Spatial correlation evolution and prediction scenario of land use carbon emissions in China

Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data

Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China's carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by "lower reach > middle reach > upper reach". The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.

Investigating spatio-temporal variations and contributing factors of land use-related carbon emissions in the Beijing-Tianjin-Hebei Region, China

The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east-west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.

Spatio-temporal evolution and prediction of carbon balance in the Yellow River Basin and zoning for low-carbon economic development

Studying the carbon effect of land use in watersheds is important for mitigating global warming, promoting coordinated emission reduction in different regions within the watersheds, and realizing high-quality development of the watersheds. Although a number of scholars have carried out relevant studies in the past, they mainly focused on carbon emissions, rarely involved the carbon balance formed by carbon sources and sinks, and lacked relevant studies on the development of low-carbon economy sub-region. Based on this, this study takes the Yellow River Basin as an example, explores the spatial and temporal evolution of carbon emissions from land use in counties in the Yellow River Basin from 1980 to 2020, and predicts the spatial pattern of carbon income and expenditure from land use under natural conditions in 2030 and 2060 using the PLUS model; and then superimposes on the main functional area planning, divides 735 counties in the Yellow River Basin into six low-carbon economic development subregions, and analyzes their economic development The model of their economic development is analyzed. The results show that: (1) the spatial and temporal differentiation of land use carbon balance in the Yellow River Basin has changed greatly over the past 40 years, (2) the spatial distribution pattern of land use carbon balance in the natural context in 2030 and 2060 is more similar to that in 1990, (3) the carbon emission reduction potentials and pattern optimization of the different low-carbon economic development subregions differ greatly, and they have different low-carbon economic development patterns. The results of this study provide a theoretical basis for scientifically and rationally formulating economic policies for low-carbon development in the counties of the Yellow River Basin, and also provide an important reference for related studies in other similar basins or regions in the world.

Multiple scenario land use simulation based on a coupled MOGA-PLUS model: a case of the Yellow River Basin

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints.

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.

Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality

The coupling relationship between land-use carbon emissions (LCE) and ecological environmental quality (EEQ) is critical for regional sustainable development. Rapid urbanization promotes a notable increase in LCE, which imparts significant stress on EEQ. This study used land use and cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data from the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) to evaluate LCE, applied a remote sensing ecological index (RSEI) model to calculate EEQ, and combined gravity and centroid movement trajectory models to analyze the spatiotemporal evolution characteristics of LCE and EEQ. Four-quadrant and coupling degree (CD) models were used to analyze the synergistic relationship and interaction intensity between LCE and EEQ based on three different scales of pixels, counties, and cities. The results show that: (1) LCE and EEQ exhibit clear spatial inequality distribution, and the total amount of LCE increased from 40.16 Mt. in 2000 to 131.99 Mt. in 2020; however, LCE has not yet reached peak carbon emissions. (2) From 2000 to 2020, cities with a strong correlation between LCE and EEQ showed an increasing trend, and the centroid of LCE moved sharply to Jiangxi during 2000-2005 and 2005-2010. (3) High-CD areas were primarily located in quadrant II, and low-CD areas in quadrant IV. The relationship between LCE and EEQ has improved over the past 21 years, and CD has been increasing. (4) The stability of the coupling results between LCE and EEQ was affected by different research scales; the larger the research scale is, the greater the change in the results. This study provides a scientific basis and practical scheme for LCE reduction, ecological environmental management, and regional sustainable development in the UAMRYR.

Balancing low-carbon and eco-friendly development: coordinated development strategy for land use carbon emission efficiency and land ecological security

Correctly identifying and handling the relationship between land use carbon emission efficiency (LUCEE) and land ecological security (LES) are important to promote carbon neutrality in the overall layout of ecological civilization construction. This study takes 30 provinces in China as the research unit and measures the level of LUCEE and LES in each province in the period from 2011 to 2020 via a super-efficient slack-based measure model considering undesirable output. The coupling coordination degree (CCD) of LUCEE and LES is calculated, and its spatiotemporal evolution pattern is explored by kernel density estimation and standard deviational ellipse (SDE). The Dagum Gini coefficient is used to study spatial regional differences and the sources of differences. Results show that (1) China's LUCEE exhibited a downward and then an upward trend, as well as a spatial pattern of "high in the west and low in the east" with obvious regional differences. The LES experienced a positive transformation of "less secure → basically secure → more secure" nationwide, with no apparent regional differences. (2) The kernel density curves showed a continuous increase in CCD in general, while interprovincial differences increased, then decreased, and shifted from multipolar to bipolar differentiation. (3) The migration of SDE centers in CCD demonstrated a path of "southeast → southwest → northeast," and the ellipticity increased from 0.167 to 0.173, showing a trend of concentrated distribution. (4) The overall Gini coefficient of the national CCD indicated a decreasing trend, but imbalances remained, with the largest annual average value in the western region (0.120) and the smallest in the northeast (0.044). The main source of regional disparity was the intensity of transvariation. Accordingly, this study proposes targeted regional development strategies to promote low-carbon sustainable land use and improve the ability of land ecosystems to prevent security risks.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

Correlation analysis of regional carbon emission intensity and green industry development-A case study of Chengdu-Chongqing region

Industrial structure markedly affects the level of carbon emissions generated in a region. It is imperative to study the industrial structure of the Chengdu-Chongqing region to obtain information to achieve regional high-quality development by promoting low-carbon development. We selected 16 cities in Chengdu-Chongqing area as the research object in this study. The total carbon emissions (CE), carbon intensity (CI) and per capita carbon emissions (PCE) were calculated for each city. The green industry development (GI) evaluation indexes were then extracted, and the comprehensive evaluation value was determined using the entropy weight-TOPSIS model (EWM-TOPSIS). The green industry development was used as the core explanatory variable to construct a system representing the dynamic relationship between green industry development and carbon intensity using the quantile regression (QR) model. The results of the study showed that: (1) the total carbon emissions of Chengdu-Chongqing region increased whereas the carbon intensity decreased from 2010 to 2020. (2) The green industry development evaluation results showed that Chengdu-Chongqing had unevenly distributed green industry development during the study period, and Chengdu and Chongqing cities had higher green industry development values than other cities. (3) The green industry development of the region had a significant negative effect on carbon intensity at low quantile and a significant positive effect on carbon intensity at high quantile. Energy supply (ES) was positively correlated with the carbon intensity of the region at 1 % level of significance, whereas urbanization rate (U) and power consumption (PEC) were negatively correlated with the carbon intensity at 1 % level of significance. We comprehensively evaluated the development of green industry and introduced it as a core explanatory variable into the quantile regression model to explore the relationship between regional carbon emission intensity and industrial development. The results provide a reference for designing strategies to promote high-quality development in the cities in the Chengdu-Chongqing region.

Spatial and socioeconomic characteristics of CO2 emissions and sequestration in Indonesian cities

In dealing with the impacts of climate change, mitigation efforts play a crucial role. As one of the G20 countries on the list of the top 5 biggest contributors to emissions, Indonesia must play an active role. With all their characteristics and as one of the most significant contributors to global emissions, cities are fully responsible as a core area for climate mitigation. By analyzing the spatial and socioeconomic characteristics within the city scope, this study examines 32 representative cities and municipalities in Indonesia to understand the condition of carbon emissions and sequestration. Emissions and sequestration in selected cities in Indonesia show varying statuses; most cities have higher emission levels than sequestration, but some cities do the opposite. In addition, emissions and sequestration are also influenced by many complex and interrelated factors, including spatial (distribution, intensity, LULC, geographical conditions, total area), social (total population, urbanization rate, employment rate), economic (GDP/GRDP), and technological (industry structure and energy sector). As an archipelagic country, the uniqueness of cities in Indonesia, primarily located in coastal and waterfront areas, also influences the emission intensity, which tends to be lower in these areas on a micro basis. Cities classified as economically developed contribute more emissions at the national level. Therefore, a characteristic-based classification of the selected cities can encourage policy implications according to the characteristics of each city. These cities can learn from each other, especially from cities with high sequestration rates, to develop in a sustainable way while supporting national mitigation targets.

Spatial-temporal dynamics of land use carbon emissions and drivers in 20 urban agglomerations in China from 1990 to 2019

Urban agglomerations (UAs) are the largest carbon emitters; thus, the emissions must be controlled to achieve carbon peak and carbon neutrality. We use long time series land-use and energy consumption data to estimate the carbon emissions in UAs. The standard deviational ellipse (SDE) and spatial autocorrelation analysis are used to reveal the spatiotemporal evolution of carbon emissions, and the geodetector, geographically and temporally weighted regression (GTWR), and boosted regression trees (BRTs) are used to analyze the driving factors. The results show the following: (1) Construction land and forest land are the main carbon sources and sinks, accounting for 93% and 94% of the total carbon sources and sinks, respectively. (2) The total carbon emissions of different UAs differ substantially, showing a spatial pattern of high emissions in the east and north and low emissions in the west and south. The carbon emissions of all UAs increase over time, with faster growth in UAs with lower carbon emissions. (3) The center of gravity of carbon emissions shifts to the south (except for North China, where it shifts to the west), and carbon emissions in UAs show a positive spatial correlation, with a predominantly high-high and low-low spatial aggregation pattern. (4) Population, GDP, and the annual number of cabs are the main factors influencing carbon emissions in most UAs, whereas other factors show significant differences. Most exhibit an increasing trend over time in their impact on carbon emissions. In general, China still faces substantial challenges in achieving the dual carbon goal. The carbon control measures of different UAs should be targeted in terms of energy utilization, green and low-carbon production, and consumption modes to achieve the low-carbon and green development goals of the United Nations' sustainable cities and beautiful China's urban construction as soon as possible.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BACKGROUND

Conducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.

RESULTS

Based on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.

CONCLUSIONS

China's provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

The study on the characteristics of carbon pressure agglomeration and the dynamic evolution of heterogeneity in China from a regional perspective

Provinces (cities and districts) with identifiable boundaries are under intense pressure to reduce emissions as a fundamental unit and research object of carbon peaking and carbon-neutrality goals. Due to the significant variability of regional development, achieving equilibrium between carbon emissions and carbon absorption is challenging, contributing to the difficulty of developing carbon emission reduction and relevant green strategic initiatives in China. Therefore, this paper explored the spatial effect of carbon balance with carbon pressure as the starting point. First, this paper defined the "carbon pressure index" (CPI) of 30 provinces (cities and districts) in China from 2000 to 2019. Second, this paper validated the CPI agglomeration evolutionary characteristics in global and local aspects based on the Moran's index. Third, this paper identified and decomposed the spatial heterogeneity of CPI using the kernel density estimation method and the Theil index, then extracted typical cities to analyze the specific causes. Finally, this paper classified the seven regions in China into four types according to a comprehensive analysis of CPI. The results indicated that China's ecological carbon cycle system was in a serious "carbon overload" state. Thirty provinces (cities and districts) showed significant spatial agglomeration characteristics. The spatial gap of CPI was gradually decreasing nationwide, and the intra-regional differences were the leading cause of CPI levels in China. This can provide policy basis for the improvement of China's balanced development system of regional carbon emission reduction.

Spatial correlation analysis and prediction of carbon stock of "Production-living-ecological spaces" in the three northeastern provinces, China

The "Production-Living-Ecological Space" (PLES) is a paramount indicator in the filed of territory space development and optimization in China, under the new era. Exploring the driving factors of the PLES'land expansion is of great significance for improving space utilization, mitigating severe climate changes, and promoting green, healthy and sustainable development. In the background of the "Carbon Emissions Peak and Carbon Neutrality" strategy, analyzing and predicting the carbon stock of PLES is effective to boosting the achievement of 'Dual-Carbon' vision. Based on the above research questions， this study constructs the PLES based on statistics about land use (Year 1990, 2000, 2010 and 2020) in three northeastern provinces, and reveals the spatial correlation of PLES' carbon stock in the research area through ArcGIS spatial analysis and InVEST model. Then, the PLUS model was used to clarify the contribution of each driver to the conversion of space land, and to predict the distribution of the PLES pattern and the carbon stock's spatial correlation in 2030 and 2060 under the Natural-Development Scene and Ecological-Protection-Development Scene. Results show that: (1) The PLES in the three northeastern provinces of China is primarily green ecological space (55.71%) and agricultural production space (38.10%), while industrial production space (3.60%) and urban living space (2.76%) expand significantly, and green ecological space (-0.17%) and blue ecological space (-0.89%) are on a recession trend. Besides, 2000-2010 is the most intense period of all kinds of space land transformation in the study area. (2) Population density, proximity to roads at all levels, annual average temperature and elevation are the prime drivers of PLES' profile within the scope of the study region, but the contribution rate is significant difference. (3) The urban living space decreases and the green and blue ecological space increases significantly in the predicted years under the scene of Natural-Development and Ecological-Protection development; the pattern of PLES is relatively stable in the predicted years under both scenarios. (4) The spatial correlation of carbon stock is closely related to the distribution of PLES, with the high-value significant regions primarily in the distribution region of green ecological space, otherwise, the low-value significant regions mostly concentrated in the region with complex spatial land use types and large spatial development intensity; the overall structure within the scope of the study region shows a layout of high value areas surrounding low value areas. It can show that insisting on the ecological civilization construction is an effective way to achieve sustainable development and practice the "double carbon" strategy; in the future spatial land use control, ecological protection measures should still be adopted to ensure the sustainable development and positive operation of the study area.

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 × 10¹¹ kg 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.

Tracking the spatial-temporal distribution and regional differences of carbon footprint in grid scale of China's construction industry

As the largest contributor to global carbon emissions (CEs), construction industry (CI) is regarded as one of the most significant sources in China. Previous studies on carbon emission (CE) of CI, however, are often limited to the quantitative level and provincial or local administrative unit scales, lacking relevant studies at the spatial raster resolution scale, due to data limits. Here, using the energy consumption, social economic data, and a series of remote sensing data from EU EDGAR, this study explored the spatial-temporal distribution and changing characteristics of CEs from CI in typical years of 2007, 2010, and 2012. This study found, from 2007 to 2010, then 2012, in addition to subtle differences, that the direct, indirect, and total CEs of CI all showed an increasing trend overall. In all provincial units except Tianjin and Guangdong, indirect CEs took up more than 50% of the total CEs, which can clearly indicate the "dominant low carbon, recessive high carbon" characteristics of CI. The direct, indirect, and total CEs of the CI in 2007, 2010, and 2012 all showed a positive spatial clustering. Specifically, hot spots were mainly distributed in Beijing-Tianjin-Hebei and Yangtze River Delta, and cold spots were mainly focused in the west and northeast of China, presenting a similar distribution pattern with population-economy characteristics. These findings can provide references for the policy formulation of regional differentiated emission reduction.

Spatial correlation between producer services agglomeration and carbon emissions in the Yangtze River Economic Belt based on point-of-interest

Agglomeration of the industry significantly impacts economic performance and environmental sustainability. In line with its strategic context of striving to achieve carbon reduction targets, China is making efforts to optimize the producer services landscape to reduce carbon emissions. Understanding the spatial correlation between industrial agglomeration and carbon emissions is particularly crucial against this background. Based on POI and remote sensing data of China's Yangtze River Economic Belt (YREB), the paper adopts the mean nearest neighbor analysis, kernel density analysis, and standard deviation ellipse to portray the agglomeration of producer services. Then uses Moran's I to present the spatial distribution characteristics of carbon emissions. Accordingly, the spatial heterogeneity of producer services agglomeration and carbon emissions is showed using the Geographic detector so as to provide strong support for industrial structure optimization and sustainable development. Here are some of the conclusions drawn from the study: (1) Producer services are a significant state of agglomeration in the provincial capitals and some central cities, with similar agglomeration patterns. (2) Carbon emissions exhibits significant spatial aggregation characteristics, with the spatial distribution pattern of "High west-Low east". (3) Wholesale and retail services industry is the primary risk factor that causes spatial differentiation of carbon emission intensity, "leasing and business services industry-wholesale and retail services industry" is the key interaction factor of the spatial differentiation. (4) Carbon emissions shows a downward trend followed by an upward trend as producer services agglomeration increases.

Spatio-Temporal Coupling Evolution of Urbanisation and Carbon Emission in the Yangtze River Economic Belt

The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial-temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of 'high in the east and low in the west'. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of 'decreasing and then increasing', with a spatial distribution of 'high in the east and low in the west'. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern's path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction.

How does urbanization affect energy carbon emissions under the background of carbon neutrality?

Global climate change caused by increasing carbon emissions has raised great concern. Urbanization is regarded as one of the main sources of carbon emissions. Studying the effect of urbanization process on carbon emissions is of great significance to realize sustainable development under the background of carbon neutrality. Based on the provincial panel data of China from 1997 to 2019, this study investigates the dynamic relationship, driving mechanism, and trend prediction between urbanization and energy carbon emissions (ECEs) using the sliding T-test, kernel density estimation (KDE), quantile regression (QR) model, and Autoregressive Integrated Moving Average (ARIMA) model. This study found that there is a continuous increase in time series and three transitions occurred in 2003, 2006, and 2009, which are in line with the transition stage of China's social and economic development. The overall urbanization rates in 1997-2019 exhibited an increasing trend temporally, and a spatial trend of "high in the north and low in the south" and "high in the east and low in the west". ECEs showed an obvious spatial distribution pattern of "high in the southeast and low in the northwest". Areas with high annual increments were mainly concentrated in the northern areas of Inner Mongolia, Shanxi, and Shandong, with annual increments of 39.35 × 10⁴ t, 66.78 × 10⁴ t, 44.54 × 10⁴ t, respectively. Overall, urbanization (U) has an obvious positive influence on ECEs at a 1% significance level, and the influence is more significant in high quantile provincial units relative to low and middle quantile provincial units. Energy Intensity (EI), Energy Structure (ES), and Gross Domestic Product (GDP), all exert a positive influence on ECEs at a 1% significance level. In the next 20 years, ECEs would present an inverted "U" shaped trend and can be expected to peak in 2036 in natural situation, it is necessary to implement low carbon emission reduction measures. The findings can provide a new reference for urban sustainable development in China.

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China's second largest river and the fifth largest one worldwide. Its watershed is key to China's economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km2 and 2407 km2, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

Spatio-temporal effect of provincial technological innovation on environmental pollution in China

The relationship between technological innovation (TL) and environmental pollution (EP) and its action mechanisms are complex and controversial aspects of discussion. Using the spatial autocorrelation analysis, standard deviation ellipse analysis, kernel density function, spatial econometric model, this study analyzed the spatial distribution, evolution characteristics, and influencing factors of the EP and TL from 2000 to 2020 in China. Results found there was a significant spatial autocorrelation between the EP and TL in 2000-2020. The standard deviation ellipse of EP was broadly distributed in the "southwest-northeast" direction, indicating that EP presented a trend of concentration in the direction of "southwest-northeast." The moving trajectory of the center of gravity for the EP in 2000-2020 was essentially moved from the northeast to southwest. Overall, the national level of TL exhibited a "north-south change, high in the east, and low in the west" trend. Regional differences were gradually expanding, and the polarization was evident. Regardless of using least squares method (OLS) or quantile regression (QR) models, TL, human capital (HC), and industrial structure (IS) all had an inhibitory effect on the EP at the effective significance level. Total population (TP), foreign direct investment (FDI), and local fiscal expenditure (LFE) were positively related to the EP.