环境科学最新文献

{"title":"[Impact of Digital Financial Inclusion on Agricultural Carbon Productivity].","authors":"Yun Tian, Hui-Ting Li, Rui Xia","doi":"10.13227/j.hjkx.202508157","DOIUrl":"https://doi.org/10.13227/j.hjkx.202508157","url":null,"abstract":"<p><p>Empirically examining the nexus between digital financial inclusion and agricultural carbon productivity provides critical insights for synergizing low-carbon agricultural transformation with China's \"dual carbon\" strategic objectives. Leveraging a provincial panel dataset （2011-2022， <i>N</i>=30）， this study empirically tested the influence of digital financial inclusion on agricultural carbon productivity based on clarifying the characteristics of agricultural carbon productivity in Chinese provinces. The findings reveal that： ① There were obvious differences in agricultural carbon productivity among provinces， among which Shandong was the highest， and Qinghai was the lowest. During the investigation period， the agricultural carbon productivity of the remaining 29 provinces except Shanghai showed an obvious upward trend. ② The development of digital inclusive finance had a significant positive impact on the improvement of agricultural carbon productivity， that is， the higher the level of digital inclusive finance， the higher the agricultural carbon productivity. ③ Dimensionally， financial usage depth exhibited a statistically significant positive effect on agricultural carbon productivity， while neither coverage breadth nor digitization level demonstrated significant associations. In terms of regional heterogeneity， digital financial inclusion in the central， western， and non-major grain-producing regions had a significant positive impact on agricultural carbon productivity， while it had no significant impact in the eastern region and major grain-producing regions. ④ The analysis of spatial spillover effects indicated that agricultural carbon productivity within a region was simultaneously influenced by the development of digital financial inclusion in both the local province and neighboring provinces， demonstrating a positive spatial spillover effect.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6014-6024"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatio-temporal Evolution and Driving Mechanism of Carbon Storage in Honghe River Basin of Yunnan Province Based on PLUS-InVEST-Geodetector Model].","authors":"Chen-Ting Zhao, De-Jin Wang, Jing Chen, Yi-Fan Cao, Jin-Kai He","doi":"10.13227/j.hjkx.202506234","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506234","url":null,"abstract":"<p><p>Exploring the impact of land use on regional carbon storage and its spatial driving factors is of great significance for enhancing regional ecosystem carbon sinks and optimizing territorial space. This study takes the Honghe River Basin in Yunnan Province as an example， couples the PLUS-InVEST-Geodector model to deeply investigate the spatial evolution patterns of land use and carbon storage and their spatial driving causes from 2000 to 2020， and predicts and analyzes the evolution trends of land use and carbon storage in 2040 under four scenarios （natural development， farmland protection， carbon sink enhancement， and water resource protection）. The results are as follows： ① In the Honghe River Basin of Yunnan Province， the land use was dominated by cultivated land， forest， and grassland， accounting for approximately 17%， 62%， and 20%， respectively. The period from 2000 to 2010 saw the most intense land use change， with a total transition area of 3 410.52 km². ② From 2000 to 2020， the carbon storage in the Honghe River Basin of Yunnan Province generally presented a pattern of \"high in the west and low in the east and middle，\" with a cumulative loss of 4.28×10⁶ t. ③ Under the natural development scenario and the cultivated land protection scenario， carbon storage amounted to 1 483.75×10⁶ t and 1 480.60×10⁶ t， respectively， decreasing by 4.71×10⁶ t and 7.86×10⁶ t compared to the 2020 level. In contrast， the carbon sink enhancement scenario and the water resource protection scenario resulted in carbon storage of 1 488.68×10⁶ t and 1 489.62×10⁶ t， representing an increase of 0.22×10⁶ t and 1.16×10⁶ t relative to that in 2020. ④ NDVI （0.108 5）， soil erosion intensity （0.068 5）， and soil type （0.058 2） were the dominant factors influencing the spatial heterogeneity of carbon storage. The interaction effects between any two factors were stronger than those of individual factors， with the most significant synergistic effect observed for the interaction between NDVI and soil type （0.147 3）.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5863-5876"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Characteristics of Spatial and Temporal Evolution of Vegetation Cover and Analysis of Driving Factors in Different Climate Zones in China from 1982 to 2022].","authors":"Yan-Wei Zhang, Jia-Yuan Wang, Ming-Chang Shi, Zhi-Dong Luo, Ze-Kun Zhao, Chu-Rui Zhang, An-Qi Wu","doi":"10.13227/j.hjkx.202506274","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506274","url":null,"abstract":"<p><p>In order to maintain the ecological environment and prevent soil erosion， it is of great significance to investigate the spatiotemporal dynamics of vegetation cover across different regions of China as well as the topographical effects of such dynamics. This study calculates vegetation cover from 1982 to 2022 across China by using AVHRR GIMMS-3G+ NDVI. Utilizing such methods as the Theil-Sen-Mann-Kendall test， Hurst index， CV test， and geographic detector， this study assesses the spatiotemporal evolution patterns of FVC in China's four climate regions， as well as the driving mechanisms of such patterns. It was found that： ① From 1982 to 2022， China's annual average FVC exhibited a spatial distribution pattern of \"high in the southeast and low in the northwest.\" With a multi-year average FVC value of 0.317 7， an overall fluctuating upward trend was shown， and the temperate monsoon climate zone made the most contribution. ② From 1982 to 2022， FVC across China generally maintained high stability， with an average CV value of 0.061 2. Areas with high vegetation coverage typically exhibited higher stability. The Hurst index indicated that the vegetation coverage in China is expected to manifest an overall positive trend in the future， but the areas undergoing continuous degradation will still account for 8.88%. ③ The primary single factors driving the spatial distribution patterns of FVC across China included precipitation， soil moisture， and temperature， with significance coefficients of 0.769 2， 0.558 1， and 0.436 4， respectively. Ecological engineering has reversed land degradation to some extent； nevertheless， extreme climate cycles may still overtake the ecosystem's recovery threshold. ④ The coupled effects between precipitation and soil moisture versus other factors were the primary drivers of vegetation growth in China. In the temperate monsoon climate zone of China， as the primary regions for such ecological engineering projects as the Three-North Shelterbelt Forest and the Beijing-Tianjin-Hebei Sandstorm Source Control Project， it has been demonstrated that human intervention could significantly enhance vegetation coverage. In the high-altitude and frigid Qinghai-Tibet Plateau climate zone， human activities have disrupted the regional microclimate water cycle structure along major rivers， particularly in ecologically fragile areas such as the Yarlung Tsangpo River Basin， thus further hindering the ecological recovery capacity. This study provides theoretical support for formulating regionally differentiated ecological recovery strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6137-6150"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial Correlation Network and Driving Factors of Tourism Ecological Resilience in the Yangtze River Delta Urban Agglomeration].","authors":"Jin-Hui Zheng, Wei-Qing Hu, Wei-Xiang Xu, Xu-Shuang Li, Xu-Nuo Lu","doi":"10.13227/j.hjkx.202507030","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507030","url":null,"abstract":"<p><p>Based on the framework of resilience theory， the level of tourism ecosystem resilience is systematically evaluated， and the structural characteristics and driving factors of its spatial correlation network are revealed， which is of great significance for practicing the concept of green development and promoting high-quality development of the tourism industry. The level of tourism ecological resilience in the Yangtze River Delta urban agglomeration from 2008 to 2022 is measured using a comprehensive level evaluation model， and its spatial network characteristics and driving factors are revealed by using the modified gravity model， core-edge analysis method， spatiotemporal geographic weighted regression， spatial econometric， and threshold model. The results show that： ① The tourism ecological resilience of the Yangtze River Delta urban agglomeration presented the spatial correlation characteristics of \"local tightness and overall looseness.\" The connection between the \"Shanghai-Nanjing-Hangzhou-Suzhou\" sub-network and other nodes was polarized， and the first connection nodes mostly appeared in core cities， showing the core-edge characteristics of the three-layered. ② Factors such as economic development， industrial structure， transportation infrastructure， human capital， opening up to the outside world， and the opening of high-speed railways significantly drove the spatial correlation of tourism ecological resilience and showed obvious belt-like or sheet-like distribution patterns. ③ The impacts of industrial structure， transportation infrastructure， opening up to the outside world， and high-speed rail opening on the spatial correlation of tourism ecological resilience had spatial spillover effects. In addition， the impacts of urbanization and marketization on the spatial correlation of tourism ecological resilience had threshold effects， showing a nonlinear characteristic of increasing marginal effects.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6304-6316"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Scenario Simulation of Carbon Emissions and Emission Reduction Pathways in Industry-dominated Cities： A Case Study of Lanzhou].","authors":"Yan Han, Yuan-Li Zhou, Shi-Hui Wang","doi":"10.13227/j.hjkx.202506302","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506302","url":null,"abstract":"<p><p>A carbon emission prediction model for Lanzhou's industries was established based on the long-term energy alternatives planning system （LEAP）， and the evolution paths of carbon emissions under different policy scenarios from 2023 to 2060 were systematically analyzed. Taking 2023 as the base year， four policy scenarios were set： the base scenario， the industrial structure optimization scenario， the energy transition scenario， and the blueprint scenario. Through quantitative simulation， the feasible paths for Lanzhou to achieve its carbon peaking and carbon neutrality goals were revealed. The results show that in the base scenario without policy intervention， Lanzhou's energy consumption will continue to grow， increasing from 34.3×10⁶ t in 2023 to 59.7×10⁶ t in 2060， and carbon emissions will rise from 64.4×10⁶ t to 81×10⁶ t， making it difficult to achieve the expected carbon peaking target. After implementing emission reduction policies， the industrial structure optimization， energy transition， and blueprint scenarios will peak in 2029， with peak values of 69×10⁶ t， 72.9×10⁶ t， and 68.2×10⁶ t， respectively. The results of the emission reduction contribution rate showed that industrial structure optimization contributed 40% of the emission reduction in the short term， but the long-term contribution rate dropped to 22%； the contribution rate of energy structure transformation steadily increased from 11% to 30%； and the blueprint scenario maintained an emission reduction contribution rate of 48%-49% through policy coordination， highlighting the effectiveness of multi-dimensional emission reduction strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5988-5999"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial-temporal Evolution of Land-use Carbon Emissions and Differentiated Carbon Compensation in the Chengdu-Chongqing Economic Circle].","authors":"Yuan-Jie Deng, Hang Chen, Lu Ming, Yi-Feng Hai, Ai-Ting Ma, Mei Zhang, Ze-Ding-di Ji, Ming-Hong Peng","doi":"10.13227/j.hjkx.202507068","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507068","url":null,"abstract":"<p><p>Understanding the spatial-temporal heterogeneity of land-use carbon emissions and corresponding carbon compensation mechanisms is essential for optimizing regional low-carbon development pathways and achieving carbon peak and neutrality targets. This study investigates 144 counties within the Chengdu-Chongqing Economic Circle （CCEC） during 2000-2022 by establishing a county-level carbon accounting framework and a compensation valuation model based on land use， agricultural production， energy consumption， and socioeconomic data. The results indicate that： ① Total carbon emissions increased sharply from 4.95×10⁷ t in 2000 to a peak of 1.20×10⁸ t in 2010 before declining to 1.04×10⁸ t in 2022. High-emission zones were concentrated in the core areas of Chengdu and Chongqing， while the Qinba Mountains and western Sichuan served as major carbon sinks. Total carbon sequestration reached 5.79×10⁷ t in 2022， representing an increase of 9.96×10⁵ t compared with that in 2000. ② Carbon compensation exhibited a pronounced \"core-periphery\" gradient： Urban cores， exemplified by Wuhou District with emissions of 5.14×10⁶ t in 2022， were the primary compensation payers， whereas ecological and agricultural regions benefitted from strong sequestration capacity. Xuanhan County， with a sequestration volume of 1.6×10⁶ t， ranked among the leading compensation recipients. ③ A strong spatial coupling existed between carbon emissions， economic development， and carbon compensation. Although carbon intensity declined in economically developed areas， absolute emissions remained high， whereas ecological function zones maintained stable carbon sink capacities owing to low human disturbance and relatively stable land use. This imbalance intensified the spatial mismatch between emission responsibilities and compensation capacities. The findings provide robust scientific evidence for implementing targeted emission reduction policies and refining differentiated carbon compensation mechanisms in the CCEC.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5925-5935"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Impact of Artificial Intelligence on Synergistic Pollution and Carbon Reduction].","authors":"De-Shan Li, Zi-Xuan Jia","doi":"10.13227/j.hjkx.202510240","DOIUrl":"https://doi.org/10.13227/j.hjkx.202510240","url":null,"abstract":"<p><p>With the rise of next-generation information technologies， artificial intelligence （AI） has demonstrated substantial potential in enhancing environmental governance and promoting green development. Using panel data from 267 prefecture-level cities in China spanning from 2014 to 2023， this study applies a variety of econometric models， including the two-way fixed effects model and spatial Durbin model， to explore the impact of AI on synergistic pollution and carbon reduction and its underlying mechanisms. The results indicate that： ① AI significantly promoted synergistic pollution and carbon reduction， with its impact exhibiting dynamic changes. ②Industrial structure upgrading， green technological innovation， and resource misallocation alleviation were key pathways through which AI drove synergistic pollution and carbon reduction. ③ The effect of AI on synergistic pollution and carbon reduction showed dual-threshold characteristics due to variations in digital infrastructure development. ④ The influence of AI on synergistic pollution and carbon reduction exhibited pronounced spatial spillover effects， which could foster regional synergistic pollution and carbon reduction through technological diffusion. This study not only uncovers the mechanisms through which AI influences synergistic pollution and carbon reduction but also provides empirical evidence for the construction of a cross-regional green collaborative governance system.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6057-6069"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Identification of Driving Mechanisms for the Evolution of Ecological Security Patterns and Multi-scenario Simulation Prediction in Xi'an].","authors":"Ting Liu, Bang-Rui Yue, Long-Jie Yao, Zong-Bin Zhu, Yu Zhang","doi":"10.13227/j.hjkx.202507028","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507028","url":null,"abstract":"<p><p>The ecological security pattern （ESP） is a crucial foundation for regional ecological protection and spatial planning. Existing research mostly focuses on the identification of driving factors of the ESP， emphasizing its evolution mechanism， but there is insufficient discussion on how key factors affect the future pattern evolution and their application value in spatial planning. The study proposes a research idea of shifting from \"explaining the past\" to \"predicting the future.\" Based on a three-dimensional identification framework of \"ecosystem service importance-ecological sensitivity-landscape connectivity，\" the ESP of Xi'an from 2000 to 2020 are constructed. The key driving factors of the evolution of the ESP are identified by using a geographical detector model， ordinary least squares （OLS） regression， and geographically weighted regression （GWR） in a progressive \"qualitative-quantitative-spatial\" analytical sequence. These key driving factors were subsequently integrated into the FLUS model to simulate the future land use patterns and assessed the ecological security response characteristics under three scenarios： business as usual （BAU）， priority economic development （PED）， and priority ecological protection （PEP）. The results show that： ① The spatial pattern of ecological source areas in Xi'an were relatively stable， with fragmented distribution in the northern areas due to urbanization disturbance， while the remaining distribution was relatively concentrated in the southern mountainous regions. The number and length of ecological corridors both decreased， mainly distributed in the central plain and eastern mountainous areas. ② There were six influencing factors that significantly affected the formation of ecological sources， among which elevation， terrain roughness， and slope played a promoting role. Land use type， terrain roughness， and distance to residential areas were the three main driving factors of resistance surface formation， all exhibiting significant positive effects， with spatial effects mainly concentrated in the southern mountainous areas. ③ The scenario simulation results showed that there were significant differences in the land use pattern of Xi'an under different scenarios. Under the PEP scenario， the forest area increased； the landscape diversity， connectivity， and aggregation degree were optimal； the landscape disturbance risk was the lowest； and the ecological security response effect was the best. The study identified the key driving factors of the evolution of Xi'an's ESP and incorporated them into the FLUS model to enhance the scientific validity of the prediction results， providing more targeted decision-making support for ecological restoration and sustainable spatial governance in Xi'an.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6328-6341"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Identification and Improvement Path of Decoupling Type of Urban Sprawl and Ecosystem Service Flow in the Yangtze River Basin， China].","authors":"Li-Juan Ran, Dong-Jie Guan, Li-Lei Zhou, Shi Chen, Yun Lu, Xu-Sen Zhu","doi":"10.13227/j.hjkx.202507210","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507210","url":null,"abstract":"<p><p>The disorderly sprawl of cities leads to the spatial transmission path of ecosystem service flows being cut off， which not only hinders the sustainable development of cities but also causes serious damage to natural ecosystems. Based on the decoupling model of the elastic coefficient， a decoupling measurement model was constructed from five dimensions， namely structural dimension， density dimension， quality dimension， efficiency dimension， and morphological dimension， combined with six ecosystem service flows， namely freshwater supply， landscape aesthetics， food production， climate regulation， water conservation， and carbon sequestration， to identify the decoupling characteristics of urban sprawl and ecosystem service flow in the Yangtze River Basin. Geodetectors and geographically weighted regression models were used to analyze the driving factors， and the sustainable development path of urban sprawl and ecosystem service flow in the Yangtze River Basin was proposed. The results show that： ① From 2010 to 2020， the degree of urban sprawl in the Yangtze River Basin increased with time in the four dimensions of structure， quality， efficiency， and form and decreased with time in the density dimension. From 2010 to 2020， the flow intensity of six ecosystem service flows in the Yangtze River Basin decreased or was blocked with time. ② From 2010 to 2020， the decoupling results of urban sprawl and ecosystem service flow in the Yangtze River Basin were mainly strong negative decoupling and expansive negative decoupling， accounting for 44% and 31% of prefecture-level cities， respectively. ③ The interaction between the quality and efficiency dimensions on ecosystem service flow was the strongest， and the explanatory power of the interaction between the other factors was also more than 30%. It is necessary to propose targeted optimization strategies for different decoupling types to promote the sustainable development of urban and ecosystem service flows in the Yangtze River Basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6365-6376"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatiotemporal Evolution and Driving Force Analysis of Ecological Environment Quality in an Industrial Area].","authors":"Xin-Ran Sun, Chun-Ming Hu, Chang-Jun Zhu, Lin-Lin Luo","doi":"10.13227/j.hjkx.202506349","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506349","url":null,"abstract":"<p><p>Industrialization has significantly affected the regional ecological environment quality. It is of great significance to analyze the spatial and temporal evolution process and identify the driving factors of the industrial area system for its sustainable development. In this study， Landsat 8 remote sensing imageries on GEE were used to construct the remote sensing ecological index （RSEI）. The migration of the gravity center， Geodetector， and the GM（1，1） grey prediction model were applied to analyze the spatiotemporal evolution of ecological environment quality in the study area from 2013 to 2024 under different ecological management strategies. The main driving factors were identified， and the future development trend was predicted， providing a scientific basis for ecological planning and management in industrial areas. The results showed that： ① There was a significant spatial differentiation in the ecological environment quality of the study area. A 26.64% increase was shown in the average RSEI of the Old Factory Area with good management， but its level was still \"relatively poor.\" Due to the large-scale expansion in the Zhonghua Area， fluctuations were observed in the ecological environment quality， and degradation was observed in the western region. After artificial restoration， an increase in the RSEI \"good\" area to more than 20% was shown in 2021. In the Planning-Construction Area， characterized by extensive natural coverage and \"good\" RSEI， management had been absent， leading to emerging signs of ecological degradation observed in recent years. Moreover， a northeastward migration of the RSEI improvement center was exhibited across the study area， while a southwestward shift of deterioration was demonstrated. ② NDBSI， NDVI， and LST were identified as the primary driving factors， with NDBSI exhibiting the strongest driving force （<i>q</i>=0.820）. The explanatory power generated by any two-factor interaction was observed to exceed that of a single factor， and the most significant explanatory effect was demonstrated through the interaction between NDBSI and LST （<i>q</i>=0.881）. ③ Based on the prediction results， the RSEI was projected to reach 0.39 in the Old Factory Area and 0.521 in the Zhonghua Area by 2031. Without intervention， continued degradation was anticipated in the Planning-Construction Area. A methodological framework was constructed in this study to facilitate dynamic monitoring and attribution analysis of industrial area ecosystems， through which ecological evolution and driving mechanisms were revealed under varying ecological managements. These findings provide theoretical support and decision-making references for ecological restoration and green transformation in industrial areas， contributing to the coordinated development of ecological and industrial systems.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6279-6290"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}