Search

The growth of global environmental concerns has prompted a worldwide movement towards sustainable development. In recent years, China's rapid economic growth has been reflected in increased environmental degradation, exacerbated by rapid urbanization, the urban–rural divide, and a government commitment to urban ecological preservation at the expense of rural areas. At the same time, rural communities require increased cultural awareness of environmental conservation and sustainable practices. To address the indiscriminate depletion of natural resources endangering China's rural landscapes, this paper presents a novel framework that combines deep convolutional neural networks (CNNs) with rural ecological governance. A thorough understanding of rural ecological dynamics is made possible by the integration of cutting-edge CNN technology, which facilitates complex data processing and image recognition from satellite and sensor data. Utilizing the Eurostat dataset, it carries out an extensive sustainability analysis of rural ecological governance by applying fundamental theories of environmental governance, such as polycentricity and symbiosis, that are adapted for rural environments. The pastoral environmental governance issues are systematically addressed by means of a multi-stage PPS hierarchical approach. The framework also suggests a novel approach to household waste management for isolated communities, focusing on natural sewage treatment and waste recycling as ways to reduce environmental impact. This all-encompassing strategy seeks to transform rural ecological management by providing customized solutions to protect resources and promote sustainable development in China's rural areas. The experimental results show a fantastic accuracy rate of 98.5% of the CNN model. This compares favorably to recognized architectures such as ResNet16, ResNet32, and ResNet64, demonstrating CNN's ability to handle complex features of rural landscapes. A comparative analysis with RNN and LSTM highlights CNN's capacity to extract hierarchical features and capture spatial dependencies. In addition, the work reveals that rural residents have an overall satisfaction rate of 85.2% with environmental governance, of which 45.6% provide high levels of satisfaction. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has significantly impacted international trade, benefiting national and international economies. It has increased trade and financial liberalization in many countries. Ecological and environmental protection have become critical issues due to the fast mobility of money and commodities. The purpose of this paper is to find a way to balance foreign trade growth with environmental protection. We aim to achieve this by using IoT to establish a co-integration model that takes into account the comprehensive environmental index, imports and exports, and the capital-labor ratio. We have collected data from Quanzhou City between 2011 and 2019, based on various environmental pollution indicators, import and export trade volume, capital-labor ratio, and other factors. Our analysis includes an examination of the empirical model of foreign trade and the ecological environment, as well as the impulse response. Additionally, we used prediction variance decomposition and the Granger causality test to understand the coordinated relationship between foreign trade and the ecological environment. Our results demonstrate that when the volume of exports (EX) increases by 1 percent, the ecological environment in Quanzhou City is negatively impacted by about 0.06%. On the other hand, when the volume of imports (IM) increases by 1 percent, there is a 0.26% positive effect on the ecological environment of the city. Finally, when the capital-to-labor ratio (K) increases by 1 percent, there is a 0.40% deterioration of the city's ecological environment. [ABSTRACT FROM AUTHOR]