Search

Effluents from paper mills are highly toxic and are a major source of aquatic pollution. In this study, we collected water and sediment samples to examine the microbial communities using denaturing gradient gel electrophoresis and identified bacterial taxa greatly affected by paper mill pollution using next-generation sequencing data. Our results indicated that bacterial communities in downstream sediments were similar to those in paper mill discharge sites, indicating obvious effects of pollution, while bacterial communities in downstream water samples showed similar profiles to those in upstream sites, both being quite different from the bacterial communities in paper mill discharge sites. This was possibly because of the short contact period. In addition, bacterial communities in the estuary were quite different from those in other water and sediment samples, which was owing to the special habitat type. Considering the storage of paper mill pollutants in sediment and the significant effect on shifts in bacterial communities, we selected Clostridia and Epsilonproteobacteria at the class level and Fusibacter and Desulfobulbus at the genus level as bacterial indicators of paper mill pollution. To monitor the remediation of polluted aquatic environments, we propose Sphingobacteria, Alphaproteobacteria, Actinobacteria, Subdivision3, Planctomycetacia and Verrucomicrobiae at the class level and Bacillus, Steroidobacter, Nocardioides, Terrimonas, Pirellula and Methylibium at the genus level.

The investigations were carried out in the form of a field reclamation experiment established in the former “Jeziorko” Sulphur Mine (Poland). For soil reclamation, various combinations of sewage sludge, mineral wool, post-flotation lime and mineral fertilisation were introduced in a single application to the degraded soil. The following parameters were determined in the reclaimed soil in spring and autumn of the 2nd and 3rd year after waste application: number of oligotrophic, cellulolytic and lipolytic bacteria, number of filamentous, cellulolytic and lipolytic fungi, respiratory activity, intensification of cellulose mineralisation, lipase activity and DNA concentration. The applied wastes had a stimulating effect on the growth of the analysed bacterial groups. The growth of fungi was stimulated only in the soil treated with sewage sludge, whereas the values of these parameters in other treatments were usually reduced. The addition of wastes contributed to the intensification of respiration processes and cellulose mineralisation. Lipase activity and DNA concentration fluctuated depending on the type of waste and the time period of investigations; these parameters exhibited the highest values in the soil treated with sewage sludge. In general, the increase in microbiological and biochemical activities was clearly evident in the soil treated with sewage sludge applied separately or in combination with other wastes. The use of sewage sludge together with post-floatation lime, rather than sewage sludge alone, and the application of mineral wool as a 5-cm pad, rather than a mixture of mineral wool and soil, proved to be more beneficial for most of the analysed parameters. The current study is important in the context of safe and effective application of waste for reclamation purposes. In addition, the work raises the innovative aspect of using DNA concentration as an indicator of degraded soil status.

This study was undertaken to confirm the reliability of the proposed potential bioindicators, Alcaligenaceae and Chromatiaceae to specifically indicate contamination in river water by palm oil mill effluent (POME) final discharge. The use of bioindicators could assist in determining the specific cause of contamination in water bodies. Alcaligenaceae and Chromatiaceae were shown by a previous study to be present in river water contaminated with the final discharge from the treatment of POME. In the present study, the compositions of the bacterial communities in the POME final discharge obtained from four different palm oil mills which used different biotreatments of POME were elucidated using high-throughput MiSeq. The four POME final discharges studied showed different species richness and evenness among them. However, the bacterial community compositions in the different final discharges exhibited almost similar patterns in that the phylum Proteobacteria was dominant in all the samples. The proposed bioindicators, the Alcaligenaceae and Chromatiaceae families, were found to be present in all the four final discharges despite the different characteristics of the mills and the different biotreatment processes used by them. These bioindicators were also strongly and positively correlated with the biochemical oxygen demand (BOD5) concentration. This makes them reliable bacterial indicators to detect the presence of POME final discharge in river water.

Monogenea is a Class of ecologically and economically significant platyhelminth parasites of freshwater and marine hosts. Contamination of the environment has been shown to variably affect parasites resulting in alterations at the population and organismal levels. The aim of this study was to perform a meta-analysis of published data to assess similarities among responses of monogeneans to pollution and estimate effect sizes of contaminant exposure on these parasites. Effect size was calculated according to Cohen’s d based on reported mean abundance for monogeneans from individual studies. A total of 57 studies were used in the present study. Each Monogenea–contaminant record represented a single data point in the meta-analysis, and this accounted for a total of 217 useable records. Most studies were concerned with species belong to Gyrodactylidae, Diplozoidae, Ancyrocephalidae and Dactylogyridae. Assessment of the effects of specific pollutants on monogeneans have mostly been restricted to exposure to eutrophication, pulp-mill effluent and metals. Comparison of effect sizes showed strong negative effects for metals and strong positive effects for eutrophication on monogeneans. Other contaminant types had weak effect sizes on monogeneans. Comparison of effect sizes among monogenean families showed that none of the effect sizes calculated were significant but metals and disturbance contaminants showed strong negative and positive effects respectively for Gyrodactylidae. The current comparisons were based on changes in the population structure and infections of monogeneans, however, organ level effects associated with exposure to poor water quality have also been noted. In this instance, exposure has been suggested to result in asymmetry of the attachment organs of some monogeneans. Despite numerous records of impacts of pollution exposure at the population level, understanding of the physiological effects and mechanisms employed by Monogenea to survive and adapt to changes in the environment are yet to be explored. Study of these aspects would enable better understanding of the variation in the trends observed at a population level for monogeneans in relation to the quality of the macroenvironment.

The paper focuses on the use and influence of indicators in European sustainable transport policy from a ‘knowledge utilization’ research point of view. The starting point is the contrast between the widely held idea that indicators are important tools for sustainable transport policy making, versus ‘knowledge utilization’ oriented research, which has often demonstrated that formal technical knowledge is used much less, or at least differently, than expected in policy and decision making. The paper looks at two cases of indicators applied for strategic policy making within the sustainable transport agenda. The first case concerns indicators tracking the fulfillment of national transport policy objectives in Sweden. The case explores the use and influence of an annual monitoring and evaluation report produced for this purpose, within a general ‘Management-by-Objectives’ regime. The second case deals with indicators applied for the Mid-Term Review of the European Commission's transport policy White Paper ‘Time to Decide’ (issued in 2001). The focus in this case is on a particular indicator based study conducted in 2005 by a group of consultants in close collaboration with European Commission staff. It is found that several indicators in both cases are actually used in policy processes, with evidence of use found in documents as well as interviews. However, ‘use’ does not automatically mean ‘influence’ on policies or processes in more than a superficial manner. Indicators seem to play a very limited direct instrumental role, while some signs of symbolic , conceptual and process roles are found. In the EU case we identify what is called a rationalization role of indicators, meaning that indicators inform and help to rationalize a change in the position towards key objectives. Several factors that characterize the indicators, the users, and policy context are found to contribute to understand the type of use and influence observed in both cases.

A thorough outlook on the effect of palm oil mill effluent (POME) final discharge towards bacterial community dynamics in the receiving river is provided in this study by using a high-throughput MiSeq. The shift of bacterial composition could be used to determine the potential bacterial indicators to indicate contamination caused by POME. This study showed that the POME final discharge did not only alter the natural physicochemical properties of the river water but also caused the reduction of bacterial diversity in the receiving river. The Chromatiaceae and Alcaligenaceae which were not detected in the upstream but were detected in the downstream part of the river are proposed as the indicator bacteria to indicate the river water contamination caused by POME final discharge. The emergence of either one or both bacteria in the downstream part of the river were shown to be carried over by the effluent. Therefore, an accurate pollution monitoring approach using bacterial indicator is expected to complement the conventional POME pollution assessment method which is currently dependent on the physicochemical properties of the final discharge. This is the first study that reported on the potential indicator bacteria for the assessment of river water contamination caused by POME final discharge.

The operation of an activated sludge wastewater treatment plant (WWTP) was assessed by determining the removal efficiency of treatment processes. For that purpose, different physicochemical and microbiological parameters, including total, active and culturable bacteria, were measured along a year. Values of both physicochemical and microbiological parameters were significantly reduced by biological treatment but no relation between these groups of parameters was obtained. Besides, results suggest that microbiological parameters were more sensitive than physicochemical ones to changes in WWTP conditions (i.e. influent quality). Additionally, high percentages of noncultured (>88%) and noncultured remaining active (until 50%) cells were detected before and after treatment. Therefore, ISO standards adopted to enumerate indicator bacteria by Spanish legislation, and based on culturability, underestimate the bacterial population present in wastewater. We have compared two culture-independent methods for bacterial enumeration, microscopy and flow cytometry (FCM) and results indicate that FCM provides a rapid and automated quantification in WWTP. We conclude that microbiological parameters are essential to monitor the correct WWTP operation and we propose quantification of active bacteria as indicator of wastewater microbiological quality.

The increasing attention on environmental problems is stimulating the development of integrated evaluation approaches to territorial transformations, where different facets have to be considered. The present paper focuses on the development of a novel integrated evaluation model for measuring the ecological states and the economic values of an environmental system employing both a system of indicators and a mathematical model of Lotka-Volterra. This integrated evaluation model aims at investigating the relations between the landscape ecology and economics, and at simulating possible and future transformation scenarios for the landscape planning and management. As case study, we consider an environmental system located in Piedmont (Italy), in the Monferrato Ovadese area. The present paper proposes a suitable support for local governments to visualize outcomes and possible future scenarios, aiding the decision-making process in the definition of territorial transformation policies and actions.

Building a beautiful and livable countryside is essential to farmers’ wellbeing, contributing to the development of New Socialist Countryside in China. Few of the existing studies have established a sound theoretical framework to evaluate rural human settlements from the perspective of farmers, who are the major beneficiaries of the rural human settlements. Therefore, this paper establishes a theoretical framework to evaluate both hard and soft rural human settlements qualitatively and quantitatively. Based on surveys from 6 provinces (cities) in central and eastern China, the theoretical framework is tested using Structural Equation Model (SEM). The empirical results show that the surveyed farmers are vaguely unsatisfied with their local rural human settlements, making improvement a must. Moreover, there is a significant correlation between rural sanitation, housing condition, rural infrastructure, farmers’ economic burden, social services and rural human settlements, with rural sanitation and social services ranking at the top among the hard and soft human settlement indexes, respectively. Thus, the government should prioritize rural sanitation and social services to improve hard and soft human settlements, respectively. The discussions in this paper could provide solid foundations for policy makers to effectively improve rural human settlements.

The impending threats of changing climate have been well documented across sectors. The climate risks are best addressed through increasing adaptive capacity and building resilience. Ever since the global call during the Rio Summit in 1992 for establishing sustainability indicators across sectors, there have been several studies across the world on developing indicators for sustainability, vulnerability and climate resilience. Agriculture, the most vulnerable system to changing climate, depends on the resilience of both social and ecological systems. This paper focuses on integrating the variability of climate into the agricultural sustainability measurement with a broad base of indicators and bringing in the localized factors for representing the agroecosystem specificities. The paper also aims at identifying indicators for measuring climate resilient agriculture in Indian sub-continent and developing a conceptual framework for profiling the spatial resilience across various agro-ecosystems for appropriate location-specific policy interventions. In the current study 1209 indicators used in various research studies were screened, grouped for similarity and purpose and classified based on the various dimensions viz., social, economic, ecological, etc. After a critical review based on their appropriateness as a measurable indicator, extent of overlap, relevance in Indian context and possible data availability, 41 indicators were shortlisted for validation through a comprehensive structured online survey among subject matter specialists (n = 225). The responses from the experts (n = 36) were analysed using weighted sum model (WSM) and analytic hierarchy process (AHP). The study identifies a list of 30 sustainability indicators for climate resilient agriculture in India, that are particularly suitable for different agro-ecosystems of the sub-continent. The authors advocate an action-oriented model called Climate Risk Management Package for Agriculture (CRiMPA) to aid in planning spatial/agro-ecosystem specific interventions, which in turn could strengthen the National Action Plan for Climate Change (NAPCC) of Government of India.

The ecological footprint (EF) methodology, which is a tool for evaluating the natural resource use level and regional carrying capacity, has been extensively used to measure the capacity to reach both an ecological supply and demand balance and a level of sustainable development. In this study, to systematically sort and analyse the distribution of EF research, its hot areas and the evolution of its fronts, papers with “ecological footprint” as the subject term in the Web of Science Core Collection Database and the China Knowledge Resource Integrated Database Core Journals since 2000 are visually analysed using CiteSpace. The characteristics of these papers, including the quantity trend, quality, author group, affiliated institution type and journal type, are summarized, and keyword co-appearance and paper co-citation knowledge maps are produced. The results show the following: (1) Chinese researchers have been closely following international research fronts and steadily improving their research results. (2) Hotspots such as carbon dioxide emissions, the carbon footprint (CF), water footprint (WF) and sustainable use have been receiving attention both in China and internationally. International researchers focus on hot topics such as public satisfaction, land and resource use, spatial equity and policy analysis. In comparison, Chinese researchers prefer to modify and improve relevant methods, such as the three-dimensional EF (3D EF) and footprint family methods. (3) The research frontier of EF is based on improving the EF accounting method, which is aimed at assessing and monitoring the relationship between ecology and the economic society on a multi-scale basis, establishing an index system that can evaluate the demand for natural resources and environmental pressure, and explore the issues of mechanisms of regional ecology resource allocation fairness, and market-led and government-led ecological governance efficiency.

Conservationists and protected area managers spend millions in establishing and maintaining protected areas every year. However, many studies revealed that the design and coverage of protected areas are still inadequate in representing key biodiversity features, are insufficient in the management approach to address the threats and pressures and have a low level of ecological integrity because of the increasing levels of environmental degradation. There is strong evidence of different anthropogenic pressures that deteriorate the effectiveness of existing protected areas, structurally, compositionally and functionally. Hence, the evaluation of a protected area’s effectiveness is very crucial in maintaining long-term biodiversity conservation. Different evaluations have been carried out to assess the effectiveness of protected areas but focus only on certain issues or a single component. Current effectiveness evaluations still inadequately incorporate important criteria to make the evaluation of protected areas more comprehensive. Therefore, this paper will first focus on reviewing the current conditions and threats towards biodiversity conservation in tropical protected areas. Then, there will be a discussion on the relationship between the threats that cause issues with the effectiveness of protected areas. From issues that influence protected areas, the criteria or components that are crucial in developing the consolidated index model are identified. The main objective of this paper is to propose a consolidated index model that integrates the components of design and planning, ecological integrity and management to assess the conservation effectiveness of individual terrestrial protected areas in tropical regions. Hence, this paper presents the first step to develop a consolidated index model that can be implemented by park managers to assess conservation effectiveness.

Ecosystem health report cards are an increasingly popular means of summarizing the results of environmental monitoring programs for broad, non-scientific audiences. This paper describes a whole-of-system report card initiative developed to monitor the condition of a multi-use port in the Great Barrier Reef World Heritage Area, Queensland, Australia. Concerns over the impacts of major industrial expansion, fish health incidents, safety of recreational harbour users and habitat loss prompted stakeholders in the Gladstone region to unite and establish the Gladstone Healthy Harbour Partnership. The objective was to monitor and report on the health of the harbour via an annual report card ( http://rc.ghhp.org.au/ ). We illustrate one approach to designing a report card against ten core considerations; 1) strong links to all stakeholders at all stages, 2) rigorous science, 3) effective communication, 4) setting clear goals and 5) realistic expectations, 6) flexibility in implementation, 7) transparency, open access and accountability, 8) results linked to actionable management recommendations, 9) regular evaluation, and 10) long term commitment. Innovative aspects of this report card include the combination of four components of harbour health: environmental, social, cultural and economic; underpinned by a sophisticated monitoring, modelling and reporting program. A review of the report card content and methodology, conducted in 2017, determined that the design meets the original vision and objectives set by Gladstone stakeholders. However, stakeholder perceptions of the effectiveness of the report card have not been comprehensively assessed to date. This paper provides a guide to others designing report card programs and environmental metrics, with advice on maximizing transparency and data sharing, coordinating otherwise disparate monitoring efforts, and linking with management through the provision of scenario analysis tools with which to interpret and respond to annual report card results.

In order to mitigate the threat that hydraulic engineering will have on the fish’s living environment, fish migration can be accomplished by building fishway. This paper takes the Diversion Dahaerteng River to Danghe Reservoir project in Gansu, China as an example. Through field investigation and data analysis, it was found that the main fish in the study area was Triplophysa. Through the comparison of the advantages and disadvantages of the fishways, it is determined that the fishway and the nature-like fishway are suitable. By constructing the evaluation method, the 19 indicators of the fishway were scored from the aspects of ability to lure fish, ability to pass fish, fish adaptability, engineering quantity, operation and maintenance, and the nature-like fishway was determined to be the best choice. The surface runoff from March to August is calculated by the P-III curve to simulate the flow field of the Dahaerteng River under the conditions of maximum, minimum and average runoff. The flow velocity of the simulation results is suitable for the Triplophysa migration, and the right bank is suitable for building nature-like fishway. The fishway section is rectangular, with a slope of 1%, a width of 2 m, and a total length of 520 m. It is arranged with a staggered baffle, and the revetment is rough masonry stones. The inside of the fishway is inlaid with pebbles so that the roughness of the fishway is more than 0.03. Then, numerical simulation calculations were carried out for the fishways composed of the partitions of 10 m and 5 m apart. The comparison shows that the fishway with the partition gap 5 m is closer to the right bank of the river, which is suitable for Triplophysa migration and survival. For later evaluation and adjustment, an observation room and a fish collection pool are provided near the entrance of the fishway, and an observation room is provided near the exit. This paper can provide reference for the planning, construction and post evaluation of fishways.

Governmental policies that can develop advantageous endogenous livelihood transition are of great importance for anti-poverty. However, the existing research finds that in the Tibetan Plateau, there is a certain contradiction between the poverty alleviation policies and the local farmers’ needs, and thus the effect of these policies will be weakened and the goal of poverty alleviation be missed. It is therefore imperative to identify and analyze the factors influencing the livelihood choice so that policy makers and development practitioners can design policies that will tackle the poverty afflicting the majority of the local people. This paper provides a new method for anti-poverty policymaking and conducts a case study to enrich the safety net/cargo net paradigms. In this paper, household data in the YNL river region are used and a two-step clustering method is developed to classify these households into three types: hanging in (type I), stepping up (type II) and stepping out (type III). Then a multinomial logit model is adopted to identify determinants of households’ livelihood choice. The results indicate that drought poses a significant negative impact on farmers’ choice of type II, while household size, educational level of labor and medical expenditure have a significant positive impact on the choice of type II; spring frost, proportion of skilled training, borrowed money and the ratio of agricultural equipment have a significant positive impact on their choice of type III. Some anti-poverty policy implications are put forward in the last on the basis of these determinants.

Fecal coliform bacteria are important indicator microorganisms that are commonly monitored monthly for the quality of oyster harvest waters and the end product, making the protection of public health challenging as oyster harvest may occur daily and fecal coliform levels in oyster harvest waters may also change daily. This paper presents an artificial intelligence-based neural network modeling approach to predict fecal coliform bacteria levels in oyster harvest areas (waters) daily. The new approach was demonstrated by developing an artificial neural network (ANN) model for daily prediction of fecal coliform levels in seven oyster harvest areas along the Northern Gulf of Mexico coast. The model input variables were selected by using the stepwise regression analysis method. It was found that the prevalence of fecal coliform bacteria in oyster growing waters was controlled by six independent environmental predictors, including wind, salinity, tide, water temperature, rainfall, and solar radiation, which were utilized as the model input variables. It was also found that the prevalence of fecal coliform bacteria in oyster growing waters is affected by not only current conditions of the six independent environmental variables but also antecedent conditions of the variables (particularly average solar radiation and cumulative rainfall over the past two days). Model prediction results indicated that the ANN model was capable to predict not only daily variations in fecal coliform levels but also seasonal fluctuations in observed fecal coliform levels characterized by high bacteria levels in the cold season and low bacteria levels in the warm season. The performance of the ANN model was demonstrated by the linear correlation coefficient (LCC) of 0.7421 and root mean square (RMSE) of 0.3844 for the model development phase and the LCC of 0.6312 and RMSE of 0.2835 for the independent validation phase. The ANN model makes it possible to reduce the harvest and consumption of fecally contaminated oysters and thereby greatly reduce the health risk to the general public and particularly oyster consumers. Although the predictive ANN model was specifically developed for oyster harvest areas along the Louisiana Gulf coast, the methods used in this paper are generally applicable to other oyster harvest areas and coastal waters.

As the gateway of “the Belt and Road”, it is of great significance for Xinjiang to fulfill the carbon reduction target without compromising the steady socio-economic development. A comprehensive understanding of the decoupling relationships between industrial growth and carbon emissions as well as its driving factors is helpful to make targeted recommendations. In this paper, decoupling analysis, index decomposition analysis, and attribution analysis were applied to analyze the decoupling effect, driving factors, and contributions of sub-sectors to each driving factor, respectively. Some conclusions were drawn. (1) Xinjiang’s decoupling relationships between industrial growth and carbon emissions experienced three stages, i.e., “negative decoupling, weak decoupling, and negative decoupling”. Xinjiang’s industrial sector was in relatively unsustainable period after 2008. (2) From 2000 to 2014, energy intensity promoted the decoupling process, and industrial structure and energy structure were the main factors which inhibited the decoupling process. But after 2008, all driving factors showed negative influences. (3) After 2008, fuel processing, textile, and mining and quarrying were primarily responsible for the energy intensity’s negative influence; fuel processing was primarily responsible for energy intensity’s negative influence; production and supply, smelting and pressing of metals, and chemicals were primarily responsible for industrial structure’s negative influence. (4) This paper suggests that Xinjiang should make targeted carbon reduction policies at sub-sector level.

To learn the regional carbon footprint of agro-ecosystem from the perspective of industrialization can help to clarify the responsibility of energy saving and carbon emission reduction in various industrial sectors. Using the input–output table of Hebei province in 2012, this study discussed the structure of the regional agro-industry carbon footprint (ACF) and its spatial difference, taking the prefecture level city as the basic spatial unit. The results indicated that the ACF was approximately 1.05 × 108 tons in 2012, which accounted for 6.7% of the total carbon footprint in Hebei. In all agro-industries, the fertilizer manufacturing industry produced the largest proportion ACF, which accounted for 23.7% of the total ACF. The gross domestic products (GDPs) of wood products, papermaking industry and agricultural food processing industry were relatively low, but their ACFs were large. The influence and induction of the fertilizer and pesticide manufacturing industries to other agro-industries were strong. The ACFs of wood, bamboo, rattan, palm and grass products manufacturing; paper and paper products manufacturing; fertilizer manufacturing; pesticide manufacturing; and agriculture, forestry, animal husbandry, and fishery special machinery manufacturing industries had significant spatial difference at the prefecture-level city spatial scale. The results from this study will be helpful for the adjustment of agro-industrial structure to develop low-carbon and energy-saving and emission-reduction agriculture in Hebei.

The ecosystem services derived from tropical plants sustain local societies and make significant contributions to the global economy. Often many types of ecosystem services are derived from a single plant species, introducing the potential for tensions between forest users. Despite the potential for forest users having different approaches to how a plant is used, little has been done to understand how a plant species’ sustainability may be impacted by opposing views on their utility. In this paper, the inherent properties of plants were used to propose a conceptual approach for comparing the values of plant species from the perspectives of commercial loggers and indigenous peoples. Using the provisioning ecosystem services associated with a sample of plants from the Rupununi, Southern Guyana, commercial logging and subsistence use indices were developed to compare how plant species may be perceived by the two opposing views of tropical forest management. The analysis suggested that for more than 84% of plants the subsistence value indices were greater than the logging indices. This study suggested that by examining opposing approaches to forest management, and adopting indices to document how different users approach plants, forest managers can begin to answer questions on where in space, and for which species, conservation and management efforts should be pursued. The methods proposed in this paper can be used by indigenous peoples and their allies to make spatial optimization decisions on their use of forest resources for conservation and payment for ecosystem services initiatives such as REDD+.

Tracking Earth’s past helps us to move from hindsight to foresight in seeking landscape sustainability, a pursuit aided by modern mapping capabilities but hindered by a dearth of historical landscape information. To fill the data gap and exemplify the use of old maps for land-use change sciences, we combined an old paper-based US civil war map and modern aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, USA, from 1867 to 2014. We also tested how advanced algorithms—object-based image analysis and Random Forests (RF)—could aid in data processing. Automatic classification of the scanned 1867 paper map proved difficult, but its manual digitization could benefit from object-based image segmentation. Classifying digital aerial images was more accurate via the object-based than pixel-based method, but only if the images were segmented appropriately. In the object-based classification, spectral-based features were much more important and useful than shape/geometry features for land-cover discrimination, as ranked by RF. During the 147 years, 32% of the region changed in land type. Settlement and roads increased in extent by 1850% and 691%, respectively, and woodland decreased by 19%. These changes fragmented the landscape, altered the hydrological regime, and affected river morphology. The utility of old maps exemplified here provides an impetus for leveraging extant old maps or historical records to support land-use and global change research. Our study also connotes the importance of preserving and geotagging current non-traditional data, such as photos, videos, and citizen science data, that can serve as a baseline to document future landscape change.

The cultural heritage of landscapes makes an important contribution to people’s local identity, which enhances the human-nature relationship and informs decision making. However, cultural heritage assessments in the context of cultural ecosystem services are still challenged by the lack of coherent methods to assess the spatial distribution of cultural heritage. This paper addresses the questions: how can complex cultural heritage be captured with objective indicators and how are cultural heritage values spatially distributed in Scotland? The aim of this paper is to develop an indicator-based framework for the mapping of cultural heritage, which will be tested for its applicability by assessing cultural heritage in a Scottish national level case study. The developed hierarchical framework includes both, indicators related to historic land uses (Time Depth) and historic elements (Historic Richness) that are aggregated to a higher methodological level for mapping the spatial distribution of cultural heritage value. The application of the framework in the case study has shown its capability to assess the spatial and temporal distribution of cultural heritage in Scottish landscapes. The results identify landscapes that represent cultural heritage hotspots of Scotland (e.g. crofting landscapes, settlements shaped by early industrialisation or the Drumlin Lowlands). Different landscape units and classes are highlighted by either historic land use or elements, which underline the specific contribution of different indicators to the overall cultural heritage indicator. Land use-based indicators highlight landscapes with early-introduced, medium-dynamic land use patterns, while element-based indicators highlight landscapes with a long tradition of settlements. The proposed framework emphasises the importance of systematic indicators for cultural heritage, which reflect both quantitative and qualitative aspects of human influence on land use and the built environment. These aspects are difficult to include in a single indicator. The findings can improve the integration of cultural heritage values in decision-making processes and the development of more objectively assessable indicators for other cultural ecosystems services.

A timely and adequate water supply to crops is a requirement for sustainable food production to the burgeoning world populace. Although sufficient water supply is necessary for crop production, the excess water in the rootzone is detrimental to crop growth and yield. Poor land drainage and associated salinization correspond to severe threats to the long-term sustainability of irrigated agriculture as these cause a reduction in crop growth and yield. To provide ideal aeration in the rootzone, an apt drainage is necessary on poorly drained agricultural lands. This paper provides an overview of various measures and their suitability and limitations in managing the land salinization and rising groundwater level problems of irrigated areas. The aptness and restrictions of surface drainage, subsurface drainage, mole drainage, tile drainage, vertical drainage, and biodrainage in managing the problems of agricultural lands are explored in the paper. Conclusions are provided that can be of use to the readers.

Grassland carbon (C) sink/source evaluation is important to terrestrial ecosystem C cycling research. In this paper, boreal ecosystem productivity simulator (BEPS) model was used to simulate the net ecosystem productivity (NEP) of Inner Mongolia (IM) from 1982 to 2008. The coupling relation between NEP and climate factors was then explored. Results showed that the mean annual value of grassland NEP is 12.2 gC/m2/yr, and regions with NEP of >0 (C sink) accounts for 73.2% of the total grassland area of IM. Additionally, the total C sequestration reaches 0.35 Pg C yearly. NEP was positively correlated with precipitation (R = 0.31), and the positive correlation percentage accounted for 90% of total grassland area. But NEP was negative correlated with temperature (R = −0.11), and the negative correlation percentage was 72% of case. And the partial correlation coefficient between NEP and precipitation and between NEP and temperature was 0.30 and −0.06 respectively. Meanwhile, the monthly NEP of the grassland is obviously lagging behind the precipitation and temperature, and the lag time is both three months. We defined a precipitation differential (PD) parameter to explore the coupling relation between grassland NEP and precipitation. Generally, areas with positive PD are typically a C sinks (72% of the grassland area of IM). However, regions with negative PD are likely a C source (28% of case). The obtained mean PD value of IM is 18 mm, indicating that IM is a C sink. Further analysis showed that 69% of regions have positive PD and positive NEP, and 20% of regions have negative PD and negative NEP. This result confirmed that precipitation deficit restrains C sequestration. However, the rest of 11% of regions was sensitive area of carbon sink and carbon source transition. Among 8% of the regions (with negative PD but positive NEP) have insufficient precipitation, although other conditions (i.e., ecological restoration program) are favorable to C sink. Moreover, 3% of the regions (with positive PD but negative NEP) have sufficient precipitation, but have negative NEP because of inappropriate management or low temperature. Thus, appropriate measures that can convert a C source to a C sink are necessary. This paper can serve as a reference for policy makers for the efficient targeted implementation of ecological engineering.

The relationship between CO2 emissions, the main gas responsible for global warming, and economic growth is among the most studied themes of environmental economics. Reducing overall emissions while keeping a high pace of economic development is at the heart of the sustainable development concept. When emissions grow less rapidly than GDP environmental economists speak of relative decoupling; if emissions even decrease relative to the pace of economic growth, then decoupling is absolute. Assessing these options requires an empirical analysis of the emissions-GDP relationship. The study of this nexus has special importance for developed countries, given their historical responsibility towards global warming. At the same time, in the last decades, the same countries have been at the forefront of the fight against climate change in terms of emissions-reduction efforts. By applying cutting-edge econometric techniques, this paper aims to investigate the decoupling options, if any, for a group of European economies which can be considered as pioneers in pursuing the sustainable development goals. This question gains further importance considering that some recent studies have found positive GDP elasticities of emissions for certain developed countries, which may be seen as a cause of concern for the sustainable development path of such countries. Unlike the bulk of the literature, in this paper, we allow the income elasticity of emissions – a critical indicator for the study of decoupling – to vary over time. The reason is that the elasticity might change through the time due to the factors affecting the main drivers of the CO2 emissions. We use a time-varying coefficients cointegration approach to investigate the CO2 emissions-GDP relationship for 12 Western European countries over a long time period ranging from 1861 to 2015. Our main finding is that the income elasticity of CO2 emissions is positive in all investigated countries. In addition, we find evidence in favor of relative decoupling in 8 out of the 12 European countries. This is in line with the fact that the selected European countries have shown more determination in adopting carbon reduction policies before and after the Kyoto protocol period and toward the Paris agreement compared to other leading economies such as China, United States, and Russia. For the remaining 4 cases, the income elasticity of CO2 emissions is in excess of unity. This can be considered as a call for policymakers to take quick and relevant measures to mitigate emissions level without harming the economic development.

Integrating forest resources into the socio-economic system correctly and reasonably is of vital importance to tackle the increasingly scarce forest resources. In this paper, forest resource input-output model and forest resource metabolism network model are established to provide new insights into the relationships among the systems, industries and sectors related to forest resources. A promising indicator named exploitation index is developed to reveal the weaker sectors in the ecological relations, which will further help to provide better corrective actions and integrated strategic measures. Guangdong is taken as an example to verify the availability of the model and solve the problem. The results show that the primary manufacturing consumes more direct timber, while advanced manufacturing and the service sector utilize timber indirectly. In addition, Guangdong forest metabolism system shows a negative correlation and the whole network does not achieve the mutualism state, leading to competition relationships between pairwise sectors that should not appear, such as Forestry sector and Papermaking and Paper Products sector. These results provide the corresponding reference for helping the decision makers to allocate forest resources and coordinate ecological and economic development.

Green transformation has become one of the most important directions in the further development of the world. It involves the development of green technologies and the creation of legal regulations enforcing, for example, saving energy or reducing the emissions of greenhouse gases, as well as any other activities aimed at changing society’s attitude towards the acceptance of – frequently more expensive but more environment-friendly – technological solutions and legal norms. Thus green transformation can be defined as combining economic growth with caring about the environment in order to guarantee a high quality of life for present and future generations at the level which is attainable due to civilisational development, as well as to an effective and rational use of the available resources. Still, there is no single globally accepted way to this transformation. It is proceeding in a variety of ways and depends on many different factors. The main purpose of the paper is a comparative analysis of the directions of changes occurring in the process of green transformation in EU countries. The added value of the research presented in the paper and its novelty is the analysis of the current patterns of green transformation in EU countries. The paper concentrated on recognizing them and drawing attention to the unfavourable directions of this transformation as well as to its positive aspects. The changes in the process of green transformation were analysed based on data used by the OECD to describe green growth in two periods: 2004 or 2005 as initial data and the last available data (usually 2018 or 2019). For this purpose the TOPSIS method was applied. The EU countries were divided into groups according to their way of applying green transformation. The provided research allows to better understand the course of this transformation in various EU countries.

Storm surge is the most severe marine disaster in China, affecting the whole coastal area. Estimating storm surge disaster loss (SSDL) is significant to disaster prevention, sustainability and decision-making. Taking 11 provincial administrative regions in the coastal areas of China as the study area, this paper estimated SSDL grades based on four machine learning (ML) algorithms. A total of 132 pieces of official open-source data of storm surge disasters were collected and divided into a cross-validation set (CV set) and a test set. First, a comprehensive indicator system was constructed from three perspectives, covering the hazard (16) of disaster-causing factors, the vulnerability (22) and resilience (12) of disaster-bearing bodies, including 50 indicators. A few data preprocessing methods are implemented to improve the model performance such as normalization, SMOTE, etc. Then, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic model tree (LMT), and K-star were applied to construct the estimation model of SSDL grades. Principal component analysis (PCA) and recursive feature elimination (RFE) are adopted for an intelligent screening of the indicators. Finally, the models’ performance is compared through Precision, Recall, F1 score and Kappa metrics. The results show that scientific and efficient data preparation is a strong guarantee for the reliability and stability of the models. RFE is verified more suitable for indicator selection in this paper compared with PCA. The importance ranking of RFE enhances the interpretability of the ML model, which shows that the hazard indicator is the most important, the vulnerability indicator is the second, and the resilience indicator is the least. The 27-indicator K-star model, with advantages of accurate estimation, strong generalization, and less workload, is the optimal SSDL estimation model. The number of input indicators of the optimal SSDL estimation model is 27, its CV Precision, Recall, F1 score, and Kappa are 0.838, 0.832, 0.827, and 0.776, and its Precision, Recall, F1 score, and Kappa for test set are 0.819, 0.786, 0.781, and 0.714, respectively. This paper provides a scientific basis for the government's decision-making and risk management, and it can be used as a typical demonstration case of SSDL research.

Delineating urban growth boundaries (UGBs) has been proved as an effective approach to constrain urban encroachment and achieve sustainable urban development. However, so far, many studies have not fully taken account of the ecosystem integrity in simulating the urban growth as well as the corresponding UGBs delineation. To bridge this research gap, this paper proposes an ESP-FLUS model to predict the future land use pattern with a comprehensive consideration of ecological protection. In this paper, on the one hand, the ecological security pattern (ESP) is integrated from four ecological aspects (i.e., hydrology, geology, biodiversity, and recreation) and classified into three levels (i.e., basic ESP, intermediate ESP, and optimal ESP). On the other hand, drawing on historical records, random forest coupled with Gini importance are applied to identify the driving factors in shaping the land use variation. Taking the classified ESP as the spatial constraint allied with the identified driving factors into a FLUS model, this paper predicts the urban growth in 2035 in a multi-scenario manner. By analysing the multi-scenario simulation results at both region and district levels, the possible urban planning option under the requirements of local master plans is suggested for sustainable urban development. Against a backdrop of an increasingly severe environmental crisis, this paper develops a new research paradigm to incorporate ESP into UGBs delineation as a way to shed light for urban planners on reaching the balance between eco-environment protection and socio-economic development.

The coordinated development of ecology and economy is one of the key issues of sustainable development. A full understanding of the relationship between ecology and economy is necessary for the coordinated development of the two. This paper takes Sichuan Province as the research area and county-level administrative districts as the research unit. Based on the medium value of the ecological quality index and economic growth index of each unit, Sichuan Province is divided into dual pressure zones (DPZ), economic backward zones (EBZ), ecological crisis zones (ECZ), and coordinated development zones (CDZ) and the coupling coordination degree (CCD) of each unit is studied. The results show that: (1) On the whole, the eastern part of Sichuan Province has a better level of economic development, and the southern part of the western region has a higher ecological quality. The ecological quality and economic growth pattern are highly correlated with topographical conditions. (2) The distribution characteristics of the four types of partitions are significantly different. The EBZ are more distributed in the western and southern of Sichuan Province, the ECZ are more distributed in the eastern, the DPZ and CDZ are relatively more sporadic. More than 90% of the partition changed zones are distributed in the eastern area. (3) The CCD of more than 95% units belongs to moderate coordination and high coordination. The overall CCD of the four types of zones has shown an upward trend from 2010 to 2015. Based on the results, Sichuan Province should follow the path of clear waters and green mountains are as good as mountains of gold and silver, and targeted sustainable development suggestions for DPZ, EBZ, ECZ, and CDZ are put forward. This paper provides guidance for formulating regional sustainable development strategies, especially in areas with unbalanced development due to congenital topographical conditions.

In this paper, we estimate the Genuine Progress Indicator (GPI), which is a measure of sustainable economic welfare, for California for a five-year period, 2010–2014. This relatively short time period, which covers the recovery from a deep recession, allows us to examine how integration of environmental degradation, nonmarket activities, and inequality affects the GPI of California. The California GPI is only 52% of Gross State Product (GSP) – comparable to other GPI to GDP proportions – because the large negative environmental components offset the large positive social components and because many government expenditures, such as those related to defence and law enforcement, are excluded. Between 2010 and 2014, California GSP grew 9.2% and GPI grew 9.8%. We evaluate our estimation of the California GPI (CA-GPI) in two specific ways. First, we compare California’s GPI to an alternative indicator of social welfare, the Human Development Index (HDI) for California. Our comparison points out that the GPI is a more holistic measure of sustainable economic well-being, although the HDI is useful for evaluating educational attainment, life expectancy, or earnings across regions or demographic groups in the state. Second, we compare our estimation of CA-GPI to the California results from a recent GPI estimation for all fifty states for 2011, in order to evaluate how different methodological decisions and data selection affect the results. Our overall estimate is 13 percent higher, with the primary differences reflecting discrepancies in methodological assumptions or data sources in the calculation of a few key variables, including the value of time used for calculating nonmarket activities. These two estimates of CA-GPI allow us to analyze the sensitivity of two widely used approaches for calculating the GPI, and the sensitivity of using California-specific public data sources compared to national public data sources (scaled to California). The variation in the two California GPI estimations demonstrates the importance of standardizing the method and the data sources, with the goal of creating a viable alternative to the GDP for measuring economic performance. Comparison of the two GPI estimations shows how the use of region-specific data increases the accuracy of estimates, which is important for evaluating regional outcomes and trends over time. However, using data and method that prioritizes standardization is essential for cross-regional comparability, even though the trade-off is diminished regional accuracy. The paper concludes with a discussion of the uses of the GPI to evaluate policies, and suggests fruitful steps forward.

In salt marsh ecology, various indicators, including environmental, biological, and anthropogenic factors, have been used to predict the patterns of plant species richness. The potential impact of spatial autocorrelation on this prediction, however, has yet to receive much attention. In this paper, two sets of regression models were developed to predict spatial patterns (in 2006) and temporal changes (from 2006 to 2012) of richness across selected tidal creeks at a Danish salt marsh: (1) traditional ordinary least squares (OLS) using soil and topographic parameters as independent variables and (2) spatial regressions in which spatial filters produced by spatial eigenvector mapping were included into the non-spatial OLS as additional independent variables. Such incorporation led to a general improvement of model outcomes, that is, increases in R2 and decreases in both Akaike’s information criterion and residual autocorrelation. Notably, only spatial filters were always significant independent variables for both the spatial and temporal dynamics of species richness. In contrast, no environmental variables were consistently significant because of the substantial reduction in their regression coefficients after spatial regression. These results imply that identifying the relevant indicators of richness patterns in salt marshes may be a much more complicated job than previously thought. By revealing the new and statistically more rigorous predictive power of these environmental (i.e., non-spatial) variables, the spatially explicit modeling employed in this paper will provide benefits to the literature on ecological indicators.

To address the pressure of the ecological degradation, an ‘Ecological Control Line’ (ECL) policy has been implemented in China since 2005. This paper aims to examine the effects of ECL and its supporting policies on maintaining ecosystem services within the context of Chinese high-speed economic growth and rapid urbanization. The paper is a product of previous research into the valuation of ecosystem services in monetary units and discusses a method of translation from the unit area value of China’s ecosystem service value (ESV) to Wuhan City by the adjustment of the functional coefficient and economic coefficient. The evaluation of the effects of the ECL policy on ESV is divided into two time-periods. One is from 2009 to 2011 before the implementation of the supporting policies, and the other is from 2012 to 2014 after the implementation of the supporting policies. The total ESV within the Wuhan ECL area dropped by 1.54% from 2009 to 2011 and then increased quickly by 38.71% from 2012 to 2014, coinciding with the application of ECL supporting policies during 2012–2014.

Improvements in the ecological attributes of inland rivers have been projected to provide considerable non-market benefits, and the monetary value of these attributes has steadily increased over the past several decades. The present study addresses the comparison between welfare estimates for changes in the conservation of river basins and improvements in various attributes of these basins. Furthermore, by comparing the transfer error, this paper examines the feasibility of transferring benefits across populations and sites; this paper also determines the impacts of spatial heterogeneity on the welfare estimates. This study reveals that the estimated benefits are higher for the lower basin than for the middle and upper basins, and respondent preferences for relative improvements in environmental attributes vary across the basins. To consider spatial heterogeneity, a spatially explicit choice experiment was used to estimate the transfer errors. The results from the random parameter logit model (RPL) reveal that the inclusion of taste variation reduces the transfer errors that in turn make the benefit transfer applicable. The transfer errors are 15% when benefits are transferred between the upper and middle basins, while transfer errors are 16% when benefits are transferred from the upper basin to the lower basin. The results suggest that the non-market benefits of the river restoration policy are transferable between the upper and middle basins and from the upper basin to the lower basin. Our results have practical implications that can inform efforts to improve ecosystem services. The results are relevant to the policy specifically in terms of a cost-benefit assessment of the management strategy as well as understanding public support for the policy.

Water supply is a significant issue for realizing sustainable water resources development at the watershed scale. Such an issue is mixed with many human and ecosystems related factors that need to be characterized, evaluated, and simulated for achieving sustainable watershed management. Thus, cascades of ecological indicators need to be used for describing and quantifying dynamics of such factors and the associated processes and conditions. They could facilitate assessment, simulation, and management of many ecological indicators at the context of watershed. A number of relevant studies were collected and compiled in this special issue. Its purpose is to report a number of the latest research related to multi-scale ecological indicators for supporting sustainable watershed management. The contributions incorporated diversified and creative applications of theories and methodologies in multiple fields, including ecology, modelling and management. All selected papers underwent a thorough peer review process, with at least two rounds of reviews. Following the journal guidelines and requirements, all submitted papers were reviewed by at least two experts. These papers contributed in different aspects to the topic of “multi-scale ecological indicators for supporting sustainable watershed management”, providing a deep understand of relevant issues and presenting novel approaches, techniques and applications for dealing with such issues. The papers can be categorized into three groups, covering studies upon multi-level indicators of inherent mechanism, internal function, management modeling, and ecosystem analysis.

In this paper, we describe methods to establish the relationships among geomorphology (physiographic) data, topography, and physical parameters in different-sized catchments using GIS techniques. GIS software allows performing interpolation point, vector or raster analysis based on topographic parameters, simultaneously with mapping land use. This technique depends on the quantity of spatial information. Our objective was to explain the most important geomorphological parameters with an emphasis on surface erosion in mountain areas. Assessment of fluvial sediment in streams is essential to evaluate surface run-off. In the present paper, Artificial Neural Networks (ANNs) were applied to show relationships among total phosphorus, nitrate nitrogen and suspended sediment concentration using architecture based on geomorphological features. Results may be applied at a catchment scale of 1 km2 to more than 50 km2. Our methods are important for investigating the land-use for final assembly depending on sediment-erosion appraisal. This understanding is critical for developing geomorphological models to predict the detachment of soil and transport from their mass, derived-debris material and surface run-off. Our approach will be useful for land management to evaluate risks of sediment transport and raindrop splash and rill erosion in mountainous catchment areas.

During the last century, soils have been over-exploited by humans through agriculture and industrial development. The need to assess different aspects of soil degradation has become a priority in the soil protection management. Among several indices developed in the last years, QBS-ar (Soil Biological Quality-arthropod) index joins the biodiversity of soil microarthropods community with the degree of soil vulnerability. Up to now, numerous publications have reported the results of the QBS-ar application. This paper starts a review process for QBS-ar assessment by taking into account its potential in highlighting the relationship between soil quality and different land uses. In order to clarify the relationship between QBS-ar values and land use, we collected 41 published papers that reported of QBS-ar applications. In this framework, another aim of this paper is to make a critical review of the QBS-ar in respect to applications in different environmental contexts, and to obtain critical indication about problem and potential of QBS-ar for monitoring activities. We collected published data on QBS-ar and we individualized eight groupages in relation to soil uses: 1) Agriculture lands (A, several crops, till and no-tillage, organic, conventional), 2) Woods (W, forests, maquis and bushes), 3) Restored (R, plant remediation, restored pit mine, peri -urban uncultivated areas), 4) Natural degradation (ND, soils in natural degraded conditions, e.g. serpentine soils, soil in the brUle), 5) Permanent grasslands, pastures and meadows (G), 6) Orchards (O), 7) Urban parks, residual urban woods, public gardens, botanical gardens, home gardens (UP), 8) Soils involved in human degradation (D). The results confirmed that land use significantly affects QBS-ar values. The overall mean of QBS-ar = 93.7 can be considered a tentative threshold that separates high quality soils and values which are typical for poor soils. In the end, we would like to affirm the validity of this index, which, allows to evaluate the suffering state of soils and its potentiality for an expeditious use to evaluate soil biological quality in recovery areas.

A number of physical, chemical and biological water quality parameters are measured to understand quality conditions of a riverine environment. Various aggregation techniques have been used to reflect the combined effect of the existing or proposed quality levels. Water Quality Index (WQI) has been developed primarily for fresh water environments. Organic pollutants are not considered in many WQIs because analyses of organics are rather expensive. In general, fecal coliform and total coliform counts are measured to reflect biological contamination of water. With growing consensus on considering rivers as ecological entity, rather than merely a source of water for human consumptions, the emphasis is changing from water quality index (WQI) to River Health Index (RHI). As benthic macroinvertebrate species are differentially sensitive to many biotic and abiotic factors in river environment, macroinvertebrate community structure has commonly been used as indicator of stream water condition. A number of river health assessment methodologies have been proposed in past and several indices such as Ecological Health Index (EHI), Ecological Quality Index (EQI), Overall Index of Pollution (OIP) and River Pollution Index (RPI) have been developed using water quality variables and aquatic species for evaluating the ecological health of river systems. This paper attempts to review and summarize some of these indices with particular emphasis on measurable parameters included in such formulations. The analyses suggest that with sufficient scientific experience and understanding in this area, it is possible to define a River Health Index (RHI) by including algae, macroinvertebrates and fish species as measurable parameters along with routine physico-chemical characteristics of river water. One such formulation on RHI has been proposed by the authors in this paper. The novelty of the proposed formulation include simple calculations and presentation of RHI value on 0–100 scale. The biological indices used in ACEDP (2011) requires biological expertise which is not normally available with water resource managers. Hence species identification and counting based simple biotic indices have been proposed so that it can be easily done by non experts also. River Health Level (RHL) can be categorized as ‘critical’ or ‘excellent’ based on RHI value 80. Thus RHI values can be used to identify the healthy or unhealthy stretches of the river. RHI can serve as scientific tool to assess the current status, need and effectiveness of any intervention required to improve the condition of the river. Application of this RHI formulation on main stream of river Ganga (India) is under study.

Este artículo contiene 11 páginas, 8 figuras, 7 tablas., Macroalgae is one of the Biological Quality Elements (BQE) used by several indexes conceived in the European Water Framework Directive (WFD) for the assessment of the Ecological status of coastal water bodies. Among them, CARLIT index, based on the cartography of rocky-shore littoral communities, has been extensively and successfully applied in the Western Mediterranean Sea. In this study CARLIT was applied for the first time in the Levantine Sea, along the Lebanese shoreline in order to test the suitability of this method in the peculiar ecological conditions of the Levantine Sea and have a first assessment of its ecological status. The choice of proper reference sites is a focal point in the fulfillment of the WFD. In order to ensure accurate calculation of the ecological status of the Lebanese coast, the calculations of the reference conditions (RC) were performed using the values calculated in the Lebanese reference area, the Northwestern (NW) Mediterranean RC proposed in the first application of the CARLIT method and the Adriatic Sea RC. The results showed that the calculated ecological quality ratio values (EQR) based on Lebanese RC is particularly important when considering the principle of the WFD to reach and maintain a good Ecological Status. Overall, the EQR values were well correlated with anthropogenic pressures, as assessed by the LUSI and MA-LUSI indexes. In addition, this method allowed the collection of accurate information on the distribution and abundance of shallow-water communities, especially of those deserving protection (e.g. Cystoseira forests). Thus, the present paper represents a baseline for future studies and gives useful tools for the management of human impacts on the Lebanese coast., This paper was funded by the National Council for Scientific Research (CNRS) in Lebanon within the PhD thesis scholarship granted to Mr. Ali BADREDDINE and the European Community’s Seventh Framework Programme (FP7/2007-2013) [grant number 290056], within the framework of the project MMMPA-Training Network for Monitoring Mediterranean Marine Protected Areas.

Vulnerability of a socio-ecological system to one or more climatic hazards depends on climatic, geophysical, socio-economic and institutional factors. In addition, vulnerability is a scale- and place-dependent concept. While decision-making on adaptation is often made by local government, only a small number of local-scale assessments have been conducted. When indicators are used, global utility functions based on additive or multiplicative aggregation implicitly assume complete compensation between indicators, implying that one source of vulnerability (e.g., proximity to the sea) can always be compensated for by an advantage in another source (e.g., strong social or economic capital) when evidence shows that this is not always the case. This paper presents an indicator-based assessment of vulnerability of eight beaches in Shoalhaven, New South Wales, that are especially exposed to sea level rise. Its goal is twofold. First, it illustrates how indicator-based vulnerability assessments can be applied at a local scale – and not just sectoral, regional and national scales – with the aim of informing local government adaptation planning and resource allocation. In the process, models operationalising the concepts of exposure, sensitivity and adaptive capacity are developed for two valued attributes identified through discussions with the local council, namely the well-being of beach residents and the well-being of users of infrastructure systems located at the beaches. Second, the paper studies, for the first time, the way outranking procedures can be applied in real-life vulnerability assessments as a form of data aggregation that better reflects the non-compensatory nature of many vulnerability indicators. The strengths and weaknesses of the outranking approach are discussed, especially the extent to which the concepts of preference, indifference and dominance thresholds can be conveyed to, and their values elicited from, stakeholders.

Evaluating the efficiency of urban metabolism is an effective tool to quantitatively analyze the relationship between social, environmental and economic input and output in an urban ecosystem. In this paper, there are the combination Emergy synthesis, Principal Component Analysis (PCA) and Date Envelopment Analysis (DEA) of time-series approach to comprehensive assessment urban metabolism for the first time. DEA of time-series has the advantage of fully considering the technology changes every year, it can reflect clearly the overall situation of urban metabolism, including the use of various resources and the development of environmental sustainability cycle. The results show that, with the overall efficiency of metabolism ensured a generally steady rise in Changsha, the trend of urban environmental load rate is increased obviously, there is facing the huge pressure of ecosystem in Changsha. This paper suggests that government and enterprises should take measures to promote the construction of the eco industrial park, gradually realize industrial symbiosis, upgrade through the adjustment of energy structure and industrial structure.

The analysis of vulnerability of socio-ecological systems to threats of different types such as extreme climate events or change in land use draws attention to the factors and processes that determine whether the ecological, social and integrated socio-ecological systems are likely to experience harm due to exposure to the threat. During the last years there has been an increasing attention to the analysis of the vulnerability of socio-ecological systems when facing the lost or degradation of ecosystem services. However, despite the existence of conceptual frameworks and empirical applications to evaluate vulnerability of socio-ecological systems some open questions and challenges still remain. How to conceptually differentiate key concepts such as sensitivity, exposition, vulnerability and threat? How to consider the differences in socio-economic characteristics of the beneficiaries and in their capacity to adapt to new conditions of the ecological system? How to link ecological vulnerability with the social system analysis to obtain an integrated risk assessment of the socio-ecological system? This paper presents a conceptual framework for vulnerability assessment of socio-ecological systems that addresses the mentioned open questions based on a review of both theoretical and empirical literature related to vulnerability and socio-ecological systems. The paper identifies the attributes and indicators of the dimensions of vulnerability for understanding both the social vulnerability and ecological vulnerability separately and then jointly, in interaction with each other. The framework offers a way to communicate with practitioners and policy makers on identifying and improving the factors that reduce vulnerability. It can thus serve as a tool for targeting the implementation of policies and practices aimed at reducing vulnerability.

There are several methods for assessing ecosystem tolerance level, each method is defined according to the target of the ecosystem development. This paper seeks a way to plan for the development of the existing ecosystems modified for human activities. For this purpose, two methods have been applied to assess the effects of local development on the local ecosystem tailored to the different demands. Thus, after gathering data in a coastal region all technologies being capable to be applied in this region regarding ecotourism development (population growth) were determined. Via different indicators, the quality of the ecosystem was evaluated. The Ecosystem Quality Index (EQI) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are methods of decision making. Both methods were applied to minimize the negative effects of the development plan. These methods have similar behavior against development. In the context of decision-making, a minimization method was used so as to minimize the distance between the values of the indicator in the current and future time horizon in the EQI method. The second method; that’s to say, minimization of negative effects is the main target to choose the technology. In the present paper, Shif Island, as a certain limited region in the south of Iran, has been investigated. In this region, industrial development might directly affect the soil and water quality and it might disrupt the development of agriculture and the natural ecosystem. In this regard, local development might have restrained the availability of resources and also might have a detrimental effect on the environment. Population growth has been one of the main pressures on the ecosystem. If the economic development of the island is aimed at the expansion of ecotourism, it shall have both positive and negative effects on the local population. The development of the wastewater treatment system in Shif Island would lead to an increase in the tolerance level in 2025. Since 2018, due to the development of industrial and rural facilities, Soil erosion has been observed. After 2027, the tolerance level will be increased. Extension of the organic phase of soil and accumulation of biomass also will improve the quality of the soil. The application of two methods and analysis of variation of ecosystem tolerance has indicated that a decreasing trend of ecosystem tolerance should be expected until 2027. Then, a steady increase in ecosystem tolerance will be observed from 2028 onwards and will go on in the years following then.

Industrial ecology (IE) was defined as a dynamic evolution process characterized by the coupling coordination relationship between ecological efficiency and industrial efficiency. This paper used data envelopment analysis and technique for order preference by similarity to ideal solution method to explore the spatiotemporal evolution of ecological efficiency and industrial efficiency in China’s loess plateau region (LPR) from 2006 to 2017. Based on the definition, LPR’s IE development and spatial correlations were studied separately using coupling evaluation model and social network analysis. Findings include: Although eco-efficiency and industrial efficiency were mutually promoted, and both have improved during the study period, LPR’s human-earth relation was still facing uncertainties. There were significant disparities in industrial efficiency, ecological efficiency, and IE levels among cities. The evolutionary patterns of cities were classified as coordinated development mode and professional development mode. The IE interactive network was characterized by “scale-free” and “small world,” where provincial capitals and big cities functioned as growth poles and intermediaries. The correlations of IE brought challenges as well as favorable conditions for setting up a cross-regional coordination mechanism. This paper provided a new perspective to understand IE and a “quantity and structure” driven development idea for policymakers.

The UN System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) aims at regular and standardised stocktaking of the extent of ecosystems, their condition, and the services they provide to society. Recording the condition of ecosystems is one of the most complex pieces in this exercise and needs to be supported by consistent guidelines. SEEA EA defines the condition of an ecosystem as its overall quality, measured in terms of quantitative metrics describing its abiotic and biotic characteristics. One of the key challenges lies in identifying the most appropriate metrics for each ecosystem type that capture these essential characteristics. The objective of this paper is to create a well-defined framework for transparent and operative development of ecosystem condition indicators, which can be used in ecosystem accounting and ecosystem assessment studies. Starting from the SEEA EA documentation and a small targeted systematic review, we identified 12 key criteria, which we grouped according to their roles during the indicator development process. Five conceptual criteria (intrinsic relevance, instrumental relevance, sensitivity, directional meaning, and framework conformity) outline the priorities for identifying relevant characteristics of the ecosystems. Five practical criteria (validity, reliability, availability, simplicity, and compatibility) provide guidance on identifying concrete quantitative metrics for the selected characteristics. Finally, two ensemble criteria (comprehensiveness, and parsimony) ensure the completeness and complementarity of the final set of metrics. To tackle the climate and biodiversity crises, the condition of ecosystems needs to be better recognized in national economic planning. The proposed framework supports the selection of a concise set of salient and credible ecosystem condition indicators through a transparent, repeatable and scientific process. This can make the compilation of ecosystem condition accounts more accessible and more standardised on a global level, which is a key prerequisite for the success of SEEA EA. Additionally, the framework presented in this paper may be useful in other contexts where ecological, environmental, or sustainability indicators need to be identified.

Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing water quality monitoring in urban rivers. In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism learning and semantic feature analysis. The results show that the coefficient of determination (R2) of GA_XGBoost algorithm for chlorophyll a (Chla), total phosphorous (TP), total nitrogen (TN), ammonia–nitrogen (NH3-N) and turbidity (TUB) is 0.855, 0.699, 0.787, 0.694, and 0.597, respectively, indicating a high precision and the predicted results are consistent with the measured data. Meanwhile, this paper compares the GA_XGBoost model with other algorithms: Deep Neural Network (DNN), Random Forest, genetic algorithm_RandomForest (GA_RandomForest), adaptive boosting (AdaBoost) and genetic algorithm_adaptive boosting (GA_AdaBoost), and the performance of the GA_XGBoost model is better. At the same time, data from different periods have been added to verify the model’s applicability. Moreover, based on the inversion results, analyze from the point of view of point source pollution, non-point source pollution, etc., to further investigate the influencing factors that cause urban river pollution. The current method has important practical significance for promoting the intelligent and automatic level of water environment monitoring technology in ecological environmental protection and urban water resources protection.

Methodological factors are often acknowledged for their impact on the urban sustainability assessment outcomes. The significance of this impact, however, divides those who assign it great importance; to the point of compromising any attempt at sustainability benchmarking using indicators, from those who regard it as having a relatively limited role. In this paper, we aim to contribute to this debate by assessing how methodological choices could influence the variation in the outcomes of indicator-based urban sustainability benchmarking. The paper starts with a literature review outlining the evolution of the approaches adopted since the pioneering experiences in the ‘90s to show a convergence in indicators and data over time. Subsequently, by using the data of the 25 most populous cities in Quebec, we empirically analyse the sensitivity of the cities’ sustainability scores to 18 combinations of estimation techniques, and observe consistency within the outcomes while maintaining general patterns regardless of the approach used. The results suggest that methodological choices play a limited role in the urban sustainability assessment outcomes. Thus, other determinants beyond the evaluation methods are most likely involved and therefore should be a major focus in future research agendas.

The study of grasslands using machine learning (ML) methods combined with proximal/remote sensing data (RS) has been steadily increasing in the last decades. Available algorithms range from a primarily academic use to more widespread practical applications intended at helping farm management. Here, we review the use of ML methods applied to aboveground biomass (AGB) estimation in grassland systems. Based on 26 recent papers, we perform a literature review of the topic to identify common practices, namely the relation between estimation performance and the ML method used, data sources, and scale (local/regional). In order to identify the relation between the characteristics of the studies and the estimation accuracy, we use descriptive and correlation analysis. In spite of a surge in the number of papers and application examples, there is no evidence that the estimation performance of the algorithms has been improving over time. In all approaches used by the authors of the papers herein considered, the number of field samples, RS data source, and species composition of the grassland systems are the most relevant variables to explain the estimation accuracy. This accuracy increases with the number of field samples until it plateaus, hinting at the existence of an optimum level for monitoring efforts. Accuracy also increases with the proximity of the sensor to the field, i.e., on average accuracy is higher using field spectroscopy than using satellite data. There is no evidence that any particular ML method is more suited to this problem. The literature also displays significant limitations in terms of its applications of the ML algorithms. For example, a limited number of papers validated the models, casting doubt on the potential of the models for generalized application. Despite those limitations, and considering the advancements verified, we expect that, in the near future, ML methods combined with RS/proximal data will continue to improve and be helpful for farm management.