Measurement of Forest Ecological Benefits Based on Big Data
Abstract
:1. Introduction
2. Methods
2.1. Forest Ecological Benefits Seemingly Unrelated Model
- 1.
- Dependent variable of forest ecological benefit
- 2.
- Independent variable set of forest ecological benefit
- 3.
- Others are constants to be estimated
2.2. Monetary Construction Model of Forest Ecological Benefits
2.2.1. Classification of the Monetary Quantity Construction Model
- (1)
- Alternative market technologies for alternative goods. According to various classical definitions of forest ecological benefits, seeking appropriate alternative goods and alternative prices, we built a monetary quantity construction model, which is called the first kind of monetary quantity construction model.
- (2)
- Direct alternative market technology. According to the field measurement data of the external economy (or external non economy) generated by forest ecological benefits, the monetary quantity construction model of forest ecological benefits was constructed directly, which is called the second type of monetary quantity construction model.
2.2.2. First Type of Monetary Construction Model
2.2.3. Second Type of Monetary Construction Model
2.3. Overall Diffusion Model
2.3.1. Canopy Interception
2.3.2. Water Holding Capacity of the Litter
2.3.3. Soil Capillary Pore Water Storage
2.3.4. Fixing Soil
2.3.5. Retaining Fertilizer
2.3.6. Absorbing Carbon Dioxide
2.3.7. Releasing Oxygen
2.3.8. Restraining Wind and Sand
3. Results
3.1. Estimation of the Total Physical Amount of Forest Ecological Benefits
3.2. Measurement of Forest Ecological Benefits
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Forest Ecological Benefit | Pi | Ri | Construction Model of Money (¥/hm2) * | Number of Samples |
---|---|---|---|---|
Improving microclimate | 0.4 | 0.8 | Ei(t) = ∑Pj × Rj × S(t)ij × 67.99605 × LZ0.4931957 | 60 |
Reducing flood and drought | 1.0 | 0.9 | Ei(t) = ∑Pj × Rj × S(t) ij × 311.6941 × LZ0.6183988 | 30 |
Recreation resource | 0.6 | 0.4 | Ei(t) = ∑Pj × Rj × S(t) ij × 12.33866 × LZ0.8235893 | 60 |
Wild animal protection | 1.0 | 0.9 | Ei(t) = ∑Pj × Rj × S(t) ij × 21.39681 × LZ0.8760093 | 30 |
Wild plant protection | 1.0 | 0.9 | Ei(t) = ∑Pj × Rj × S(t) ij × 64.11374 × LZ0.82359.8 | 60 |
Reducing noise | 0.1 | 0.8 | Ei(t) = ∑Pj × Rj × S(t) ij × 62.74023 × LZ0.2500285 | 30 |
Items | Canopy Interception | Litter Holding Water | Soil Holding Water | Fixing Soil | Retaining Fertilizer | Absorbing CO2 | Releasing Oxygen | Restraining Wind and Sand hm2 | Restraining Wind and Sand t | General Eco-Benefits |
---|---|---|---|---|---|---|---|---|---|---|
Para. 1 | 6.9 | 25.374 | −5085.55 | 4 | −1.5 | 0.95355 | 0.702 | 1.68262 | 28.7 | 0.4 |
Para. 2 | −0.7849 | 16.542 | −254.8 | 6 | −3.3125 | −0.13631 | −0.13631 | 0.10423 | 1.68262 | 0.8 |
Para. 3 | 0.0052 | 68.58 | −72.46 | −2.567 | −0.89391 | 0.0789 | 0.0789 | 0.06526 | 0.10423 | 122.6 |
Para. 4 | −0.1834 | 4.83 | 0 | 0 | 0 | 0.02197 | 0.00293 | 0 | 0.06526 | 1 |
Para. 5 | 0 | −1.81 | 79.8 | −0.28445 | 0.05195 | 0 | 0 | 0.01376 | 0 | 0.9 |
Para. 6 | −0.4921 | 0 | −91.8 | 0.87825 | 0.00039 | 0.00252 | 0.00252 | −0.03955 | 0.01378 | 91 |
Para. 7 | −0.1919 | −7.42 | 0.75 | 0.01762 | 0.00009 | −0.00293 | −0.00293 | −0.00067 | −0.03955 | 0.6 |
Para. 8 | 0 | −3.04 | 0 | 17 | 0.5 | −0.0002 | −0.0000 | 0.41924 | −0.00067 | 0.4 |
Para. 9 | 0.7612 | 0 | 0 | 0 | 0 | 0.00236 | 0.00236 | 0 | 0.41924 | 60.1 |
Para. 10 | 1.2388 | −0.59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Para. 11 | 0 | 0.4415 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Para. 12 | 0 | 0.0015 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 129 |
Effective area coefficient | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 0 | 0.1 |
Market approximation coefficient | 0.8 | 0.8 | 0.8 | 0.9 | 0.1 | 1 | 0.2 | 0.8 | 0 | 1 |
Price/ha | 0.66024 | 0.66024 | 0.66024 | 14.88 | 843.7 | 128.33 | 1269.7 | 450 | 0 | 71 |
Age Group | Stand | Total Area hm2 | Total Volume m3 | Total Physics | Water Source Cultivation y | Water Source Cultivation Total | Fixing Soil y | Fixing Soil Total | Retaining Fertilizer y | Retaining Fertilizer Total | Absorbing CO2 y | Absorbing CO2 Total | Releasing Oxygen y | Releasing Oxygen Total | Restraining Wind and Sand y | Restraining Wind and Sand Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6334 | 1,820,433 | 14,516,716 | 2215.8 | 14,034,662 | 33.2 | 210,462 | 4.7 | 29,972 | 15 | 94,913 | 8.8 | 55,794 | 0.003 | 3167.9 | ||
Young | 748 | 120,026 | 1,441,877 | 1838.5 | 1,375,208 | 40.1 | 29,961 | 2.3 | 1724 | 17.7 | 13,235 | 13 | 9743 | 0.854 | 418.4 | |
Korean pine | 35 | 6778 | 67,724 | 1839.2 | 64,373 | 40.2 | 1407 | 2.3 | 81 | 21.4 | 748 | 15.7 | 551 | 15 | 19.7 | |
Coniferous | 312 | 63,908 | 598,553 | 1820.2 | 567,912 | 40.4 | 12,595 | 2.3 | 722 | 22.6 | 7054 | 16.6 | 5193 | 0.567 | 176.9 | |
Mixed | 203 | 33,853 | 412,405 | 1941.7 | 394,158 | 39.8 | 8080 | 2.3 | 466 | 18.4 | 3731 | 13.5 | 2747 | 0.941 | 112.3 | |
Hard broad-leaved | 15 | 585 | 27,137 | 1745.5 | 26,183 | 38.7 | 581 | 2.3 | 34 | 4.3 | 64 | 3.1 | 47 | 7.467 | 8 | |
Soft broad-leaved | 182 | 14,852 | 334,685 | 1765.1 | 321,248 | 39.9 | 7268 | 2.3 | 419 | 9 | 1638 | 6.6 | 1206 | 2.159 | 101.3 | |
Sub arbor | 1 | 50 | 1374 | 1335 | 1335 | 29 | 29 | 2 | 2 | 14 | 0.3 | |||||
Middle | 2418 | 796,924 | 5,427,877 | 2165.9 | 5,237,228 | 31.5 | 76,219 | 4.7 | 11,424 | 18.5 | 44,632 | 9.2 | 22,206 | 0.003 | 1260.2 | |
Korean pine | 720 | 402,608 | 1,622,049 | 2155.2 | 1,551,730 | 31.3 | 22,524 | 4.7 | 3397 | 31.3 | 22,539 | 15.6 | 11,212 | 0.14 | 371 | |
Coniferous | 999 | 276,356 | 2,240,161 | 2167.7 | 2,165,508 | 31.7 | 31,666 | 4.7 | 4725 | 15.5 | 15,487 | 7.7 | 7708 | 0.137 | 525 | |
Mixed | 366 | 71,227 | 862,443 | 2288.9 | 837,720 | 31.5 | 11,540 | 4.7 | 1729 | 10.9 | 3991 | 5.4 | 1986 | 0.437 | 190.8 | |
Hard broad-leaved | 26 | 3172 | 56,855 | 2124.9 | 55,247 | 31.8 | 826 | 4.7 | 123 | 6.8 | 178 | 3.4 | 88 | 13.7 | ||
Soft broad-leaved | 307 | 43,562 | 646,369 | 2042.4 | 627,023 | 31.5 | 9664 | 4.7 | 1450 | 7.9 | 2437 | 3.9 | 1212 | 159.7 | ||
Mature | 3168 | 903,483 | 7,646,962 | 2342.9 | 7,422,227 | 32.9 | 104,283 | 5.3 | 16,824 | 11.7 | 37,047 | 7.5 | 23,845 | 1489.1 | ||
Korean pine | 668 | 224,243 | 1,636,089 | 2374.1 | 1,585,905 | 33.5 | 22,405 | 5.4 | 3586 | 13.6 | 9092 | 8.9 | 5914 | 320 | ||
Coniferous | 1368 | 392,493 | 3,299,191 | 2340.8 | 3,202,150 | 33.3 | 45,524 | 5.4 | 7383 | 11.3 | 15,491 | 7.5 | 10,306 | 638.9 | ||
Mixed | 838 | 232,454 | 2,084,974 | 2417.7 | 2,026,065 | 32.3 | 27,084 | 5.2 | 4364 | 12 | 10,017 | 7.4 | 6174 | 392.7 | ||
Soft broad-leaved | 294 | 54,294 | 626,709 | 2068.4 | 608,108 | 31.5 | 9270 | 5.1 | 1491 | 8.3 | 2448 | 4.9 | 1450 | 137.4 |
Age Group | Stand | Total Area hm2 | Total Volume m3 | Total Money | Water Source Cultivation | Fixing Soil | Retaining Fertilizer | Absorbing CO2 | Releasing Oxygen | Restraining Wind and Sand | Improving Micro Climate | Reducing Water Disaster | Recreation | Living Things Protection | Reducing Noise |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6334 | 1,820,433 | 9431 | 2402 | 761 | 455 | 2850 | 2550 | 103 | 45 | 93 | 16 | 147 | 8 | ||
Young | 748 | 120,026 | 1263 | 235 | 108 | 26 | 397 | 445 | 14 | 5 | 11 | 2 | 17 | 1 | |
Korean pine | 35 | 6778 | 67 | 11 | 5 | 1 | 22 | 25 | 1 | 1 | 1 | ||||
Coniferous | 312 | 63,908 | 624 | 97 | 46 | 11 | 212 | 237 | 6 | 2 | 5 | 1 | 7 | ||
Mixed | 203 | 33,853 | 355 | 67 | 29 | 7 | 112 | 126 | 4 | 1 | 3 | 1 | 5 | ||
Hard broad-leaved | 15 | 585 | 12 | 4 | 2 | 1 | 2 | 2 | |||||||
Soft broad-leaved | 182 | 14,852 | 204 | 55 | 26 | 6 | 49 | 55 | 3 | 1 | 3 | 4 | |||
Sub arbor | 1 | 50 | |||||||||||||
Middle | 2418 | 796,924 | 3860 | 896 | 276 | 173 | 1340 | 1015 | 41 | 17 | 36 | 6 | 56 | 3 | |
Korean pine | 720 | 402,608 | 1635 | 266 | 81 | 52 | 677 | 512 | 12 | 5 | 11 | 2 | 17 | 1 | |
Coniferous | 999 | 276,356 | 1440 | 371 | 114 | 72 | 465 | 352 | 17 | 7 | 15 | 3 | 23 | 1 | |
Mixed | 366 | 71,227 | 446 | 143 | 42 | 26 | 120 | 91 | 6 | 3 | 5 | 1 | 8 | ||
Precious hard broad-leaved | 26 | 3172 | 25 | 9 | 3 | 2 | 5 | 4 | 1 | ||||||
Soft broad-leaved | 307 | 43,562 | 313 | 107 | 35 | 22 | 73 | 55 | 5 | 2 | 5 | 1 | 7 | ||
Mature | 3168 | 903,483 | 4308 | 1270 | 377 | 255 | 1112 | 1090 | 48 | 22 | 47 | 8 | 74 | 4 | |
Korean pine | 668 | 224,243 | 993 | 271 | 81 | 54 | 273 | 270 | 10 | 5 | 10 | 2 | 16 | 1 | |
Coniferous | 1368 | 392,493 | 1849 | 548 | 165 | 112 | 465 | 471 | 21 | 10 | 20 | 4 | 32 | 2 | |
Mixed | 838 | 232,454 | 1148 | 347 | 98 | 66 | 301 | 282 | 13 | 6 | 12 | 2 | 19 | 1 | |
Soft broad-leaved | 294 | 54,294 | 319 | 104 | 34 | 23 | 74 | 66 | 4 | 2 | 4 | 1 | 7 |
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Li, H.; Liu, S.; Cai, T. Measurement of Forest Ecological Benefits Based on Big Data. Sustainability 2022, 14, 7248. https://doi.org/10.3390/su14127248
Li H, Liu S, Cai T. Measurement of Forest Ecological Benefits Based on Big Data. Sustainability. 2022; 14(12):7248. https://doi.org/10.3390/su14127248
Chicago/Turabian StyleLi, Hua, Shuo Liu, and Tijiu Cai. 2022. "Measurement of Forest Ecological Benefits Based on Big Data" Sustainability 14, no. 12: 7248. https://doi.org/10.3390/su14127248