Public Works and Housing Infrastructure Planning using Environmental Carrying Capacity Consideration:Case Study on Planning Dam Development in Kalimantan Island, Indonesia

This article aims to explain how the environmental carrying capacity indicators could benefit public works and housing infrastructure planning. Law No. 32/2009 about environmental protection and management stated that the government is obliged to implement the Strategic Environment Assessment (SEA/KLHS) in the preparation of policies, plans, and/or programs that have the potential to cause environmental impacts and/or risks. This research aims to understand the process of using ecosystem services as part of the environmental carrying capacity. This approach would be relevant to the public works and housing infrastructure planning and is related to the National Medium Term Development Plan (RPJMN) goals in considering the environmental carrying capacity. This means that if the development of infrastructure does not meet the criteria of the environmental carrying capacity, it will cause negative impacts that could lead to futile infrastructures. The process of considering the environmental carrying capacity will be explained in quantitative methodology as an analysis process with a matrix as an overlay result. The overlay result will be interpreted as the basic information on whether a building in that location is feasible or not for carrying capacity conditions. The overlay result will be used as a basis for providing suggestions and recommendations.

Public Participation as a Tool for Solving Socio-Spatial Conflicts of Smart Cities and Smart Villages in the Sustainable Transport System

The article presents modern international approaches to public participation in Sustainable Transport System planning. It discusses the causes of social conflicts during the implementation of transport infrastructure projects using the example of implementation of several Polish strategic road infrastructure projects. It provides the assessment of the form, scope, and scale of stakeholders’ involvement in the decision-making process. Among mitigation measures, the authors propose a model solution based on a comprehensive approach to public participation in road infrastructure planning in smart cities and smart villages within a Sustainable Transport System. The proposed idea involves a model of multi-criteria spatial analysis using the analytic hierarchy process (AHP) developed in the geographical information systems (GIS) environment, which—apart from technical-functional, environmental, cultural, economic, financial, and social criteria—also encompasses preferences expressed by local community representatives. The model includes eight stages of public participation in the decision-making process, involving all the rungs of a ladder of citizen participation. The presented solution departs from typical social participation methods used in road infrastructure planning processes.

Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%.

Design of Infrastructure for Pumped Storage Power Station and Automatic Monitoring System Using Geographic Information System

The pumped storage power station realizes grid connected power generation through the conversion between the potential energy of surface water and mechanical energy. It has become the strategic resource of UHV power grid with its low valley peak regulation and emergency standby function. The green basic design and design of the pumped storage power station needs systematic research. Based on the collaborative analysis method of production and ecological safety of storage disk, this paper takes Ninghai pumped storage power station as an example to carry out green infrastructure planning and design research. Through the comprehensive evaluation and analysis of construction land based on GIS, from the perspective of adaptability of power station construction to mountain creek pit environment, the function of horizontal layout is constructed in parallel and vertical layout, and a modular and distributed spatial structure of green infrastructure is constructed; Then, based on the continuous recycling of water resources, the monitoring and early warning system of power plant production ecological safety is constructed, which is “one water, two lines and three slopes”. It is hoped that the correlation between the production and the ecological dispatching of the storage tray can be found through the dynamic tracking of multiple factors and quantitative analysis of production cycle, which can provide reliable basis for timely mitigation of damage and formulation of compensation strategies, Scientific control of green infrastructure planning, design and construction.