Forestry, Faculty of
Non UBC
DSpace
Wang, Benxu
Yang, Xuanqin
Dou, Yaquan
Wu, Qingjun
Wang, Guangyu
Li, Ya
Zhao, Xiaodi
Vegetation, serving as the primary constituent of terrestrial ecosystems, plays a crucial role in regulating energy flow and material cycles and providing vital resources for human socio-economic activities. This study analyzes the spatio-temporal patterns of economic density and vegetation coverage in the Yellow River Basin (YRB) based on forest resource inventory and socio-economic data from 448 counties in 2008, 2013, and 2018. A three-tiered criterion layer is constructed using economic density as the core explanatory variable, encompassing social development factors, land use factors, and natural factors. A two-way fixed effects model is then utilized to analyze the impact of economic density on vegetation coverage. Results reveal that: (1) Spatially, economic density demonstrates a “low in the west and high in the east” pattern, with an overall upward trend in the YRB. Conversely, vegetation cover exhibits a “high in the west and low in the east” pattern, displaying a downward trend. (2) Over the 2008–2018 period, a significant negative correlation between economic density and vegetation cover is observed in each county of the YRB, with vegetation cover decreasing by 1.108% for every 1 unit increase in economic density. Notably, the upstream areas of the YRB experience a significant increase in vegetation coverage, while the middle and lower reaches witness a decrease. (3) Considering control variables, the proportion of the primary industry, urbanization rate, forest protection level, and cultivated land area exert a significant influence on vegetation coverage across the entire basin. Policymakers should formulate relevant policies to achieve sustainable development in the YRB, as discussed in the proposed countermeasures. This study delineates a practical pathway for high-quality economic development and high-level ecological protection in the YRB, offering a valuable reference for analogous research in other regions.
Citation: Wang, B.; Yang, X.; Dou, Y.;Wu, Q.; Wang, G.; Li, Y.; Zhao, X.Spatio-Temporal Dynamics ofEconomic Density and VegetationCover in the Yellow River Basin:Unraveling Interconnections. Land2024, 13, 475. https://doi.org/10.3390/land13040475Academic Editors: SalvadorGarcía-Ayllón Veintimilla andJosep Lluís Miralles GarciaReceived: 29 February 2024Revised: 2 April 2024Accepted: 5 April 2024Published: 7 April 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).landArticleSpatio-Temporal Dynamics of Economic Density and VegetationCover in the Yellow River Basin: Unraveling InterconnectionsBenxu Wang 1,†, Xuanqin Yang 1,†, Yaquan Dou 2 , Qingjun Wu 3, Guangyu Wang 4 , Ya Li 1 and Xiaodi Zhao 2,4,*1 College of Economics and Management, Southwest Forestry University, Kunming 650224, China;wangbenxu@swfu.edu.cn (B.W.); xuanqin23@swfu.edu.cn (X.Y.); liy@swfu.edu.cn (Y.L.)2 Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China;douyq@caf.ac.cn3 College of Economics and Management, China Agricultural University, Beijing 100083, China;wqj2020@cau.edu.cn4 Faculty of Forestry, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada;guangyu.wang@ubc.ca* Correspondence: zhaoxiaodi@caf.ac.cn; Tel.: +86-136-7105-3260† These authors contributed equally to this work.Abstract: Vegetation, serving as the primary constituent of terrestrial ecosystems, plays a crucial rolein regulating energy flow and material cycles and providing vital resources for human socio-economicactivities. This study analyzes the spatio-temporal patterns of economic density and vegetationcoverage in the Yellow River Basin (YRB) based on forest resource inventory and socio-economic datafrom 448 counties in 2008, 2013, and 2018. A three-tiered criterion layer is constructed using economicdensity as the core explanatory variable, encompassing social development factors, land use factors,and natural factors. A two-way fixed effects model is then utilized to analyze the impact of economicdensity on vegetation coverage. Results reveal that: (1) Spatially, economic density demonstrates a“low in the west and high in the east” pattern, with an overall upward trend in the YRB. Conversely,vegetation cover exhibits a “high in the west and low in the east” pattern, displaying a downwardtrend. (2) Over the 2008–2018 period, a significant negative correlation between economic density andvegetation cover is observed in each county of the YRB, with vegetation cover decreasing by 1.108%for every 1 unit increase in economic density. Notably, the upstream areas of the YRB experience asignificant increase in vegetation coverage, while the middle and lower reaches witness a decrease.(3) Considering control variables, the proportion of the primary industry, urbanization rate, forestprotection level, and cultivated land area exert a significant influence on vegetation coverage acrossthe entire basin. Policymakers should formulate relevant policies to achieve sustainable developmentin the YRB, as discussed in the proposed countermeasures. This study delineates a practical pathwayfor high-quality economic development and high-level ecological protection in the YRB, offering avaluable reference for analogous research in other regions.Keywords: economic density; vegetation cover; spatio-temporal pattern; two-way fixed effects model;the Yellow River Basin1. IntroductionIn the context of global warming and extreme climatic events, the loss of biodiver-sity is increasingly evident [1], making the study of vegetation cover changes a crucialtopic in discussions about global ecosystem shifts. Forest ecosystems play a vital role inmitigating climate change, preserving biodiversity, and safeguarding water resources [2].However, climate change can alter forest vegetation distribution, impact forest growth anddevelopment, and potentially lead to ecosystem instability and species loss [3]. Vegetationdynamics, heavily influenced by climate change, are crucial for forest ecosystems to effec-tively respond to human disturbances and the significant impacts of climate change. InLand 2024, 13, 475. https://doi.org/10.3390/land13040475 https://www.mdpi.com/journal/landLand 2024, 13, 475 2 of 22recent decades, intensified human activities combined with ongoing climate change haveled to rapid changes in vegetation. This is particularly evident in the Yellow River Basin(YRB), where vegetation restoration has become a critical aspect of ecological protection andsocioeconomic development. The degradation and dysfunction of ecosystems, driven byhuman activities and climate change, pose significant threats to biodiversity, productivity,and the basin’s ecological services, impacting regional socio-economic development andenvironmental protection [4].As an important ecological shield and economic zone in China, the YRB plays animportant role in China’s economic and social development and ecological security. TheYRB plays an important role in preventing wind, stabilizing sandy soil and maintainingecological balance [5,6]. However, the fluctuations in vegetation cover within the YRB notonly impact regional ecological equilibrium but also exert profound effects on climate andthe environment in the basin and beyond. In addition to climate factors, human activitieshave become a major contributing factor to vegetation cover change [7]. The study revealedthat climatic factors enhance vegetation suitability in the YRB, whereas the influence ofhuman activities on vegetation suitability is dependent on specific circumstances [8]. Exces-sive human activities will interfere with vegetation suitability, such as over-irrigation andover-fertilization [9], economic density [10], and gross domestic product [11]. Scientificallydriven vegetation restoration is crucial for promoting socioeconomic development in thebasin to mitigate the YRB’s ecological fragility.The potential contributions of this paper are multifaceted. Firstly, historically, schol-ars have often concentrated on segmenting research areas, focusing primarily on specificriver basins or sections. This research tended to explore the impact of natural factorsor human activities on vegetation while overlooking the similarities and differences ineconomic density. Therefore, it is imperative to examine vegetation coverage while con-sidering regional heterogeneity from an economic density perspective. This involvescomparing and analyzing the economic development and resource endowment of differ-ent regions. Secondly, previous studies have predominantly taken a macro perspectivewhen discussing the influence of socio-economic factors on vegetation coverage. De-spite a general consensus within the academic community, further research is neededto delve deeper into the depth and specific mechanisms of this influence. Thirdly, interms of index selection, many scholars have focused on factors such as economic devel-opment level, industrial structure, and economic development speed. However, there islimited research on the relationship between economic density and vegetation coverage.In this study, we integrate three primary indicators—social development factors, landuse factors, and climate factors—along with nine secondary indicators, comprehensivelyconsidering various aspects. This approach minimizes the impact of external factors onexperimental results.The YRB encompasses diverse landscapes, ranging from densely populated urbanareas to rural agricultural regions, spanning multiple provinces. Understanding the rela-tionship between economic density and vegetation cover in this vital watershed is crucialfor informing sustainable development strategies, environmental conservation efforts, andland use planning initiatives. This study utilizes data from the 7th to 9th National ForestResource Inventory and ArcGIS (version 10.8) software to analyze the spatio-temporalpatterns of economic density and vegetation cover changes across 448 counties in the YRB.We employ a two-way fixed effects model to investigate the impact of economic densityon vegetation cover, explore underlying mechanisms, and identify spatial variations inthese impacts. Based on the findings, actionable recommendations are proposed to enhanceecological protection and support the socioeconomic development of the YRB.The structure of the paper is as follows: The second part comprises the literaturereview, followed by the third part detailing the materials and methods, including anintroduction to the study area, data sources, the Fraction of Vegetation Cover (FVC) indexsystem, ArcGIS spatial analysis, two-way fixed effects model, and variable selection. TheLand 2024, 13, 475 3 of 22fourth part presents the empirical results, followed by the discussion in the fifth part, andconcluding remarks in the sixth part.2. Literature ReviewAs the paramount socio-economic center in northern China and a traditionally delicateecological zone, changes in vegetation coverage within the YRB have consistently capturedthe attention of scholars. However, previous studies have predominantly focused onspecific river basins or sections. For instance, in the Gansu section of the YRB, vegetationcover has exhibited a continuous decline from the southwest to the northeast between2000 and 2018 [12]. In the Henan section, areas with high and medium-high vegetationcover are predominantly concentrated in the western Funiu Mountain, northern TaihangMountain, and eastern plain areas. Conversely, regions with medium, medium-low, andlow vegetation cover are mainly found in the central Luoyang to Zhengzhou section, withbare land primarily distributed along the water system and both sides of the YRB [13].The Inner Mongolia section, situated in an arid-semiarid region, relies on groundwater forvegetation, and increased rainfall stands as the primary driver for heightened vegetationcover [14]. In the Shanxi section, the most notable increase in vegetation cover occurs in thereforestation (grass) areas in the north of Shanxi [15]. Evidently, substantial variations invegetation cover exist across different sections of the YRB.In terms of influencing factors, climate change emerges as a crucial element affectingvegetation growth, while human activities also wield influence on changes in vegetationcover, often with predominant negative effects. However, there are periods where positiveeffects become more pronounced [16,17]. Specifically, in the middle section of the YRB,vegetation cover demonstrates a positive correlation with precipitation and temperature.The impact of topographic factors on vegetation cover is primarily manifested throughelevation and slope. As elevation and slope increase, vegetation cover experiences an initialrise followed by a subsequent decline. Under diverse topographic conditions, vegetationcover appears to be more influenced by temperature than rainfall. From 2000 to 2002, theFVC of the urban agglomeration in central Yunnan showed an overall improvement trend,with an average value of 0.496 and an annual growth rate of 0.0024, showing a temporaland spatial distribution of high in the west and low in the east, while human activitieshad a positive impact on its FVC, accounting for 55.1% of the regional proportion [18].Vegetation cover stands as a primary indicator of ecosystem alterations, and understandingvegetation dynamics and their responses to both natural and anthropogenic factors play apivotal role in enhancing ecosystem structure and function amid the backdrop of globalwarming [19].Regarding research methodologies, previous research has utilized various indica-tors to measure vegetation cover, encompassing FVC [20], Ecological Vulnerability Index(EVI) [21], MODIS NDVI vegetation index [22,23], Moran’s index and coefficient of vari-ation [24], and Ratio Vegetation Index (RVI) [25], among others. These indices provide acomprehensive framework for monitoring vegetation dynamics and offer critical insightsfor environmental management and conservation strategies. Various statistical approaches,such as the Dimidiate Pixel Model, linear regression, mean method, partial correlationanalysis, and regression residual method, have been employed for spatio-temporal analysesof vegetation cover in the YRB [26–28]. Geographic detectors are commonly utilized toanalyze the driving factors behind spatio-temporal changes in vegetation cover [29]. Exist-ing studies have predominantly concentrated on vegetation cover changes within specificsections of the YRB, with limited research encompassing the entire basin. Therefore, thecomparability of research conclusions necessitates further examination. Additionally, cur-rent studies on the influencing factors of vegetation cover tend to adopt a more macroscopicapproach. While scholars generally agree on the impact of socio-economic factors on vege-tation cover, further exploration is required to understand the extent and mechanisms ofthis impact.Land 2024, 13, 475 4 of 22Economic density refers to the concentration of labor, population, and tangible capitalin a specific area, reflecting the efficient operation of economic activities and the inten-sive utilization of land resources. Various factors such as population, land, production,basic environmental conditions, transportation infrastructure, socio-economic infrastruc-ture, industrialization level, structure, and regional economic development strategy andpolicies contribute to measuring the overall economic density level [30]. In the YangtzeRiver Delta, the quality of human capital has notably improved, attributed mainly toadvancements in human capital levels, urbanization promotion, foreign direct investmentinflux, and economic scale expansion [31]. This has led to enhanced economic prosperityand ecological health in the YRB, signifying a high degree of coordination and balancebetween economic development and environmental protection [32]. The harmonious coex-istence of economic development and ecological environment bodes well for the region’ssocioeconomic development. The ecological status of the YRB has shown a trend of initialdecline followed by improvement, indicating a correlation between ecological environ-ment enhancement and economic development. However, significant disparities existin ecological coordination among different regions, with light intensity being linked tourban economic development levels, showing a consistent upward trajectory [33]. Fur-thermore, scholars have evaluated economic development and ecological situations, aswell as the coupling degree, in the YRB based on economy, energy consumption, ecol-ogy, and water resources [34]. Given the critical ecological protection and socioeconomicdevelopment role of areas such as the YRB in China, economic development, albeit pro-gressing towards in a good direction, advances at a relatively slow pace. Notably, thereduction in resource and environmental pressures suggests a relaxation of the couplingrelationship between economic growth and resource utilization, with the environmentaldecoupling index significantly influencing the overall decoupling scenario [35]. In China,achieving the coordinated development of the industrial economy, resource conservation,and environmental protection has become imperative for promoting ecological civiliza-tion construction and socioeconomic development [36]. The eco-economic coordinationmodel serves as an effective tool for evaluating the consistency between ecosystem valueand economic growth, often measured by gross domestic product. From 2001 to 2015,the ecological carrying capacity of the YRB exhibited a unique changing trend, with thetotal ecological carrying capacity and ecological carrying capacity per unit area showinga “U” pattern. While about 55.6% of the region experienced an increase in ecosystemservice value (ESV), ESV decline was mainly concentrated in the western and northernregions of the basin, illustrating the complex interplay between economic developmentand ecological protection in the YRB [37]. As an indicator of efficient economic activityand intensive land resource utilization, economic density comprehensively reflects variouseconomic aspects through indicators such as labor concentration, population density,capital investment, land utilization rate, gross domestic product, and industrial structure.This provides a comprehensive and in-depth perspective for evaluating regional economicdevelopment. Given the YRB’s significance as an ecological protection and socioeco-nomic development experimental area, special attention to the coordinated relationshipbetween economic development and the ecological environment is necessary to achievesustainable development.In terms of economic density and vegetation coverage, the Vegetation Coverage Rate(VFC) stands as a critical global indicator used to assess land use changes, ecosystemhealth, and landscape characteristics. It quantifies the ratio of vegetation to land surface,offering insights into land use alterations, vegetation distribution shifts, and ecosystemfunctionality enhancements [38]. Economic density is intertwined with factors such aswages, innovation, rent, accessibility to amenities, public service provision costs, trans-portation, and environmental conditions [39]. Furthermore, economic density indirectlyinfluences vegetation coverage trends through urbanization, vegetation types, gross do-mestic product, and population density. For instance, urban economic density servesas a robust measure of a city’s economic scale and elucidates the drivers behind rapidLand 2024, 13, 475 5 of 22urbanization efforts in the region. Against the backdrop of global urbanization, vegetationcoverage in urban areas is undergoing transformations. While vegetation coverage isgenerally declining in the southern hemisphere, it is increasing in eastern North Americaand certain European urban locales. Moreover, numerous urban areas with high vegetationcoverage are witnessing expansion [40]. Changes in vegetation coverage in the urbancore and peripheral areas (buffer zones 5 km, 10 km, and 15 km away from the urbancore) correlate with variations in urbanization rates, gross domestic product, populationdensity, and urban development stages among cities [41]. Despite their advantageousgeographical locations, urban expansions result in an overall loss of vegetation. How-ever, San Diego’s urban dynamic index has risen, albeit with only a slight increase invegetation coverage in its urban core area [42]. In the Qaidam Basin, vegetation coveragevaries across different vegetation types. The Vegetation Classification Inversion (VCI)can estimate the Fraction of Vegetation Cover (FVC). In areas with relatively high vegeta-tion coverage density (FVC > 60%), the empirical model (EM) for mixed vegetation FVCestimation differs from that of mixed vegetation inversion (VMI). Pixel DecompositionModel (PDM)-derived VCI yields lower inversion values for very low-density vegeta-tion cover (FVC ≤ 10%) and higher inversion values for high-density vegetation cover(FVC > 80%) [43]. Nevertheless, research on whether economic density directly impactsvegetation coverage remains nascent and necessitates further development. Through athorough investigation of this relationship, we can better strike a balance between economicdevelopment and the ecological environment, ultimately achieving sustainable social andeconomic development.In conclusion, previous studies predominantly focused on segmented research ar-eas, particularly specific river basins or sections, often discussing the impact of naturalfactors or human activities on vegetation while neglecting differences in economic den-sity. To comprehensively understand vegetation coverage, it is crucial to fully considerregional heterogeneity from the perspective of economic density and conduct a detailedanalysis of the region’s economic development and resource endowment. Moreover, al-though past studies have analyzed the impact of socio-economic factors on vegetationcoverage from a macro level, further exploration is needed to delve into the deep-seatedand specific mechanisms of this impact. Investigating the internal logic of this relation-ship can reveal its essence more comprehensively. Additionally, while many scholarsfocus on indicators such as economic development level, industrial structure, and eco-nomic development speed, research on economic density and vegetation coverage isrelatively scarce. Hence, this paper integrates social development, land use, and climateand selects nine secondary indicators—including the proportion of primary and secondaryindustries—to enhance the comprehensiveness of the study and reduce the influence ofexternal factors on experimental results. Furthermore, previous research data have beenrelatively macroscopic and rough, necessitating improved data accuracy. In this paper, isemployed to ensure data accuracy based on forest resource inventory and socio-economicdata from 448 counties in 2008, 2013, and 2018. Lastly, regarding the research method,the two-way fixed effects model adopted herein can simultaneously model causal andreverse relationships, providing a comprehensive perspective on the influence betweenvariables. This model not only addresses endogeneity issues but also fully considersindividual and time heterogeneity, thereby enhancing the accuracy and reliability oflresearch results.The effects of economic density on vegetation coverage are analyzed in this paperbased on forest resource inventory and socio-economic data from the YRB in 2008, 2013, and2018. ArcGIS software is employed, utilizing the existing literature to analyze the spatio-temporal patterns of economic density and vegetation cover changes in the YRB, with atwo-way fixed effects model utilized to analyze the relationship between economic densityand vegetation cover. Through result analysis and comparison with relevant scholars’research conclusions, suggestions for economic development and ecological protectionLand 2024, 13, 475 6 of 22in the YRB and other areas are proposed. The research hypotheses in this paper areas follows:Hypothesis 1: economic density in the YRB exhibits a negative correlation with vegetation cover.Hypothesis 2: economic density varies in its impact on vegetation cover across different watersheds.The YRB spans a total length of 5464 km, with a water surface difference of 4480 mand a total basin area of 795,000 km2. The geographical landform, climate, and ecologicalenvironment of the upper, middle, and lower reaches significantly differ, manifestingvarying degrees of conflict between human activities and ecological preservation acrossdifferent sections. In the upper reaches, characterized by steep terrain, soil erosion, anddesertification pose significant challenges. Prolonged periods of excessive human activities,irrational agricultural practices, and overgrazing have resulted in extensive soil erosion anddesert encroachment. Conversely, the downstream areas exhibit high levels of urbanization,with industrial and mining activities surrounding urban centers leading to significant riverpollution. Rapid urbanization exacerbates environmental degradation, marked by landoverexploitation and indiscriminate logging.The YRB spans nine provinces in China, with downstream regions experiencing higherpopulation densities. Increased human activities, particularly urbanization, contribute toforest degradation. This study considers economic density as the core explanatory variableto test the aforementioned hypotheses and incorporates nine control variables, such associal development, land use, and natural factors. The study utilizes ArcGIS softwarespatial analysis and a two-way fixed effects model to analyze forest resource inventory datafrom 2008, 2013, and 2018, exploring the influence of economic density and other variableson vegetation cover across different areas of the YRB.3. Materials and Methods3.1. Study AreaThe YRB is situated between 96◦ E and 119◦ E, 32◦ N and 42◦ N [44]. By the endof 2021, the total population of the YRB will be 420.63 million, accounting for 29.14% ofChina, while Gross Domestic Product is USD 396634 × 104, accounting for 25.08%; theforest area is 7.32 × 107 hm2, accounting for 31.72%; the forest stock is 4.60 × 108 m3,accounting for 26.22%. The topography of the region exhibits higher elevations in thewest, with the average elevation in the western source area surpassing 4000 m, and lowerelevations in the east, ranging from 1000–2000 m in the central area [45]. The YRB, span-ning 19% of China’s land area, the YRB is divided into three basins: upstream, middleand downstream (refer to Figure 1), traverses nine provincial-level administrative regions:Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shan-dong. Within the basin, there are 168 counties in the upper reaches, 219 in the middlereaches, and 61 in the lower reaches (refer to Appendix A). The basin exhibits three distinctclimate zones: the plateau climate zone, the temperate zone, and the warm temperatezone [46]. Water resources within the basin are inherently scarce, characterized by anaverage annual precipitation of less than 450 mm and notable regional differences inaverage temperatures [47]. The vegetation coverage in the YRB generally exhibits a dis-tribution feature with high coverage in the southeast and west and lower coverage inthe northeast and north [48]. The primary land use types contributing to vegetation cov-erage include forest land, grassland, and cultivated land [49]. Population growth, landreclamation, deforestation, and urbanization have hastened the exploitation of land andwater resources in the YRB, presenting significant challenges to ecosystem restoration inrecent decades.Land 2024, 13, 475 7 of 22Land 2024, 13, x FOR PEER REVIEW 7 of 23 Figure 1. Map of the study area. In the regional map of China above, pink represents the YRB and green represents other regions. The boundary point between the middle and upper reaches of the YRB is Hekou Town, Toktuo County, Inner Mongolia Autonomous Region, and the boundary point between the middle and lower reaches of the YRB is Taohuayu, Xingyang County, Zhengzhou City, Henan Province. 3.2. Data Sources and Variable Selection 3.2.1. Data Sources Vegetation cover: Vegetation cover data are sourced from the forest inventory data in periods 7–9 (2008, 2013, 2018). The vegetation cover level for each county is calculated utilizing the weighted average method based on the sample plot data. Economic and social development: data from the China County Statistical Yearbook. Land use: land use data is extracted from the datasets of 2008, 2013, and 2018, as released by the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. Climate: climate data are obtained from the World Climate Database (https://www.worldclim.org/, accessed on 12 January 2024). 3.2.2. Variable Selection (1) Explained variable: vegetation cover. Vegetation cover, an essential indicator for gauging regional ecological changes, typically denotes the ratio of forest area to the total land area [50]. As a widely used indicator of vegetation response, it mirrors the growth status of surface vegetation and serves as a measure for regional ecological environment changes [51]. Thus, this study adopts the vegetation coverage of each county (district) as the dependent variable for the empirical model. (2) Core explanatory variable: The correlation between socio-economic factors and vegetation cover or land use change in urban systems has garnered escalating interest [52]. Thus, delineating the relationship between socio-economic indicators and vegetation cover assumes pivotal significance in enhancing both socio-economic development and ecological environments. Economic density: This study investigates the influence of eco-nomic development levels on vegetation coverage, selecting economic density as the key measure based on previous research findings. “Economic density” refers to the measure Figure 1. Map of the study area. In the regional map of China above, pink represents the YRB andgreen represents other regions. The boundary point between the middle and upper reaches of theYRB is Hekou Town, Toktuo County, Inner Mongolia Autonomous Region, and the boundary pointbetween the middle and lower reaches of the YRB is Taohuayu, Xingyang County, Zhengzhou City,Henan Province.3.2. Data Sources and Variable Selection3.2.1. Data SourcesVegetation cover: Vegetation cover data are sourced from the forest inventory datain periods 7–9 (2008, 2013, 2018). The vegetation cover level for each county is calculatedutilizing the weighted average method based on the sample plot data.Economic and social development: data from the China County Statistical Yearbook.Land use: land use data is extracted from the datasets of 2008, 2013, and 2018, asreleased by the Resource and Environmental Science Data Center of the Chinese Academyof Sciences.Climate: climate data are obtained from the World Climate Database (https://www.worldclim.org/, accessed on 12 January 2024).3.2.2. Variable Selection(1) Explained variable: vegetation cover. Vegetation cover, an essential indicator forgauging regional ecological changes, typically denotes the ratio of forest area to thetotal l nd area [50]. As a widely used indicator of vegetation response, it mirrors therowth status of surfa e ve tation and serves a a me sure or gional ecologicalenvironment changes [51]. Thus, this study adopts the vegetation coverage of eachcounty (district) as the dependent variable for the empirical model.(2) Core explanatory variable: The correlation between socio-economic factors and vege-tation cover or land use change in urban systems has garnered escalating interest [52].Thus, delineating the relationship between socio-economic indicators and vegetationcover assumes pivotal significance in enhancing both socio-economic developmentand ecological environments. Economic density: This study investigates the influenceof economic development levels on vegetation coverage, selecting economic densityas the key measure based on previous research findings. “Economic density” refers tothe measure of economic activity per unit area of land, often quantified as the outputLand 2024, 13, 475 8 of 22value per square kilometer of land. It is typically calculated as the gross regionalproduct divided by the administrative area (billion yuan/km2). Beyond measuringthe efficiency of economic activities and land use intensity in a city, economic densityalso represents the intensity of economic activities within a region [53]. Moreover, itserves as a crucial foundation for devising regional policies and evaluating implemen-tation processes [54]. Consequently, this article designates the economic density ofeach county (district) as the core explanatory variable.(3) Control variables: Previous studies indicate that, apart from economic density, numer-ous factors can influence the vegetation coverage of each county (district). This articleestablishes three primary indicators by building upon prior research conclusions [55]:social development factors, land use factors, and natural factors. Correspondingsecondary indicators are constructed, encompassing the proportion of the primaryindustry, the proportion of the secondary industry, population density, urbanizationrate, forest protection level, proportion of cultivated land area, proportion of construc-tion land, annual precipitation, and monthly average temperature. Specific variabledescriptions are outlined in Table 1.Table 1. Description of variables.Variable Types Variable Names Abbreviation DescriptionExplained variable Vegetation cover vc The ratio of forest area to total land areaCore explanatory variable Economic density eco Gross regional product/area of theadministrative region (billion yuan/km2)ControlvariablesSocial developmentfactorsThe proportion of primaryindustry agriThe ratio of the value added of primaryindustry to the gross regional domesticproduct of the county (%)The proportion ofsecondary industry indusThe ratio of the value added of secondaryindustry to the gross regional domesticproduct of the county (%)Population density ps The ratio of permanent population tocounty area (thousands/km2)Urbanization rate urbi Urbanization rate of permanentpopulation (%)Forest protection level protectBased on the weighted average of “forestland protection level” in the sample landattribute tableLand use factorsProportion of cultivatedland area farmlandThe ratio of cultivated land area to countyarea (%)Proportion of constructionland construThe ratio of the construction area tocounty area (%)Natural factorsAnnual precipitation rainfall Annual precipitation in each county anddistrict (mm)Monthly averagetemperature tempeAnnual average temperature in eachcounty and district (◦C)3.3. Methods3.3.1. ArcGIS Spatial AnalysisSpatial analysis involves the manipulation of geospatial information using geographictechnology and associated mathematical models. It encompasses a set of functions thatdescribe spatial correlations within and beyond a single dataset. In recent years, GIStechnology has assumed an increasingly pivotal role in surveying and data processing.ArcGIS software, being a well-established GIS software, holds the highest market share andpopularity in the industry [56]. It boasts robust tools and functionalities for map productionand analysis. This study employs ArcGIS software to process forest resource inventory datafrom the 7th to 9th periods, extracting vector data. Subsequently, it conducts an analysis ofthe vector data pertaining to vegetation coverage and economic density in the YRB.Land 2024, 13, 475 9 of 223.3.2. Two-Way Fixed Effects ModelThis study employs a two-way fixed effects model for fundamental regression anal-ysis to assess the extent of the influence of socio-economic activities on vegetation coverin the YRB [57]. The two-way fixed effects model is a statistical technique primarilyutilized for panel data analysis. It excels in managing inter-regional heterogeneity thatremains constant over time, effectively mitigating biased coefficient estimation arising fromomitted variables.The equation of the model is as follows:Yit = β0 + β1ecoit + β2Xit + µi + σt + εitwhere Yit denotes the vegetation cover of the 448 counties in the YRB, ecoit is theeconomic density of each county (district), and β1 is the main coefficient under ob-servation in this study, representing the coefficients reflecting the influence of socio-economic development on vegetation cover. Xit denotes the control variables, en-compassing variables that may impact vegetation cover, excluding the level of socio-economic development. µi represents the individual effect, which remains constantover time, σt represents the time-fixed effect, unchanging based on individual dif-ferences between cities, and ε it denotes the random interference term. This paper’sdata analysis is grounded in panel data, and hence, the two-way fixed effects modelis utilized for analysis. This approach offers advantages such as improved accuracyin observations.4. Results4.1. Spatial-Temporal Evolution of Vegetation Cover and Economic DensityThis study delves into the spatio-temporal evolution patterns of vegetation coverageand economic density in 2008, 2013, and 2018, as illustrated in Figures 2–7. Figures 2–4depict the spatiotemporal evolution patterns of vegetation coverage in 2008, 2013, and 2018.Figures 5–7 depict the spatiotemporal evolution patterns of economic density in 2008, 2013,and 2018.Land 2024, 13, x FOR PEER REVIEW 10 of 23 Figure 2. Spatio-temporal pattern of vegetation cover from 2008. Figure 3. Spatio-temporal pattern of vegetation cover from 2013. Figure 4. Spatio-temporal pattern of vegetation cover from 2018. Figure 2. Spatio-te poral pattern of vegetation cover from 2008.Land 2024, 13, 475 10 of 22Land 2024, 13, x FOR PEER REVIEW 10 of 23 Figure 2. Spatio-temporal pattern of vegetation cover from 2008. Figure 3. Spatio-temporal pattern of vegetation cover from 2013. Figure 4. Spatio-temporal pattern of vegetation cover from 2018. Figure 3. Spatio-te poral pattern of vegetation cover from 2013.Land 2024, 13, x FOR PEER REVIEW 10 of 23 Figure 2. Spatio-temporal pattern of vegetation cover from 2008. Figure 3. Spatio-temporal pattern of vegetation cover from 2013. Figure 4. Spatio-temporal pattern of vegetation cover from 2018. Figure 4. Spatio-te poral pattern of vegetation cover from 2018.Land 2024, 13, x FOR PEER REVIEW 11 of 23 Figure 5. Spatio-temporal pattern of economic density from 2008. Figure 6. Spatio-temporal pattern of economic density from 2013. Figure 7. Spatio-temporal pattern of economic density from 2018. Figure 5. Spatio-temporal pattern of economic density from 2008.Land 2024, 13, 475 11 of 22Land 2024, 13, x FOR PEER REVIEW 11 of 23 Figure 5. Spatio-temporal pattern of economic density from 2008. Figure 6. Spatio-temporal pattern of economic density from 2013. Figure 7. Spatio-temporal pattern of economic density from 2018. Figure 6. Spatio-temporal pattern of economic density from 2013.Land 2024, 13, x FOR PEER REVIEW 11 of 23 Figure 5. Spatio-temporal pattern of economic density from 2008. Figure 6. Spatio-te poral pattern of economic density from 2013. Figure 7. Spatio-temporal pattern of economic density from 2018. Figure 7. Spatio-te poral pattern of economic density from 2018.Vegetation cover serves as a reliable measure of an area’s ecological environment [58].The analysis results reveal that in 2008, certain regions in the middle and upper reachesof the YRB exhibited low vegetation cover. However, as the government increasinglyprioritizes ecological environment protection and various ecological projects are graduallyimplemented, there has been a significant improvement in vegetation cover in the middleand upper reaches of the YRB over the 10-year period from 2008 to 2018. Particularlynoteworthy is the sustained high vegetation cover in certain counties and cities in Sichuanand Qinghai, with a more concentrated distribution of areas with elevated vegetation cover.In 2008, the vegetation cover in the lower reaches of the YRB was relatively robust, withsome counties and cities in Henan Province exceeding 75%. However, over the past decade,the vegetation cover in the lower reaches of the YRB has experienced a noteworthy declinecompared with the previous period. The number of areas with higher vegetation cover hasbecome scarce in this region.In terms of spatial distribution, the economic development in the middle and lowerreaches of the YRB is more pronounced, particularly in Shandong and Henan, whereeconomic density exhibits a substantial difference compared to the other seven provinces.Over the period of 2008 to 2018, the regions within the YRB witnessed rapid economicdevelopment, marked by a significant increase in economic density compared with thepreceding period. In the middle reaches of the YRB, the economic level has experienced aLand 2024, 13, 475 12 of 22substantial and notable increase in the past decade. The economic development of the lowerreaches continues to maintain a high growth rate, with a more concentrated distribution ofareas characterized by high economic density. However, the upper reaches of the YRB aredeveloping at a slower pace, and the distribution of areas with high economic density ismore dispersed.As illustrated in Figures 2–7, there exists a correlation between the temporal and spatialchanges in economic density and vegetation coverage. As economic density continues torise across various regions of the YRB, vegetation coverage has, to some extent, experienceda decrease. The subsequent discussion employs a two-way fixed effects model to provide amore intuitive description of the relationship between economic density and vegetationcoverage in diverse regions of the YRB.4.2. Regression ResultsIn this section, the fixed effects model is applied to examine the forest resource inven-tory data in the YRB. Following a comprehensive analysis of the YRB, the study furtherdivides it into three segments: upstream, midstream, and downstream. The outcomes ofthis analysis are presented in Table 2.Table 2. Regression results.Variable The Entire Basin Upper Middle LowerVariable Coefficient t Coefficient t Coefficient t Coefficient teco −1.108 *** −3.920 −0.579 −0.460 −1.103 *** −3.580 −0.706 −0.340agri 0.114 ** 2.010 0.197 *** 3.600 0.019 0.150 −0.152 −0.440indus −0.019 −0.350 0.065 1.270 −0.092 −1.370 −0.004 −0.020ps −0.109 −1.120 0.023 0.040 −0.102 −1.020 0.081 0.020urbi 0.095 * 1.710 −0.049 −0.610 0.145 ** 2.080 −0.017 −0.050protect −2.165 * −1.850 −0.053 −0.030 −3.364 * −1.890 −0.340 −0.110farmland 0.120 * 1.800 −0.133 −1.020 0.224 ** 2.510 0.015 0.080constru 0.171 1.600 −0.167 −1.090 0.133 0.740 0.689 1.570rainfall 0.001 0.170 0.024 ** 2.540 0.008 0.840 −0.015 −0.570tempe 0.959 1.490 1.196 1.590 1.800 * 1.660 −1.270 −0.180Note: ***, **, and * represent significant levels at 10%, 5%, and 1%, respectively.Simultaneous with socio-economic development, it exerts a certain impact on veg-etation coverage in various regions [59]. Owing to substantial differences in economicdevelopment levels and quality among counties in the YRB, the influence of economicdensity on vegetation coverage also varies. According to the results of the regressionanalysis covering the entire basin, the economic density of each county in the YRB exhibiteda negative impact on vegetation coverage from 2008 to 2018. The correlation coefficient ofeconomic density is −1.108, indicating a particularly significant impact on the dependentvariable, vegetation coverage. Every increase of 1 km2 in land output value correspondsto a 1.108% decrease in vegetation coverage. There was no significant correlation be-tween economic density and vegetation coverage in the upper and lower reaches of theYRB from 2008 to 2018 after analyzing the watershed-specific regressions. However, asignificant negative correlation was observed between economic density and vegetationcoverage in the middle reaches of the YRB. For every 1 km2 increase in land output value,vegetation coverage experiences a decrease of 1.103%. This suggests that the economicdensity in the middle reaches of the YRB has the most substantial impact on vegetationcoverage, resulting in a negative correlation between economic density in the YRB andvegetation coverage.Considering the control variables, it is evident that the proportion of the primaryindustry, urbanization rate, forest protection level, and cultivated land area in the entireYRB significantly impact vegetation coverage. Specifically, in the following:Land 2024, 13, 475 13 of 22Every 1% increase in the proportion of the primary industry results in a 0.114% increasein vegetation coverage; every 1% increase in the urbanization rate leads to a 0.095% increasein vegetation coverage; every 1 unit increase in the forest land protection level correspondsto a 2.165% decrease in vegetation coverage; every 1% increase in the proportion of arableland leads to a 0.120% increase in vegetation coverage.Moreover, there is a notable correlation between the proportion of the primary industry,annual precipitation in the upper reaches of the YRB, and vegetation coverage. Specifically,in the following:Every 1% increase in the proportion of the primary industry results in a 0.197%increase in vegetation coverage; every 1 mm increase in annual precipitation leads to a0.024% increase in vegetation coverage.In the middle reaches of the YRB, economic density, urbanization rate, forest protectionlevel, and the proportion of cultivated land area also exhibit significant correlations withvegetation coverage. Specifically, in the following:Every 1 km2 increase in land output value leads to a 1.103% decrease in vegetationcoverage; every 1% increase in the urbanization rate results in a 0.145% increase in vege-tation coverage; every 1 unit increase in the forest land protection level corresponds to adecrease of 3.364% in vegetation coverage; every 1% increase in the proportion of arableland leads to a 0.224% increase in vegetation coverage.4.3. Robust TestThis paper employs a variable replacement method to conduct a robustness test on theoriginal basic regression model to enhance the robustness and credibility of the researchfindings. The core variable, economic density, is replaced by a comparable variable, GDP(the final outcome of production activities in the region over a certain period of time), for atwo-way fixed effects model test. The regression results obtained are presented in Table 3.Table 3. Robustness test results.Variable The Entire Basin MidstreamVariable Coefficient t Coefficient tGDP −0.009 *** −2.690 −0.010 ** −2.140agri 0.098 * 1.720 −0.003 −0.020indus −0.018 −0.420 −0.093 −1.370ps −0.128 −1.310 −0.113 −1.120urbi 0.110 ** 1.990 0.160 ** 2.280protect −2.370 ** −2.020 −3.594 ** −2.000farmland 0.103 1.520 0.197 ** 2.190constru 0.139 1.290 0.060 0.340rainfall 0.000 0.000 0.008 0.820tempe 0.982 1.510 2.004 ** 1.810Note: ***, **, and * represent significant levels at 10%, 5%, and 1%, respectively.The results indicate that the correlation coefficient of the replaced core variable GDPis negative, and it has a significant impact on the explained variable, vegetation coverage.Therefore, the economic development level of the entire basin and the middle reaches ofthe region align with the basic regression results in terms of direction and significance.5. Discussion5.1. Analysis of Spatio-Temporal Pattern5.1.1. Vegetation Coverage TrendFrom 2008 to 2018, the upper reaches of the YRB experienced a significant increase invegetation coverage, with notable improvements in regions like Ningxia Hui AutonomousRegion and Gansu. However, some areas, such as Sichuan, Gansu, and Qinghai, maintainhigh vegetation coverage. The middle reaches of the YRB witnessed a significant decreaseLand 2024, 13, 475 14 of 22in vegetation coverage, particularly in areas like Shaanxi and Shanxi, which experienced asubstantial decline compared with 2008. Although there was an increase from 2013 to 2018,it remained lower than 2008 levels. The lower reaches of the YRB saw a significant decreasein vegetation coverage, especially from 2008 to 2013. However, there was a certain degreeof improvement in vegetation coverage by 2018. Some areas in Henan Province that hadhigh vegetation coverage in 2008 experienced a significant decrease by 2018.5.1.2. Economic Density TrendFrom 2008 to 2018, economic development in the upper reaches of the YRB wasrelatively slow, significant changes occurred in the overall economic density pattern inthe middle reaches, and rapid economic development was observed in the lower reaches,particularly in Shandong and Henan. The YRB has been an agricultural area for a long timein history, and the industrial development started slowly, which has a certain impact on thedevelopment of the social economy. The geographical location of this region is relativelyharsh, natural disasters such as soil erosion are serious, and the ecological environment isfragile, which brings no small challenge to the development of an urban economy.5.2. Analysis of the Relationship between Vegetation Coverage and Economic DensityEconomic density growth has had a certain impact on vegetation coverage, but thedecline in vegetation coverage is attributed to multifaceted factors such as climate change,human activities, and natural disasters [60]. Rapid social and economic development,coupled with human construction activities and the acceleration of industrialization, haveled to vegetation destruction. Initiatives like the Grain Greening Project and the NaturalForest Protection Project in the middle and upper reaches of the YRB have promoted vege-tation growth [61]. However, despite these efforts, overall vegetation coverage continues toexhibit a downward trend.5.2.1. The Whole Basin of the YRBUpon a comprehensive analysis of the YRB, it becomes evident that economic densityexhibits a negative correlation with vegetation coverage. The intricate interplay of variousfactors contributes to the fluctuation in vegetation, with key influences being the proportionof primary industry, urbanization rate, and cultivated land area.A notable insight arises from the regression findings, suggesting a seemingly negativeassociation between forest protection levels and vegetation coverage. However, a more nu-anced interpretation emerges when considering the classification criteria for forest resourceinventory’s continuous forest land protection levels. Under the classification standards offorest protection levels in forest resource inventory, there are four levels ranging from I toIV. Interestingly, lower forest protection levels correspond to higher vegetation coverage.The forest resource inventory process entails continuously addressing structural changes indemand for forest products and services, as well as ecological environment protection, inresponse to social, economic, and environmental developments. Starting from the growth,distribution patterns, and characteristics of forest resources themselves, combined withChina’s national conditions, forest conditions, and forest resource management charac-teristics, it comprehensively prices the current situation and changes in forest resourcesand provides corresponding technical drawings. Therefore, the higher the protection level,the more positively it influences vegetation coverage, aligning with on-ground protectionpractices. The positive correlation between forest land protection and vegetation coveragehighlights the beneficial impact of conservation efforts. This study underscores a crucialpoint—economic development and ecological protection in the YRB can coexist harmo-niously. The path forward involves prioritizing the improvement of industrial qualityand ensuring that economic growth aligns with environmental preservation. The complexeconomic system, encompassing factors like economic growth, urbanization, agriculturalactivities, and commercialization, collectively shapes and influences vegetation coverage.Land 2024, 13, 475 15 of 225.2.2. The Sub-Basin of the YRBConsidering the significant disparity in economic development levels among the upper,middle, and lower reaches of the YRB, industrial development priorities vary accordingly.Upon integrating the analysis of economic density’s role, it becomes evident that the upperreaches of the YRB, characterized by relatively lagging economic development, seek toenhance their economic status by leveraging geographical advantages. Initiatives such asthe establishment of the Qingtongxia Hydropower Station and the Laxiwa HydropowerStation have not only safeguarded the ecological integrity of the upper reaches but alsospurred economic growth. Meanwhile, the middle reaches serve as a crucial energy huband industrial center in China. However, the absence of incentives for traditional industrialtransformation and upgrading has led to the proliferation of resource-intensive industries,resulting in environmental degradation and diminished vegetation cover. As the economycontinues to evolve, downstream counties and cities witness escalating levels of economicprosperity. The Huanghuaihai Plain, occupying a substantial portion of arable land in thedownstream region, offers opportunities for enhancing vegetation coverage through thedevelopment of a modernized agricultural economy.The ecological sensitivity and strategic geographical location designate the upperreaches of the YRB as a key restricted development zone at the national level. Despite arelatively gradual pace of economic development, this region holds immense potential forlarge-scale forest nurturing [62]. Notably, the fluctuations in economic density have mini-mal repercussions on vegetation coverage in this area. Instead, the paramount influenceemerges from natural environmental factors, with annual precipitation significantly shap-ing the vegetative landscape. While emphasizing the preservation of vegetation coverage,it remains imperative for the upper reaches of the YRB to address economic development.The resolute implementation of the Western Development Strategy [63] stands as a crucialdirective. This strategy encompasses a comprehensive approach, aiming not only at eco-logical conservation but also at enhancing the economic landscape. It calls for concertedefforts to improve the income and welfare of residents in the upper reaches, addressingthe challenge of economic backwardness. By striking a harmonious balance between eco-logical well-being and economic progress, the upper reaches can chart a sustainable andprosperous future.The middle reaches of the YRB witnessed robust economic development during theperiod from 2008 to 2018, accompanied by significant environmental challenges, particularlyevident in the Loess Plateau, where soil erosion became a prominent issue. The region,endowed with abundant coal resources, underwent extensive coal exploitation as part ofits economic expansion. Unfortunately, this rapid development, coupled with an outdatedtechnological infrastructure, contributed to a range of ecological problems, hastening thedecline of the local ecological environment [64]. A notable consequence of this economicsurge was the concurrent increase in urbanization rates. As urbanization progressed, therewas a positive correlation observed with vegetation coverage—a phenomenon often drivenby public awareness of the importance of ecological sustainability. The rise in wealth actedas a catalyst, intensifying the demand for both high-quality and substantial vegetationcoverage [55]. Recognizing the ecological toll of economic activities, various regions inthe middle reaches of the YRB have taken proactive measures to address environmentalconcerns. These initiatives include the implementation of ecological and environmentalprotection measures and a steadfast commitment to policies encouraging the conversionof farmland back to forests and grasslands. Through such concerted efforts, the aim is tomitigate the adverse impact of economic activities on vegetation coverage and restore theregional ecological equilibrium.The lower reaches of the YRB, boasting an advantageous geographical location withproximity to the capital, Beijing, and encompassing coastal cities, laid a strong foundationfor robust economic development [65]. In comparison to the upper and middle reaches ofthe YRB, the lower reaches exhibited a higher level of economic development, a superiorecological environment, and a more pronounced degree of ecological and economic coor-Land 2024, 13, 475 16 of 22dination, albeit still below the national average [66]. The economic density in the lowerreaches of the YRB demonstrated a minimal impact on vegetation coverage. However,the continuous march of industrialization underscores the need to guard against potentialdeclines in vegetation coverage arising from ecological and economic imbalances. Theheightened population density in the lower reaches amplifies the importance of judiciouslyutilizing limited land resources, presenting a pivotal factor in enhancing vegetation cov-erage. As the lower reaches navigate the intricate interplay between economic prosperityand environmental preservation, it becomes imperative to implement strategic measuresthat foster a harmonious coexistence. Addressing ecological and economic imbalances,especially in the face of increasing industrialization, stands as a key consideration forsustaining and enhancing vegetation coverage in this vital region.5.3. LimitationThis study delineates a pragmatic trajectory for the high-level preservation of forestresources and the high-quality advancement of the social economy across the upper, middle,and lower reaches of the YRB, offering valuable insights for other developing nations andregions. Nonetheless, it is subject to certain limitations. Primarily, in terms of studyscope, our analysis is grounded in socio-economic and vegetation coverage data fromthe YRB spanning 2008 to 2018, thus limiting its generalizability to other countries andregions. The YRB, characterized by ecological fragility, exhibits a weak resistance tointerference, with human activities exerting a profound impact on its ecological landscape.The basin’s unique geographical features, predominantly sloping hills and its passagethrough the Loess Plateau, render it susceptible to soil erosion and other natural calamities.In contrast, regions like the Yangtze River Basin boast high vegetation suitability owingto their favorable geographic positioning, with forest reserves accounting for 25% of thenational total [67]. Consequently, the findings of our study are specific to the YRB. Futureresearch endeavors will expand the scope to explore the nexus between China’s nationaleconomic development and forest resource conservation. Secondly, our analysis mayoverlook variables, potentially omitting other factors influencing vegetation cover in theYRB. Subsequent research will comprehensively consider the interplay of climate changeand economic development, which jointly impact forest vegetation cover.6. Conclusions6.1. ConclusionsBased on vector data, the spatial-temporal pattern of vegetation coverage and eco-nomic density in the YRB from 2008 to 2018 was analyzed using ArcGIS software. Theresults revealed that vegetation coverage displayed a pattern of high levels in the west andlow levels in the east, with an overall declining trend across the YRB. Conversely, economicdensity exhibited a pattern of low levels in the west and high levels in the east, displayingan overall increasing trend. The outcomes of the two-way fixed effects model indicated anegative correlation between economic density and FVC in the YRB, particularly evidentin the middle reaches of the YRB. Specifically, across the entire YRB, vegetation coveragedecreased by 1.108% for every 1 unit increase in economic density, while in the middlereaches of the Yellow River, it decreased by 1.103% with a similar increase in economicdensity. The encroachment of large-scale industrial activities on forest land has adverselyimpacted vegetation coverage [68], suggesting that the development and utilization ofnatural resources may undermine vegetation coverage in the Yangtze River Delta amideconomic growth. Notably, no significant correlation between economic density and vegeta-tion coverage was observed in the upper and lower Yellow River regions during 2008–2018.Furthermore, factors such as forest protection level, urbanization rate, and proportion ofprimary industry influence vegetation coverage through various mechanisms, exacerbatingspatial disparities in vegetation coverage within the YRB. Results indicate a positive impactof the primary industry on vegetation coverage in the YRB, attributed to advancementsin agricultural modernization, adoption of water-saving irrigation technologies, and im-Land 2024, 13, 475 17 of 22proved production conditions, all contributing to agricultural improvements and increasedvegetation coverage. Additionally, higher forest protection levels in the YRB correspond tomore pronounced improvements in vegetation coverage. Ecological restoration projects ini-tiated by the Chinese government since the early 21st century have augmented vegetationcoverage across parts or even the entire YRB [69]. Previous research on the dynamic natureof China’s vegetation area underscores the significant influence of socio-economic factorson vegetation change, offering valuable insights for sustainable development strategies inthe YRB and beyond [70].6.2. CountermeasuresIn conclusion, the YRB, endowed with abundant resources but facing ecologicalfragility, necessitates a transformative approach to economic development, prioritizinggreen practices and emphasizing responsible resource exploitation and utilization. Severalkey recommendations emerge.Upstream: Environmental ConservationImplement afforestation policies by closing mountain areas to human activities, allow-ing natural ecology to recover. Strengthen ecological protection in mountainous regions,focusing on safeguarding vegetation and water sources and preventing overgrazing anddeforestation. Enhance the sustainable development of the primary industry, encouraginggreen agricultural practices to minimize damage to vegetation.Midstream: Economic and Ecological HarmonyRecognize the negative correlation between vegetation coverage and economic densityin the middle reaches. Reduce human activities based on the environmental carryingcapacity of vegetation, continuing initiatives like returning farmland to forest and grass-land for promoting vegetation restoration. Control the growth rate of economic density,implement rational land use planning, and mitigate industrial impacts on vegetation.Plan urban construction thoughtfully, prioritizing the protection of natural landscapesand ecological environments, strengthening forestland protection, and improving forestrymanagement systems.Downstream: Urban Development with Ecological FocusFocus on ecological restoration projects in downstream areas, including wetland pro-tection, riverbank greening and urban forest and green space development. Implement mea-sures to control pollution and promote regional ecological protection and socio-economicdevelopment in human settlement areas. By incorporating these recommendations, theYRB can strike a balance between economic development and ecological preservation,fostering sustainability and resilience in the face of environmental challenges.Themes such as “global forest coverage and changes”, “global land degradationsituation”, and “global major natural disasters and their impacts” have become focal pointsin the ecological discourse, with the coordinated development of green ecology and theeconomy emerging as a shared objective for nations worldwide. Since the onset of the 21stcentury, there has been a slight decline in the global forest coverage area, prompting othercountries to consider emulating China’s green ecological policies. Firstly, countries shouldprioritize the protection and restoration of the ecological environment, striving to enhanceand sustain forest vegetation coverage to contribute significantly to maintaining the globalforest coverage equilibrium. Secondly, adopting China’s ethos of “Lucid waters and lushmountains are Invaluable Asset” is essential, wherein economic development is pursuedalongside the preservation of green ecology. Improving global forest vegetation coveragenot only mitigates land degradation but also helps mitigate the impacts of climate change.Author Contributions: Conceptualization, X.Z. and Y.D.; methodology, Q.W. and B.W.; validation, G.W.;formal analysis, X.Y. and B.W.; writing—original draft preparation, B.W. and X.Y.; writing—review andediting, Y.D., Q.W. and X.Z.; visualization, Y.L.; supervision, G.W.; funding acquisition, X.Z. Allauthors have read and agreed to the published version of the manuscript.Land 2024, 13, 475 18 of 22Funding: This research was funded by the Fundamental Research Funds of CAF, Theoretical Frame-work and Method for Accurate Estimation of the Full Life-Cycle Carbon Sink Potential in Masson PineArtificial Monoculture Forests (CAFYBB2023MB019), China Scholarship Council Chinese GovernmentScholarship (202203270009), and the Fundamental Research Funds of CAF, Precision Estimation Studyof Carbon Sink Potential in Deciduous Forests in the Yellow River Basin (CAFYBB2023PA006-02).Informed Consent Statement: This article does not contain any studies with human participants oranimals performed by any of the authors. Informed consent was obtained from all the individualparticipants included in the study.Data Availability Statement: The data presented in this study are available upon request from thecorresponding author.Acknowledgments: Thank you to the anonymous reviewer and editor.Conflicts of Interest: The authors declare no conflicts of interest.Appendix ABasin Province City CountryUpperreachesQinghaiXiningChengdong, Central, Chengxi, Chengbei, Huangzhong, Datong Hui andTu Autonomous, HuangyuanHaidongLedu, Ping An, Minhe Hui and Tu Autonomous, Huzhu Tu Autonomous,Hualong Hui Autonomous, Xunhua Salar AutonomousHaibei TibetanAutonomous PrefectureMenyuan Hui Autonomous, Qilian, Haiyan, Gangcha, Tongren, Jainca,Zeku, Henan Mongol Autonomous, Gonghe, Tongde, Guide, Xinghai,Guinan, Maqin, Banma, Gande, Dari, Jiuzhi, Maduo, Chengduo,Qumalai, Dulan, TianjunSichuanAba Tibetan and QiangAutonomous PrefectureMalcolm, Songpan, Aba, Ruoergai, HongyuanGanzi TibetanAutonomous PrefectureShiquGansuLanzhou Chengguan, Qilihe, Xigu, Anning, Honggu, Yongdeng, Gaolan, YuzhongBaiyin Baiyin, Pingchuan, Jingyuan, Huining, JingtaiTianshuiQinzhou, Maiji, Qingshui, Qin’an, Gangu, Wushan, Zhangjiachuan HuiAutonomousWuwei Gulang, Tianzhu Tibetan AutonomousZhangYe ShandanPingliang Kongtong, Jingchuan, Lingtai, Chongxin, Huating, Zhuanglang, JingningQingyang Xifeng, Qingcheng, Huan, Huachi, Heshui, Zhengning, Ning, ZhenyuanDingxi Anding, Tongwei, Longxi, Weiyuan, Lintao, Zhang, MinLongnan Dangchang, LiLinxia Hui AutonomousPrefectureLinxia (a), Linxia (b), Kangle, Yongjing, Guanghe, Hezheng, DongxiangAutonomous Condado, Jishishan Bao’an Dongxiang Salar AutonomousGannan TibetanAutonomous PrefectureHezuo, Lintan, Zhuoni, Diebu, Maqu, Luqu, XiaheNingxia HuiAutonomousRegionYinchuan Xingqing, Xixia, Jinfeng, Yongning, Helan, LingwuShizuishan Dawukou, Huinong, PingluoWuzhong Litong, Hongsibao, Yanchi, Tongxin, QingtongxiaGuyuan Yuanzhou, Xiji, Longde, Jingyuan, PengyangZhongwei Shapotou, Zhongning, HaiyuanLand 2024, 13, 475 19 of 22Basin Province City CountryUpperreachesInnerMongoliaAutonomousRegionHohhotXincheng, Huimin, Yuquan, Saihan, Tumote Left Banne, Tokto, Helingeer,Qingshuihe, WuchuanBaotouDonghe, Kundulun, Qingshan, Shiguai, Bayan Obo, Jiuyuan, TumedRight Banner, Guyang, Darhamo Ming’an United BannerWuhai Haibowan, Hainan, WudaOrdosDongsheng, Kangbashi, Dalad Banner, Jungar Banner, Otog Front Banner,Etuoke Banner, Hangjin Banner, Wushen Banner, Ejin Horo BannerBayan NurLinhe, Wuyuan, Dengkou, Urat Front Banner, Urad Middle Banner, UratRear Banner, Hanggin Rear BannerUlanqab Zhuozi, Liangcheng, Chahar Right Middle Banner, Siziwang BannerAlxa Alxa Left BannerMiddlereachesShaanxiXi’anXincheng, Beilin, Lianhu, Baqiao, Weiyang, Yanta, Yanliang, Lintong,Chang’an, Gaoling, Huayi, Lantian, ZhouzhiTongchuan Wangyi, Yintai, Yaozhou, YijunBaojiWeibin, Jintai, Chencang, Fengxiang, Qishan, Fufeng, Mei, Long,Qianyang, Linyou, Feng, TaibaiXianyangQindu, Yangling, Weicheng, Sanyuan, Jingyang, Qian, Liquan, Yongshou,Changwu, Xunyi, Chunhua, Wugong, Xingping, BinzhouWeinanLinwei, Huazhou, Tongguan, Dali, Heyang, Chengcheng, Pucheng,Baishui, Fuping, Hancheng, HuayinYan’anBaota, Ansai, Yanchang, Yanchuan, Zhidan, Wuqi, Ganquan, Fu,Luochuan, Yichuan, Huanglong, HuangLing, ZichangHanzhong FopingYulinYuyang, Hengshan, Fugu, Jingbian, Dingbian, Suide, Mizhi, Jia, Wubao,Qingjian, Zizhou, ShenmuAnkang NingshanShangluo Shangzhou, Luonan, Danfeng, ZhashuiShanxiTaiyuanXiaodian, Yingze, Xinghualing, Jiancaoping, Wanbailin, Jinyuan, Qingxu,Yangqu, Loufan, GujiaoDatong ZuoyunYangquan urban area, mining area, suburbs, Pingding, YuChangzhi Shangdang, Tunliu, Zhangzi, Wuxiang, Qin, QinyuanJincheng urban area, Qinshui, Yangcheng, Lingchuan, Zezhou, GaopingShuozhou Shuocheng, Pinglu, YouyuJinzhongYuci, Taigu, Yushe, Heshun, Xiyang, Shouyang, Qi, Pingyao, Lingshi,JiexiuYunchengYanhu, Linyi, Wanrong, Wenxi, Jishan, Xinjiang, Jiangxian, Yuanqu, Xia,Pinglu, Ruicheng, Yongji, HejinXinzhouXinfu, Ningwu, Jingle, Shenchi, Wuzhai, Kelan, Hequ, Baode, Pianguan,YuanpingLinfenYaodu, Quwo, Yicheng, Xiangfen, Hongdong, Gu, Anze, Fushan, Ji,Xiangning, Daning, Xi, Yonghe, Pu, Fenxi, Houma, HuozhoulvliangLishi, Wenshui, Jiaocheng, Xing, Lin, Liulin, Shilou, Lan, Fangshan,Zhongyang, Jiaokou, Xiaoyi, FenyangHenanZhengzhou Jinshui, Shangjie, Huiji, Zhongmou, Gongyi, Xingyang, Xinmi, DengfengLuoyangOld Town, Xigong, Chanhe Hui Autonomous Region, Jianxi, Jili,Luolong, Mengjin, Xin’an, Luanchuan, Song, Ruyang, Yiyang, Luoning,Yichuan, YanshiPingdingshan RuzhouAnyang Hua, NeihuangHebi XunJiaozuo Bo’ai, Wuzhi, Wen, Qinyang, MengzhouSanmenxia Hubin, Shaanzhou, Mianchi, Lushi, Yima, LingbaoNanyang XixiaJiyuan JiyuanLand 2024, 13, 475 20 of 22Basin Province City CountryLowerreachesHenanKaifeng Longting, Shunhe Hui, Gulou, Yuwangtai, Xiangfu, LankaoXinxiangHongqi, Xinxiang, Huojia, Yuanyang, Yanjin, Fengqiu, Weihui,ChangyuanPuyang Hualong, Fan, Taiqian, PuyangShandongJinanLixia, Shizhong, Huaiyin, Tianqiao, Licheng, Changqing, Zhangqiu,Jiyang, Laiwu, Gangcheng, PingyinZibo Zichuan, Boshan, Gaoqing, YiyuanDongying Dongying, Hekou, Kenli, LijinJining Wenshang, Sishui, Liangshan, QufuTai’an Taishan, Daiyue, Ningyang, Dongping, Xintai, FeichengLinyi Pingyi, MengyinDezhou QiheBinzhou Bincheng, Huimin, Boxing, ZoupingHeze Mudan, Yuncheng, Juancheng, DongmingReferences1. 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Vancouver : University of British Columbia Library
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economic density
vegetation cover
spatio-temporal pattern
two-way fixed effects model
the Yellow River Basin
Spatio-Temporal Dynamics of Economic Density and Vegetation Cover in the Yellow River Basin: Unraveling Interconnections
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