How will ecosystem carbon sequestration contribute to the reduction of regional carbon emissions in the future? analysis based on the MOP-PLUS model framework

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) contribute to both carbon storage and carbon emissions. Therefore, regulating the LULC is an important means of achieving carbon neutrality under global environmental change. Here, the West Liaohe River Basin, a semiarid watershed, was taken as a case study. Based on the assessment of the carbon storage and emissions induced by LULC from 2000–2020, we set up three different coupled shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) scenarios (SSP119, SSP245, and SSP585), from 2030–2060, to optimize the LULC. Then, the LULC patterns under each scenario were simulated using the patch-generating land use simulation (PLUS) model, and the corresponding changes in carbon storage and emissions were compared and analyzed. It was found that, since 2000, with the expansion of forest, cropland, and construction land, as well as the degradation of grassland, the carbon storage and emissions induced by LULC have significantly increased, but the increase in storage was lower than that of emissions. The scenario simulations revealed that, when we optimize the LULC, mainly including the protection and expansion of ecological land such as forest and grassland in the western and southern edges of the basin, as well as the control and management of cropland land and construction land in the northeast and central parts of the basin, there will be a significant increase in the carbon storage and a significant reduction in carbon emissions from 2030–2060. This indicates that zone-based management measures with rational LULC regulation can contribute to the achievement of carbon neutrality in the study area. Supported by the results of this study, a direct decision-making basis for land use policy regulation to promote regional sustainable development can be undertaken in the basin. This study also provides a reference for low-carbon development in other regions.

Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework

Land use and land cover (LULC) change is a pattern of alteration of the Earth’s land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances ED and EP. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.

Land use and land cover (LULC) change in tropical regions can cause huge amounts of carbon loss and storage, thus significantly affecting the global climate. Due to the differences in natural and social conditions between regions, it is necessary to explore the correlation mechanism between LULC and carbon storage changes in tropical regions from a broader geographical perspective. This paper takes Hainan Island as the research object, through the integration of the CA-Markov and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models, based on multi-source data, analyses the dynamics of LULC and carbon storage from 1992 to 2019 and the relationship between the two, and predicts future LULC and carbon storage under different scenarios. The results show that (1) the built-up land area of Hainan Island expanded from 103.59 km2 to 574.83 km2 from 1992 to 2019, an increase of 454.91%; the area of cropland and shrubland decreased; and the area of forest increased. (2) Carbon storage showed an upward trend during 1992–2000, and a downward trend during 2000–2019. Overall, LULC changes during 1992–2019 reduced carbon storage by about 1.50 Tg. (3) The encroachment of cropland in built-up land areas is the main reason for the reduction of carbon storage. The conversion of shrubland to forest is the main driving force for increasing carbon storage. The increase and decrease of carbon storage have obvious spatial clustering characteristics. (4) In the simulation prediction, the natural trend scenario (NT), built-up land priority scenario (BP) and ecological priority scenario (EP) reduce the carbon storage of Hainan Island, and the rate of decrease is BP> NT > EP. The cropland priority scenario (CP) can increase the LULC carbon storage, and the maximum increase in 2050 can reach 0.79 Tg. This paper supplements and improves the understanding of the correlation between LULC and carbon storage changes in tropical regions, and can provide guidance for the optimization of LULC structure in tropical regions with high economic development from a low-carbon perspective.

In the context of climate change and rapid urbanization, there have been unparalleled changes in land use and land cover (LULC), resulting in substantial impacts on the surrounding habitat quality (HQ), particularly in ecologically vulnerable arid regions. However, previous studies on the influencing mechanisms of HQ in arid urban agglomerations and future multi‐scenario simulations remain limited. To fill this knowledge gap, this study aimed to reveal the influencing mechanisms in HQ changes and to develop a multi‐scenario HQ assessment framework within arid urban agglomerations. We assessed the spatiotemporal variations in HQ using the InVEST model and three periods of LULC data for the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM), and the partial least squares structural equation model was introduced to explore the interactions between natural and non‐natural factors and their impacts on HQ. Additionally, we coupled multi‐objective programming and PLUS models to predict the LULC under different optimization scenarios (natural development scenario (NDS), ecological protection scenario (EPS), ecological–economic coordinated scenario, and economic development scenario) for the UANSTM in 2030, and to assess HQ. Results show that (1) the HQ index of the UANSTM was 0.507, 0.520, and 0.495 in 2000, 2010, and 2020 respectively, with a spatial distribution pattern of high values in the west, low values in the east, and high in the central and low in the north and south; (2) geomorphic, climatic, and LULC factors have direct positive effects on HQ, while socio‐economic factors have a direct negative effect on HQ. In addition, geomorphic, socio‐economic, and climatic factors also influence HQ through potential indirect paths. Climatic and LULC factors enhance the positive effect of geomorphic on HQ while counteracting the direct negative effect of socio‐economic factors on HQ. Climatic factors have the largest negative effect on HQ through their influence on LULC; (3) according to the four scenarios in 2030, the highest HQ index (increased by 0.13%) was found under the EPS, which also aligns more closely with SDGs. Conversely, NDS showed the lowest HQ index (declined by 2.59%). The research results could provide a scientific basis for promoting sustainable land management and ecological conservation for the UANSTM.

Assessing and decoupling ecosystem services evolution in karst areas: A multi-model approach to support land management decision-making

Land use pattern is a dominant factor causing carbon storage changes in terrestrial ecosystems and is crucial for maintaining the stability of carbon storage. Understanding the impact of land use on carbon storage variations in drylands is of great significance for local ecological protection and the sustainable management of land resources. Based on the land use data of the Gonghe Basin from 1990 to 2020, the InVEST model was applied to analyze the spatiotemporal changes in carbon storage, and the PLUS model was used to predict the changes in carbon storage under three different development scenarios in 2030. The results are as follows: (1) From 1990 to 2020, the main land use types in the Gonghe Basin were grassland and unused land, with an overall increase in grassland and a marked decrease in unused land. (2) The spatial distribution of carbon storage was generally characterized by being low in the center and high at the edge, and grassland was the most important land use type with the highest carbon storage. Over the past 30 years, it has shown an increase followed by a decline, with an overall increase of 1.84%. (3) The carbon storage under the natural trend, urban development, and ecological protection scenarios will be 158.80 × 106 Mg, 158.66 × 106 Mg, and 159.83 × 106 Mg in 2030, respectively. The grassland and cropland areas were larger under the ecological protection scenario, which was more conducive to improving the carbon storage in this region. This study provides an effective reference for optimizing land use and achieving carbon neutrality (“dual carbon” goals) in drylands.

Under the background of the carbon peaking and carbon neutrality goals, the evolution of the spatiotemporal pattern of carbon storage has recently emerged as a research hotspot. The change in land use and land cover (LULC) is the primary driver of carbon storage changes. Understanding the spatiotemporal variations of LULC and carbon storage at the small scale of district and county level and proposing strategies to improve carbon sink, will contribute to the ecological conservation, restoration and sustainable development of districts or counties. With Yanqing District in Beijing as an example, we calculated carbon storage from 1990 to 2020 based on the InVEST model and used the PLUS model to predict LULC type changes under three scenarios (natural growth, ecological conservation and economic development) from 2020 to 2050. We further predicted the carbon storage and proposed mea-sures to improve carbon sink. The results showed that the key LULC change in Yanqing between 1990 and 2020 were the conversion of 88.9% of grassland to forest, 50.1% of farmland to forest, and 39.5% of cropland to impervious surface. The total carbon storage showed an upward trend, with an increase of 3.34×106 Mg. The spatial distribution of carbon storage presented "high in the northeast, low in the southwest, and high in the mountainous areas, low in the riverine areas." The increase in forest and the decrease in grassland were the main reasons for the increase and decrease in carbon storage, respectively. Between 2020 and 2050, the ecological restoration efforts under the ecological protection scenario increased, and the probability of other LULCs transforming into forest increased, resulting in a 5.8% increase in carbon storage, which had the highest increase and carbon storage under the three scenarios. High-value carbon storage areas were concentrated in the northeast, northwest, and south of Yanqing District, basically corresponding to the mountainous regions of Yanqing with high forest coverage, and the low-value areas generally corresponded to the plains with high development intensity and low forest coverage. We could implement comprehensive ecological protection and restoration measures, including forest and grassland ecosystem protection, water environment ecological restoration, farmland ecological restoration, to promote sustainable development in Yanqing District and to achieve the "dual carbon" goal.

ContextDecades of intensifying human activities have led to drastic changes in land use and land cover (LULC) in the Poyang Lake Basin (PLB), resulting in significant changes in landscape pattern and ecosystem service value (ESV), thereby affecting regional sustainability.ObjectivesWe focused on understanding the impact of LULC changes on the landscape pattern and ESV of the PLB and used the Patch-generating Land Use Simulation model (PLUS) to predict LULC changes in 2050.MethodsWe evaluated landscape patterns using landscape metrics and calculated ESV using the ecosystem service equivalent factor method. The Pearson correlation coefficient was used to analyze the correlation between landscape patterns and ESV from 1990 to 2020. Then, we combined the PLUS model and the ecosystem service equivalent factor method to calculate the ESV under multiple scenarios from 2020 to 2050.ResultsFrom 1990 to 2020, the LULC of the PLB changed to varying degrees. The PLB has undergone a rapid process of landscape fragmentation, and the total ESV of the PLB has decreased. The total ESV was positively correlated with the CONTAG index and negatively correlated with the SHDI index. Between 2020 and 2050, the ESV of the PLB is projected to decrease under the NDS (nature development scenario) and EDS (economic development scenario) and increase under the EPS (ecological protection scenario).ConclusionsESV responded to changes in landscape pattern. We recommend that the PLB should increase patch connectivity. Additionally, future development in the PLB should prioritize ecological protection to prevent further declines in ESV.

Land use and land cover (LULC) changes play a crucial role in regional carbon dynamics and climate regulation. This study assesses the impact of LULC changes on carbon stocks in Hunan Province, China, from 2000 to 2035 using a MOP-PLUS–InVEST–OPGD integrated modeling framework. Results show that carbon stock declined by 45.96 million tons from 2000 to 2020 due to rapid urban expansion and conversion of forest and grassland to construction land. Scenario simulations reveal that by 2035, carbon stock will increase by 4.82% under the ecological protection scenario (EP) but decrease by 3.26% under the natural trend scenario (NT). Economic development scenario (ED) and sustainable development scenario (SD) produce intermediate outcomes. Spatially, high-carbon regions are concentrated in high-altitude forested areas, while urbanized lowlands exhibit the lowest carbon density. The optimal parameters-based geographical detector (OPGD) model identifies land use intensity, elevation, and net primary productivity as the dominant drivers of carbon stock variation, with significant interactions between natural and socioeconomic factors. These findings underscore the need for integrated land-use planning and ecological conservation policies that align with carbon neutrality goals. This study provides a replicable spatial framework and policy-oriented insights for managing carbon stocks in rapidly developing regions.

Given the ecological significance and extensive coverage of mountainous regions in Türkiye, this study evaluates the effects of land use and land cover (LULC) changes in these areas on national greenhouse gas (GHG) emissions between 2000 and 2022. Collect Earth (CE), an open-source program developed by the Food and Agriculture Organization of the United Nations (FAO), was used to monitor the LULC changes. Collect Earth monitoring relies on high-resolution imagery from Google Earth and Bing Maps, enabling detailed plot-level assessments from local to national scales. The coefficients and emission factors to estimate GHG emission and removals were taken from the most recent National GHG Inventory Report (NIR) to ensure consistency and comparability. The land monitoring system revealed that 60.96 million hectares, constituting more than three-fourths of the country, are classified as mountainous. During the monitoring period, the settlement was the land use class with the most significant proportional increase (30.36%), mainly consuming cropland and grassland. Our results revealed that cropland (0.05%) and grassland (2.19%) areas shrank, while forestland (0.28%) and reservoir areas (6.03%) increased since the year 2000. Even though net forest cover (afforestation-deforestation) increased by around 59.3 Kha, the sole forest-related conversions increased the GHG emissions by 25.1 Mt CO2eq. The total effect of LULC changes on the GHG balance was a net 47.1 Mt CO2eq of emissions. Thus, LULC changes in Türkiye's mountainous areas acted as a net emission source, adding over 2 MtCO2eq annually during the 20-year period.

With the gradual prominence of global water shortage and other problems, evaluating and predicting the impact of land use change on regional water conservation function is of great reference significance for carrying out national spatial planning and environmental protection, and realizing land intelligent management. We first analyzed 8,416 remote sensing images in the upper reaches of the Hanjiang River Basin (URHRB) by GEE platform and obtained the land use and land cover (LULC) results of fours periods. Through our field investigation, the accuracy of remote sensing image interpretation is obviously higher than that of other comprehensive LULC classification results. Then, through the coupling of InVEST-PLUS model, not only the results of URHRB water conservation from 1990 to 2020 were calculated and the accuracy was assessed, but also the LULC results and water conservation of URHRB under different development scenarios in 2030 were predicted. The results showed as follows: From 1990 to 2020, the forest area of URHRB increased by 7152.23 km2, while the area of cropland, shrub and grassland decreased by 3220.35 km2, 1414.72 km2 and 3385.39 km2, respectively. The InVEST model reliably quantifies the water yield and water conservation of URHRB. In the past 30 years, the total amount of water-saving in China has shown a trend of increasing first and then decreasing. From the perspective of vegetation types, URHRB forest land is the main body of water conservation, with an average annual water conservation depth of 653.87 mm and an average annual water conservation of 472.10×108 m3. Under the ecological protection scenario of the URHRB in 2030, the maximum water conservation in the basin is 574.92×108 m3, but compared with the water conservation in 2010, there is still a gap of 116.28×108 m3. Therefore, through the visualization analysis of the LULC changes of URHRB and water conservation function, it is found that the land and resources department should pay attention to the LULC changes of water sources and adjust the territorial spatial planning in time to cope with the huge water conservation gap in the future.

Liaoning Province, as an old industrial urban agglomeration since the founding of China, is an important link between the Bohai Economic Zone and the Northeast Economic Zone, and it has made great contributions to the economic development of China. The transformation of China’s economy and heavy industrial development have posed great challenges to the long-lasting growth of Liaoning Province. In this study, the driving force of land expansion was detected using the patch-generating land use simulation (PLUS) model in Liaoning Province, and the land situation in 2030 was predicted under natural development, ecological protection, and economic development scenarios. We then further coupled the PLUS model with the integrated valuation of ecosystem services and trade-offs (InVEST) model to explore the spatial autocorrelation and synergistic relationship between carbon storage and habitat quality. The results indicated the following: (1) The total accuracy of the simulation in 2020 using the PLUS model reached 94.16%, and the Kappa coefficient reached 0.9089; therefore, the simulation result was highly reliable. (2) The overall spatial pattern of both carbon storage and habitat quality decreased from the northwest and southeast to the middle, and habitat quality had an impact on carbon storage to a certain extent, with a positive spatial correlation. (3) The ecological protection (EP) scenario was the only development prospect with increasing total carbon storage, which could increase carbon sequestration by approximately 7.83 × 106 Mg/C, and development prospects with optimal habitat quality. (4) Weak trade-off and weak synergy dominated in the 2030 natural development (ND) scenario; most regions showed weak synergy in the ecological protection scenario, spatial heterogeneity became more pronounced in the economic development (ED) scenario, and a strong trade-off and strong synergy emerged in individual regions. The results of the study have a positive feedback effect on establishing an ecological security barrier in Liaoning Province and furthering long-lasting low-carbon urban development.

This chapter presents a methodology for estimating carbon sequestration and storage (i.e., change in storage over time) on a landscape and the social value of the sequestration process. It presents two approaches for estimating carbon sequestration and storage on a landscape. In the simpler tier 1 approach, carbon storage is a function of land use and land cover (LULC), and sequestration is given by the difference in storage at two points in time. Carbon sequestration is not accounted for in a parcel of land unless its LULC changes or it is subject to harvest of wood. In the more complex tier 2 model, carbon storage in a parcel of land is a function of its LULC, the age of its LULC, and its previous LULC. Carbon sequestration is accounted for even if LULC or land management does not change over time, such as when a forest accumulates carbon in its standing biomass due to tree growth. The chapter illustrates both of these approaches with examples from Tanzania and the United States, and also demonstrates a method for determining the economic value of sequestration using the social cost of carbon.