The Netherlands Climate Change Agreement aims to reduce CO₂ emissions and seismic events by halting natural gas usage by 2050. This will require widespread societal acceptance by 90% of households. The study investigates the social acceptance of a district heating network (DHN) among social housing tenants in Haarlem, Netherlands. The findings of a survey administered to ninety-five tenants revealed a substantial level of support for the DHN project. A significant portion of respondents, 75%, expressed their approval for the DHN, surpassing the legally required threshold of 70% for implementing building retrofits. Findings imply that although the participants possess an adequate comprehension of the rationale for energy transition, their familiarity with the precise particulars and practical information pertaining to the proposed transition to DHN is inadequate. The level of trust in housing corporations, energy providers, and the municipality is uniformly low, indicating a lack of institutional trust. Generally, the interpersonal trust among tenants tends to be lower than their trust in the broader public, which in turn restricts their capacity for self-organization and exercising influence over energy institutions. Although DHNs are typically regarded as environmentally friendly and secure, there are several challenges that need to be addressed, including the uncertainty about who will cover the costs of transitioning and the doubts surrounding DHN feasibility (warmth and reliability). We suggest implementing interventions to improve tenants' comprehension of the DHN project's particulars (capability), provide practical information regarding costs and feasibility (motivation), and foster trust at both interpersonal and institutional levels (opportunity).

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the <inline-formula> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.