Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities

Greencoin as an AI-Based Solution Shaping Climate Awareness

Our research aim was to define possible AI-based solutions to be embedded in the Greencoin project, designed as a supportive tool for smart cities to achieve climate neutrality. We used Kamrowska-Załuska's approach for evaluating AI-based solutions' potential in urban planning. We narrowed down the research to the educational and economic aspects of smart cities. Furthermore, we used a systematic literature review. We propose solutions supporting the implementation process of net zero policies benefiting from single actions of urban dwellers based on the Greencoin project developed by us. By following smart city sectors, the paper introduces AI-based solutions which can enrich Greencoin by addressing the following needs: (1) shaping pro-environmental behaviors, (2) introducing instruments to reinforce the urban management process, (3) supporting bottom-up initiatives allowing to shape urban resilience, (4) enhancing smart mobility, (5) shaping local economies supporting urban circularity, and (6) allowing better communication with residents. Our research fills the gap in the limited group of studies focused on shaping climate awareness, enhancing smart governance, and supporting social participation and inclusion. It proves that AI-based educational tools can be supportive when implementing adaptation policies toward climate neutrality based on our proposed AI-based model shaping climate awareness.

Decision-Making Model Based on Discriminant Analysis Fuzzy Method for Low-Carbon and Eco-Friendly Residence Design: Case Study of Conghua District, Guangzhou, China

Many countries aim to promote low-carbon and eco-friendly development and find a route to sustainable development. For such development, a model that helps design and build appropriate constructions is necessary. Thus, this study is carried out to establish such a model by combining the Delphi method, analytical hierarchy process (AHP), and fuzzy logic theory (FLT) (DAFuzzy model). In the Delphi method, the expert’s opinions are reflected in three dimensions (green facilities, ecological facilities, and community participation) and nine factors (green building materials, photovoltaic power generation, energy-saving equipment (green facilities), green roof, planting/vegetation, rainwater collection/water recycling (ecological facilities), subsidies, resident participation, appropriate norms (community participation)). Then, AHP is used to calculate the relative weight of each factor. Finally, by using FLT, the output value of each factor is calculated to find out the best scenarios and influencing factors for the scenario. The result shows that green facilities are the most important dimension, followed by community participation and ecological facilities. Among 45 different scenarios for the model, the best is to consider green facilities and ecological facilities with community participation. The important factors in the best scenario include photovoltaic power generation, planting/vegetation, energy-saving equipment, green building materials, appropriate norms, rainwater collection/water recycling, subsidies, and green roof. The proposed model is validated with residential houses in Conghua District, Guangzhou city, China. Considering the dimensions and factors of the best scenario, the proposed DAFuzzy model proves that a low-carbon and eco-friendly development requires support with appropriate policies and a large investment. The research result suggests that building a low-carbon and eco-friendly house needs the support of the government and people’s understanding and participation in eco-friendly development.

Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province

Rural areas in southern China receive ample rainfall annually as well as over 1600 h of annual sunshine. Despite a generally severe urban–rural development imbalance, these rural areas feature well-developed basic infrastructure and diverse economic activities. Rural revitalization policies in these areas have emphasized the development of cultural and ecological tourism, which has spurred economic development and given rise to a trend of villa construction. Residential buildings sit on large areas where natural resources are abundant. These advantages are conducive to the development and use of sustainable resources. This study proposes an incentive policy encouraging rural residents to renovate their buildings to include rainwater conservation and solar power generation. The Delphi method, an analytic hierarchy process, and fuzzy logic theory were combined to establish an AI-MCDM model, with applications of artificial intelligence and multiple-criteria decision making. Using Conghua District, Guangdong Province as an example, the study suggested that the model is beneficial to increasing the willingness of rural residents to reconstruct and renovate their residences, promoting the development of a low-carbon ecological region, Wenquan Township. We conducted the Delphi process twice to assess and validate incentives for installing natural resource conservation structures in agricultural areas. Nine criteria were identified, which can be divided into three main dimensions of participation situation, generating capacity, and storage facilities. The proposed AI-MCDM model developed using the Delphi–Fuzzy Analytic Hierarchy Process Model has high objectivity and can support rural areas in developing low-carbon, sustainable characteristics. The findings can serve as a reference for governments formulating incentives to encourage the installation of rainwater conservation and solar energy generation structures by rural households.

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of urban big data analytics based on AI-related tools to support the design and planning of cities. To this end, the author introduces a conceptual framework to assess the influence of the emergence of these tools on the design and planning of the cities in the context of urban change. In this paper, the implications of the application of artificial-intelligence-based tools and geo-localised big data, both in solving specific research problems in the field of urban planning and design as well as on planning practice, are discussed. The paper is concluded with both cognitive conclusions and recommendations for planning practice. It is directed towards urban planners interested in the emerging urban big data analytics based on AI-related tools and towards urban theorists working on new methods of describing urban change.