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Understanding the extent of collaboration among stakeholders is key to supporting mangrove management. Despite the existence of robust policies, collaboration among stakeholders of mangrove co-management remains largely unexplored in Sri Lanka. This was partly due to the civil war, natural disasters, and other socio-economic changes over the past 30 years. Our study aimed to identify the collaboration between stakeholders of mangrove management and their perceptions regarding mangrove co-management in Sri Lanka using social network analysis and content analysis. Surveys were conducted in all five coastal provinces of Sri Lanka. Stakeholders included in the study were from government departments, non-governmental organizations, and private institutes. Our results showed that there were differences between coastal provinces in the mangrove management networks, specifically in the number of stakeholders involved and their degree of collaboration. Some important stakeholders (for example the Land Use and Policy Planning Department) were excluded from the social networks in certain provinces (Eastern and Western provinces). There were various issues hampering effective mangrove management such as inefficient communication, inconsistencies between policies, and insufficient financial capacity of government stakeholders responsible for policy implementation. According to the stakeholders in our study, providing mangrove management initiatives with long-term collaboration, post-care, continuous monitoring, and funding may help to overcome these challenges. Additionally, we suggest the establishment of a common platform to coordinate stakeholders. We further encourage increasing the participation of academics, researchers, and students from national universities in the mangrove co-management of Sri Lanka. Insights from this island-wide survey can be adapted to mangrove and other natural resource management trajectories in other countries as well.

Sardine cooking effluents contain a high level of organic matter, such as proteins and lipids, which allows them to be forward into a chain exploiting high added-value compounds attained from these effluents, increasing their economic value while reducing their environmental effect. Thus, the purpose of this work was to develop an innovative pilot-scale integrated membrane process, with or without enzymatic hydrolysis, to obtain fractions with high protein/peptide and low NaCl contents, as well as optimized bioactive properties. The research strategy followed involved the use of ultrafiltration (UF) and nanofiltration (NF) technologies of the pretreated sardine cooking effluent followed by reverse osmosis (RO) at a pilot scale levels. Moreover, it allowed for the attainment of fractions rich in protein/peptides that might be used in the food, pharmaceutical, or cosmetic industries, particularly after RO, as they present a lower NaCl content. The RO retentate (hydrolyzed sample) coupled with UF and NF resulted in the fractions with the best bioactive properties (higher antioxidant capacity and antimicrobial activity) of all the analyzed samples. Overall, the current work demonstrated the feasibility of exploiting liquid by-products as a source of functional components as well as reinforcing this strategy’s potential relevance in future effective management strategies for this type of effluents.

Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation.

Mainland Southeast Asian (MSEA) countries (Cambodia, Laos, Thailand, Myanmar, and Vietnam) are likely to become one of the next hotspots for emission reduction, since CO2 emissions in this area will have a two-thirds increase by 2040 due to rapid economy growth and associated energy consumption. As one of the most vulnerable areas to climate change, MSEA countries need to develop low-carbon roadmaps based on accurate emission data. This study provides emission inventories for MSEA countries for 2010-2019, based on the IPCC territorial emission accounting approach, including emissions from five types of fuels (i.e., coal, crude oil, oil products, natural gas, and biofuels & waste) used in 47 economic sectors. The results show that the emissions in MSEA countries are on the rise, with average annual growth rates ranging from 2.5% in Thailand to 19.3% in Laos. Biomass is one of the most important sources of carbon emissions, contributing between 11.8% and 76.7% of total carbon emissions, but its share has been declining in most countries, whereas the share of emissions from coal has risen sharply in Laos, Vietnam, and Cambodia. We further examine the drivers behind the changes in emissions using index decomposition analysis. Economic growth was the strongest driver of growth in emissions, while population growth has only had a small effect on emission growth. Energy intensity varies widely across nations, but only significantly reduced CO2 emission growth in Thailand. The secondary sector considerable contributed to an increase in CO2 emissions in Laos and Vietnam, while the tertiary sector only moderately contributed to emissions in Thailand. Our study provides a better understanding of the composition and underlying factors of emission growth in MSEA countries, this could shape their low-carbon development pathway. Our results could also inform other emerging economies, which may become emission hotspots in the next decades, to develop low-carbon roadmaps, thereby contributing to the achievement of global climate change targets.

Air pollution is a global challenge, and especially urban areas are particularly affected by acute episodes. Traditional approaches used to mitigate air pollution primarily consider the technical aspects of the problem but not the role of citizen behaviour and day-to-day practices. ClairCity, a Horizon 2020 funded project, created an impact assessment framework considering the role of citizen behaviour to create future scenarios, aiming to improve urban environments and the wellbeing and health of its inhabitants. This framework was applied to six pilot cases: Bristol, Amsterdam, Ljubljana, Sosnowiec, Aveiro Region and Liguria Region, considering three-time horizons: 2025, 2035 and 2050. The scenarios approach includes the Business As Usual (BAU) scenario and a Final Unified Policy Scenarios (FUPS) established by citizens, decision-makers, local planners and stakeholders based on data collected through a citizen and stakeholder co-creation process. Therefore, this paper aims to present the ClairCity outcomes, analysing the quantified impacts of selected measures in terms of emissions, air quality, population exposure, and health. Each case study has established a particular set of measures with different levels of ambition, therefore different levels of success were achieved towards the control and mitigation of their specific air pollution problems. The transport sector was the most addressed by the measures showing substantial improvements for NO2, already with the BAU scenarios, and overall, even better results when applying the citizen-led FUPS scenarios. In some cases, due to a lack of ambition for the residential and commercial sector, the results were not sufficient to fulfil the WHO guidelines. Overall, it was found in all cities that the co-created scenarios would lead to environmental improvements in terms of air quality and citizens’ health compared to the baseline year of 2015. However, in some cases, the health impacts were lower than air quality due to the implementation of the measures not affecting the most densely populated areas. Benefits from the FUPS comparing to the BAU scenario were found to be highest in Amsterdam and Bristol, with further NO2 and PM10 emission reductions around 10%–16% by 2025 and 19%–28% by 2050, compared to BAU.

Conventional green roofs have often been criticised for their limited water buffer capacity during extreme rainfall events and for their susceptibility to droughts when additional irrigation is unavailable. One solution to these challenges is to create an extra blue water retention layer underneath the green layer. Blue-green roofs allow more stormwater to be stored, and the reservoir can act as a water source for the green layer throughout capillary rises. An automated valve regulates the water level of the system. It can be opened to drain water when extreme precipitation is expected. Therefore, the water buffer capacity of the system during extreme rainfall events can be maximised by integrating precipitation forecasts as triggers for the operation of the valve. However, the added value of this forecast-based operation is yet unknown. Accordingly, in this study, we design and evaluate a hydrological blue-green roof model that utilises precipitation forecasts. We test its performance to capture (extreme) precipitation and to increase evapotranspiration and evaporative cooling under a variety of precipitation forecast-based decision rules. We show that blue-green roofs can capture between 70 % and 97 % of extreme precipitation (>20 mm/h) when set to anticipate ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). This capture ratio is considerably higher than that of a conventional green roof without extra water retention (12 %) or that of a blue-green roof that does not use forecast information (i.e., valve always closed; 59 %). Moreover, blue-green roofs allow for high evapotranspiration rates relative to potential evapotranspiration on hot summer days (around 70 %), which is higher than from conventional green roofs (30 %). This serves to underscore the higher capacity of blue-green roofs to reduce heat stress. Using the city of Amsterdam as a case study, we show the high upscaling potential of the concept: on average, potentially suitable flat roofs cover 13.3 % of the total area of the catchments that are susceptible to pluvial flood risk. If the 90th percentile of the ECMWF forecast is used, an 84 % rainfall capture ratio can translate into capturing 11 % of rainfall in flood-prone urban catchments in Amsterdam.

This paper reviews the architecture of collaboration that exists within inter-organizational natural resource management (NRM) networks. It presents an integrative conceptual framework designed to help operationalize the multi-level interactions that occur between different dimensions of trust, risk perception, and control as key concepts in inter-organizational collaboration. The objective is to identify and justify a series of propositions considered suitable for assessing inter-organizational NRM network collaboration through empirical work. Such an integrative conceptualization goes beyond the existing trust scholarship related to collaborative NRM, and, we argue, offers a useful starting point for further exploring some of the 'inner' social dynamics affecting collaborative performance using complex systems thinking. To help establish the relevance of the conceptual framework to transboundary resource governance, a survey operationalizing different dimensions of trust, perceived risk, and control is piloted in the Salish Sea, an ecosystem that spans the Canada-US border between British Columbia and Washington State. Key challenges associated with operationalizing the framework and future research needs are identified.

Participatory techniques are widely recognized as essential in addressing the challenges of agri-environmental policy and decision-making. Furthermore, it is well known that stakeholder analysis and social network analysis are useful methods in the identification of actors that are involved in a system and the connections between them. To identify key stakeholders and improve the transfer of information from national-to farm-level, we compared a stakeholder analysis with farmer-centric networks for primary productivity, carbon regulation and biodiversity through the case study of Latvia. Farmer-centric networks show a higher number of stakeholders communicating on the topic of primary productivity network comparing to other topics. We found three pathways for improving knowledge transfer in agri-environmental governance: horizontal strengthening of farming community, horizontal strengthening of policy departments, and vertical strengthening between policy departments and farmers. The first step is to ensure that policy-makers have a common understanding of the results that should be achieved. The second step is the transfer of know-how between farmers to develop new solutions. The third step is the training of advisers in the land multifunctionality and the strengthening of communication and knowledge transfer between policy departments and farmers in order to jointly achieve the desired direction at that national level. Long-term cooperation between many stakeholders, including knowledge transfer, the development and implementation of solutions, and monitoring are essential in order to adequately address global societal challenges. The application of our mixed methods approach to elucidate pathways for improved governance of knowledge and information is of direct relevance to other jurisdictions seeking to transition towards multifunctional and sustainable land management.

Against a background of intensifying climate-induced disturbances, the need to enhance the resilience of forests and forest management is gaining urgency. In forest management, multiple trade-offs exist between different demands as well as across and within temporal and spatial scales. However, methods to assess resilience that consider these trade-offs are presently lacking. Here we propose a hierarchical framework of principles, criteria, and indicators to assess the resilience of a social-ecological system by focusing on the mechanisms behind resilience. This hierarchical framework balances trade-offs between mechanisms, different parts of the social-ecological system, ecosystem services, and spatial as well as temporal scales. The framework was developed to be used in a participatory manner in forest management planning. It accounts for the major parts of the forest-related social-ecological system and considers the multiple trade-offs involved. We demonstrate the utility of the framework by applying it to a landscape dominated by Norway spruce (Picea abies (L.) Karst.) in Central Europe, managed for three different management goals. The framework highlights how forest resilience varies with the pursued management goals and related management strategies. The framework is flexible and can be applied to various forest management contexts as part of a participatory process with stakeholders. It thus is an important step towards operationalizing social-ecological resilience in forest management systems.

Tourism in ecological functional zones (EFZs) is rapidly becoming an increasing trend; however, its impact on ecosystem services remains poorly understood owing to the absence of a consistent quantification framework. This study uses the Taihang Mountains (THM), an EFZ in China, as an example to develop a framework for evaluating the direct and indirect impact pathways of scenic spots on the trade-offs between multiple ecosystem services by identifying the linkages between scenic spot development, socioeconomic change, land use transitions, and ecosystem services. The results show that the continued conversion of agricultural land, grassland, and forest to constructed land around scenic spots in 2000–2020 was accompanied by a decline in water yield (WY) and habitat quality (HQ); while food production (FP), carbon storage (CS), and soil retention (SR) increased. Land use and ecosystem service changes around scenic spots in the THM also exhibited significant spatial gradient effects. In particular, a 10-km buffer area was identified as a distinct “influence zone” where the ecosystem services trade-offs and land use changes were the most pronounced. In 2010, scenic spot revenue was the dominant factor that increased the trade-offs between SR with FP and CS via direct pathways. However, in 2020, the dominant factor was scenic spot level, which shifted the impact toward the relationship between CS and WY and HQ by intensifying the trade-offs to facilitating synergies. This was accomplished in an indirect manner, such as the facilitation of local population growth, industrial restructuring, and infrastructure development. This study reveals the varying effects of scenic spot development via different pathways, thereby providing useful insights for global EFZs to more precisely design policies that can adequately balance human activities with ecosystem services.

Controlling non-point source pollution is often difficult and costly. Therefore, focusing on areas that contribute the most, so-called critical source areas (CSAs), can have economic and ecological benefits. CSAs are often determined using a modelling approach, yet it has proved difficult to calibrate the models in regions with limited data availability. Since identifying CSAs is based on the relative contributions of sub-basins to the total load, it has been suggested that uncalibrated models could be used to identify CSAs to overcome data scarcity issues. Here, we use the SWAT model to study the extent to which an uncalibrated model can be applied to determine CSAs. We classify and rank sub-basins to identify CSAs for sediment, total nitrogen (TN), and total phosphorus (TP) in the Fengyu River Watershed (China) with and without model calibration. The results show high similarity (81%-93%) between the identified sediment and TP CSA number and locations before and after calibration both on the yearly and seasonal scale. For TN alone, the results show moderate similarity on the yearly scale (73%). This may be because, in our study area, TN is determined more by groundwater flow after calibration than by surface water flow. We conclude that CSA identification with the uncalibrated model for TP is always good because its CSA number and locations changed least, and for sediment, it is generally satisfactory. The use of the uncalibrated model for TN is acceptable, as its CSA locations did not change after calibration; however, the TN CSA number decreased by around 60% compared to the figures before calibration on both yearly and seasonal scales. Therefore, we advise using an uncalibrated model to identify CSAs for TN only if water yield composition changes are expected to be limited. This study shows that CSAs can be identified based on relative loading estimates with uncalibrated models in data-deficient regions.

Multilayer Blue-Green Roofs are powerful nature-based solutions that can contribute to the creation of smart and resilient cities. These tools combine the retention capacity of traditional green roofs with the water storage of a rainwater harvesting tank. The additional storage layer enables to accumulate the rainwater percolating from the soil layer, that, if properly treated, can be reused for domestic purposes. Here, we explore the behavior of a Multilayer Blue-Green Roof prototype installed in Cagliari (Italy) in 2019, that have been equipped with a remotely controlled gate to regulate the storage capacity of the system. The gate installation allows to manage the Multilayer Blue-Green Roof in order to increase the flood mitigation capacity, minimizing the water stress for vegetation and limiting the roof load with adequate management practices. In this work, 10 rules for the management of the Multilayer Blue-Green Roof gate have been investigated and their performances in achieving different management goals (i.e., mitigating urban flood, increasing water storage and limiting roof load on the building) have been evaluated, with the aim to identify the most efficient approach to maximize the benefits of this nature based solution. An ecohydrological model have been calibrated based on field measurements carried out for 6 months. The model has been used to simulate the system performance in achieving the proposed goals, using as input nowdays and future rainfall and temperature time series. The analysis reveled the importance of the correct management of the gate, highthing how choosing and applying a specific management rule helps increasing the performance in reaching the desired goal.