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The previous literatures have insufficient content on spatial dependence and heterogeneity of agricultural carbon emissions (ACEs), which is inconsistent with the actual situation, weakening the practical significance of research conclusions. To fill this knowledge gap, this study attempts to explore the spatial evolution pattern of ACEs at the city-scale in the Fujian Province of China from spatio-temporal latitudes and adopts the exploratory spatial data analysis method (ESDA) to analyze the spatial correlation effects of ACEs. The findings revealed that ACEs in Fujian show a downtrend as a whole. From the perspective of carbon sources of ACEs, agricultural materials and livestock breeding caused the largest emissions, accounting for 73.82% of the total ACEs, while rice growth led to the smallest carbon emissions, accounting for 26.18% of the total ACEs. We also found that there is obvious non-equilibrium in the spatial distribution of ACEs and their intensity, showing a strong spatial correlation; and although a relatively obvious clustering area has been formed, the spatial autocorrelation of most regions is not significant. Accordingly, we suggest that exploring the “carbon compensation mechanism”, is conducive to stimulating the low-carbon agricultural production behavior with positive externalities, to reduce agricultural carbon emissions.

To find out the spatial dependence of carbon emissions and its evolution characteristics is the key to achieving regional differential emission reduction strategy. In this study, 30 provinces with different population sizes and in different stages of development in China, were selected to explore the spatial heterogeneity of carbon emissions by exploratory spatial data analysis (ESDA), combined with geographically and temporally weighted regression (GTWR). The findings revealed that (1) energy-related carbon emissions at the province-level in China increased from 1997 to 2016, with an increment of 8,893 million tons; (2) there is a significant positive spatial correlation between provincial carbon emissions, which showed the characteristics of rising first and then falling; this indicated that provincial carbon emissions have obvious spatial dependent characteristics; (3) the tertiary industry ratio had a restraining effect on carbon emissions, whereas the other three variables, namely GDP, urbanization rate, and energy intensity had a positive effect on carbon emissions of provinces in China; and (4) province-scale spatial differences in and distribution patterns of carbon emissions within the same countrywide, which will help decision making in terms of carbon trading and ecological compensation mechanisms. Therefore, we suggested that in the formulation of reduction policies for carbon emissions, policymakers need to adapt to local conditions which accord to the characteristics of the province.

Optical edge detection is a useful method for characterizing boundaries, which is also in the forefront of image processing for object detection. As the field of metamaterials and metasurface is growing fast in an effort to miniaturize optical devices at unprecedented scales, experimental realization of optical edge detection with metamaterials remains a challenge and lags behind theoretical proposals. Here, we propose a mechanism of edge detection based on a Pancharatnam–Berry-phase metasurface. We experimentally demonstrated broadband edge detection using designed dielectric metasurfaces with high optical efficiency. The metasurfaces were fabricated by scanning a focused laser beam inside glass substrate and can be easily integrated with traditional optical components. The proposed edge-detection mechanism may find important applications in image processing, high-contrast microscopy, and realtime object detection on compact optical platforms such as mobile phones and smart cameras.

In many regions and at the planetary scale, human pressures on the environment exceed levels that natural systems can sustain. These pressures are caused by networks of human activities, which often extend across countries and continents due to global trade. This has led to an increasing requirement for methods that enable absolute environmental sustainability assessment (AESA) of anthropogenic systems and which have a basis in life cycle assessment (LCA). Such methods enable the comparison of environmental impacts of products, companies, nations, etc, with an assigned share of environmental carrying capacity for various impact categories. This study is the first systematic review of LCA-based AESA methods and their applications. After developing a framework for LCA-based AESA methods, we identified 45 relevant studies through an initial survey, database searches and citation analysis. We characterized these studies according to their intended application, impact categories, basis of carrying capacity estimates, spatial differentiation of environmental model and principles for assigning carrying capacity. We then characterized all method applications and synthesized their results. Based on this assessment, we present recommendations to practitioners on the selection and use of existing LCA-based AESA methods, as well as ways to perform assessments and communicate results to decision-makers. Furthermore, we identify future research priorities intended to extend coverage of all components of the proposed method framework, improve modeling and increase the applicability of methods.

Assessing changes in the extent and management intensity of land use is crucial to understanding land-system dynamics and their environmental and social outcomes. Yet, changes in the spatial patterns of land management intensity, and thus how they might relate to changes in the extent of land uses, remains unclear for many world regions. We compiled and analyzed high-resolution, spatially-explicit land-use change indicators capturing changes in both the extent and management intensity of cropland, grazing land, forests, and urban areas for all of Europe for the period 1990–2006. Based on these indicators, we identified hotspots of change and explored the spatial concordance of area versus intensity changes. We found a clear East–West divide with regard to agriculture, with stronger cropland declines and lower management intensity in the East compared to the West. Yet, these patterns were not uniform and diverging patterns of intensification in areas highly suitable for farming, and disintensification and cropland contraction in more marginal areas emerged. Despite the moderate overall rates of change, many regions in Europe fell into at least one land-use change hotspot during 1990–2006, often related to a spatial reorganization of land use (i.e., co-occurring area decline and intensification or co-occurring area increase and disintensification). Our analyses highlighted the diverse spatial patterns and heterogeneity of land-use changes in Europe, and the importance of jointly considering changes in the extent and management intensity of land use, as well as feedbacks among land-use sectors. Given this spatial differentiation of land-use change, and thus its environmental impacts, spatially-explicit assessments of land-use dynamics are important for context-specific, regionalized land-use policy making.

The COVID-19 pandemic has severely affected the normal socioeconomic operation of countries worldwide, causing major economic losses and deaths and posing great challenges to the sustainable development of cities that play a leading role in national socioeconomic development. The strength of urban resilience determines the speed of urban social and economic recovery. This paper constructed a comprehensive evaluation index system for urban resilience under the COVID-19 pandemic scenario considering four dimensions—economy, ecology, infrastructure, and social systems—conducted a quantitative evaluation of urban resilience in the Yangtze River Delta of China, revealed its spatiotemporal differences and change trends, and proposed targeted strategies for improving urban resilience. The results show that (1) the Yangtze River Delta urban resilience system is growing stronger every year, but there are significant differences in the level of urban resilience, its spatial distribution and regional urban resilience. (2) In the Yangtze River Delta urban agglomeration, there is less distribution of areas with a higher resilience index, while those with high and medium resilience levels are more distributed. However, the resilience of most cities is low. (3) The resilience index of eastern coastal cities is significantly higher, and the resilience of cities under the COVID-19 scenario presents obvious east–west differentiation. (4) When constructing urban resilience, the individual situation of cities should be taken into account, measures adjusted according to local conditions, reasonable lessons drawn from effective international urban resilience construction, and reasonable planning policies formulated; it is important to give play to the relationship between the whole and the parts of resilience to achieve unified and coordinated development.

PurposeRegionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still face multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provide an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software.MethodsA survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method’s spatial resolution and scale, the spatial resolution of input parameters, the choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and the variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors.Results and discussionSurvey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modeled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated model CFs are not easily available, archetype models are sometimes developed.ConclusionsRegionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency, and robustness in the development and application of regionalized LCIA methods.

Accurate and quantitative assessments of rural development can offer important information for the formulation of rural development strategies. The purpose of this study was to provide a new perspective on the evaluation of rural sustainable development, overcoming the limitations of past studies that were based on simple analyses using a single method. In doing so, we aimed to provide a theoretical reference for formulating differentiated policies for regional rural development. In the present study, the rural development levels of 31 provincial administrative regions in China from 2000 to 2020 were analyzed based on the rural revitalization index framework, using five dimensions proposed in a previous study, i.e., industrial prosperity, ecological livability, rural civilization, effective governance, and a rich life. China’s rural sustainable development level was calculated using kernel density estimation and least squares estimation of temporal and spatial analyses. The results revealed that the development levels of rural areas in China are improving, but the improvement is not spatially consistent across rural areas. On the basis of the driving factors and causal mechanism, seven types of rural development levels were identified. We further analyzed the main reasons for the spatial differences in rural development levels and offer suggestions for improvement.

This study examines 357 sports-related intangible cultural heritage (ICH) items in Shandong Province, employing an integrated methodology combining GIS spatial analysis, geodetector modeling, and comparative approaches to investigate their multi-tiered spatial distribution patterns. Through six analytical dimensions—natural ecological environment, sociodemographic conditions, economic development level, regional cultural foundations, transportation accessibility, and policy frameworks—the research systematically evaluates differential influences on spatial configuration. Key findings reveal: (1) Shandong possesses a substantial concentration of sports-related ICH resources with widespread municipal distribution. Among them, a series of results such as the geographic concentration index G = 28.56, Moran’s I 0.94, and Nearest neighbor ratio 0.1 indicate significant clustering characteristics; (2) Macroscale clustering occurs predominantly along the Yellow River Basin and Jiaodong Peninsula coastal zones. (3) Regional high-density clusters emerge in Jinan(1.48–2.03 cores/km 2 ), Heze(1.06–1.31 cores/km 2 ), Zibo(1.18–1.54 cores/km 2 ), and Tsingtao(1.07–1.48 cells/km 2 ), with medium-density clusters spanning Liaocheng, Jining, and Tai’an. (4) Multivariate analysis identifies economic development and sociodemographic factors as primary spatial determinants, with transportation networks and cultural foundations serving as significant contributors. Natural environmental factors and policy interventions exhibit comparatively limited explanatory power. The study proposes targeted recommendations for optimizing spatial distribution patterns and advancing preservation strategies for both Yellow River civilization and regional sports traditions.

Spatial development and landscape pattern are fundamental elements of the land system of village. Analysing the spatial differentiation and coupling relationship between spatial development intensity and landscape pattern is of great significance for the development and protection of village land resources. In order to address the current research lack on the coupling response between village spatial development intensity and landscape pattern, a technical method for analysing the spatial differentiation and coupling relationship between village spatial development intensity and landscape pattern is constructed based on the methods of village spatial development intensity model, landscape pattern index, bivariate spatial autocorrelation model, coupling degree and coupling coordination degree model. Taking 100 villages in Anhui Province, China as an example, the spatial distribution characteristics and coupling characteristics of village spatial development intensity and landscape pattern are analysed. The results show that there are obvious regional differences in the spatial distribution of village spatial development intensity and landscape pattern in Anhui Province. The village spatial development intensity shows a pattern of the Northern Anhui plain region (NAPR) > along the Yangtze River plain region (YRPR) > Jiang-huai Hilly region (JHHR) > Southern Anhui mountainous region (SAMR) > Western Anhui mountainous region (WAMR). The village landscape pattern in NAPR and YRPR are high fragmentation, while the village in JHHR has the lowest fragmentation, and the villages in SAMR and WAMR show relatively low fragmentation. The spatial coupling relationship between village spatial development intensity and landscape pattern is mainly characterised by high-high clustering and low-high clustering. The coupling coordinated development of villages in NAPR is the best, followed by YRPR, JHHR and SAMR, and WAMR is the worst. There is only a significant multi-linear relationship between village landscape pattern and multiple spatial development intensity indicators in WAMR and NAPR. The spatial differentiation and coupling relationship are influenced by both natural geographical factors and human activity factors. Finally, the study puts forward some targeted countermeasures and suggestions. The research results can provide theoretical method and practical application reference for village land space development and protection and village planning.