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The Social-Ecological Systems (SES) framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. Yet, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change that institutions and natural resources systems co-evolve to provide a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how Data Cube technology could overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users.

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

China’s research on and specific implementation of energy saving for buildings are mainly concentrated in urban areas, but according to 2016 statistics, the rural population accounts for 42.65% of the total population, so rural housing has considerable energy-saving potential. However, the degree of attention to the energy consumption of rural houses needs to be improved. Regarding the research on and implementation of passive energy-saving strategies for residences, compared with centralized urban high-rise residences, rural residences mainly have independent courtyards, with a flexible layout and easier transformation. In this study, a system that uses the common cold lanes in traditional villages and buildings’ exterior walls was constructed, and the indoor spaces of courtyard buildings in southern Shaanxi were completely passively cooled in summer. This system can be completely separated from the supply of artificial energy by relying on the accumulation and buoyancy effects of air in patios and cold lanes and the hot-pressure ventilation in buildings to cool the buildings and greatly improve indoor ventilation efficiency. As the building is ventilated and cooled, the air wall formed in the system can effectively prevent direct contact between the outdoor and indoor temperatures and reduce the impact of thermal wall radiation on the interior. In previous studies on the passive design of courtyard houses, scholars considered the effect of thermal wall radiation on indoor temperature in simulations. Therefore, in this study, we also separately calculated whether to consider the difference between the situation with and without wall heat radiation (WHR) when simulating thermal conversion. The final results show that when the cooling system was adopted, the annual cooling load of the whole building was 4786.494 kW·h without WHR. However, with WHR, the cooling load reduction was 2989.128 kW·h, a difference of 1797.336 kW·h.

Transitioning to the Enhanced Transparency Framework (ETF) under the Paris Agreement (PA) from the current measurement, reporting and verification (MRV) arrangements under the United Nations Framework Convention on Climate Change (UNFCCC) requires developing countries to enhance their national reporting systems in a sustainable way. There are various challenges that developing countries face in preparing national reporting under the existing MRV framework. The objective of this paper is to identify practical solutions as good practices in the short and long term to address the common challenges facing many developing countries as they prepare their national climate reporting. We collected information on practical solutions implemented by developing countries to overcome common challenges during preparation of their national reporting. There were some limitations regarding data collection using a country survey since we received responses from only six countries and two organizations, resulting in a small sample. Good practices for addressing common challenges include developing a national legal framework for transparency-related work, assigning a main coordinating institution, collaborating with domestic institutions for methodology development on emission reduction, and establishing university curricula on transparency. Replicating these solutions as good practices on how to deal with the challenges would be one way for developing countries to enhance their respective reporting systems in a sustainable manner. Furthermore, the paper highlights three underlying factors (efficiency, accountability, and sustainability) behind those practical solutions. These underlying factors should be considered important as guiding principles for building sustainable reporting systems in order to be able to successfully implement the ETF.

Sustainable nitrogen (N) management in agriculture is one of the most important issues affecting the environmental performance of modern agriculture. It is actually well perceived that coordinated efforts and holistic approaches are required to regulate N use by farmers. The purpose of this study was to provide an initial examination of stakeholders’ views in Japan regarding N use in agriculture and challenges to increase its sustainability. The analysis was based on a questionnaire study of five types of stakeholders (farmers, advisors, researchers, suppliers, policy makers). By means of multivariate analysis techniques it was revealed that consensus was lacking either in the acknowledgment of the causes and effects of unsustainable N management or in the challenges that need to be addressed. N losses from farms and the effects of N use were perceived but not conceived equally by all stakeholders. Organic farming and mandatory measures were the most controversial challenges, while those involving awareness, training and advisory were the most popular. This study cannot provide safe conclusions that can be generalized in the Japanese context, but it indicates domains where further research is required and orientations for future policy design towards more sustainable N use.

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.