Spatial Model Research Articles

Agricultural carbon emission efficiency and agricultural practices: Implications for balancing carbon emissions reduction and agricultural productivity increment

The current Ukraine War underlines the importance of grain self-sufficiency. After the adoption of the Paris Agreement, two major challenges developing countries are facing in the coming decades are increasing agricultural production to ensure food security and reducing carbon emissions (CE). The key to such an “environment-development dilemma” is to improve agricultural carbon emission efficiency (CEE). Using China as the study site, we systematically analyze the impacts of agricultural management activities on agricultural CEE from 1997 to 2019. Global and local Moran’s I index tests provide evidence of a positive spatial dependence of agricultural CEE. Using the LISA cluster map, we observe that high CEE regions tend to be distributed together, dominated by environmental conditions. However, with the promotion of agricultural management activities, such a clustering pattern vanished. Our spatial Durbin model (SDM) estimation results indicate that there are significant nonlinear relationships between agricultural practices and agricultural CEE. While the consumption of fertilizers and pesticides has economies of scale effects, the deployment of agricultural machinery and irrigation have diseconomies of scale effects on local CEE. Based on the SDM results, the direct and indirect effect estimation results suggest that the significant direct and spillover effects of many practices on agricultural CEE have opposite nonlinear shapes, implying a more complicated situation in promoting these activities, as the positive regional effect of an agricultural activity might have a negative impact on adjacent regions. All the results indicate that local policymakers should carefully tailor agricultural development policies based on local environmental conditions.

How does rapid urban construction land expansion affect the spatial inequalities of ecosystem health in China? Evidence from the country, economic regions and urban agglomerations

Identifying the adaptation mechanisms between urban construction land expansion (UCE) and ecosystem health (EHI) is a crucial aspect of sustainable management. However, there is still progress room for existing studies in distinguishing the impact mechanisms of UCE on the EHI of China's cities, different economic subregions and urban agglomerations. These gaps hinder the development of differentiated supportive frameworks for urban land management policies and ecosystem restoration initiatives across various scales in China. This study integrated multivariate satellite remote sensing data to monitor the heterogeneous evolution patterns of EHI and UCE in Chinese cities, economic subregions and urban agglomerations. A set of spatial measure models are utilized to distinguish the variability impact of UCE on China's EHI from a multi-dimensional perspective, including global and local, whole-area and sub-area, and direct and indirect influences. The results indicate that as UCE continues to grow, EHI steadily declined, reaching 0.512 with an average annual decrease of 1.507% from 2000 to 2020. A diminishing cluster pattern of EHI was observed, primarily concentrated in urban agglomerations such as BTH, GBA, and YRD. UCE exhibited a significant negative spatial dependence on EHI, and both global and local perspectives, UCE had a gradually weakening negative effect on the EHI of Chinese cities and economic subregions. However, the impacts of UCE on EHI in mature urban agglomerations (e.g., GBA, BTH, and YRD) were dominated by spatial spillovers, whereas growing urban agglomerations (e.g., MRYRU and CCU) were more affected by direct shocks from UCE. Our investigation informs site-specific ecologically sustainable management strategies in China based on cross-scale analyses, providing new perspectives into establishing cooperative mechanisms for cross-regional ecological management while emphasizing ecological risks management under the integration of urban agglomerations.

Revealing the Urban Heat Island: Investigating Spatiotemporal Surface Temperature Dynamics, Modeling, and Interactions with Controllable and Non-controllable Factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and XGBoost models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = -0.67). Additionally, Modified Built-up Index (MBI) demonstrates a noteworthy negative correlation (r = -0.48) with LST. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Stochastic microbial dispersal drives local extinction and global diversity

Airborne dispersal of microorganisms is a ubiquitous migration mechanism, allowing otherwise independent microbial habitats to interact via biomass exchange. Here, we study the ecological implications of such advective transport using a simple spatial model for bacteria–phage interactions: the population dynamics at each habitat are described by classical Lotka–Volterra equations; however, species populations are taken as integer, that is, a discrete, positive extinction threshold exists. Spatially, species can spread from habitat to habitat by stochastic airborne dispersal. In any given habitat, the spatial biomass exchange causes incessant population density oscillations, which, as a consequence, occasionally drive species to extinction. The balance between local extinction events and dispersal-induced migration allows species to persist globally, even though diversity would be depleted by competitive exclusion, locally. The disruptive effect of biomass dispersal thus acts to increase microbial diversity, allowing system-scale coexistence of multiple species that would not coexist locally.

Model-based disease mapping using primary care registry data

Background:Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference. Methods:Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation. Results:Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation. Conclusion:Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.

Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou

A refined spatial understanding of carbon emissions is crucial for advancing low-carbon development. This study aims to develop a comprehensive, open data-based approach for spatial modelling of carbon emissions at street level, covering five sectors: industry, transportation, residential & public service, commerce, and agriculture in Guangzhou. Two sets of open data, including statistical yearbook data and urban morphology data, were analyzed using a comprehensive methodology that integrates both bottom-up and top-down approaches to map the spatial distribution of carbon emissions. The findings delineate the carbon emission hierarchy across five distinct sectors as follows: Industrial (37.9%), Transportation (31.3%), Residential (18.6%), Commercial and Public Services (11.6%), and Agriculture (0.6%). The industrial sector emerges as the largest contributor, emitting 61.59 million tons, chiefly situated in suburban industrial zones like Huangpu and Panyu. Following closely is transportation, emitting 50.87 million tons, concentrated around Baiyun International Airport, ports, and urban areas with heavy traffic. Commercial and residential sectors emit 18.94 million tons, primarily within densely populated areas such as Tianhe and Haizhu. Agricultural emissions total 1.02 million tons, predominantly located on the city's outskirts, notably in Nansha. The findings of this study could provide information support for identifying carbon emission hotspots and developing sector-specific low-carbon urban planning strategies.

Mapping Dust Risk under Heterogenous Vulnerability to Dust: The Combination of Spatial Modelling and Questionnaire Survey

Mapping Dust Risk under Heterogenous Vulnerability to Dust: The Combination of Spatial Modelling and Questionnaire Survey

Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

Supporting zero-waste building: A novel spatial explicit material flow analysis model for construction waste

The accurate quantification of construction waste (CW) is prerequisite for achieving zero-waste in the building sector. This study employs a Spatial Material Flow Analysis model considering building life cycle to estimate the spatio-temporal dynamics of CW in Nanjing, China, over two decades. Additionally, it employs an ARIMA model for forecasting CW generation over the forthcoming decade. During 2000 to 2020, Nanjing's CW witnessed a CW increase from 0.8 to 59.0 Tg, predominantly comprising non-metallic materials. Meanwhile, the spatial distribution of CW has widened, accompanied by the emergence of numerous CW hotspots, in parallel with economic and population growth. Future projections suggest a continuous yet decelerating increase in CW generation, highlighting a significant opportunity for resource recovery. The application of this hybrid approach not only enhances CW management accuracy but also supports the broader goal of fostering sustainable building practices. This research expects to enlighten the stakeholders committed to advancing sustainable construction and waste management strategies in the context of zero-waste buildings.

Examining Patterns and Trends of Change in Greater Yola, Adamawa State, Nigeria: A 10 Years Spatio-Temporal Investigation

This research paper explores the dynamics of land cover and land use in Greater Yola, Adamawa State, Nigeria, spanning the years 2013 to 2023. Through comprehensive analysis, including remote sensing techniques and spatial modeling, the study examines the temporal trends and implications of urbanization on ecosystem diversity and resilience within the region. The findings reveal a significant trend of rapid urbanization, characterized by the expansion of built-up areas at the expense of natural vegetation, which poses challenges to ecosystem integrity. Furthermore, the analysis highlights the implications of these changes for ecosystem diversity, including the loss of natural habitats and fragmentation of ecosystems, threatening biodiversity and essential ecosystem services. The study underscores the critical need for a balanced approach to urban development and environmental conservation to achieve sustainable development goals. Recommendations are provided to guide policymakers and stakeholders in fostering ecosystem conservation, promoting sustainable land use practices, and mitigating the adverse impacts of urbanization on ecosystem health. By implementing these recommendations, Greater Yola can strive towards achieving a harmonious balance between urban growth and environmental stewardship, ensuring the long-term sustainability of ecosystems and the well-being of its residents.

Understanding Urbanization Patterns and Demographic Shifts in Vaniyambadi Town: A Spatial Approach

Abstract: During the last several decades, the urbanization trends in worldwide push the people into the urban areas due to large number of opportunities i.e., educational, employment, better quality of life, and land use in these areas experienced significant changes and restructuring. This paper delves into the nuanced interplay between demographic shifts and the spatial structure of urban settings, employing Vaniyambadi Town as a case study. Through a combination of demographic analyses, spatial modelling, and field surveys, we systematically explore the transformations in population dynamics and its impact in the spatial structure of the town. Based on a site survey, this paper analyses the spatial characteristics of land use change in Vaniyambadi town. The results reveal a reduction in agricultural land from 44% in 1984 to 24.62% in 2023 and further to 14.76% in 2043. This reduction is primarily attributed to population growth, the establishment of settlements, mixed residential usage, and has led to a significant loss of cultivated land, indicating increasing land scarcity in the future.

Unveiling the Dynamics of Rural Revitalization: From Disorder to Harmony in China’s Production-Life-Ecology Space

This study utilizes provincial panel data from China spanning the period from 2011 to 2020 to assess the coupled and coordinated development of spatial functions related to production, life, and ecology (PLE) in rural areas. The assessment is based on quantifying the spatial function indices for PLE in China’s rural regions. Additionally, it examines the characteristics of their spatial and temporal evolution, spatial correlation, and driving factors. The findings indicate a modest upward trend in the spatial coupling and coordination levels of these functions across rural China, although a significant proportion of provinces still exhibit a near-disordered decline. Exploratory spatial data analysis reveals a geographical disparity, with higher levels of coupled and coordinated development observed in the eastern regions, lower levels in the west, and noticeable spatial clustering. By employing the spatial Durbin model to investigate the determinants of coupling degrees, we discovered that factors such as regional economic development, urbanization, the urban–rural income gap, financial support for agriculture, science and technology investment level, and agricultural structural adjustments significantly influence the spatial coupling of rural PLE functions. Furthermore, using the geographic detector model, the analysis identifies science and technology investment level, economic development, and financial support for agriculture as key drivers influencing the spatial coupling and coordination of these functions. These findings provide valuable reference points for policies and strategies related to rural management.

Construction of a Spatial Equalization Assessment System for Medical Facilities

The spatial equalization of medical facilities can alleviate the wastage of medical resources and improve the efficiency of medical services. Therefore, it is necessary to carry out spatially balanced planning and assessment of medical facilities in cities. Existing studies on the balanced planning, design, and evaluation of medical facilities have been conducted from the perspective of hospital buildings in terms of spatial utilization efficiency, service satisfaction, and their physical environment on one hand, and from the perspective of regional planning of medical facilities in terms of spatial accessibility to medical facilities and the suitability of medical facilities to the social environment on the other hand. This study hopes to break down the boundaries of each perspective and effectively integrate the architecture, planning, and social well-being of medical facilities, taking spatial equilibrium as the core, in order to establish a spatial equilibrium system for medical facilities and achieve a spatial equilibrium-based assessment of the current state of medical facilities. First, the factors influencing the spatial equilibrium of hospital buildings with the support of the system and environment of hospital buildings are determined. Second, the indicators of the spatial equilibrium of hospital buildings are extracted through the consideration of influencing factors, and the indicator weights are determined by discussing the degree to which they contribute to the influence of the operation of hospital building spatial equilibrium systems, thus forming a system of equilibrium indicators for hospital buildings. Finally, a spatial equilibrium evaluation model for hospital buildings is established to assess the effects of equilibrium. The results obtained in this study provide insights into the regional planning of medical facilities and the design of hospital buildings.

Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods.

Research on Human Pose Estimation Model Based on Long-Range Fine-Grained Modeling

To address the issues of lacking long-range spatial position learning ability and excessive loss of fine-grained feature information during spatial feature pooling in human pose estimation models, a novel human pose estimation model based on the Yolopose network is proposed. Firstly, an enhanced coordinate attention module is introduced and embedded into the backbone network to endow the model with long-range spatial position modeling capability. Secondly, a fine-grained cascaded spatial pyramid pooling module is proposed to mitigate the loss of fine-grained feature information caused by spatial feature pooling. Finally, an implicit knowledge learning module is incorporated to reduce the model parameter count and enhance the model's capability for multi-task joint optimization.

Analysis of the Coupling Coordination between the Ecosystem Service Value and Urbanization in the Circum-Bohai-Sea Region and Its Obstacle Factors

In light of the tremendous pressure for improving urbanization levels and expanding construction land on ecosystems, regional sustainable development is premised on the coordinated development of the ecological environment and urbanization. This paper focuses on the Circum-Bohai-Sea Region and assesses the ecosystem service value (ESV) and urbanization level using the equivalence factor and entropy-weighted TOPSIS methods. Based on these assessments, it examines the coupling coordination between the ESV and urbanization as well as the obstacles to this coordination, utilizing the coupling coordination degree model, spatial autocorrelation model, and obstacle model. The results revealed several key findings. First, the Circum-Bohai-Sea Region experienced a continuous improvement in the urbanization level from 2005 to 2020, which presented a “center–periphery” spatial pattern. Overall, the ESV exhibited an N-shaped trend, namely, initially increasing, then decreasing, and then rapidly increasing. The ESV per unit of area exhibited a spatial difference, with the boundary of Baoding and Cangzhou as the dividing line. The ESV per unit of area in the northern parts was higher than that in the southern parts. Second, the degree of the coupling coordination between the ESV and urbanization exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. It also exhibited significant spatial heterogeneity, with the degree of the coupling coordination in the northern parts being higher than that in the southern parts and that in the central parts being higher than those in the eastern and western parts. Additionally, the ESV showed a significant spatially positive correlation with urbanization. H-H clusters were predominantly found in central and southern Liaoning Province, while L-L clusters were primarily located in southern Hebei Province and western Shandong Province. Third, the obstacle factors remained stable during the study period. The top five obstacles in the ESV system were the water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. Moreover, the top five obstacles in the urbanization system were the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values.

Towards Intercity Mobility System – Insights into the Spatial Interaction Gravity Model and Determination Approach

The current development of urban agglomeration greatly promotes the intercity connection and elevates the significance of intercity mobility system. However, intercity mobility often exhibits extreme spatiotemporal imbalances due to the diverse urban characteristics. This poses a huge challenge for traffic management and reveals the necessity on understanding the urban attractiveness for intercity mobility, which is represented as spatial interaction gravity in this study. While recent works have explored relevant aspects, they failed to provide insights into temporal variations in spatial interaction gravity or capture the determining factors from multiple perspectives. To fill this gap, this study proposed a two-phase framework to measure the urban spatial interaction gravity and developed determination approaches utilising the large-scale location-based services (LBS) dataset. Specifically, the inverse gravity model was adopted for the measure within multiple urban agglomerations and city sets during weekdays, weekends and holidays. Then, we developed the fitting equations of spatial interaction gravity by incorporating the correlated features associated with social, economic, network accessibility and land use. The findings present spatial interaction gravity across different periods and substantiate the distinct determination effects of features, with a high fitting accuracy. They provide promising supports for the intercity mobility prediction and pre-emptive traffic management.

Spillover effects and influencing factors of tourism eco‐efficiency for sustainable development: A case study of cities in the Yangtze River Delta

AbstractTourism often brings about negative environmental impacts. Tourism eco‐efficiency (TEE) and its spatial coordination are of great significance for sustainable tourism development and environmental protection in the Yangtze River Delta (YRD). TEE index system was established and applied in this study. The inter‐city TEE spatial correlation network is established by the modified gravity model and analyzed by social network analysis (SNA). Spatial Durbin model (SDM) was used to analyze the spatial spillover effects of the two centrality indicators of the network. Based on fuzzy set qualitative comparative analysis, we analyzed TEE enhancement configuration. The results show that: TEE in 26 cities in the study area has improved overall; the phenomenon of unbalanced development within the region still exists. In addition, the TEE of each city in the spatial correlation network of TEE in the YRD urban agglomeration is connected with each other, and there is a significant negative spillover effect. TEE is mainly affected by the level of economic development, industrial status of the tourism industry, tourism industry concentration, tourism investment level, tourism resource endowment, urban landscaping, and information development level. The combination of these factors and TEE can form five configuration patterns for TEE enhancement. This study combines SNA and SDM for the first time, and the conclusions obtained can more deeply describe the spatial correlation between different urban eco‐efficiency in the YRD. Aiming at the sustainable development of regional tourism, this study provides help for strengthening the protection of tourism ecological environment in the YRD.

Analysis of the Current Situation of Sustainable Development of the Yangtze River Economic Belt based on SDGs

This paper evaluates the level of sustainable development of 11 provinces and cities in the Yangtze River Economic Belt from 2011 to 2020, and analyzes its spatial and temporal differentiation and trend evolution characteristics. This paper adopts the coupling coordination model based on CRITIC-entropy weight combination weight, spatial autocorrelation model, spatial Markov chain and other methods, based on the measurement indicators of the United Nations Sustainable Development Goals, and conducts a comprehensive evaluation from four aspects: innovation-driven, welfare guarantee, resources and environment, and economic development. The research results show that the sustainable development level of the Yangtze River Economic Belt presents the characteristics of continuous improvement, regional difference, hierarchical leap, spatial dependence, spillover effect, etc. According to the research findings, this paper puts forward some suggestions, such as strengthening regional coordinated development, optimizing spatial planning and governance, coordinating policies and work, promoting rule of law and innovation, etc., aiming to promote the high-quality development of the Yangtze River Economic Belt, achieve the coordinated development of economy, society and ecology, and localize the SDGs. This paper’s research has important theoretical significance and practical value for deeply understanding the current situation and problems of sustainable development of the Yangtze River Economic Belt.