59 prob > chi2 = 0.000 |
lnperco2 | lnCO2E | |
---|---|---|
(1) | (2) | |
ER | −0.0356 *** | −0.0429 *** |
(−2.8998) | (−2.9062) | |
ENS | −0.4008 *** | −0.3895 *** |
(−3.9826) | (−3.6464) | |
URBAN | 0.0109 ** | 0.0132 ** |
(2.0532) | (2.4647) | |
PIFI | 0.1861 | 0.2835 ** |
(1.4808) | (2.1892) | |
TRADE | −1.1829 ** | −1.1291 * |
(−2.6109) | (−1.9561) | |
lnTP | −0.6458 ** | 0.0935 |
(−2.7144) | (0.3318) | |
Constant | 6.6062 *** | 3.7136 |
(3.4296) | (1.6266) | |
N | 330 | 330 |
individual effect | yes | yes |
year effect | yes | yes |
Adj. R2 | 0.8967 | 0.9097 |
lnperco2 | lnCO2E | ||
---|---|---|---|
(1) | (2) | ||
Local effect | ER | −0.0206 *** | −0.0260 *** |
(0.0075) | (0.0090) | ||
ENS | −0.3655 *** | −0.3430 *** | |
(0.0434) | (0.0520) | ||
URBAN | 0.0145 *** | 0.0174 *** | |
(0.0029) | (0.0035) | ||
lnTP | −0.7144 *** | 0.1433 | |
(0.1510) | (0.1812) | ||
PIFI | 0.1414 ** | 0.2734 *** | |
(0.0618) | (0.0740) | ||
TRADE | −1.2534 *** | −1.2583 ** | |
(0.4096) | (0.4915) | ||
Neighbor effect | ER | 0.0532 *** | 0.0625 *** |
(0.0191) | (0.0229) | ||
ENS | 0.0505 | 0.0815 | |
(0.0939) | (0.1109) | ||
URBAN | −0.0291 *** | −0.0310 *** | |
(0.0059) | (0.0071) | ||
lnTP | −0.4409 | −0.7852 ** | |
(0.2984) | (0.3423) | ||
PIFI | 0.5098 *** | 0.5026 *** | |
(0.1211) | (0.1454) | ||
TRADE | −1.4161 * | −2.0924 ** | |
(0.7955) | (0.9550) | ||
spatial-rho | −0.1780 ** | −0.2321 *** | |
(0.0814) | (0.0847) | ||
sigma2_e | 0.0035 *** | 0.0050 *** | |
(0.0003) | (0.0004) |
(1) | (2) | |
---|---|---|
lnperco2 | lnCO2E | |
ER | −0.0356 *** | −0.0429 *** |
(−2.8998) | (−2.9062) | |
ENS | −0.4008 *** | −0.3895 *** |
(−3.9826) | (−3.6464) | |
URBAN | 0.0109 ** | 0.0132 ** |
(2.0532) | (2.4647) | |
PIFI | 0.1861 | 0.2835 ** |
(1.4808) | (2.1892) | |
TRADE | −1.1829 ** | −1.1291 * |
(−2.6109) | (−1.9561) | |
lnTP | −0.6458 ** | 0.0935 |
(−2.7144) | (0.3318) | |
Constant | 7.0118 *** | 4.1708 * |
(3.6418) | (1.8276) | |
N | 330 | 330 |
individual effect | yes | yes |
year effect | yes | yes |
Adj. R2 | 0.9744 | 0.9889 |
(1) | (2) | (3) | |
---|---|---|---|
First Stage | Second Stage | ||
Variables | ER | lnperco2 | lnCO2E |
ER | −0.2803 *** | −0.3447 *** | |
(−4.8627) | (−5.1988) | ||
L.ER | 0.4186 *** | ||
(7.2416) | |||
ENS | −0.2252 ** | −0.4878 *** | −0.4954 *** |
(−2.2710) | (−10.8224) | (−9.5559) | |
URBAN | 0.0011 | 0.0112 *** | 0.0131 *** |
(0.1484) | (3.4733) | (3.5413) | |
PIFI | 0.1105 | 0.1925 *** | 0.2929 *** |
(0.6896) | (2.8380) | (3.7548) | |
TRADE | −0.2362 | −1.1817 *** | −1.1336 ** |
(−0.2245) | (−2.6818) | (−2.2366) | |
lnTP | 0.6584 * | −0.3718 ** | 0.4550 *** |
(1.9678) | (−2.4719) | (2.6298) | |
Constant | −4.7877 * | 4.6881 *** | 1.0738 |
(−1.6510) | (3.6884) | (0.7345) | |
Anderson canon. corr. LM statistic | 51.172[0.0000] | ||
Cragg–Donald Wald F statistic | 52.441{16.38} | ||
N | 300 | 300 | 300 |
individual effect | yes | yes | yes |
year effect | yes | yes | yes |
Adj. R2 | 0.978 | 0.991 |
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Song, J.; Li, M.; Wang, S.; Ye, T. To What Extent Does Environmental Regulation Influence Emission Reduction? Evidence from Local and Neighboring Locations in China. Sustainability 2022, 14, 9714. https://doi.org/10.3390/su14159714
Song J, Li M, Wang S, Ye T. To What Extent Does Environmental Regulation Influence Emission Reduction? Evidence from Local and Neighboring Locations in China. Sustainability. 2022; 14(15):9714. https://doi.org/10.3390/su14159714
Chicago/Turabian StyleSong, Jing, Mengyuan Li, Shaosong Wang, and Tao Ye. 2022. "To What Extent Does Environmental Regulation Influence Emission Reduction? Evidence from Local and Neighboring Locations in China" Sustainability 14, no. 15: 9714. https://doi.org/10.3390/su14159714