Evaluation and Prediction of Ecosystem Service Value in the Zhoushan Islands Based on LUCC

Modifications on the coastal atmospheric sulfur and cloud condensation nuclei along the Eastern China seas by shipping fuel transition

Marine fuel combustion from shipping releases SO2 and forms sulfate particles, which may alter low cloud characteristics. A series of strategies were implemented to control the sulfur content of ship fuel oil from 2018 to 2020, offering insights into the effects of the ship fuel oil transition on sulfur-related pollutants and the consequent cloud condensation nuclei (CCN) in the atmosphere. Compared to 2018 in the southeast China waters, shipping SO2 emission decreased by 78 % in 2020, resulting in a 76 % reduction in ship-related total sulfur concentration, and a decrease of 54 % in CCN number concentration under supersaturation 0.2 % (CCN0.2) contributed by shipping. The response of CCN0.2 to ship-related sulfate modification is more pronounced in relatively clean environments than polluted environments, highlighting the uneven changes in coastal CCN along the Eastern China Sea induced by the ship fuel policies. CCN can trigger the formation of cloud droplets, 2020 fuel regulation may have and will reduce the cooling radiative forcing effect with strong spatial heterogeneity. The study provides insights into the variations in coastal atmospheric sulfur-related pollutants and CCN in uneven response to changes in ship fuel oil, prompting the need for further comprehensive assessments of the climate effects resulting from potential shifts in ship fuel use in the future.

Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model

Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.

Quantifying the Relationship between Land Use Intensity and Ecosystem Services' Value in the Hanjiang River Basin: A Case Study of the Hubei Section

An increased land use intensity due to rapid urbanization and socio-economic development would alter the structure and function of regional ecosystems and cause prominent environmental problems. Revealing the impact of land use intensity on ecosystem services (ES) would provide guidance for more informed decision making to promote the sustainable development of human and natural systems. In this study, we selected the Hanjiang River Basin (HRB) in Hubei Province (China) as our study area, explored the correlation between land use intensity and ecosystem Services' Value (ESV), and investigated impacts of natural and socio-economic factors on ESV variations based on the Geographical Detector Model (GDM) and Geographically Weighted Regression (GWR). The results show that (1) from 2000 to 2020, land use intensity in HRB generally showed an upward trend, with a high spatial agglomeration in the southeast and low in the northwest; (2) the total ESV increased from 295.56 billion CNY in 2000 to 296.93 billion CNY in 2010, and then decreased to 295.63 CNY in 2020, exhibiting an inverted U-shaped trend, with regulation services contributing the most to ESV; (3) land use intensity and ESV had a strong negative spatial correlation, with LH (low land use intensity vs. high ESV) aggregations mainly distributed in the northwest, whereas HL (high land use intensity vs. low ESV) aggregations were located in the southeast; (4) natural factors, including annual mean temperature, the percentage of forest land, and slope were positively associated with ESV, while socio-economic factors, including GDP and population density, were negatively associated with ESV. To achieve the coordinated development of the socio-economy and the environment, ES should be incorporated into spatial planning and socio-economic development policies.

Ecosystem Service Value Estimation of Paddy Field Ecosystems Based on Multi-Source Remote Sensing Data

A paddy field ecosystem (PFE) is an important component of an agricultural land ecosystem and is also a special artificial wetland with extremely high value. Taking Tianjin (a municipality city in China) as the research area and using multi-source remote sensing data, we improved the accounting framework of the ecosystem service value (ESV) of PFEs and the calibration of model parameters. The ESV of PFEs was mapped at medium-high resolution and fine-grain at the provincial scale. The results showed that: (1) the net ESV of PFEs in Tianjin in 2019 was RMB 29.68 × 108, accounting for 0.21% of GDP. The positive ESV was RMB 35.53 × 108, the negative ESV was RMB 5.84 × 108, and the average ESV per unit area was RMB 5.47 × 104/ha; (2) as a proportion of the ESV of PFE, the value of climate regulation (61.27%) was greater than the value of carbon fixation and oxygen release (15.29%), which was greater than the value of primary products supply (8.08%). The production value of PFEs is far lower than their ESV; (3) the total net ESV in Baodi District was RMB 16.85 × 108, accounting for 56.77% of Tianjin’s ESV, and the net ESV per unit area was RMB 5.72 × 104/ha, both of which were higher than in other districts; (4) the pixel-based hot spots analysis showed that the number of hot spots (high-value ESV) and cold spots (low-value ESV) reached 98.00% (hot spots 56.9%, cold spots 41.1%) with a significant cluster distribution. The hot spots were mostly distributed in Baodi District (37.8%) and the cold spots were mostly distributed in Ninghe District (17.2%). The research results can support agricultural development, improve countermeasures according to local conditions, and provide theoretical support for regional land use planning, ecological compensation policy formulation and ecological sustainable development. Our methodology can be used to assess the impact of land use change on ESV.

The Spatiotemporal Evolution and Prediction of Carbon Storage: A Case Study of Urban Agglomeration in China’s Beijing-Tianjin-Hebei Region

Due to rapid urban expansion, urban agglomerations face enormous challenges on their way to carbon neutrality. Regarding China’s urban agglomerations, 25% of the land contains 75% of the population, and all types of land are used efficiently and intensively. However, few studies have explored the spatiotemporal link between changes in land use and land cover (LULC) and carbon storage. In this work, the carbon storage changes from 1990 to 2020 were estimated using the InVEST model in China’s Beijing–Tianjin–Hebei (BTH) region. By coupling the Future Land Use Simulation (FLUS) model and InVEST model, the LULC and carbon storage changes in the BTH region in 2035 and 2050 under the natural evolution scenario (NES), economic priority scenario (EPS), ecological conservation scenario (ECS), and coordinated development scenario (CDS). Finally, the spatial autocorrelation analysis of regional carbon storage was developed for future zoning management. The results revealed the following: (1) the carbon storage in the BTH region exhibited a cumulative loss of 3.5 × 107 Mg from 1990 to 2020, and the carbon loss was serious between 2000 and 2010 due to rapid urbanization. (2) Excluding the ECS, the other three scenarios showed continued expansion of construction land. Under the EPS, the carbon storage was found to have the lowest value, which decreased to 16.05 × 108 Mg in 2035 and only 15.38 × 108 Mg in 2050; under the ECS, the carbon storage was predicted to reach the highest value, 18.22 × 108 Mg and 19.00 × 108 Mg, respectively; the CDS exhibited a similar trend as the NES, but the carbon storage was found to increase. (3) The carbon storage under the four scenarios was found to have a certain degree of similarity in terms of its spatial distribution; the high-value areas were found to be clustered in the northwestern part of Beijing and the northern and western parts of Hebei. As for the number of areas with high carbon storage, the ECS was found to be the most abundant, followed by the CDS, and the EPS was found to be the least. The findings of this study can help the BTH region implement the “dual carbon” target and provide a leading example for other urban agglomerations.

Coupling and Coordination Relationships between Urban Expansion and Ecosystem Service Value in Kashgar City

The growing urbanization of oasis cities in arid and semi-arid regions of Northwest China has an adverse influence on the fragile local ecological system. Therefore, improved understanding of the coupling and coordination between urban expansion (UE) and ecosystem services value (ESv) is critical to long term sustainable development. Here, we study the urbanization trend of a typical oasis city of Northwest China (Kashgar) using Landsat TM/ETM+/OLI imagery from 1990 to 2015. Land use types are classified and the spatio-temporal features of UE are analyzed; ESv of each land use types and the ecosystem services function (ESf) are determined; the driving factors of UE and the spatio-temporal change of ESv are analyzed; and the coupling and coordination relationship between UE and ESv is quantitatively determined. Results show that: (1) The land use structure has changed significantly between 1990 and 2015, with construction land (40.51 km2) showing the highest growth and farmland (28.42 km2). (2) UE values during 2000–2005 (16.65 km2) and 2010–2015 (21.09 km2) are relatively large, and during 1990–2015, the city extended from the center to the outskirts at a dynamic growth rate of 13.17% and a comprehensive expansion index of 1.54‰. (3) The total ESv was reduced by CNY 35.76 million (USD ~ 5.26 million), ranked from high to low as: waste treatment (CNY 9.94 million, USD ~1.46 million), water source conservation (CNY 7.95 million, USD ~ 1.17 million), soil formation (CNY 4.60 million, USD ~ 0.68 million), biodiversity protection (CNY 3.37 million, USD ~ 0.5 million), climate regulation (CNY 3.15 million, USD ~ 0.46 million), food production (CNY 2.83 million, USD ~ 0.42 million), gas regulation (CNY 1.96 million, USD ~ 0.29 million), entertainment and leisure (CNY 1.26 million, USD ~ 0.19 million), and raw materials (CNY 0.68 million, USD ~ 0.1 million). (4) The coupling degree between UE and ESv is relatively small (<0.5), though this value has increased yearly. The coordination degree between UE and ESv is relatively low, indicating that UE already poses a serious danger to the ecological environment. (5) The rapid growth of the population and economy and government policies are the main driving factors of intensive UE. Increasing climatic factors such as precipitation, temperature, and runoff impact ESv in some positive ways whereas UE leads to a reduction of ESv. Our results here can help to guide long-term sustainable development of arid regions, reasonable urban planning of oasis cities, and protection of the local ecological environment.

Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method

Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

Prediction of Ecosystem Service Function of Grain for Green Project Based on Ensemble Learning

The Grain for Green Project (GGP) was implemented over 20 years ago as one of six major forestry projects in China, and its scope of implementation is still expanding. However, it is still unclear how many ecosystem services (ESs) the project will produce in the future. The GGP’s large-scale ecological monitoring officially started in 2015 and there is a lack of early monitoring data, making it challenging to predict the future ecological benefits. Therefore, this paper proposes a method to predict future ESs by using ecological monitoring data. First, a new ensemble learning system, auto-XGBoost-ET-DT, is developed based on ensemble learning theory. Using the GGP’s known ESs in 2015, 2017, and 2019, the missing ESs of the past decade have been evaluated via reverse regression. Data from 2020 to 2022 within a convolution neural network and the coupling coordination degree model have been used to analyze the coupling between the prediction results and economic development. The results show that the growth distributions of ESs in three years were as follows: soil consolidation, 3.70–6.34%; forest nutrient retention, 2.72–.71%; water conservation, 2.52–6.09%; carbon fixation and oxygen release, 3.00–4.64%; and dust retention, 3.82–5.75%. The coupling coordination degree of the ESs and economic development has been improved in 97% of counties in 2020 compared with 2019. The results verify a feasible ES prediction method and provide a basis for the progressive implementation of the GGP.