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Production-living-ecological space (PLES) reflects the main function of land use types. It is one of the main directions that many scholars researched to evaluate, predict, and optimize the land space from the perspective of PLES. Yunnan Province is affected by such factors as economy, topography, and natural environment. The conflict of PLES is serious, and the problems of spatial planning development are prominent. This study aims at the current status of PLES, based on the establishment of restrictive constraints such as ecological red line, arable land minimum, and natural reserves. Meanwhile, these constraints were combined with the development planning of the Yunnan Province to forecast the quantitative structure change in the PLES in Yunnan Province in 2035 and 2050, coupling Markov and PLUS models to optimize the future space layout. This study can provide a scientific basis for the optimization of land space in Yunnan Province and other areas. The prediction accuracy of the Markov–PLUS model is 98.55%, which can be effectively used to simulate and predict the distribution of PLES in Yunnan in 2035 and 2050. From 2010 to 2015, the disordered layout of PLES in the Yunnan Province was obvious, and the ecological space (ES) seriously occupied the production space (PS) and living space (LS). In 2035 and 2050, the industrial production space (IPS) of Yunnan Province expands and presents distinct regional aggregation. LS and the water ecological space (WES) areas have increased. The layout of PLES in 2035 and 2050 of Yunnan Province mainly centers on PS. The orderly development of IPS promotes the regional economic growth, ensures that the agricultural production space (APS) will not be damaged and allocates the ES reasonably. It will also promote the overall optimization and coordinated development of PLES in Yunnan Province.

Research investigates the transformations in production–living–ecological spaces (PLES) across diverse scenarios and their ecological effects, with the aim of offering advice for environmental preservation and long-term growth in Sichuan Province. Utilizing the PLUS model, we simulated the PLES configuration in Sichuan Province for the year 2030 and subsequently evaluated its ecological impacts using an ecological effect assessment model. The findings reveal that: (1) population and GDP are key drivers of the expansion of Industrial-Production Spaces (IMPS), Urban-Living Spaces (ULS), and Rural-Living Spaces (RLS), whereas altitude has a crucial influence on shaping the expansion of Agricultural-Production Spaces (APS), Forest-Ecological Spaces (FES), Grassland-Ecological Spaces (GES), Water-Ecological Spaces (WES), and Other-Ecological Spaces (OES); (2) significant changes in PLES are observed in Sichuan Province by 2030 across four scenarios, with notable distinctions between the production priority scenario and the other three; (3) variations in ecological quality exist among the four scenarios concerning PLES; (4) the reasons behind better or worse ecological conditions differ across scenarios. The research demonstrates that the PLUS model can effectively simulate PLES in Sichuan Province under multiple scenarios for 2030, offering various potential development pathways and their corresponding ecological effects, thereby aiding in the selection of optimal development pathways.

The southwestern mountainous and hilly regions of China are vital ecological barriers upstream of the Yangtze River. Assessing and simulating changes in ecosystem service value (ESV) in this area is essential for ensuring sustainable ecological development. In this study, our purpose was to evaluate and simulate the spatial patterns as well as trends in the changes shown by the ESV in Yanting County, China, from 2020 to 2030 via a grid-scale using the equivalent factor method and the Markov–patch-generating land use simulation model. The results indicated that (1) from 2020 to 2030, forest, cultivated, and construction lands would be the main types of land use. (2) In 2030, the projected ESVs in Yanting County under the as-usual, low-carbon, and shared development scenarios were 5.31, 5.30, and 4.99 billion RMB, respectively. Compared to the 2020 ESV of Yanting County, the as-usual scenario and low-carbon scenario ESVs increased. The shared development scenario ESVs decreased. It reflects the contra-diction between urbanization, industrialization, agricultural production, and ecological protection in Yanting County. The spatial distribution of the ESVs of all three scenarios showed an agglomeration trend. (3) Given the background of national food security and carbon peaking and carbon neutrality, the setting of a low-carbon development scenario combined with a shared development scenario appears to be more suitable for the future development of Yanting County, which is conducive to the rational planning of land-use patterns and the sci-entific growth of ESV in Yanting County. This study underscores the critical importance of integrating ESVs in sustainable land-use planning and management, and provides a reference for the rational use of land resources, land spatial planning, and policy-making for ecological protection in Yanting County.

Exploring the spatial distribution of land use/cover change (LUCC) and ecosystem carbon storage under future climate change scenarios can provide the scientific basis for optimizing land resource redistribution and formulating policies for sustainable socioeconomic development. We proposed a framework that integrates the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and tradeoffs (InVEST) model to assess the spatiotemporal dynamic changes in LUCC and ecosystem carbon storage in Guangdong based on shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios provided by the Coupled Model Intercomparison Project 6 (CMIP6). The future simulation results showed that the distribution patterns of LUCC were similar under SSP126 and SSP245 scenarios, but the artificial surface expanded more rapidly, and the increase in forest land slowed down under the SPP245 scenario. Conversely, under the SSP585 scenario, the sharply expanded artificial surface resulted in a continuous decrease in forest land. Under the three scenarios, population, elevation, temperature, and distance to water were the highest contributing driving factors for the growth of cultivated land, forest land, grassland, and artificial surface, respectively. By 2060, the carbon storage in terrestrial ecosystems increased from 240.89 Tg in 2020 to 247.16 Tg and 243.54 Tg under SSP126 and SSP245 scenarios, respectively, of which forest ecosystem carbon storage increased by 17.65 Tg and 15.34 Tg, respectively; while it decreased to 226.54 Tg under the SSP585 scenario, and the decreased carbon storage due to forest destruction accounted for 81.05% of the total decreased carbon storage. Overall, an important recommendation from this study is that ecosystem carbon storage can be increased by controlling population and economic growth, and balancing urban expansion and ecological conservation, as well as increasing forest land area.

To support the “dual-carbon” strategy and develop a carbon-sink-oriented coastal spatial planning framework, this study applies the coupled InVEST–PLUS model to Qingdao using 30 m resolution land-use data. Six spatial drivers (DEM, slope, GDP, population, road, and water proximity) are used to simulate land-use change and evaluate its impact on carbon storage. Model validation results indicate that the PLUS model shows good performance (Kappa ≈ 0.79, FoM = 0.168). The results indicate that (1) during 2010–2020, land-use patterns in the study area changed markedly, characterized by a decrease in farmland and an expansion of architecture area, while forest increased slightly and overall ecological land declined. (2) Total carbon storage dropped from 5.3174 × 10 7 t (2010) to 5.2749 × 10 7 t (2020), with a net loss of 4.25 × 10 5 t. Spatially, carbon storage showed a “clustered-high, contiguous-medium, radial-low” pattern. (3) DEM and water proximity primarily drove the expansion of farmland, forest, grassland, and waters, while population density and DEM dominated architecture growth; bare land expansion was mainly driven by population. Based on these findings, carbon storage transfer pathways are quantified, providing a scientific basis for low-carbon-oriented territorial spatial governance in coastal zones.

Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land.

Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

Applying higher nitrogen (N) rates than required for optimum potato (Solanum tuberosum L.) growth leads to economic and environmental losses. The extent to which the N rate associated with maximum potato yields differs from that maximizing net revenue (NR) or potato specific gravity is not fully understood. The objectives of this three-year study (2013–2015) conducted at five sites in three Canadian provinces (MB-1; MB-2; QC-1; QC-2; PEI) (15 site-years) were to: (i) assess potato marketable yield, NR, and specific gravity responses to increasing N application; (ii) calculate the N rate maximizing marketable (Nmax) yield and NR using different statistical models. The year, N fertilizer, and their interaction were significant on marketable yield and NR except at the MB-1 site where no significant effect of N was observed. No significant yield increases were observed at a N rate above 60 kg N ha−1 at four site-years and above 120 kg N ha−1 at five site-years, implying that the current recommended N rate could be reduced. All models fitted the marketable and NR data equally based on R2, mean bias error or root mean square error and resulted in comparable predicted yield and NR values. However, Nmax values were different depending on the model with higher values being predicted by the quadratic- (161.4 to 191.9 kg N ha−1) and the quadratic plateau models (60 to 191.9 kg N ha−1), while lower Nmax values were obtained with linear plateau- (60.6 to 129.8 kg N ha−1) and Mitscherlich–Baule plateau models (60.9 to 130. 9 kg N ha−1). Nitrogen rate maximizing NR was on average 4% lower than the N rate maximizing marketable yields, except at one site where it was higher by 26 kg N ha−1 when the quadratic plus plateau model was used. Specific gravity tended to decrease with the N rate. Our study confirms trade-offs between the N rate maximizing yields or NR with that maximizing specific gravity. Nitrogen rate maximizing marketable yield and NR varies depending on the selected model.

Land use change is an important cause of habitat quality change. In order to reveal the impact of urban land use change on habitat quality, and to explore sustainable development planning, this paper uses the city of Tianjin, China, as a case study. Based on land use data from 2000, 2010, and 2020, the PLUS model was first used to predict land use in 2030 under three scenarios, and the InVEST model was then used to assess habitat quality from 2000 to 2030. This study showed that habitat quality was highly correlated with land use change. The rapid expansion of construction land was the main reason for the year-by-year decline in habitat quality. From 2000 to 2030, habitat quality in Tianjin declined year-by-year according to the average habitat quality values for 2030 for the three scenarios: the Ecological Protection Scenario (EPS) > Natural Development Scenario (NPS) > Economic Construction Scenario (ECS). In the EPS, habitat quality will deteriorate and improve. It would be ecologically beneficial to continue to work on the revegetation of the Jizhou area. In the ECS, habitat quality will decline sharply. In Tianjin, urbanization will continue to accelerate. This is a threat to the sustainable development of the city.

Carbon storage in terrestrial ecosystems, which is the basis of the global carbon cycle, reflects the changes in the environment due to anthropogenic impacts. Rapid and effective assessment of the impact of urban expansion on carbon reserves is vital for the sustainable development of urban ecosystems. Previous studies on future scenario simulations lacked research regarding the driving factors of changes in carbon storages within urban expansion, and the economic value accounting for changes in carbon storages. Therefore, this study examined Wuhan, China, and explored the latent effects of urban expansion on terrestrial carbon storage by combining the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Patch-generating Land Use Simulation (PLUS) model. Based on different socioeconomic strategies, we developed three future scenarios, including Baseline Scenario (BS), Cropland Protection Scenario (CP) and Ecological protection Scenario (EP), to predict the urban built-up land use change from 2015 to 2035 in Wuhan and discussed the carbon storage impacts of urban expansion. The result shows that (1) Wuhan’s urban built-up land area expanded 2.67 times between 1980 and 2015, which is approximately 685.17 km 2 and is expected to continuously expand to 1349–1945.01 km 2 by 2035. (2) Urban expansion in Wuhan has caused carbon storage loss by 5.12 × 10 6  t during 1980–2015 and will lead to carbon storage loss by 6.15 × 10 6  t, 4.7 × 10 6  t and 4.05 × 10 6  t under BS, CP, and EP scenarios from 2015 to 2035, accounting for 85.42%, 81.74%, and 78.79% of the total carbon loss, respectively. (3) The occupation of cropland by urban expansion is closely related to the road system expansion, which is the main driver of carbon storage reduction from 2015 to 2035. (4) We expect that by 2035, the districts facing carbon loss caused by the growth of urban built-up land will expand outward around secondary roads, and the scale of outward expansion under various scenarios will be ranked as BS > CP > EP. In combination, the InVEST and the PLUS model can assess the impact of urban expansion on carbon storage more efficiently and is conducive to carrying out urban planning and promoting a dynamic balance between urban economic development and human well-being.