Understanding the factors that affect residents’ waste separation behaviors helps in constructing effective environmental campaigns for a community. Using the theory of planned behavior (TPB), this study examines factors associated with waste separation behaviors by analyzing responses to questionnaires distributed in Guangzhou, China. Data drawn from 208 of 1000-field questionnaires were used to assess socio-demographic factors and the TPB constructs (i.e., attitudes, subjective norms, perceived behavioral control, intentions, and situational factors). The questionnaire data revealed that attitudes, subjective norms, perceived behavioral control, intentions, and situational factors significantly predicted household waste behaviors in Guangzhou, China. Through a structural equation modeling analysis, we concluded that campaigns targeting moral obligations may be particularly effective for increasing the participation rate in waste separation behaviors.

China—the largest developing country in the world—is experiencing both rapid economic maturation and large-scale urbanization. These situations have led to waste disposal problems, and the need to identify alternative energy sources. Waste-to-energy (WTE) conversion processes, a source of renewable energy, are expected to play an increasingly important role in China’s sustainable management of municipal solid waste (MSW). The purpose of this research is to investigate the key problems and opportunities associated with WTE, to provide recommendations for the government. This paper begins by describing China’s current MSW management situation and analyzing its waste disposal problems. The major challenges associated with China’s WTE incineration are then discussed from economic, environmental and social points of view. These include the high costs associated with constructing necessary facilities, the susceptibility of facilities to corrosion, the lower heating value of China’s MSW, air pollutant emissions and especially public opposition to WTE incineration. Since discarded waste can be used to produce energy for electricity and heat—thus reducing its volume and the production of greenhouse gas (GHG) emissions—with government policies and financial incentives, the use of WTE incineration as a renewable energy source and part of a sustainable waste management strategy will be of increasing importance in the future. The paper concludes by summarizing the management, economic and social benefits that could be derived from developing the country’s domestic capacity for producing the needed incineration equipment, improving source separation capabilities, standardizing regulatory and legal responsibilities and undertaking more effective public consultation processes.

Anthropogenic land-use change is one of the main drivers of global environmental change. China has been on a fast track of land-use change since the Reform and Opening-up policy in 1978. In view of the situation, this study aims to optimize land use and provide a way to effectively coordinate the development and ecological protection in China. We took East Guangdong (EGD), an underdeveloped but populous region, as a case study. We used land-use changes indexes to demonstrate the land-use dynamics in EGD from 2000 to 2020, then identified the hot spots for fast-growing areas of built-up land and simulated land use in 2030 using the future land-use simulation (FLUS) model. The results indicated that the cropland and the built-up land changed in a large proportion during the study period. Then we established the ecological security pattern (ESP) according to the minimal cumulative resistance model (MCRM) based on the natural and socioeconomic factors. Corridors, buffer zones, and the key nodes were extracted by the MCRM to maintain landscape connectivity and key ecological processes of the study area. Moreover, the study showed the way to identify the conflict zones between future built-up land expansion with the corridors and buffer zones, which will be critical areas of consideration for future land-use management. Finally, some relevant policy recommendations are proposed based on the research result.

With the transition in the regional development discourse to knowledge- and innovation-based economics, the cultivation of innovation capacity has gained importance as an initiative to enhance regional sustainability and has emerged as a policy goal. An understanding of the formation of innovation clusters is critical to the cultivation of regional innovation capabilities. Except for the location and regional development conditions’ factors, researchers emphasize a built environment’s role in the formation of innovation clusters. Based on the spatial database of 12,516 high-tech firms in 2017 in the Pearl River Delta (PRD), this study developed a conceptual framework for the built environment. The framework comprises living, service, perceptual, industrial, and physical aspects. The direction and intensity of the correlation between built environment factors and high-tech firms are discussed using the spatial regression model and geographical detector (GD) technique. The results show a highly concentrated spatial distribution pattern of high-tech firms in the PRD. A significant county-level spatial autocorrelation is revealed through Moran’s I. According to the model, we determine the positive impacts of technology support, transport infrastructure, and living service levels on the agglomeration of high-tech firms as well as the negative impact of the public service level. The GD’s result demonstrates different levels of impact intensity of built environmental factors. We argue that a comprehensive understanding of the influence of built environment factors on innovation agglomeration will help policymakers develop targeted policies to foster local innovation capabilities and promote sustainable regional development.

Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation.