Spatiotemporal Relationship Between Carbon Emissions and Ecosystem Service Value in Hutuo River Basin

Ecosystem services are related to human well-being， and land use change （LUCC） can affect carbon emissions and ecosystem service value （ESV）. Studying the spatiotemporal relationship between the two is of great significance for promoting green and sustainable development. Therefore， in the context of the "dual carbon" goal and the construction of ecological civilization， Shanxi， as a coal-producing province， was taken as the research area， based on land use and socio-economic data from 2000 to 2020， using spatial autocorrelation analysis methods to explore the spatiotemporal evolution characteristics and interaction laws of carbon emissions and ESV. The results indicate that： ① From 2000 to 2020， the main land types in Shanxi Province were cultivated land， grassland， and forest land. The area of construction land and forest land showed an increasing trend， while the area of other land types showed a decreasing trend. ② The net carbon emissions from land use in Shanxi Province have been increasing year by year， from 34.65 million tons in 2000 to 124.80 million tons in 2020. The high value areas of land use carbon emissions intensity were mainly concentrated in the central region and continued to extend to the surrounding areas， while the low value areas were mainly distributed on the edges of various cities， and their area continued to decrease. ③ The ecosystem service value in Shanxi Province was 269.13 billion yuan in 2000 and 264.69 billion yuan in 2020， with a slow decline in value. Forest and grassland had the highest contribution rates to ESV. The spatial distribution of ESV varied significantly， with significant annual changes. ④From 2000 to 2020， there was a significant negative correlation between land use carbon emission intensity and ESV intensity in Shanxi Province. The stronger the land use carbon emission intensity， the weaker the ESV intensity， and it passed the p-value test. The research results contribute to providing scientific basis for land use planning and ecological environment policy formulation in Shanxi Province， thereby promoting industrial transformation and upgrading as well as sustainable socio-economic development.

Changes in land use restructuring significantly impact carbon emissions and the provision of ecosystem service value (ESV). This study focuses on the Guanzhong Plain Urban Agglomeration, and accounts for carbon emissions and ecosystem service values caused by land use in the years 2000, 2005, 2010, 2015, and 2020. Carbon sources, carbon sinks, net carbon emission intensity, and ESV intensity were introduced as research variables and the spatial and temporal divergence and correlation patterns between them were examined. The results show that: 1) The carbon emission intensity of land use in the Guanzhong Plain Urban Agglomeration has increased significantly over the study period, showing a distribution pattern of high intensity in the centra regions and low intensity in the surrounding regions. Construction land was identified as the largest carbon source. 2) The overall ESV follows an increasing trend, with the total value increasing from 215,263.7 million to 216,776.2 million. The distribution of ESV intensity is low in the central regions and high in the surrounding regions, and significant changes were observed in the ESV loss and gain intensity of farmland and water body. 3) Carbon emissions and ESV show a significant negative spatial correlation, and both are dominated by low-high and high-low aggregation patterns. A spatial spillover effect of carbon emissions on ESV was observed. Through the correlation analysis of carbon emissions and ESV, theoretical support is provided for promoting regional low-carbon green development and eco-economic synergistic development.

Exploring future land use changes in river basins and assessing habitat quality are crucial for ensuring the sustainable development of basin ecosystems. Based on land use data from 2000 to 2020 in the Hutuo River Basin, this study applied the future land use simulation （FLUS）-integrated valuation of ecosystem services and tradeoffs （InVEST） model to simulate and analyze the spatial-temporal evolution characteristics of land use and habitat quality in the basin from 2030 to 2070 under typical shared socioeconomic pathway （SSPs）-representative concentration pathway （RCPs） scenarios （SSP1-2.6, SSP2-4.5, and SSP5-8.5）. The results indicate that： The primary land use types in the Hutuo River Basin were cropland, grassland, and forest land, with cropland and grassland continuously converting into construction land in densely populated areas over the 20-year period. Under the SSP1-2.6 and SSP2-4.5 scenarios, cropland and grassland decreased, while forest land and construction land increased, with SSP1-2.6 exhibiting the greatest reduction in cropland and the highest increase in forest land. Under the SSP5-8.5 scenario, construction land expanded continuously and intensively. The overall habitat quality in the Hutuo River Basin demonstrated a spatial distribution pattern of "high in the central region and low in the periphery." Habitat quality declined continuously over the 20-year period, with more severe degradation from 2000 to 2010 and a slight mitigation in the degradation rate from 2010 to 2020. Under the SSP1-2.6 scenario, overall habitat quality in the basin improved, whereas it declined under the SSP2-4.5 and SSP5-8.5 scenarios, with the most pronounced decline observed under the SSP5-8.5 scenario. The SSP1-2.6 and SSP5-8.5 scenarios primarily showed an increase in hotspots and coldspots, respectively. These findings provide valuable insights for enhancing habitat quality in the basin and promoting the synergistic development of ecological and economic systems in the future.

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km2), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km2), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 104 t in 1990 to 169.17 × 104 t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals.

Human activities cause land use change and affect ecosystem service value and carbon emissions. It is of great significance to explore the relationship between ecosystem service value and land use carbon emissions to promote regional low-carbon development and promote the construction of ecological civilization. The Loess Plateau is selected as the study area, and the ecosystem service value (ESV) and land use carbon emissions in the study area are estimated by using the land use data of five periods from 2000 to 2020. The spatio-temporal characteristics and correlation between the two are analyzed, and the sustainable development capacity of the ecosystem is evaluated based on the four-quadrant model. The results show that: (1) From 2000 to 2020, the ecosystem service value of the Loess Plateau increased from 579.032 billion yuan to 582.470 billion yuan, an overall increase of only 0.15%. (2) The carbon emissions from land use in the Loess Plateau increased from 1.37 ×108t during the study period increased to 4.58×108t, with an average annual growth rate of 6.20%. Energy consumption in the process of industrialization and urbanization is the main factor for its growth. (3) There is a certain spatial positive correlation between ecosystem service value and land use carbon emissions, and the regional scope and distribution of significant correlation between 2000 and 2020 have little change. (4) The intensity of ecosystem service value and land use carbon emissions at the county scale change steadily over time. Most counties are located in the general area of ecosystem sustainable development capacity, indicating that there is a certain room for improvement in ecosystem sustainable development capacity. The research results can provide reference for the formulation of regional refined ecological management and differentiated carbon emission reduction policies.

Analyzing the spatiotemporal differences in land use carbon emissions systematically and exploring their influencing factors for the rational allocation of land resources is of great importance and promoting collaborative emission reduction in this region. Based on the calculation of land use carbon emissions in Ningxia and its prefecture-level cities from 2000 to 2021, the regional differences in carbon emissions, economic efficiency, and carbon sink capacity were reflected through the difference index, carbon emission intensity, economic contribution rate, and carbon sink ecological carrying capacity. The results were as follows： ① From 2000 to 2021, the land use carbon emissions in Ningxia showed a significant increase by 110 919 400 t. Construction land was the main carbon source land, accounting for 99.57% of the total carbon emissions in 2021, and forest land was the main type of carbon absorption, accounting for 79.22% of the total carbon absorption in 2021. ② During the research period, the carbon emission difference among prefecture-level cities showed a trend of first rising and then slightly falling, with the gap reaching the maximum in 2016. ③ Although the overall difference in carbon emission intensity among prefecture-level cities showed a trend of narrowing and convergence, the economic contribution coefficient and carbon sink ecological carrying coefficient had significant differences, and the economic contribution rate and carbon emission contribution rate were both in a relatively unbalanced state, with obvious regional differences. ④ Land use carbon emission intensity, land use structure, economic development level, and population all played a promoting role in land use carbon emission, with contribution rates of 56.48%, 41.27%, 85.20%, and 9.29%, respectively. The contribution value of land use carbon intensity per unit GDP was negative, which inhibited the increase of land use carbon emission.

In the context of escalating global concerns for “carbon neutrality and peak carbon” and the urgent need for ecological conservation, deciphering the spatiotemporal interactions between carbon emissions and the ecosystem service value (ESV) in relation to land use changes becomes critically significant. Identifying areas to bolster ecosystem services and curtail carbon emissions, especially within the Guanzhong urban agglomeration, is crucial for advancing sustainable and low-carbon regional development. The study focuses on the urban agglomeration of Guanzhong, using land use and socio-economic data from three periods between 2010 and 2020. Methods such as grid analysis and bivariate spatial autocorrelation models are employed to explore the temporal and spatial evolution characteristics and interaction patterns of carbon emissions and ESV in relation to land use. The findings reveal: (1) during 2010–2020, the Guanzhong urban agglomeration experienced varied transitions in land use types, marked by a significant net decrease in arable land and net increases in grasslands and urban construction areas. (2) The ESV in the Guanzhong urban agglomeration witnessed a consistent rise, exhibiting a spatial distribution pattern with higher values in the southwest and lower in the northeast. Among the categorized ecosystem service functions, services related to hydrological and climate regulation stood out. (3) The Guanzhong urban agglomeration observed an average annual growth rate of 5.03% in carbon emissions due to land use, with a spatial trend that was higher in the center and tapered towards the periphery. Predominant carbon sources included arable lands and urban construction areas, while forests accounted for 94% of carbon sequestration. (4) A pronounced negative correlation between the ESV and carbon emissions was discerned in Guanzhong. Regions with a stronger correlation were primarily centered in Guanzhong, notably around Xi’an and Baoji. The results emphasize the pivotal role of the primary sector’s qualitative development in harmonizing the ESV and carbon emission dynamics in the Guanzhong urban agglomeration. This research provides valuable insights for optimizing land resource management, aligned with the rural revitalization strategy, streamlining carbon dynamics, bolstering ESV, augmenting carbon sequestration efficiency, and guiding ecological spatial planning.

Carbon emissions triggered by land use changes have become the main source of regional carbon emissions. Taking Beijing-Tianjin-Hebei， where land use changes are extreme and the intensity of carbon emissions far exceeds the national average， as the study area and taking the county as the study unit， we systematically investigated the spatial and temporal evolution of regional land use carbon emissions and its driving factors， which has great significance in guiding the optimization and adjustment of the low-carbon land use in Beijing-Tianjin-Hebei and the realization of the goal of the "dual-carbon" strategy. This study integrated data from multiple sources， including land use， nighttime lighting， and socioeconomic data， and also integrated the carbon emission coefficient method， nighttime lighting inversion， improved Kaya model， and LMDI model to study the characteristics of the spatial and temporal evolution of carbon emissions from land use in Beijing-Tianjin-Hebei counties and identify the driving factors in the period of 2000?2020. The study produced several results： ① From 2000 to 2020， land use in Beijing-Tianjin-Hebei was mainly characterized by a dramatic conversion of construction land and cropland， with construction land as the main carbon source and forest land as the main carbon sink. ② During the study period， the net carbon emission from land use in Beijing-Tianjin-Hebei increased from 71.857 9 million t to 159.698 5 million t， with a change rate of 122.24%. Carbon emissions in Caofeidian District， Fuping County， and Luanping County increased by a higher amount， whereas carbon emissions in Dongcheng， Xicheng， and Chaoyang Districts of Beijing decreased. ③ The economic contribution coefficient of carbon emissions from land use in Beijing-Tianjin-Hebei showed a decreasing trend in general， while the spatial pattern of ecological carrying coefficients was more stable， with high ecological carrying coefficient （>1） counties concentrated in the northwestern ecological conservation area and other districts and counties consistently having lower ecological carrying coefficients. ④ The level of economic development was the main factor contributing to carbon emissions， and the economic efficiency of land use was the main factor inhibiting carbon emissions. Therefore， pursuing high-quality and sustainable economic development， enhancing land use intensification， promoting technological innovation， and building a strong ecological barrier is the best strategy for reducing carbon emissions from land use in Beijing-Tianjin-Hebei.

With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

Land use is an important part of the human-land system, which can provide huge ecosystem services. Land use changes also lead to changes in the value of ecosystem services. Considering primary production, maintaining carbon dioxide and oxygen balance, nutrient cycling, water conservation, soil erosion and other main services functions, this study establishes a land use ecosystem services value (ESV) estimation method based on the terrestrial ecosystem simulator (TESim) implemented for regional scales (TESim-R model) coupled with the Land Use and Land Cover Change (LUCC) model (the TES-LUC model). This ESV estimation model facilitates understanding the true economic costs of land use projects as it considers ecosystem services. This model can be utilized to calculate the ecosystem data of different types of land use by substituting in data of meteorology, vegetation, soil, and control attribute, and then evaluate the ESVs of land use according to different ecosystems. Following Constanza et al. (2007), this study classifies the ESV of an area into five categories: primary production, climate regulation, nutrient cycling, water conservation, and erosion control. Using the net primary productivity (NPP) output value simulated by the TES-LUC model as the basis, the ESVs of five categories can be calculated, respectively. The sum of the five ESVs yields the total ESV. This study introduces the model through the example of China. To test the model, two cost benefit analysis, one on a land use development project of a small community, and the other one on a large national project, are conducted. The effectiveness of the model is evaluated for both projects. This study first investigates the “Grain-for-Green” Project in Long County, Sha’anxi Province, China based on the remote sensing image of the county and the data published by the local bureau of statistics from 2000 to 2015. Using the model, this study calculates and analyzes the change of ESV in Long County during the study period. The results demonstrate that the area of woodland, construction land, and grassland increased in different proportion, among which the area of grassland increased the most at 15.15%. The increase in land area mainly came from the decrease in farmland, the reduced area of which is up to 6,055.40hm2. During the study period, the total ESV increased by 1.670×107 yuan, an increase of 0.52%. This is mainly attributed to the increase in the ESV of woodland and grassland. However, due to the sharp decrease in the area of farmland and water body, the NPP and the nutrient cycling function of Long County were affected, exhibiting decreasing ESVs during the period. Therefore, based on the model and results, this study puts forward policy implications for land use projects from perspectives of water resources. The model is further tested by evaluating the environmental costs of a large national land use project, the “Rapid Urbanization” Project of Yangtze River Delta, which is in the Outline of China’s National Land Planning. Using data of land use gathered from six terms of remote sensing image interpretation data of Resource and Environmental Science Data Center of the Chinese Academy of Sciences from 1990 to 2015, the results derived from the model find that rapid urbanization leaded to unbalanced transition among different types of land, which caused a significant loss of ESV. From 1990 to 2015, the ESV of Yangtze River Delta decreased from 171.701 billion yuan to 168.267 billion yuan. Moreover,among five E5V components, the loss in the ESV of soil erosion was the highest, which decreased by 1.518 billion yuan due to the violent transformation between farmland and construction land. As a concluding remark, this study proposes a series of implications for large land use projects from the perspective of planning, implementation, and management.

Land use changes significantly impact both carbon emissions and ecosystem service value (ESV). However, few studies have been conducted on the spatial relationship between land use carbon emissions (LUCE) and ESV. Thus, focused on the Yellow River Basin (YRB), this study independently calculates carbon emissions from land use change (LUCE) and ecosystem service values (ESV) in the region. Utilizing spatial autocorrelation methods, we analyze the spatiotemporal pattern of LUCE and ESV and subsequently apply the bivariate spatial autocorrelation method to explore their spatial relationship. The results prove that: (1) The YRB's LUCE has continuously increased, with construction land acting as the dominant carbon source and woodland acting as the main carbon sink. The LUCE in the YRB had a positive spatial autocorrelation. (2) The YRB's ESV increased. Spatially, the ESV in the YRB showed a positive autocorrelation. (3) Both LUCE and ESV exhibited negative spatial autocorrelation, with predominant patterns of bivariate localized spatial autocorrelation identified as High-Low agglomeration (H-L) and Low-High agglomeration (L-H). Cities with the L-H pattern were primarily located in Qinghai Province and Inner Mongolia. In contrast, cities with the H-L pattern were mainly observed in the western section of Shandong and the northeastern region of Henan. The study revealed the negative impact of increased carbon emissions from land use on the value of ecosystem services, providing assistance in the development of relevant environmental policies and promoting sustainable development in the YRB.

The dramatic changes in land use caused by human economic activities have a profound impact on carbon emissions and ecosystem service value （ESV）. In order to explore the evolution characteristics of carbon emissions and ESV on the spatial and temporal scales， based on the land use data of the Yellow River Basin from 2000 to 2020， this study used spatial autocorrelation and multivariate Logit regression models to study the spatial and temporal characteristics and spatial correlation of total carbon emissions and ESV in counties of the Yellow River Basin， then to explore the influencing factors of spatial correlation. The research findings were as follows： ① In the past 20 years， the total amount of land use carbon emissions in the basin has shown an overall growth trend， and the increasing counties were concentrated in energy-rich areas such as Inner Mongolia， Ningxia， and northern Shaanxi. The total amount of ESV increased first and then decreased， and the high value counties were mainly distributed on the edge of the Yellow River Basin， among which Qumalai County in Qinghai Province had the most ESV. The low value counties of ESV were mainly located in the economically active urban agglomerations such as the Shandong Peninsula Region， Central Plains Region， Guanzhong Plains Region， and cities along the yellow river in Ningxia. The lowest value of ESV has always been located in Xi'an. ② There was a spatial negative correlation between total carbon emissions and total ESV. The number of counties with high carbon emissions and high ESV has been increasing， mainly distributed in southern Inner Mongolia， eastern Ningxia， and northern Shaanxi， which was related to the location near the Yellow River and energy development. The double low type was mainly located in the gully area of the Loess Plateau， which is connected to the strip from the east and west. The low-high class was contiguously distributed in Qinghai， Sichuan， and western Gansu， and some were island-like distributed around the double-low class. The number of high-low classes was increasing year by year， mainly located in the core city area. ③ In low ESV counties， regions with better economic development and higher population were more likely to increase their carbon emissions. Taking the low carbon emissions from land use as a reference， the per capita GDP， energy use efficiency， and rainfall were significantly negatively correlated with the high-high and high-low categories. This indicates that most counties with high carbon emissions had relatively dense populations and less rainfall， resulting in higher energy dependence. Additionally， there was a positive correlation between low-high class areas and total population. When located in areas with low land use carbon emissions， areas with higher ESV values tended to have more a concentrated population distribution. The increase in land reclamation rate may encroach on forests and grasslands that can provide higher ecosystem services， reducing the value of regional ecosystem services. The research findings have certain reference significance for ecological protection and high-quality development decision-making in the Yellow River Basin.

Ecological and environmental effects of land use change in rapid urbanization: The case of hangzhou, China

The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.

Land optimization simulation and ecosystem service value (ESV) estimation can better serve land managers in decision-making. However, land survey data are seldom used in existing studies, and land optimization constraints fail to fully consider land planning control, and the optimization at the provincial scale is not fine enough, which leads to a disconnection between academic research and land management. We coupled ESV, gray multi-objective optimization (GMOP), and patch-generating land use simulation (PLUS) models based on authoritative data on land management to project land use and ESV change under natural development (ND), rapid economic development (RED), ecological land protection (ELP), and sustainable development (SD) scenarios in 2030. The results show that construction land expanded dramatically (by 97.96% from 2000 to 2020), which encroached on grassland and cropland. This trend will continue in the BAU scenario. Construction land, woodland, and cropland are the main types of land used for expansion, while grassland and unused land, which lack strict use control, are the main land outflow categories. From 2000 to 2030, the total amount of ESV increases steadily and slightly. The spatial distribution of ESV is significantly aggregated and the agglomeration is increasing. The policy direction and land planning are important reasons for land use changes. The land use scenarios we set up can play an important role in preventing the uncontrolled expansion of construction land, mitigating the phenomenon of ecological construction, i.e., “governance while destruction”, and promoting food security. This study provides a new approach for provincial large-scale land optimization and ESV estimation based on land survey data and provides technical support for achieving sustainable land development.