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In recent years, Portugal has witnessed the siting of 250 wind farms, particularly in mountainous and rural areas. Even though, unlike other European countries, general public consensus seemed at first to prevail, protests by local population and ENGOs have been increasing of late (many broadcast by the media) – the outcomes of Environmental Impact Assessments (EIAs) provide a good example. This chapter has two main objectives. On one hand, it examines how rural landscapes are discursively framed in the press when the Portuguese media picks up wind energy issues. On the other hand, by analysing EIA reports, it aims at identifying the social actors involved in the decision process of the siting of wind farms in rural or peri-urban areas, the arguments for and against the location of these facilities and how the (rural) landscape is framed and represented. The empirical material is drawn on three different sources: media analysis of the public discourse on landscape issues related to wind farms; an analysis of EIA reports regarding wind farms in Portugal and an analysis of official positions on this issue assessed through the Environmental Impact Declarations (EID) of EIA processes. It is concluded that despite the lack of media attention to landscape impacts’ of wind farms, the existing discursive frames are often attached to dichotomized cultural meanings: it either deems wind farms as technological tools for landscape progressive transformation or as a risk to its pristine image. As to the EIA reports, landscape matters are more visible and important and at times sufficient to reject approval or change of the siting of a wind farm.

The role of railways within urban areas is analysed, covering ‘metro’ systems (self-contained heavy rail networks, often with substantial underground sections), light rail (both upgraded street tramways and newer systems), and travel by regional and national railways within urban areas. Basic operating characteristics, system capacity, capital costs, and technological change are examined. ‘Sustainability’ is analysed in respect of energy use and environmental impact, railways’ role in supporting high-density urban living (with associated benefits through greater use of non-motorised modes), and financial aspects (coverage of operating costs and ability to finance capital renewals). Current issues examined include the effect of users shifting to more flexible working patterns, the Covid pandemic, automation, and ownership. In general, urban railways can be seen to support a sustainable lifestyle, although some issues do arise in respect of longer distance commuting.

Purpose – The purpose of this research is to examine the diversity of macrozoobenthos as well as its relationship with water quality and substrate in the pond culture area.

Design/Methodology/Approach – The method of sampling area is on five observation stations by purposive sampling. The research was done indirectly (ex situ) for macrozoobenthic identification at the Ecology Laboratory, Faculty of Mathematics and Natural Sciences.

Findings – The fairness/uniformity index obtained from the five research stations ranging from 0.483 to 0.923 indicates a high degree of uniformity. This indicates that the macrozoobenthos biological index at the study site can be used as an indicator that water quality is in good condition and has the potential to be developed into an aquaculture area as well as supporting the success of the shrimp farming as one of the sub-systems of the shrimp agribusiness.

Research Limitations/Implications – This research can be a source of information for the management and utilization of environment in the research area, so that shrimp harvest can be optimized in the pond farming area.

Originality/Value – This research has found that macrozoobenthos included 61 species.

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.

Introduction: With the proliferation and amalgamation of technology and the emergence of artificial intelligence and the internet of things, society is now facing a rapid explosion in big data. However, this explosion needs to be handled with care. Ethically managing big data is of great importance. If left unmanageable, it can create a bubble of data waste and not help society achieve human well-being, sustainable economic growth, and development.

Purpose: This chapter aims to understand different perspectives of big data. One philosophy of big data is defined by its volume and versatility, with an annual increase of 40% per annum. The other view represents its capability in dealing with multiple global issues fuelling innovation. This chapter will also offer insight into various ways to deal with societal problems, provide solutions to achieve economic growth, and aid vulnerable sections via sustainable development goals (SDGs).

Methodology: This chapter attempts to lay out a review of literature related to big data. It examines the implication that the big data pool potentially influences ideas and policies to achieve SDGs. Also, different techniques associated with collecting big data and an assortment of significant data sources are analysed in the context of achieving sustainable economic development and growth.

Findings: This chapter presents a list of challenges linked with big data analytics in governance and achievement of SDG. Different ways to deal with the challenges in using big data will also be addressed.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.