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Faced with increasingly serious environmental risks, it is necessary to conduct a comprehensive evaluation of the regional environment to provide a solid foundation for environmental policies and actions in the future. This article builds a composite environment risk index that considers spatiotemporal factors and uses annual socio-economic and environmental data of China’s 31 provincial administrative regions from 2004 to 2019 to quantitatively analyze environmental risks. Furthermore, the article employs a panel data model to empirically test the key factors that lead to environmental risks. Moreover, this article employs SVAR models to analyze the dynamics of regional environmental systems in China. The study finds that, at least at this stage, the environmental risks in provincial regions in China are still relatively high, and the key factors of the risks are economic growth, urbanization development, secondary industry growth, and green policy. Therefore, China must adopt more stringent environmental protection policies and actions in the future.

In the United States, there is a growing interest in understanding heat stress in lower-income and racially isolated neighborhoods. This study spatially identifies heat-vulnerable neighborhoods, evaluates the relationship between race/ethnicity and temperature exposure, and emphasizes differences among Hispanics by origin to capture environmental injustices in Santa Clara County (SCC), CA. The current methodology uses Landsat 8 via Google Earth Engine to measure the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to assess the physical environment. The human environment is evaluated using the Modified Darden-Kamel Composite Socioeconomic Index to determine the spatial variability of socioeconomic status (SES) and the Index of Dissimilarity to determine the level of segregation between Hispanics and non-Hispanic Whites and among Hispanics/Latinos. The combination of these assessments comprises a comprehensive human–environment approach for health exposure evaluation by which to define environmental injustice. Results reveal socioeconomic inequalities and an uneven residential distribution between Hispanics and non-Hispanic Whites. Low NDVI and high LST values were found in Mexican neighborhoods, implying possible environmental racism. Almost half the Mexican population lives in highly segregated neighborhoods with low and very low SES, mainly located in East San Jose, where, historically, they have been ghettoized. Mexicans, in general, could be at a higher risk of heat stress and heat mortality during heat waves. Future work should examine additional variables (e.g., housing characteristics, crime, social cohesion, and collective behaviors) to comprehensively evaluate the at-risk Mexican population.

Mapping social vulnerability is a prominent way to identify regions in which the lack of capacity to cope with the impacts of weather extremes is nested in the social setting, aiding climate change adaptation for vulnerable residents, neighborhoods, or localities. Calculating social vulnerability usually involves the construction of a composite index, for which several construction methods have been suggested. However, thorough investigation of results across methods or applied weighting of vulnerability factors is largely missing. This study investigates the outcome of the variable addition—both with and without weighting of single vulnerability factors—and the variable reduction approach/model on social vulnerability indices calculated for New York City. Weighting is based on scientific assessment reports on climate change impacts in New York City. Additionally, the study calculates the outcome on social vulnerability when using either area-based (person/km2) or population-based (%) input data. The study reveals remarkable differences between indices particularly when using different methods but also when using different metrics as input data. The variable addition model has deductive advantages, whereas the variable reduction model is useful when the strength of factors of social vulnerability is unknown. The use of area-based data seems preferable to population-based data when differences are taken as a measure of credibility and quality. Results are important for all forms of vulnerability mapping using index construction techniques.

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r =  − 0.81), SDI-3 (r =  − 0.78) and VCI-5 (r =  − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems.

This study focuses on the Kadalundi estuary, Kerala’s first community reserve, investigating the prevalence and impacts of microplastics on both the estuarine environment and selected fish species. This study presents the initial evidence indicating the consumption of microplastic particles by 12 commercially important edible fish species inhabiting the Kadalundi estuary. Analysis revealed significant accumulations of microplastic fibers within the surface water. In examining 12 fish species from demersal and pelagic habitats, microplastics were found in both the gastrointestinal tracts and gills. In the digestive tracts, microplastic fragments constituted the highest proportion (46%), while in the gills, microplastic fibers were dominant (52.4%). This study observed a prevalence of blue microplastics over other colors in both water and fish samples. Notably, demersal species showed a higher incidence of ingested microplastics. Polymer analysis identified Polypropylene (PP), Nylon, Low-Density Polyethylene (LDPE), Polyethylene (PE), Polypropylene isotactic (iPP), PE 1 Octene copolymer, and Rayon in water samples, while fish samples predominantly contained LDPE, PP, PE, and Nylon. Risk assessment utilizing the Polymer Hazard Index (PHI) categorized certain polymers as posing minor to moderate risks. Pollution Load Index (PLI) computations indicated moderate to high levels of microplastic contamination across various sampling sites in the estuary. Principal Component Analysis (PCA) revealed a lack of correlation between fish size and microplastic ingestion, underscoring environmental factors’ influence on microplastic intake. The study emphasizes the implications of microplastic pollution on the fragile ecosystem of the Kadalundi estuary, posing potential risks to biodiversity and human health. Graphical abstract

Paschim medinipur is one of the backward as well as highly flood prone district in the state of West Bengal, India. For many reason, the most frequent choice should be protection from the flooding by means of physical control of river. Though it is not possible to control the flood disaster totally. To minimize the flood damages analysis of flood vulnerability and risk assessment is an important strategy. For this work some physical and social variables has been selected who’s potential to be harmed during the time of floods. While hazards are a potential threat to population and environment, risk is interplay between hazard and vulnerability. To evaluate the vulnerability analysis and risk assessment of flood, analytic hierarchy process and weighted linear combination has been used to functionalize the conceptual model within a GIS framework. To delineate the flood risk zones a composite vulnerability index map has been prepared which is accomplish with Physical Vulnerability Index, Social Vulnerability Index and Coping Capacity Index. Analysis revealed that 24.25% of the population lives in high to very high flood risk zones. Almost 17.76% of mud houses which are severe damage by floods located in high to very high risk zones.

The effects of climate change are diverse and impact several aspects of the environment, coastal activities, social dynamics, economic factors, and drivers of growth. This paper investigates the vulnerability assessment caused by climate change in one of Malaysia's coastal areas. We analyze the relationships between indicators of vulnerability by correlating the perceptions of a community characterised by both identified and anticipated climate change situations, whereby we develop a risk perception index that comprises cognitive, contextual, and affective factors as one of the indicators under exposure indices. The Composite Vulnerability Index (CVI) methodology was used to provide a comprehensive assessment of the community's vulnerability to climate change in Kuala Gula. The area is very vulnerable to dwindling fresh water supplies, changing climate, and poor socioeconomic resources of the local population. Data on three components of CVI (exposure, sensitivity, and adaptive capacity) were collected from household members using a close-ended questionnaire-based survey. Our analysis demonstrates that these coastal communities are prone to vulnerability and greater consequences regarding threats associated with climate change, as the CVI of Kuala Gula was considerably high. In addition, the results indicated that the exposure index was high, while the sensitivity and adaptive capacity indices were at moderate levels. From the statistical analysis of the exposure and perceptions indices, we found that an increased level of vulnerability to the impacts of climate change led to greater perceptions among Kuala Gula coastline communities regarding climate change. Furthermore, a lack of financial capital and low-income households has a detrimental impact on the overall adaptive ability of the local community. As a result, the methodology may assist local policymakers in integrating and enhancing local multi-hazard knowledge in relation to the concepts of exposure, sensitivity, and adaptive ability, as well as in making information usable for mitigation and adaptation of climate change within these communities.

A web-based software, called MCDA Index Tool (https://www.mcdaindex.net/), is presented in this paper. It allows developing indices and ranking alternatives, based on multiple combinations of normalization methods and aggregation functions. Given the steadily increasing importance of accounting for multiple preferences of the decision-makers and assessing the robustness of the decision recommendations, this tool is a timely instrument that can be used primarily by non-multiple criteria decision analysis (MCDA) experts to dynamically shape and evaluate their indices. The MCDA Index Tool allows the user to (i) input a dataset directly from spreadsheets with alternatives and indicators performance, (ii) build multiple indices by choosing several normalization methods and aggregation functions, and (iii) visualize and compare the indices’ scores and rankings to assess the robustness of the results. A novel perspective on uncertainty and sensitivity analysis of preference models offers operational solutions to assess the influence of different strategies to develop indices and visualize their results. A case study for the assessment of the energy security and sustainability implications of different global energy scenarios is used to illustrate the application of the MCDA Index Tool. Analysts have now access to an index development tool that supports constructive and dynamic evaluation of the stability of rankings driven by a single score while including multiple decision-makers’ and stakeholders’ preferences.

Because of its distinct function and geographic conditions, the impact of climate change on the operation, safety, and income of airports in coastal areas is becoming increasingly significant. The measurement of climate resilience can help identify priority needs and measures to adapt to climate change, which is a crucial step in developing an aviation adaptation plan. At present, the concept of climate resilience is relatively complex and lacks a clear uniformity of composition, which has made it challenging to effectively support the development of adaptation strategies. Based on the definition of climate resilience, our first step was to construct an evaluation system for coastal airports to visually represent the level of climate resilience. Next, in this study, we introduced a coupling coordination and obstacle degree model to analyze the coordinated development and key drivers of climate resilience, which could be used to develop a targeted improvement strategy based on the calculation results. In the future, additional measures can be combined from the natural environment, socioeconomics, governance capacity, and climate change risk to enhance the capacity development of the aviation industry to address climate change and foster the establishment of a sustainable development model between the industry and the environment.

Addressing inequality is recognized a worldwide development objective. The literature has primarily focused on examining economic or social inequality, but rarely on environmental inequality. Centering the discussion on economic or social factors does not provide a holistic view of inequality because it is multidimensional and several facets may overlap imposing a disproportionate burden on vulnerable communities. This study investigates the magnitude of air quality inequality in conjunction with economic and social inequalities in Bogotá (Colombia). It explores where inequalities overlap and assesses alleviation measures by tackling air pollution. We develop a composite index to estimate performance in socioeconomic and air quality characteristics across the city and evaluate inequality with a variety of measures. Using an atmospheric chemical transport model, we simulate the impact of three air pollution abatement policies: paving roads, industry fuel substitution, and diesel-vehicle renewal on fine particle concentrations, and compute their effect on inequality. Results show that allocation of air quality across Bogotá is highly unequal, exceeding economic or social inequality. Evidence also indicates that economic, social and air quality disparities intersect, displaying the southwest as the most vulnerable zone. Paving roads is found to be the most progressive and cost-effective policy, reducing overall inequality between 11 and 46 percent with net benefits exceeding US$1.4 billion.