Can Investment in Forestry Resource Management Reduce Haze Pollution and Carbon Emissions? Evidence from China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Mechanism Analysis
2.2. Dependent Variable
2.3. Independent Variable
2.4. Data Source
2.5. Methods
2.5.1. Global Spatial Autocorrelation
2.5.2. Spatial Econometric Model
2.5.3. Spatial Weight Matrix
3. Results
3.1. Temporal Evolution Characteristics
3.2. Spatial Evolution Characteristics
3.3. Spatial Correlation Characteristics
3.4. Model Verification and Identification
3.5. Benchmark Regression Analysis
3.6. Spatial Spillover Effect
3.7. Robustness Test
4. Discussion
- (1)
- Strengthen the supervision and management of IFRM and promote the sustainable development of forestry. For a long time, due to the extensive and loose forestry development mode and the lack of awareness of forest resources protection, forest resources in some areas of China are in crisis, and ecological problems occur frequently in forestry. Local governments should formulate relevant laws and regulations, strengthen publicity on forest resource protection, prevent deforestation and illegal logging, and increase the cost for those who destroy forest resources. In addition, it is necessary to establish a forestry ecological evaluation and management mechanism, make timely and scientific judgments on the forestry ecological status, and improve the forest resource protection system.
- (2)
- Introduce market mechanisms to accelerate the development of forestry carbon sinks. The Chinese government has proposed that the national forest coverage rate will reach 24.1% in 2025 and about 25% in 2030, indicating that China has a huge forest carbon sink market. The key to constructing a forestry carbon sink market is to cultivate market demand and to encourage and guide multiple entities to participate in market supply. On the one hand, conduct forest ecosystem carbon sink surveys and carbon stock assessments, and establish an ecosystem carbon sink monitoring and accounting system. On the other hand, local governments should guide multiple entities to actively participate in forestry carbon sink projects and continuously improve the forestry carbon sink trading laws and regulations.
- (3)
- Establish a joint prevention and control mechanism and social participation mechanism for haze pollution and carbon emissions. Our research shows that haze pollution and carbon emissions have strong spatial autocorrelation and cross-regional diffusion characteristics. Therefore, it is necessary to break through administrative divisions, abandon the concept of “local protectionism” and form a joint force of pollution control. In addition, a diversified ecological compensation system should be established to coordinate the imbalance of interests among relevant stakeholders. Finally, through tax breaks and financial subsidies, enterprises and individuals are encouraged to participate in environmental protection to raise public awareness of environmental protection.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Energy Type | Average Low Heat | Folding Standard Coal Coefficient | CO2 Emission Coefficient |
---|---|---|---|
Coal | 20,908 kJ/kg | 0.7143 kgce/kg | 1.9003 kg-CO2/kg |
Coke | 28,435 kJ/kg | 0.9714 kgce/kg | 2.8604 kg-CO2/kg |
Crude oil | 41,816 kJ/kg | 1.4286 kgce/kg | 3.0202 kg-CO2/kg |
Gasoline | 43,070 kJ/kg | 1.4714 kgce/kg | 2.9251 kg-CO2/kg |
Kerosene | 43,070 kJ/kg | 1.4714 kgce/kg | 3.0719 kg-CO2/kg |
Diesel | 42,652 kJ/kg | 1.4571 kgce/kg | 3.0959 kg-CO2/kg |
Fuel oil | 41,816 kJ/kg | 1.4286 kgce/kg | 3.1705 kg-CO2/kg |
Natural gas | 38,931 kJ/m3 | 1.3300 kgce/m3 | 2.1622 kg-CO2/m3 |
Primary Indicator | Primary Symbol | Secondary Indicator | Secondary Symbol | Indicator Type |
---|---|---|---|---|
Forestry economy | X1 | annual growth rate of total forestry output value (%) | X11 | + |
proportion of forestry output value in GDP (%) | X12 | + | ||
forest land value per unit area (104 m2) | X13 | + | ||
per capita forestry output value (104 m2) | X14 | + | ||
proportion of forestry investment in GDP (%) | X15 | + | ||
Forestry status | X2 | forest coverage rate (%) | X21 | + |
per capita afforestation area (104 m2) | X22 | + | ||
volume per unit area (104 m2) | X23 | + | ||
forestry land area (104 m2) | X24 | + | ||
forest area (104 m2) | X25 | + | ||
Forestry governance | X3 | forestry pest control rate (%) | X31 | + |
forest fire area (104 m2) | X32 | − | ||
total standing stock of living trees (104 m2) | X33 | + | ||
forest pest area (104 m2) | X34 | − | ||
disaster-affected forest area (104 m2) | X35 | − |
Test | Haze Pollution | Carbon Emission |
---|---|---|
LM-spatial lag | 623.2468 *** | 2311.2233 *** |
Robust LM-spatial lag | 421.1659 *** | 261.6899 *** |
LM-spatial error | 269.6642 *** | 2057.6678 *** |
Robust LM-spatial error | 67.5833 *** | 8.1345 *** |
Wald-spatial lag | 69.4150 *** | 34.8682 *** |
LR-spatial lag | 69.9755 *** | 35.0248 *** |
Wald-spatial error | 74.5058 *** | 37.7588 *** |
LR-spatial error | 75.2563 *** | 38.4978 *** |
Hausman | 247.4248 *** | 195.1548 *** |
EVs | Haze Pollution | Carbon Emissions | ||
---|---|---|---|---|
OLS | SDM | OLS | SDM | |
Investment in forestry resource management | −0.1514 *** (−3.0260) | −0.0655 * (1.7335) | −0.1179 ** (−2.0348) | −0.1169 ** (−2.1956) |
Industrial structure | 0.0005 (0.0132) | −0.0555 (−1.3951) | −0.0364 (−0.9053) | −0.0408 (−0.7277) |
Urbanization | −1.0124 *** (−6.5343) | 0.0545 (0.3599) | 0.2807 * (1.7172) | 0.8419 *** (3.9438) |
Foreign direct investment | 0.0045 (0.3584) | −0.0267 ** (−2.9271) | −0.0189 (−1.4144) | −0.0231 * (−1.7916) |
Economic development | 0.1171 ** (2.1315) | −0.0163 (−0.3042) | 0.2310 *** (3.9873) | −0.0087 (−0.1149) |
Population density | −0.9975 *** (−6.3982) | −0.4136 ** (−3.0146) | 0.0880 (0.5351) | 0.3577 * (1.8479) |
Environmental regulation | −0.0599 *** (−5.4668) | −0.0129 * (−1.7546) | −0.0155 (−1.3371) | −0.0220 * (−1.8642) |
WX | YES | YES | YES | YES |
ρ | 0.6300 *** (9.6907) | 0.2579 *** (2.5925) | ||
R2 | 0.5268 | 0.9683 | 0.5183 | 0.9940 |
Log-L | 446.6510 | 328.9292 |
Evs | Haze Pollution | Carbon Emissions | ||
---|---|---|---|---|
Direct Effect | Spillover Effect | Direct Effect | Spillover Effect | |
Investment in forestry resource management | −0.0826 ** (−2.1359) | −0.5767 ** (−2.1099) | −0.1133 ** (−2.1059) | 0.0748 (0.3946) |
Industrial structure | −0.0504 (−1.2491) | 0.1773 (0.7700) | −0.0381 (−0.6849) | 0.1237 (0.7462) |
Urbanization | −0.0332 (−0.2268) | −1.4033 *** (−3.4604) | 0.8192 *** (3.8273) | −1.0580 * (−1.7938) |
Foreign direct investment | −0.0305 *** (−2.8065) | −0.1382 (−0.8718) | −0.0193 (−1.5464) | 0.2089 * (1.9730) |
Economic development | 0.0121 (0.2266) | 1.0474 *** (2.7626) | −0.0019 (−0.0258) | 0.3483* (1.6858) |
Population density | −0.4443 *** (−3.4665) | −0.8881 (−0.5885) | 0.3451 * (1.8652) | −0.6067 (−0.5727) |
Environmental regulation | −0.0186 ** (−2.1575) | −0.1943 *** (−3.2154) | −0.0209 * (−1.7746) | 0.0606 (1.4675) |
EVs | Haze Pollution | Carbon Emissions | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Investment in forestry resource management | −0.0854 ** (−2.4543) | −0.0654 * (−1.7335) | −0.0824 ** (−2.0047) | −0.1216 ** (−2.3512) | −0.1131 ** (−2.0836) | −0.1629 *** (−3.0501) |
Industrial structure | −0.0575 (−1.5964) | −0.0555 (−1.3950) | −0.0347 (−0.8215) | −0.0597 (−1.1149) | −0.0251 (−0.4381) | −0.0035 (−0.0647) |
Urbanization | 0.0761 (0.5784) | 0.0544 (0.3599) | 0.1753 (0.9827) | 0.8909 *** (4.5601) | 0.9940 *** (4.5667) | 0.5671 *** (2.4489) |
Foreign direct investment | −0.0292 *** (−3.3013) | −0.0266 *** (−2.9270) | −0.0290 *** (−2.9737) | −0.0257 * (−1.9554) | −0.0253 * (−1.9335) | −0.0202 * (−1.7413) |
Economic development | −0.0136 (−0.2651) | −0.0162 (−0.3042) | −0.0400 (−0.6790) | −0.0129 (−0.1699) | −0.0731 (−0.9505) | 0.0267 (0.3522) |
Population density | −0.3187 ** (−2.3182) | −0.4136 *** (−3.0145) | −0.5816 *** (−3.7540) | 0.5177 ** (2.5371) | 0.4609 ** (2.3357) | 0.4351 ** (2.1802) |
Environmental regulation | −0.0120 (−1.5386) | −0.0129 (−0.0129) | −0.0150 * (−1.6860) | −0.0206 * (−1.7758) | −0.0205 * (−1.7026) | −0.0217 * (−1.9512) |
Lag(-1) | 0.8422 * (18.4534) | 0.7349 *** (22.5381) | 0.7552 *** (24.6435) | 0.4659 *** (28.5287) | 0.5387 *** (17.6547) | 0.8854 *** (26.4657) |
WX | YES | YES | YES | YES | YES | YES |
ρ | 0.5564 *** (8.5343) | 0.6450 *** (5.3437) | 0.6869 *** (7.6759) | 0.3469 *** (5.5634) | 0.4536 *** (3.6745) | 0.5541 *** (5.5645) |
R2 | 0.9845 | 0.9833 | 0.9654 | 0.9866 | 0.9856 | 0.9827 |
Log-L | 433.5778 | 364.4540 | 5643.3496 | 367.2478 | 676.4358 | 535.4269 |
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Deng, Z.; Zhang, Y.; Supriyadi, A.; Wang, L.; Zhang, F. Can Investment in Forestry Resource Management Reduce Haze Pollution and Carbon Emissions? Evidence from China. Forests 2024, 15, 1534. https://doi.org/10.3390/f15091534
Deng Z, Zhang Y, Supriyadi A, Wang L, Zhang F. Can Investment in Forestry Resource Management Reduce Haze Pollution and Carbon Emissions? Evidence from China. Forests. 2024; 15(9):1534. https://doi.org/10.3390/f15091534
Chicago/Turabian StyleDeng, Zhen, Yizhen Zhang, Agus Supriyadi, Luwei Wang, and Fang Zhang. 2024. "Can Investment in Forestry Resource Management Reduce Haze Pollution and Carbon Emissions? Evidence from China" Forests 15, no. 9: 1534. https://doi.org/10.3390/f15091534