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

Spatial heterogeneity and interaction mechanism of human activity intensity and land-use carbon emissions along the urban-rural gradient: A case study of the Yellow River Delta

Advancing our understanding of the spatial heterogeneity and interaction mechanism between human activity intensity (HAI) and land-use carbon emissions (LCE) along the urban-rural gradient holds significant importance for achieving integrated carbon reduction in urban-rural development. Therefore, this study focused on the Yellow River Delta as the study area and introduced the gradient analysis method to establish gradient rings and gradient bands respectively, with the city center as the origin; meanwhile, and applied the improved human footprint modelling and LCE accounting methods, Lorenz curve & Gini coefficient, and cross-wavelet analysis to quantify the gradient differences of HAI and LCE and their equilibrium, directionality and spatial multi-scale correlations. The findings showed that: 1) In the urban-rural gradient rings and gradient bands, the mean levels of HAI and LCE exhibited certain characteristics of the ring structure, with high-value and low-value areas alternating. 2) In the urban-rural gradient, the equilibrium between HAI and LCE gradually decreased; moreover, the positive effect between HAI and LCE was significant, and the characteristic scales of the 4 gradient bands were identified as the most suitable spatial scales for explaining their association. Furthermore, this study provides a new perspective on carbon reduction strategies by proposing a universal urban-rural gradient partitioning scheme.

Decoupling and peak prediction of industrial land carbon emissions in East China for developing countries' prosperous regions

Urban energy consumption is mostly concentrated in industrial regions, and carbon emissions from industrial land use have significantly increased as a result of fast urbanization and industrialization. In the battle against climate change, the affluent regions of developing countries are increasingly being used as models for reducing carbon emissions. Therefore, in order to accomplish global sustainable development, it is crucial to understand how industrial land use and carbon emissions are decoupled in wealthy areas of rising nations. This study investigates the decoupling effects and the factors influencing them in six East Chinese provinces and one city between 2005 and 2020 using the Tapio decoupling model and the LMDI decomposition approach. At the same time, the industrial carbon emissions from 2021 to 2035 were predicted using a BP neural network model combined with scenario analysis. The findings indicate that: (1) From 29.921 million tons in 2005 to 40.2843 million tons in 2020, the carbon emissions from industrial land in the East China area have nearly doubled. Of these, Shandong and Jiangsu emit more than half of the region's total emissions around East China. (2) The decoupling effect analysis shows the East China region's decoupling trajectory's phased characteristics, with the degree of decoupling gradually increasing from weak decoupling (2006-2012) to strong decoupling (2013-2018) and finally to negative decoupling (2019-2020). (3) The primary causes of the rise in carbon emissions in the East China region are the scale of per capita economic output and industrial land use. (4) The overall industrial carbon peak time in East China is roughly distributed between 2028 and 2032. It is expected that Shanghai, Shandong, Jiangsu, and Zhejiang will be among the first to achieve carbon emission peak.

Surplus or deficit? Quantification of carbon sources and sinks and analysis of driving mechanisms of typical oasis urban agglomeration ecosystems

The relationship between carbon sources and sinks, along with their balance, is a crucial indicator for assessing ecosystem health. Ecosystem protection mitigates climate change and promotes the carbon cycle balance. Consequently, to effectively assess the status of carbon sources and sinks in an oasis ecosystem, this study utilized remote sensing and statistical data to estimate the natural carbon sources and sinks, energy carbon emissions(ECE), and carbon surpluses or deficits in the urban agglomeration of the northern slopes of the Tianshan Mountains(UANSTM) for a period 2000-2022 from a perspective of the natural-social-economic system. The driving mechanisms were analyzed using the optimal geodetector model (OPGD) and generalized divisia index method (GDIM). The results show(1) throughout the study period, natural carbon sources and sinks in the UANSTM exhibited a distribution pattern that diminished from the Tianshan Mountains axis northward and southward, accompanied by a fluctuating increase over time. ECE predominantly occurred in urban built-up areas and adjacent cultivated lands, totaling an increase of 88.48 × 104 TgC. (2) The change trend showed an overall predominance of carbon sources, with the rate of increase of carbon sources being greater than that of carbon sinks. Grassland-cultivated land and construction land interaction area exhibited significant changes in carbon sources and sinks. (3) During the study period, the UANSTM experienced a consistent carbon deficit, averaging -68.11 TgC annually. Spatially, this deficit evolved from discrete points to linear and then to areal patterns. (4) Precipitation and elevation were the primary determinants of natural carbon sources and sinks. Whereas GDP with a contribution of 50.4%,was the predominant driver of ECE across each prefecture-level city. Development of policies that synergize regional natural and socioeconomic systems is an important means of achieving a carbon balance. The results of this study provide a reference for adding a research paradigm on carbon sources and sinks in oasis urban agglomerations and for low-carbon development.

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.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

A dual-core system dynamics approach for carbon emission spillover effects analysis and cross-regional policy simulation

As carbon emission continue to rise and climate issues grow increasingly severe, countries worldwide have taken measures to reduce carbon emission. However, carbon dioxide is continuously flowing in the atmosphere and is easily influenced by neighboring cities' policies. Therefore, how to solve the problem of carbon emission spillover effect has become the key to improve policy efficiency. Cross-regional carbon governance provides a perspective on solving the carbon emission problem by regulating and guiding the cooperative behavior of cross-regional governance actors. Taking Chengdu-Chongqing area as an example, this study used the SDM to analyze the influencing factors and spatial spillover effects of emission. Then we used the system dynamics method to construct a dual-core carbon emission system, and simulated the spillover effect and emission reduction potential of Chengdu and Chongqing emission reduction policies under different policy schemes. The results reveal that the mobility of population and enterprises have a significant impact on carbon emission prediction. Carbon reduction policies exhibit the phenomena of "carbon transfer" and "free-riding." When Chengdu lowers its economic growth rate, it leads to the transfer of high energy-consuming enterprises to Chongqing, increasing carbon emission in Chongqing. The implementation of comprehensive carbon reduction policies in Chongqing has a positive effect on Chengdu. Emission reduction policies exhibit issues related to their temporal efficacy, as the effects of industrial structural policies in Chengdu yield opposite outcomes in the short and long term. Each city's unique circumstances necessitate tailored carbon reduction policies. In order to reduce carbon emissions, Chengdu and Chongqing require opposite population policies.

Regional differences, dynamic evolution, and spatial spillover effects of carbon emission intensity in urban agglomerations

Taking three major urban agglomerations in China as examples, this paper uses the Dagum Gini coefficient and its decomposition method, a Kernel density estimation method, and Markov chain and spatial Markov chain to study the regional differences, dynamic evolution characteristics, and spatial spillover effects of carbon emission intensity (CEI) of urban agglomerations, and accordingly, it proposes differentiated emission reduction and carbon reduction policies. The following results were obtained: (1) The overall CEI of the three major urban agglomerations and each individual urban agglomeration were found to have declined significantly over time, with an overall spatial pattern of "high in the north and low in the south," with inter-group differences being the main source of the overall differences. (2) The imbalance in CEI between cities was more obvious within the Beijing-Tianjin-Hebei (BTH) urban agglomeration, while the synergistic emission reduction effect of the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations increased over the study period. (3) The probability of a city maintaining a stable level of CEI was much higher than the probability of a state shift, and there was a spatial spillover effect of carbon emissions between neighboring cities. This study can provide theoretical support for the global response to greenhouse gas emissions, promoting green development and carbon reduction in various countries and urban agglomerations and providing a quantitative basis for the formulation of relevant policies.

Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino-Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino-Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

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.

Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model

BACKGROUND

Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space.

METHODOLOGY

The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data.

PRINCIPAL/FINDINGS

MGWR was the best-fitting model (R2: 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water.

CONCLUSIONS/SIGNIFICANCE

Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control.

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.

Spatiotemporal Dynamic Distribution, Regional Differences and Spatial Convergence Mechanisms of Carbon Emission Intensity: Evidence from the Urban Agglomerations in the Yellow River Basin

Low-carbon transition is of great importance in promoting the high-quality and sustainable development of urban agglomerations in the Yellow River Basin (YRB). In this study, the spatial Markov chain and Dagum's Gini coefficient are used to describe the distribution dynamics and regional differences in the carbon emission intensity (CEI) of urban agglomerations in the YRB from 2007 to 2017. Additionally, based on the spatial convergence model, this paper analyzed the impact of technological innovation, industrial structure optimization and upgrading, and the government's attention to green development on the CEI's convergence speed for different urban agglomerations. The research results show that: (1) The probability of adjacent type transfer, cross-stage transfer, and cross-space transfer of the CEI of urban agglomerations in the YRB is small, indicating that the overall spatiotemporal distribution type of CEI is relatively stable. (2) The CEI of urban agglomerations in the YRB has decreased significantly, but the spatial differences are still significant, with a trend of continuous increase, and regional differences mainly come from the differences between urban agglomerations. (3) Expanding innovation output, promoting the optimization and upgrading of industrial structure, and enhancing the government's attention to green development has a significant positive effect on the convergence rate of the CEI of urban agglomerations in the YRB. This paper holds that implementing differentiated emission reduction measures and actively expanding regional collaborative mechanisms will play an important role in reducing the spatial differences in carbon emissions in urban agglomerations in the YRB, realizing the goals of peak carbon and carbon neutrality.