Sustainable Development Goals

ABSTRACT The unflinching surge in global warming has left the world in ecological chaos. Consequently, governments across the globe are committing to achieving decarbonizing the environment with a target aimed at 2050, of which China is a key player. In effect, this study seeks to contribute to the strides by querying the extent to which shocks in technological innovation make or mar the sustainability of the Chinese environment from 1990 to 2019. The other impacts of foreign direct investment, renewable and nonrenewable energy, and economic growth are considered in the model estimated through nonlinear ARDL robust for ascertaining instantaneous positive and negative effects. The results reveal that technological innovation promotes and hinders environmental sustainability based on the positive and negative shocks, respectively, whereas the adverse shocks impede it. Besides, economic growth and nonrenewable energy turn out to be negative predictors of environmental sustainability while renewable energy and FDI facilitate it. The results of the Fourier Toda Yamamoto corroborate the NARDL results. The EKC hypothesis is not validated for China based on estimates from the novel Itkonen (2012) approach. Following the empirical results, practicable policy insights toward sustaining the environment in China and beyond are formulated.

Do the asymmetric effects of technological innovation amidst renewable and nonrenewable energy make or mar carbon neutrality targets?

To date, tourism is the fastest growing industry globally, but one of the least developed in terms of environmentally sustainable practices. However, only a small portion of documents elaborate on how the introduction of new technologies can impact a more sustainable development route for tourism. This study’s objective is to provide an overview on literature state-of-the-art related to sustainable tourism and technological innovations, offering insights for further advancing this domain. We employ a bibliometric analysis and a comprehensive review of 139 articles, collected from Web of Science and Scopus databases, for the purpose of: (i) exploring and discussing the most relevant contributions in the publication network: (ii) highlighting key issues and emerging topics; (iii) uncovering open questions for the future. Our findings reveal contradictory views on the risks and benefits of technology adoption. Artificial intelligence, internet of things, circular economy, big data, augmented and virtual reality emerge as major trends. Five work streams are identified and described, leading to a broader perspective on how technology can shape the future of sustainable tourism. Relevant theoretical and managerial implications are derived. Finally, a research agenda is proposed as guidance for future studies addressing the outcomes of digital disruption on sustainable tourism.

Shaping a View on the Influence of Technologies on Sustainable Tourism

Is fintech the new path to sustainable resource utilisation and economic development?

Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities.

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Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

This paper examines artificial intelligence (AI) and sustainable development in China. The Chinese government has developed ambitious policies for global leadership in the field of AI and sustainab...

Subin Liengpunsakul

Papers

Artificial Intelligence and Sustainable Development in China