Ecological Indicators最新文献

Lead contamination remains a persistent and insidious threat, particularly affecting young children and vulnerable communities. This study aims to develop a comprehensive sources-based lead exposure index map for New Jersey municipalities by integrating diverse lead contamination data, including lead-based paint, lead service lines, proximity to superfund and brownfield sites, road density, and gas stations, analyzing how municipality socioeconomic and land use factors impact lead exposure. The research employs Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP) to create two distinct multi-criteria lead exposure indices. The indices are mapped to understand the spatial distribution and magnitude of lead exposure risks and are further examined through spatial regression models to explore the relationship between lead exposure and community characteristics. Both indices identified high lead exposure risk areas in the northeastern region of New Jersey, an area that is historically heavily industrialized. Regression results indicate a significant correlation between lead exposure and socioeconomic factors, particularly in areas with higher concentration of ethnic minority populations. Though other factors exhibit varied relationships with the indices, the study highlights the importance of combining empirical data with expert knowledge to develop effective lead mitigation strategies. By identifying lead exposure hotspots and understanding their underlying causes, the findings provide valuable insights for policymakers and public health officials to prioritize interventions and allocate resources effectively, ultimately aiming to reduce the adverse health effects of lead exposure in New Jersey.

Previous optimization approaches with regard to the overall urban form can no longer meet the demand for accurately implementing building energy consumption carbon emission reduction at small spatial scales. This study constructed building energy consumption-oriented urban functional area division method using points of point-of-interest (POI), three-dimensional building and roadway data with urban functional areas as units. The method linked POI to buildings, using building volume as an auxiliary parameter to determine the functional attributes of blocks while taking into account the building operational energy consumption. The residential functional areas, commercial functional areas and industrial functional areas, which account for more than 90% of the total building operation energy consumption and the top three in terms of share, are selected as the analysis objects. Geographic detector was applied to analyze the mechanism of carbon metabolism in the compact spatial patterns of these three functional areas. The case study in Xiamen, China reveals that (1) Three-dimensional building compactness is an important factor influencing building energy consumption(normalized difference vegetation index q-value is 0.227); (2) the difference in energy consumption between different compactness classes in residential and commercial functional areas is significant, while not in industrial functional areas; (3) according to the three-dimensional building compactness optimization path of this study, the building operation energy consumption in central Xiamen City could be optimized to reduce by 7% in 2015. Based on the self-created building energy consumption-oriented functional area division method, it is concluded at the functional areas scale that different types of urban functional zones have different optimization methods for three-dimensional building compactness. Such optimization can save construction resources and achieve double carbon more effectively.

A crucial strategic choice for China’s sustainable growth in the new era is the carbon peak & carbon neutral (30–60) aim, which has set new and higher requirements for the green and sustainable development of cities in the new era, and the carbon peak & carbon neutral level is gradually becoming one of the important indicators of the green and sustainable development of cities. Based on disentangling the connotation of urban green development under the vision of carbon neutrality, this study constructed a framework system for assessing urban green development in the new era, measured and portrayed the spatial and temporal variation characteristics of green development efficiency (C-GDE) under the carbon neutrality constraint in 284 prefecture-level and above cities in China using the SBM-Undesirable model, and further explored the effect of multi-subject factors on urban C-GDE using the panel Tobit model. The main findings are as follows. (1) The average allocation level of C-GDE in Chinese cities has remained at a low level from 2009 to 2019, with an overall “V”-shaped trend of “down → up”, increasing from 0.4031 to 0.5206, an overall increase of 29.15 %. (2) The overall C-GDE of Chinese cities showed the decreasing characteristics of “western cities > eastern cities > central cities”, “super cities > small cities > medium cities > large cities > mega cities > mega cities”, “non class cluster cities > class II cities > class III cities > class I cities”, and “agriculture-led cities > industry-led cities > balanced development cities > business and tourism service cities”. (3) The spatio-temporal pattern of urban C-GDE was influenced by multiple factors such as individual-level factors, enterprise-level factors, government-level factors, and other basic-level characteristic factors. Each factor, through its own evolution or coupling with other factors, continuously shaped the internal development of the urban system and promotes the greening process and urban C-GDE. Finally, targeted recommendations were made based on the findings of the study.

Ecological control line (ECL) has become an important policy for enhancing ecological conservation and achieving sustainable urban development. Landscape connectivity of ecological network provides a method for exploring the effect of ECL policy on biodiversity conservation. This study used Shenzhen as an example to analyze the distribution of important habitats based on species occurrence points, environmental factors and artificial neural network methods. Four-phase ecological networks of focal species (Ardea cinerea, Callosciurus erythraeus, Copsychus saularis, Egretta garzetta, Pycnonotus sinensis) in 2000, 2010, 2015, and 2020 were constructed, and the effects and changes of ECL on habitat connectivity of species, geographical zone, and species zone scales were sequently analyzed using the difference-in-difference method. The results showed that: (1) Forty-one important habitats were identified, with a total area of 743 km², and the average area of each habitat was 18.1 km². The number of ecological corridors and the area of ecological pinch points in Shenzhen decreased in the first ten years but remained stable over the final ten years. (2) ECL delineation can promote habitat connectivity of regional species and with the passage of time, this promoting effect increases. The protective effect in the high habitat quality zone was greater than that in the low habitat quality zone. (3) City managers can develop habitat connectivity conservation schemes for different species according to the five habitat quality zones: high, mid-high, middle, mid-low, and low. This study proposes a method to assess the effectiveness of the existing ecological control line, and provide a scientific basis for formulating, adusting and optimizing ecological management.

Typhoon disasters are the most frequent and severe natural disasters in China’s southeastern coastal region. The strong wind and rainstorms during typhoons result in secondary disasters, such as storm surges, floods, and landslides. This phenomenon is referred to as a typhoon disaster chain that causes significant loss of life and property every year. Accurately assessing the disaster mitigation capacity and reducing limitations are crucial for improving typhoon disaster risk prevention. However, assessing the capacity of mitigating typhoon disaster chains requires further study. In this study, we select Fujian Province, China, as a case study to identify typical typhoon disaster chains, drawing on historical disasters and the sensitivity of the disaster-forming environment. We establish a county-based framework to assess the disaster mitigation capabilities for typhoon disaster chains. The mitigation capacities of 84 counties within Fujian Province are evaluated using the Random Forest (RF) algorithm, which determines the weights of various indicators. The results show the following. 1) The dominant types of typhoon disaster chains in Fujian Province are typhoon-rainstorm-urban waterlogging (TRU), typhoon-rainstorm-flooding (TRF), typhoon-rainstorm-landslide (TRL), and typhoon-strong wind-storm surge (TWS). Significant spatial differences are observed. 2) The assessment framework, which includes disaster prevention, disaster relief, and government management capabilities, accurately reflects the spatial differences in the disaster chain. The results can be extended to other regions or other disaster chains. 3) Significant spatial heterogeneity is observed in the disaster prevention, relief, and disaster mitigation capacity for typhoon disaster chains in the counties in Fujian Province. The eastern coastal areas have high mitigation capacity, whereas the northwest has low capacity. The disaster prevention capacity is very high in Changle City and Xiuyu District (2.4 %), and the disaster relief capacity is very high in Gulou District, Taijiang District, Changle City, and Huli District (4.8 %). The government disaster management capacity is very high in 7 counties (e.g., Longhai City, Minhou County, and Hui’an City (8.3 %)). The comprehensive disaster mitigation capabilities are very high in Changle City, Longhai City, and Xiuyu County (3.6 %). This study provides a scientific reference for assessing disaster chain mitigation capabilities and enhancing grassroots disaster mitigation efforts.

Amidst escalating food insecurity and climate change threats, which exacerbate food shortages and increase agricultural emissions, this paper explores transformative strategies in cereal production within the BRICS countries from 1990 to 2021. The uncontrolled growth of intensive agriculture, aimed at satisfying the growing global demand for food in a context already threatened by climate change, has led to a uniformity of crops with devastating impacts on biodiversity and ecosystem functioning, resulting in a transformation of soil and its capacity to implement ecosystem services, such as food, fiber, and raw material production, nutrient recycling, carbon sequestration, clean water availability, and the regulation of water regimes and local temperatures. These changes have had negative consequences on agricultural production. Thus, sustainable agriculture faces three closely related challenges: reducing environmental impact, in-creasing productivity, and adapting to and mitigating climate change. This analysis utilizes advanced econometric tools such as panel second-generation unit root tests, Westerlund’s cointegration test, second-generation long-run estimators, and the Dumitrescu-Hurlin causality test, together with several machine learning algorithms, to investigate the influence of technological innovations and improved land management on cereal yields. The findings demonstrate a positive correlation between technological advancements, enhanced land management for cereal cultivation, and the food production index with increased cereal output. At the same time, emissions from agriculture significantly reduce yields over time. Furthermore, an interaction analysis reveals that the comprehensive integration of these factors significantly boosts cereal productivity. The study also identifies directional causal relationships between technological and emission factors and cereal production, suggesting a complex interplay with land use. Sustainable land use is one of the key conditions for ensuring the ecological resilience of agricultural practices in terms of providing ecosystem services. Implementing these strategies calls for a collaborative approach among governments, policymakers, farmers, researchers, and other stakeholders, considering each BRICS nation’s unique environmental, socio-economic, and local contexts, and fostering regional cooperation to promote sustainable agricultural practices.

Improving nitrogen (N) management are crucial for food security and ecological protection in China and globally. Assessing the contribution and influence characteristics of N input components on crop growth is a key component. There are challenges in conducting such assessments at large regional scales, particularly in developing models that fit regional N inputs and crop N uptake. An improved scheme that fit regional N inputs and crop N uptake with consideration of spatial heterogeneity of cultivation conditions was proposed. Based on the division of homogeneous cultivation conditions zones, linear models and Random Forest models were developed to assess the contribution and influence characteristics of N input components on crop growth at the provincial scale in mainland China. And synthetic N contribution rate (SNCR) and soil fertility N contribution (SFNC) were innovatively proposed to represent the contribution of fertilizer and soil fertility to crop growth. The results showed that the overall N use efficiency ranged from 35 % to 55 % in 1985–2020, and higher NUE could be seen in major grain producing provinces. The SNCR generally declined, with a spatial pattern higher in the southwest, northwest, and northeast regions. The SFNC showed an increasing trend, with a spatial pattern higher in the northeast than southeast than west. For each N input component, Synthetic N input was critical in the northwest, southwest regions and north China, positively affecting crop N uptake. The improved scheme, which incorporated considerations for spatial heterogeneity, demonstrated superior accuracy compared to the model fitted by year, increasing from 0.27 (0.11) to 0.42 (0.47). Over the past 35 years, the crop N uptake and N surplus of mainland China had experienced a process of extensive-unsustainable-sustainable-conservative pattern changes. The response of crop N uptake and N use efficiency to the regulation of N inputs in typical regions was simulated, providing reference for provincial N inputs regulation in China. This study can provide support for the design of N management strategies in China to reduce N pollution, and the method can provide guidance for other regions to assess N use in different scales.

Ecosystems are vulnerable to accelerated climate change, population pressure, and urbanization, so ecological vulnerability assessments and impact analyses are essential if regional development is to be managed sustainably. This study develops a novel assessment framework based on resilience-sensitivity. Various techniques—including the landscape index, the ecological elasticity limit model, and the soil erosion equation—are incorporated to examine spatiotemporal variation characteristics across diverse ecological systems of China’s Jiangsu province through the decade 2010–2020, to reveal mechanisms by which various factors influence vulnerability. We arrived at a stable average yearly ecological vulnerability index value of 0.56–0.57, and found high vulnerability concentrated in cities and townships, areas along the Yangtze River, and the province’s northern coast. Results also show that vulnerability is affected more by ecological sensitivity than by ecological resilience. Stepping up from one ecological sensitivity class to another has the greatest impact on the probability of an upward step in ecological vulnerability class, accounting for 16.2% of the total area undergoing such a change. Urbanization rates and differences in human-induced landscape composition were the main factors, and their cumulative contribution to ecological vulnerability variations ranged from 59.3% to 65.8%. We found our proposed framework capable of enhancing the strength and applicability of ecological vulnerability assessments, specifically in areas with extensive river and lake networks. The framework could be used in assessments throughout China, and very likely beyond.

Actively addressing the negative effects of global climate change on vegetation has always been a hot topic of academic concern. How to accurately and comprehensively assess the vegetation risk due to climate change has been rarely reported. By comprehensively considering three dimensions—species structure (species richness), carbon sequestration function (Net Primary Production, NPP), and physiological processes (transpiration)—and based on the prediction of the future distribution characteristics of 3,370 plant species in China, this study quantified the vegetation risk and its driving mechanisms under different Shared Socioeconomic Pathways. The composite index of vegetation risk decreased to the west of the Hu Huanyong Line (NT and QTP regions) but increased to the east (NE and ST regions), with the magnitude of increase growing with the intensification of emission scenarios. In the 2070 s, the proportion of high risk and extremely high-risk areas in the east increased from 14.5 % under SSP126 to 50.0 % under SSP585. NPP and transpiration generally show an increasing trend, and species richness changes similarly to vegetation risk. In the 2070 s under SSP585, 39.2 % of QTP areas see a species richness increase over 50 %, while 33.0 % of ST areas experience a decrease over 30 %. The increase in vegetation risk in the NE region is driven by increased soil moisture, while in the ST region, it is mainly due to decreased runoff and SPEI. Therefore, China should actively respond to the risk of vegetation degradation in the east due to future climate change.

Increasing carbon sequestration (CS) in soils and biomass is an important land-based solution in mitigating global warming. Ecosystems provide a wide range of ecosystem services (ESs). The necessity to augment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to evaluate the destabilization risk resulting from alterations in ecosystem service relationships dominated by CS. We selected Northeastern China as study area to estimate six ESs and identified areas of destabilization risk among the three services most relevant to CS, including food production (FP), soil retention (SR), and habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and introduced the Shapley additive interpretation (SHAP) method for driving mechanism analysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of CS, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destabilization risk. The study has potential practical value for destabilization risks prevention, while also providing a scientific basis for formulating comprehensive carbon management policies and maintaining ecosystem stability.