Abstract
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.
Highlights
The literature review of relevant key issues is presented : Section 2.1 introduces Wenquan, Guangdong Province; Section 2.2 offers a summary of energy consumption by buildings and households in China; Section 2.3 describes sustainable resource use subsidies for rural residences; Section 2.4 provides a compilation of initial criteria; and Section 2.5 discusses a multiple-criteria decision-making artificial intelligence model (MCDM-AI)
To reduce CO2 emissions and resolve water shortage issues, this study proposes that coastal rural areas in China have sunlight and rainwater resource conditions conducive to including solar photovoltaic systems and stormwater reuse as development items for rural revitalization
The Delphi Fuzzy (DFuzzy) model fuzzy logic inference system (FLIS) quantification algorithm indicated that the optimal assessment score of subsidy applications for farm building renovation to facilitate the use of natural resources was 93.4
Summary
Sustainability 2021, 13, 12505 generated environmental pollution and other challenges due to considerable CO2 emissions. This has resulted in aggravating global warming and climate change as well as irreversible, abnormal climate conditions (e.g., extreme cold, extreme heat, flooding, and drought) [2]. Chinese society encountered various problems with the prosperity it attained through economic reform, such as environmental pollution and high CO2 emissions caused by industrial development and a severe gap between urban and rural development [17]. In addition to causing abnormal climates globally, high CO2 emission levels severely influence the quantity and distribution of world water resources, such as through changes in surface and ground water [20]. Using Conghua District, Guangdong Province as an example, the proposed 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
