Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models
Carbon storage is a critical ecosystem service provided by terrestrial environmental systems that can effectively reduce regional carbon emissions and is critical for achieving carbon neutrality and carbon peak. We conducted a study in Kunming and analyzed the land utilization data for 2000, 2010, a...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-05-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/15285.pdf |
| _version_ | 1827599395879649280 |
|---|---|
| author | Yimin Li Xue Yang Bowen Wu Juanzhen Zhao Wenxue Jiang Xianjie Feng Yuanting Li |
| author_facet | Yimin Li Xue Yang Bowen Wu Juanzhen Zhao Wenxue Jiang Xianjie Feng Yuanting Li |
| author_sort | Yimin Li |
| collection | DOAJ |
| description | Carbon storage is a critical ecosystem service provided by terrestrial environmental systems that can effectively reduce regional carbon emissions and is critical for achieving carbon neutrality and carbon peak. We conducted a study in Kunming and analyzed the land utilization data for 2000, 2010, and 2020. We assessed the features of land utilization conversion and forecasted land utilization under three development patterns in 2030 on the basis of the Patch-generating Land Use Simulation (PLUS) model. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to estimate changes in carbon storage trends under three development scenarios in 2000, 2010, 2020, and 2030 and the impact of socioeconomic and natural factors on carbon storage. The results of the study indicated that (1) carbon storage is intimately associated with land utilization practices. Carbon storage in Kunming in 2000, 2010, and 2020 was 1.146 × 108 t, 1.139 × 108 t, and 1.120 × 108 t, respectively. During the 20 years, forest land decreased by 142.28 km2, and the decrease in forest land area caused a loss of carbon storage. (2) Carbon storage in 2030 was predicted to be 1.102 × 108 t, 1.136 × 108 t, and 1.105 × 108 t, respectively, under the trend continuation scenario, eco-friendly scenario, and comprehensive development scenario, indicating that implementing ecological protection and cultivated land protection measures can facilitate regional ecosystem carbon storage restoration. (3) Impervious surfaces and vegetation have the greatest influence on carbon storage for the study area. A spatial global and local negative correlation was found between impervious surface coverage and ecosystem carbon storage. A spatial global and local positive correlation was found between NDVI and ecosystem carbon storage. Therefore, ecological and farmland protection policies need to be strengthened, the expansion of impervious surfaces should be strictly controlled, and vegetation coverage should be improved. |
| first_indexed | 2024-03-09T04:12:16Z |
| format | Article |
| id | doaj.art-f8fd891a913c4482a5047e2949f7e961 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T04:12:16Z |
| publishDate | 2023-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-f8fd891a913c4482a5047e2949f7e9612023-12-03T13:58:51ZengPeerJ Inc.PeerJ2167-83592023-05-0111e1528510.7717/peerj.15285Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST modelsYimin Li0Xue Yang1Bowen Wu2Juanzhen Zhao3Wenxue Jiang4Xianjie Feng5Yuanting Li6School of Earth Sciences, Yunnan University, Kunming City, Yunnan, ChinaSchool of Earth Sciences, Yunnan University, Kunming City, Yunnan, ChinaSchool of Earth Sciences, Yunnan University, Kunming City, Yunnan, ChinaInstitute of International Rivers and Ecological Security, Yunnan University, Kunming City, Yunnan, ChinaSchool of Earth Sciences, Yunnan University, Kunming City, Yunnan, ChinaInstitute of International Rivers and Ecological Security, Yunnan University, Kunming City, Yunnan, ChinaInstitute of International Rivers and Ecological Security, Yunnan University, Kunming City, Yunnan, ChinaCarbon storage is a critical ecosystem service provided by terrestrial environmental systems that can effectively reduce regional carbon emissions and is critical for achieving carbon neutrality and carbon peak. We conducted a study in Kunming and analyzed the land utilization data for 2000, 2010, and 2020. We assessed the features of land utilization conversion and forecasted land utilization under three development patterns in 2030 on the basis of the Patch-generating Land Use Simulation (PLUS) model. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to estimate changes in carbon storage trends under three development scenarios in 2000, 2010, 2020, and 2030 and the impact of socioeconomic and natural factors on carbon storage. The results of the study indicated that (1) carbon storage is intimately associated with land utilization practices. Carbon storage in Kunming in 2000, 2010, and 2020 was 1.146 × 108 t, 1.139 × 108 t, and 1.120 × 108 t, respectively. During the 20 years, forest land decreased by 142.28 km2, and the decrease in forest land area caused a loss of carbon storage. (2) Carbon storage in 2030 was predicted to be 1.102 × 108 t, 1.136 × 108 t, and 1.105 × 108 t, respectively, under the trend continuation scenario, eco-friendly scenario, and comprehensive development scenario, indicating that implementing ecological protection and cultivated land protection measures can facilitate regional ecosystem carbon storage restoration. (3) Impervious surfaces and vegetation have the greatest influence on carbon storage for the study area. A spatial global and local negative correlation was found between impervious surface coverage and ecosystem carbon storage. A spatial global and local positive correlation was found between NDVI and ecosystem carbon storage. Therefore, ecological and farmland protection policies need to be strengthened, the expansion of impervious surfaces should be strictly controlled, and vegetation coverage should be improved.https://peerj.com/articles/15285.pdfInVEST modelCarbon storagePLUS modelKunming citySpatial autocorrelation analysis |
| spellingShingle | Yimin Li Xue Yang Bowen Wu Juanzhen Zhao Wenxue Jiang Xianjie Feng Yuanting Li Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models PeerJ InVEST model Carbon storage PLUS model Kunming city Spatial autocorrelation analysis |
| title | Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models |
| title_full | Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models |
| title_fullStr | Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models |
| title_full_unstemmed | Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models |
| title_short | Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models |
| title_sort | spatio temporal evolution and prediction of carbon storage in kunming based on plus and invest models |
| topic | InVEST model Carbon storage PLUS model Kunming city Spatial autocorrelation analysis |
| url | https://peerj.com/articles/15285.pdf |
| work_keys_str_mv | AT yiminli spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT xueyang spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT bowenwu spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT juanzhenzhao spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT wenxuejiang spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT xianjiefeng spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels AT yuantingli spatiotemporalevolutionandpredictionofcarbonstorageinkunmingbasedonplusandinvestmodels |