Effectiveness of China’s Protected Areas in Mitigating Human Activity Pressure
Abstract
:1. Introduction
2. Literature Review
2.1. Human Pressure on PAs
2.2. A Review of Effectiveness Assessment of PAs
3. Materials and Methods
3.1. HAP Index
3.2. Propensity Score Matching Method
3.3. The Panel Model
3.4. Data Source and Processing
4. Results
4.1. Spatial Distribution of the HAP Index in China and Its PAs
4.1.1. Spatial Distribution of the HAP Index in China
4.1.2. Spatial Distribution of the HAP Index in PAs in China
4.2. Balance Test of the Propensity Score Matching Method
4.3. Assessment of China’s PAs regarding the Alleviation of the Pressure of Human Activities
4.4. Assessment of the HAP Mitigation of PAs in China under the SSP Scenarios
4.4.1. Population Pressure Mitigation of PAs in China under the SSP Scenarios
4.4.2. Urban Land Pressure Mitigation by PAs in China under the SSP Scenarios
5. Discussion
5.1. The Pressure of Human Activities in 75.15% of China’s PAs Shows an Upward Trend
5.2. Regional Differences in China’s PAs in Alleviating the Pressure of Human Activities
6. Conclusions
- (1)
- From 2000 to 2020, the pressure of human activities began to spread from the urban center to the surrounding areas. In total, 80.78% of China’s land is under pressure from human activities to varying degrees, while only 45.96 % of the protected land has a HAP index greater than 0. The land area with a rising trend of the HAP index accounts for 64.71% of the total area, and 75.15% of the reserves show an upward trend in the HAP index, but their area accounts for only 37.98% of the total area of PAs.
- (2)
- PAs in China can relieve the pressure of human activities by 1.37%, and there are regional differences in the ability of PAs to alleviate the pressure of human activities. The reserves in Northeast, East and Central China have the most significant effects on relieving the pressure of human activities, with coefficients of −0.339, −0.328 and −0.199, respectively. Meanwhile, PAs in Southwest and Northwest China are increasing the pressure of human activity.
- (3)
- Under the five SSP scenarios, the urban land pressure index shows an overall increasing trend, and the increase is mainly concentrated in Eastern and Central China, but there is no similar pattern under the five SSP scenarios. For example, under the SSP3 scenario, the average increase of urban land pressure in the Eastern China PAs exceeds that outside the PAs.
7. Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Category | Nature | Year | Data Source |
---|---|---|---|
Data of PAs | |||
Boundary data of PAs in China | Vector data | 1957–2012 | Geographic Information Database of China Nature Reserve Specimen Resource Sharing Platform http://www.papc.cn/html/folder/946895-1.htm (accessed on 20 May 2019) |
List of National Nature Reserves (2017) | Text data | 2017 | Ministry of Ecology and Environment of the People’s Republic of China. http://www.mee.gov.cn/ (accessed on 20 May 2019) |
Data used to construct HAP index | |||
Land cover data | Raster data (300 m) | 2000–2019 | European Space Agency, http://maps.elie.ucl.ac.be/CCI/viewer/index.php (accessed on 20 May 2019) |
Future urban land use data | Raster data (300 m) | 2020–2050 | https://doi.pangaea.de/10.1594/PANGAEA.905890 (accessed on 4 July 2020) |
Population density data | Raster data (1 km) | 2000–2020 | NASA Center for Socio-Economic Data and Applications. https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11 (accessed on 4 July 2020) |
Future population data | Raster data (1 km) | 2020–2050 | NASA Center for Socio-Economic Data and Applications. https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-km-downscaled-pop-base-year-projection-ssp-2000-2100-rev01 (accessed on 4 July 2020) |
Complementary DMSP and VIIRS night light data | Raster data (1 km) | 2000–2018 | https://figshare.com/articles/dataset/Harmonization_of_DMSP_and_VIIRS_nighttime_light_data_from_1992-2018_at_the_global_scale/9828827/2 (accessed on 4 July 2020) |
Other data | |||
Temperature | Text data | 2000–2020 | NOAA National Environmental Information Center Database https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/ (accessed on 4 July 2020) |
Precipitation | Text data | 2000–2020 | NOAA National Environmental Information Center Database https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/ (accessed on 4 July 2020) |
Elevation | Raster data (1 km) | Food and Agriculture Organization of the United Nations http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/zh/ (accessed on 4 July 2020) | |
Data set of major roads across the country | Vector data | 2000 | Geographic Data Platform of Peking University https://geodata.pku.edu.cn/index.php?c=content&a=show&id=1399 (accessed on 4 July 2020) |
National road data sets | Vector data | 2018 | Geographic Data Platform of Peking University. https://geodata.pku.edu.cn/index.php?c=content&a=show&id=713 (accessed on 4 July 2020) |
Land area data | Raster data (1 km) | 2010 | NASA Center for Socio-Economic Data and Applications. https://sedac.ciesin.columbia.edu/data/set/gpw-v4-land-water-area-rev11 (accessed on 4 July 2020) |
Variable | Mean Value | Standard Deviation (%) | Reduction in the Standard Deviation (%) | t-Test | |||
---|---|---|---|---|---|---|---|
The Treatment Group | The Control Group | t Statistic | Associated Probability of the t-Test | ||||
lnpecp | Before the match | −2.7261 | −3.1 | 42.1 | 52.90 | 0.000 | |
After the match | −2.7261 | −2.7284 | 0.3 | 99.4 | 0.26 | 0.796 | |
lntemp | Before the match | 3.8746 | 3.8061 | 26.9 | 34.26 | 0.000 | |
After the match | 3.8746 | 3.8794 | −1.9 | 92.9 | −1.91 | 0.056 | |
lnslope | Before the match | 0.7148 | 0.21309 | 27.6 | 37.80 | 0.000 | |
After the match | 0.7148 | 0.70359 | 0.6 | 97.8 | 0.63 | 0.528 | |
lnelev | Before the match | 6.7748 | 6.7164 | 4.0 | 5.14 | 0.000 | |
After the match | 6.7748 | 6.7708 | 0.3 | 93.2 | 0.28 | 0.781 | |
lntourban | Before the match | 10.333 | 10.392 | −5.5 | −6.58 | 0.000 | |
After the match | 10.333 | 10.321 | 1.1 | 80.5 | 1.08 | 0.279 | |
lntoroad | Before the match | 9.9819 | 10.026 | −3.7 | −4.56 | 0.000 | |
After the match | 9.9819 | 9.9951 | −1.1 | 70.4 | −1.11 | 0.265 | |
lnlandcover | Before the match | 0.80104 | 0.8439 | −7.1 | −8.70 | 0.000 | |
After the match | 0.80104 | 0.80166 | −0.1 | 98.6 | −0.11 | 0.913 |
Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
---|---|---|---|---|---|---|---|
Nationwide | Northeast China | North China | East China | Central South Region | Northwest China | Southwest China | |
PAS | −0.0137 * | −0.339 *** | −0.0388 * | −0.328 *** | −0.199 *** | 0.0465 ** | 0.0482 *** |
(0.0070) | (0.0158) | (0.0206) | (0.0106) | (0.0062) | (0.0202) | (0.0112) | |
lnelev | −0.491 *** | −0.279 *** | −0.616 *** | −0.140 *** | −0.141 *** | −0.797 *** | −0.911 *** |
(0.0018) | (0.0080) | (0.0056) | (0.0032) | (0.0029) | (0.0091) | (0.0045) | |
lnslope | 0.112 *** | −0.0825 *** | 0.216 *** | −0.0651 *** | −0.0873 *** | 0.109 *** | 0.0832 *** |
(0.0016) | (0.0044) | (0.0043) | (0.0025) | (0.0020) | (0.0039) | (0.0033) | |
lnpecp | 0.0635 *** | 0.00874 *** | 0.0460 *** | −0.00984 *** | −0.0240 *** | 0.102 *** | 0.0202 *** |
(0.0004) | (0.0014) | (0.0009) | (0.0016) | (0.0009) | (0.0007) | (0.0011) | |
lntemp | 0.319 *** | 0.140 *** | 0.0735 *** | 0.675 *** | 0.222 *** | 0.533 *** | 0.366 *** |
(0.0018) | (0.0074) | (0.0041) | (0.0088) | (0.0048) | (0.0058) | (0.0023) | |
_cons | 3.577 *** | 2.952 *** | 5.138 *** | 0.202 *** | 2.072 *** | 5.138 *** | 6.736 *** |
(0.0142) | (0.0549) | (0.0414) | (0.0401) | (0.0261) | (0.0735) | (0.0358) | |
N | 807292 | 79403 | 138076 | 82089 | 111441 | 195871 | 200343 |
R2 | 0.4122 | 0.2150 | 0.2584 | 0.4839 | 0.4793 | 0.2188 | 0.5306 |
SSPs Scenario | Number of PAs | Index (Negative, %) | Index (Positive, %) | Relative Validity (Average) |
---|---|---|---|---|
SSP1 | 670 | 74.03 | 25.97 | −0.076 |
SSP2 | 670 | 76.72 | 23.28 | −0.054 |
SSP3 | 670 | 79.55 | 20.46 | −0.031 |
SSP4 | 670 | 76.12 | 23.88 | −0.087 |
SSP5 | 670 | 74.03 | 25.97 | −0.076 |
SSPs Scenario | Number of PAs | Index (Negative, %) | Index (Positive, %) | Relative Validity (Average) |
---|---|---|---|---|
SSP1 | 670 | 94.63 | 5.37 | 0.032 |
SSP2 | 670 | 95.37 | 4.63 | 0.022 |
SSP3 | 670 | 95.67 | 4.33 | 0.016 |
SSP4 | 670 | 94.93 | 5.07 | 0.028 |
SSP5 | 670 | 93.88 | 6.12 | 0.031 |
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Chen, J.; Shi, H.; Wang, X.; Zhang, Y.; Zhang, Z. Effectiveness of China’s Protected Areas in Mitigating Human Activity Pressure. Int. J. Environ. Res. Public Health 2022, 19, 9335. https://doi.org/10.3390/ijerph19159335
Chen J, Shi H, Wang X, Zhang Y, Zhang Z. Effectiveness of China’s Protected Areas in Mitigating Human Activity Pressure. International Journal of Environmental Research and Public Health. 2022; 19(15):9335. https://doi.org/10.3390/ijerph19159335
Chicago/Turabian StyleChen, Jian, Hong Shi, Xin Wang, Yiduo Zhang, and Zihan Zhang. 2022. "Effectiveness of China’s Protected Areas in Mitigating Human Activity Pressure" International Journal of Environmental Research and Public Health 19, no. 15: 9335. https://doi.org/10.3390/ijerph19159335