Land-use Carbon Emissions Research Articles

Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan

How to identify variables for carbon reductions was considered as one of the most important research topics in related academic fields. In this study, the characteristics of landuse carbon emissions of the economic belt on the northern slope of Tianshan (NST) were tentatively investigated. Taking 12 cities in NST as the case study, land use carbon emissions and carbon intensities were estimated and analyzed based on the Landsat remote sensing image and socio-economic statistical data in 1990, 2000, 2010, and 2020. Moreover, Moran’s I model was applied to study spatial autocorrelation between carbon emissions and carbon intensities. Results show that (1) urban land and cropland were increased rapidly during the past three decades; (2) carbon emissions were increasing significantly, and the urban land was responsible for the majority of the carbon emission; (3) negative spatial correlations on both net carbon emissions and carbon intensities were obtained between 12 cities; and (4) based on carbon balance zoning analysis, NST could be divided into four different zones. The rising ratio of carbon emissions and intensities was significantly higher than urbanization expending speed. Results could provide references and useful insights into related arrangements of policies and attempts on carbon reduction for cities in NST.

Spatial–Temporal Characteristics and Influencing Factors of Land-Use Carbon Emissions: An Empirical Analysis Based on the GTWR Model

An in-depth comprehension of the spatial–temporal characteristics of land-use carbon emissions (LUCE), along with their potential influencing factors, is of high scientific significance for the realization of low-carbon land use and sustainable urban development. Academic investigations pertaining to LUCE predominantly encompass three key dimensions: assessment, optimization, and characterization research. This study aimed to investigate the spatial and temporal variations in LUCE within Zhejiang Province by analyzing data from 11 cities and identifying the key factors influencing these emissions. This research work employed the geographically and temporally weighted regression (GTWR) model to explore the patterns of variation in these factors across each city. The results reveal that (1) the temporal changes in LUCE display two predominant trends, while the spatial distribution exhibits a distinct “high in the northeast and low in the southwest” divergence; (2) the average intensity of each factor follows the order of economic level > government intervention > urban compactness > public facilities level > urban greening level > industrial structure > population density; (3) and the influencing factors exhibit significant spatial and temporal heterogeneity, with varying direction and intensity of effects for different cities at different stages of development. This study integrated the dimensions of time and space, systematically examining the evolutionary trends of influencing factors on LUCE within each region. Consequently, it contributes to the comprehension of the spatiotemporal effects associated with the driving mechanisms of LUCE. Moreover, it offers a foundation for formulating customized patterns and strategies to mitigate such emissions, taking into account specific local contexts.

Spatial correlation evolution and prediction scenario of land use carbon emissions in the Yellow River Basin

Carbon dioxide emission is an important driving factor of global warming and it has threatened the ecological environment and human survival. Among them, land use has led to significant carbon emissions that profoundly affect climate system change. The Yellow River Basin (YRB) is one of the regions with the most concentrated contradictions in population, resources, and environment in China; thus, studying the current situation and land use carbon emissions (LUCE) is significant for mitigating global warming, promoting coordinated emission reduction among different regions in the basin, and achieving ecological conservation and high-quality development of the YRB. This study based on land use and socio-economic data, and is carried out from the perspective of social network analysis. The spatiotemporal variation of LUCE in the YRB was analyzed using the carbon emission coefficient method. The spatial spillover effects of LUCE were discussed using social network analysis methods. The PLUS model was used to simulate the differences in LUCE under different scenarios. The results indicate that: (1) The LUCE in the YRB showed an increasing trend during the study period, with significant differences in spatial distribution. (2) There is a significant spatial spillover effect and correlation between cities in the YRB LUCE network, and cities with superior economic had a greater impact on other cities. (3) In 2030, under the ecological protection scenario, the LUCE in the YRB were the lowest, with a reduction of 2.7 × 106 tons compared to the natural development scenario, further illustrating the importance of ecological land. Compared with previous studies, this study explores the spatial correlation between LUCE in various cities of the YRB from a new perspective of social network analysis. On the other hand, it makes land use prediction for 2030 by setting different land use development scenarios. The research results have broadened the application scope of social network analysis methods, and have important practical significance for promoting carbon reduction in major river basins and scientifically formulating land use policies.

Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China

Climate change has become a global concern, and the prediction of carbon emissions is key to achieving carbon-reduction goals. The existing framework cannot accurately reflect the spatial distribution of carbon emissions, making it difficult to guide urban planning and management. Therefore, in this study, a carbon emission spatial simulation and prediction model was established. The model includes the GIS-Kernel Density sub-model for subdividing built-up area, the Land Use-Carbon Emission sub-model for establishing the correlation between land use and carbon emissions, the Multi Objective Programming-Principal Component Analysis-BP neural network sub-model for presetting development scenarios, and the Patch-generating Land-use Simulation sub-model for predicting. Xi'an was chosen as the study site, and two extreme scenarios were determined. A total of 373,318 development paths were segmented from the interval, and the optimal path was selected. All scenarios were simulated, and the carbon emissions and their spatial distribution were calculated. The results showed that the overall accuracy of the simulation exceeded 90%. Under the optimal path, Xi'an's carbon emissions reach 60.6 million tons at peak time, which will be reduced to 47.38 million tons by 2060. In addition, the model analyzed the temporal and spatial changes of carbon sources and sinks and drew up the path of carbon reduction by technology and urban planning. This model can provide a reference for regional carbon-reduction planning and carbon reduction technology implantation. It can propose strategies from the perspective of planning and management.

Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province

Land use change is one of the key elements leading to carbon emission changes, and is of great significance to the process of achieving the goals of carbon peaking and carbon neutrality. In this study, we calculated the land-use carbon emissions (LCE) in the Guanzhong area (GZA) of Shaanxi province from 2000 to 2019 by using an improved LCE measurement model. Meanwhile, the spatial and temporal changes of LCE were analyzed and the driving forces were investigated based on the correlation analysis and multi-scale geographical weighting regression (MGWR). The results showed that the total amount of LCE showed a significant increasing trend from 2000 to 2019. Regions where the LCE significantly increased occupied 71.20% of the total area; these regions were distributed in the central and eastern parts of the study area. The LCE showed a significant positive spatial correlation and had a remarkable aggregation state. The H-H agglomeration area of LCE was distributed in the central urban agglomeration. The L-L agglomeration areas were always distributed in the southwest part of the GZA with low carbon emissions. The average correlation coefficients between LCE and nighttime light (NTL), population density (PD), and gross primary productivity (GPP) were 0.13, 0.21, and −0.05, respectively. The NLT and PD had obvious positive effects on LCE, while GPP has obvious negative effects on carbon emissions, which can be ascribed to the carbon sink effect of forests and grasslands. The results of this study have important reference value regarding the formulation of carbon emission reduction policies and the development of a low-carbon social economy.

Land Use Structure Optimization and Ecological Benefit Evaluation in Chengdu-Chongqing Urban Agglomeration Based on Carbon Neutrality

Optimizing land use structure in urban agglomerations is essential to mitigating climate change and achieving carbon neutrality. However, the studies on low-carbon (LC) land use in the urban agglomeration based on carbon neutrality are still limited and lack the consideration of the optimized land ecological benefits. To reduce land use carbon emissions (LUCEs) and improve the ecological benefits of urban agglomerations, we constructed the framework of land use structure optimization (LUSO) under carbon neutrality. Then, in view of land use quantity structure and spatial distribution, we compared the results of LUCEs and the ecological benefits of the Chengdu–Chongqing urban agglomeration (the CCUA) in 2030 under different scenarios. The results showed that in 2030, the LUCEs of the CCUA is 3481.6632 × 104 t under the carbon neutral scenario (CN_Scenario), which is significantly lower than the baseline scenario (BL_Scenario) and 2020. In the CN_Scenario, the land use/cover change (LUCC) in the CCUA is more moderate, the aggregation degree of the forestland (FL), grassland (GL), wetland (WL), and water (WTR) patch area deepens, and the overall landscape spreading degree is increased, which is more conducive to play the ecological benefit of carbon sink land. The results can provide a reference for the more efficient use of land resource areas and the formulation of land use and spatial planning.

Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020.

Exploring the process of carbon emissions under the "carbon peaking and carbon neutrality goals" can contribute to sustainable economic development. This research takes Shaanxi Province as an example. We elaborated on the spatial and temporal characteristics of land-use change from 2000 to 2020 and adopted the carbon emission model method to calculate land-use carbon emissions, also used urban morphological indicators to reveal the main factors of carbon emission changes. The results show that from 2000 to 2020, the land-use change in Shaanxi Province is mainly reflected in the increase in construction land area and the decrease in agricultural land area. Among them, the construction land area increased by 2192 km2, and the agricultural land area decreased by 5006 km2. Land-use carbon emissions increased by 1.28 × 1011kg during this period. Construction land is a major contributor to carbon emissions. The forestland is the main carbon sink. Carbon emissions showed a spatial pattern of "high in the north, low in the south, and concentrated in the middle." Urban form change is the driving factor affecting land-use carbon emissions in Shaanxi Province. The results of the research contribute to the understanding of regional carbon emission mechanisms and provide a scientific basis for reducing carbon emissions.

Progress and Hotspots of Research on Land-Use Carbon Emissions: A Global Perspective

Carbon emissions from land use change are the leading causes of the greenhouse effect. Exploration of the progress and hotspots of research on land-use carbon emissions (LUCE) is crucial for mitigating global climate warming. However, a comprehensive and systematic review of LUCE research from a global perspective is still lacking. We used the WoS Core Collection Database to analyze the current status of research on LUCE from a global perspective with the aid of a bibliometrix tool, aiming to reveal research hotspots and future development trends. We found that (1) the process of LUCE research has gone through a nascent exploration stage (1992–2001), a problem-focused stage (2002–2011), and a prosperous development stage (2012–2022) under different policy orientations. European and North American countries prioritize LUCE research more than others. (2) Overseas research hotspots mainly focus on the climate effects of land-use change, the impact of deforestation and fire on carbon stocks, the impact of soil organic carbon stocks on climate change and biodiversity, and agricultural carbon emissions. Research hotspots in China mainly focus on the study of the influencing factors of land-use carbon emissions, the path to achieving the dual carbon goal, and the transition to a low carbon economy. (3) Research frontiers show that China mainly researches low-carbon land use intensification in the context of a “dual carbon” strategy; carbon emission reduction based on energy transition; and the multi-dimensional, dynamic, and accurate tracking and monitoring of land-use carbon emission systems using remote sensing satellite data. Other countries have shifted from measuring historical land-use carbon emissions, deforestation, degradation and fire carbon emissions to biomass combustion and global warming mitigation research. This study enhances the depth and breadth of LUCE research, which can provide a theoretical foundation and scientific reference for subsequent research on LUCE.

Analysis of Spatiotemporal Variation and Influencing Factors of Land-Use Carbon Emissions in Nine Provinces of the Yellow River Basin Based on the LMDI Model

The Yellow River Basin assumes an important ecological and economic function in China. The study of carbon emissions from land use in the nine provinces (regions) of the pathway is important to achieve carbon reduction. Based on the dynamic data of land use, energy, and economic changes in nine provinces (regions) for the past 30 years from 1990 to 2018, this study analyzed the spatial and temporal evolution characteristics of land-use carbon emissions by using the carbon emission coefficient method in the IPCC inventory method and evaluating the low-carbon development model of the nine provinces (regions) by land-use carbon emission intensity. Finally, the LMDI model was used to analyze the factors influencing land-use carbon emissions. The results showed that: (1) in the past 30 years, the net carbon emissions have shown a continuously increasing trend, and the difference in the spatial distribution of carbon emissions in different periods was obvious. The carbon sink effect was not significant enough to offset the carbon emissions generated. (2) The continuously decreasing carbon emission intensity values per unit of GDP indicate that the coordination between land-use and economic development was getting better. (3) The factors of population size, economic size, and land-use structure accelerated land-use carbon emissions, whereas land-use efficiency limited land-use carbon emissions. Accordingly, this paper puts forward some corresponding policy suggestions.

Spatiotemporal dynamic decoupling states of eco-environmental quality and land-use carbon emissions: A case study of Qingdao City, China

Spatiotemporal dynamic association between eco-environmental quality and land-use carbon emissions (LCE) is an indispensable part of regional and city planning and land management and a necessary condition for maintaining sustainable development. With the increasing awareness of the importance of sustainable development, quantifying the relationship between eco-environmental quality and LCE needs to be improved. Based on raster datasets of carbon emissions from fossil fuels, remotely sensed environmental variables, and land-use and land-cover change, this study aimed to combine an improved model of LCE, remote sensing ecological index (RSEI), and decoupling theory along with the help of the Google Earth Engine (GEE) and geographical information system platforms to explore the spatiotemporal patterns and trends of the eco-environmental quality and LCE over Qingdao City in the coastal economic belt of China for 2005–2019. There are three significant findings in this study. First, during 2005–2019, RSEI in the study area showed an overall upward trend (0.4365 in 2005 and 0.5378 in 2019), with a significant difference in the spatial distribution. Second, during 2005–2019, the total LCE value showed an increasing trend (4.028 million tonnes (Mt) in 2005 and 7.929 Mt. in 2019), with the study area acting as a carbon source. Third, the study area exhibited the four decoupling states of mainly weak decoupling (WD), expansive negative decoupling (END), strong decoupling (SD), and strong negative decoupling (SND). Finally, space management policies should be formulated based on different spatiotemporal characteristics and decoupling states to achieve carbon emission reductions and high-quality development.

Bibliometric Analysis of the Research (2000–2020) on Land-Use Carbon Emissions Based on CiteSpace

Carbon emissions are critical to climate change, and land-use change is an essential source of growth in carbon emissions. Research on land-use carbon emissions has become one of the hotspots in academic research. To explore the research hotspots and development trends of land-use carbon emissions in the last 20 years, CiteSpace software was used to conduct a quantitative analysis of relevant literature. This paper was based on the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) database literature on land-use carbon emissions from 2001 to 2020. The scientific research cooperation network CiteSpace software, with keyword co-occurrence, clustering, and burst word detection, was used to systematically analyze the main research strengths, hotspots and frontiers and clarify the research progress. The research results are as follows: (1) the amount of literature and the depth of research on land-use carbon emissions have increased yearly. However, there is little cooperation between research institutions and scholars, and there is still a lack of large-scale and stable research teams. (2) At the research hotspot level, the English literature focuses on building models and theoretical frameworks to study the internal mechanisms and driving factors of carbon emissions and climate change. The Chinese literature focuses on achieving regional carbon emissions reductions and carbon cycle goals and optimizing a low-carbon economy, transportation and land-use structure. (3) Research frontiers and trends show that the English literature first explored carbon sequestration, organic carbon, and carbon accounting. In China, the research frontiers are gradually becoming focused on influencing factors, decoupling analysis, and the built environment. The study will strengthen the intensity and depth of global carbon emission research and provide a reference for improving global climate change, protecting ecology and balancing economic development.

Analysis of spatio-temporal pattern of land-use carbon emission in Chang-Zhu-Tan metropolitan area at the grid scale

Analysis of spatio-temporal pattern of land-use carbon emission in Chang-Zhu-Tan metropolitan area at the grid scale

Land-Use Carbon Emissions in the Yellow River Basin from 2000 to 2020: Spatio-Temporal Patterns and Driving Mechanisms.

In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China's "dual-carbon" goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin's land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. "Low-Low" was largely constant and concentrated in the upper reaches, whereas "High-High" was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components' driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance.

Land Use Carbon Emission Measurement and Risk Zoning under the Background of the Carbon Peak: A Case Study of Shandong Province, China

Land use and cover change (LUCC) has a non-negligible impact on both carbon emissions and carbon sinks. Based on the analysis of land use dynamics in Shandong Province, this study simulates land use changes in Shandong Province in 2030 under the Natural Development Scenario (NDS) and Sustainable Development Scenario (SDS), classifies the risk level of carbon emissions in Shandong Province using the Land Use Carbon Emission (LUCE) risk indexes, and compares the differences between the risk level regions under NDS and SDS. This study shows that under the influence of LUCC, the carbon emissions in Shandong province increased significantly, from 90.5591 million tons in 2000 to 493.538 million tons in 2020, with urban land being the main source of carbon emissions, which increased from 90.0757 million tons in 2000 to 490.139 million tons in 2020. The main source of the increase in urban land was cropland. The LUCE was positively correlated with urban land area. The LUCE of SDS was 7.2423 million tons less than that of NDS. From 2000 to 2020, the risk areas of LUCE in Shandong province were mainly no-risk and mild-risk areas. The number of moderate-risk areas and high-risk areas of SDS was less than that of NDS. The rational organization of land use is important for Shandong Province to achieve low-carbon development.

Spatiotemporal variation of land-use carbon emissions and its implications for low carbon and ecological civilization strategies: Evidence from Xiamen-Zhangzhou-Quanzhou metropolitan circle, China

• LUCE increased by 1284.78×10 4 tons from 2000 to 2020. • LCES scenario decreased LUCE with 128.74 × 10 4 tons than that in BAU scenario. • Area and management of arable land, forest land and construction land should be regulated. • The policy effectiveness and adaptation to local conditions should be ensured. This study investigated the spatiotemporal variation of historical and future land-use carbon emission (LUCE) in the Xiamen-Zhangzhou-Quanzhou metropolitan circle, China, to obtain policy implications for low carbon and ecological security by simulating land use change under low carbon and ecological security (LCES) scenario and business as usual (BAU) scenario in 2030. Our results showed that: (1) Under LCES scenario, the area of arable land and construction land was 1.17% and 0.40% less than that under BAU scenario, whereas the area of forest land was 1.90% more than that. (2) LUCE increased by 1284.78×10 4 tons totally in 2000–2020. Whereas under LCES scenario, LUCE was 128.74×10 4 tons less than that under BAU scenario, and the carbon absorption of forest land increased by 24.90×10 4 tons. (3) At city scale, LUCE in cities followed the order of Quanzhou>Zhangzhou>Xiamen in 2000–2030. The county scale witnessed rapid-growth and heavy LUCE in coast over time. LUCE at grid scale showed an expansion from coast to inland all the time, but it was alleviated under LCES scenario. The quantity and spatial structure of land use types should be optimized, and low-carbon and ecological classification management should be implemented at arable land, forest land and construction land strictly.

Spatial Patterns Exploration and Impacts Modelling of Carbon Emissions: Evidence from Three Stages of Metropolitan Areas in the YREB, China

Metropolitan areas in China are not only the core spatial carriers of urbanization development but also the main generators of land use carbon emission (LUCE). However, existing research lacks comparative studies on the differential patterns and impact factors of LUCE in different stages of metropolitan areas. Therefore, this paper deeply analyzes the spatial characteristics of LUCE and the coupling coordination degree (CCD) of the economy contributive coefficient (ECC) and ecological support coefficient (ESC) in three different stages of metropolitan areas in the Yangtze River Economic Belt (YREB), China. Moreover, quantitative modelling of the impact factors of LUCE in these different stages of metropolitan areas is furtherly revealed. Results show that: (1) The more mature stage of the metropolitan area, the higher the amount of LUCE, and the more districts or counties with high carbon emissions levels are clustered. (2) At the metropolitan area scale, the more mature the metropolitan area is and the lower the CCD between ECC and ESC is, while at the finer scale, more developed counties have lower CCD. (3) Resident population, per capita GDP, and urbanization rate have good explanatory effects on carbon emissions in these three metropolitan areas; however, except for the urbanization rate, which has a negative effect on LUCE in Nanchang metropolitan area (NMA), the other two factors have positive effects on LUCE in these three metropolitan areas. This study has important implications for different stages of metropolitan areas to formulate targeted LUCE reduction policies.

Comprehensive assessment of land use carbon emissions of a coal resource-based city, China

Carbon emissions reduction is crucial for global sustainable development. While few studies pay attention to land use carbon emissions (LUCEs) in coal-based cities. We firstly used the emission factor method proposed by IPCC to estimate the carbon sources and carbon sinks of land use in Huainan, a typical city of China, from 1991 to 2020. Then we used Logarithmic mean Divisia index (LMDI) model and Tapio decoupling elasticity to identify the driving factors of land use carbon emission changes and analyze their intrinsic relationship with economic growth of Huainan. The results revealed that the total LUCEs in Huainan increased significantly, and the carbon sources from land use were always higher than the carbon sinks. It also showed that economic development and built-up land change were the main driving factors of LUCEs growth in Huainan. Moreover, LUCEs had a synergistic relationship with the decoupling elasticity of economic growth in different development stages of the coal-based city. Therefore, we predicted LUCEs in Huainan City from 2020 to 2035 under three scenarios. Finally, we proposed some recommendations to reduce the LUCEs and develop the low-carbon economy. We hope to not only extend the research of land use carbon emissions on coal-based city level, but also promote the low-carbon development of coal-based cities in China.

Cultivated land use efficiency and its driving factors in the Yellow River Basin, China

China is accelerating green and low-carbon transformations of cultivated land to achieve a carbon peak by 2030 and to actively respond to global climate change. Evaluating the cultivated land use efficiency (CLUE) and determining its driving factors help reduce cultivated land-use carbon emissions and promote green and low-carbon transformations of cultivated land. However, previous carbon emission-based studies of CLUE and its driving factors have been insufficient. Therefore, in this study, we explored the spatiotemporal characteristics and driving factors of CLUE in the Yellow River Basin (YRB), China, based on carbon emissions. A slacks-based model with undesirable (bad) outputs, spatial autocorrelation models, and geographic detector models was used for the analysis, with statistical data from 2005 to 2017 as inputs. The overall CLUE values in the YRB increased and subsequently decreased with time. The Huang-Huai-Hai Plain (HHHP) consistently had the highest values, followed by the northern arid and semi-arid regions (NASR), the Loess Plateau (LP), and the Qinghai-Tibet Plateau (QTP). According to the global spatial autocorrelation, the CLUE had a positive association with themselves. The major cluster types moved from high–high and low–low to high–high cluster types in eastern Henan and southern Shanxi provinces, according to local spatial autocorrelation. Per capita grain possession was the main driver of CLUE in the YRB, HHHP, LP, and NASR, and road network density was the key driver of CLUE in the QTP, according to Geodetector analysis. Furthermore, non-linear enhancement dominates the driving factors of CLUE in the YRB, with non-linear enhancement dominating the HHHP, LP, and NASR, and bi-factor enhancement dominating the QTP. The findings of this study can be used to assist policymakers in formulating feasible, green, and efficient cultivated land-use policies.

The temporal and spatial pattern evolution of land-use carbon emissions in China coastal regions and its response to green economic development

Carbon emissions based on land use change have attracted extensive attention from scholars, but the current land use carbon emission accounting model is still relatively rough. Despite the continuous promotion of China’s ecological civilization strategy, whether green economic development promotes carbon emission reduction remains to be studied. This study uses the Exploratory Spatial-temporal Data Analysis (ESTDA) framework system to revise the land-use carbon emission accounting model; it integrates the NDVI adjustment index and systematically analyzes the spatial and temporal patterns and evolutionary path characteristics of carbon emissions from 2000 to 2020 for 130 prefecture-level cities in the eastern coastal region of China, a high carbon emission region. The spatial econometric model is further used to explore the impact of green economy development on carbon emissions. The results show that the spatial distribution of carbon sources and sinks in the eastern coastal cities demonstrates a year-on-year increase during the study period. The spatial distribution of carbon sources is higher in the north than in the south, and the economically developed regions are more elevated than less developed economic areas. Net carbon emissions show prominent spatial clustering characteristics. The south has a more stable internal spatial structure than the north, and the inland has a more stable internal spatial structure than the coast. Green economic development can significantly reduce carbon emission intensity and has a significant spatial spillover effect. The findings imply that policy-makers need to consider the spatial and temporal distribution and spatial correlation of carbon emissions among cities; they can achieve carbon emission reduction by formulating a more reasonable green economy development approach and implementing regional linkages.

Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H–H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were −0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.