What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts.

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

What determines the performance of low-carbon cities in China? Analysis of the grouping based on the technology-Organization-Environment framework.

BACKGROUND AND OBJECTIVES: Addressing climate change and reducing greenhouse gas emissions have emerged as shared global objectives. Enhancing the development performance of low-carbon cities has become an urgent and widely acknowledged concern for both government policy-making departments and academics. Drawing upon the complex grouping perspective and resource allocation theory, this study investigates how varying conditions related to technology, organization, and environment in Chinese low-carbon pilot cities can effectively allocate resources to shape the governance performance of low-carbon cities. METHODS AND DATA: This paper employs a comprehensive grouping analysis perspective, treating the research object as a combination of various ways between condition variables. It integrates the advantages of case studies and variable studies, and investigates the collective relationships between elemental groupings and outcomes using the fsQCA analysis method. This approach facilitates the understanding of multiple concurrent causal relationships within the technology-organization-environment (TOE) framework, accounting for different performance levels in Chinese low-carbon pilot cities, as well as addressing complex causal issues such as asymmetry and multiple scenario equivalence. Data from 30 representative low-carbon pilot cities in China were employed to validate the TOE theoretical framework. CONCLUSION: No single element alone can be considered a necessary condition for low-carbon city governance performance. However, environmental enhancement plays a more prominent role in the governance performance of low-carbon cities. Additionally, the presence of "multiple concurrent" technical, organizational, and environmental conditions leads to a diverse range of governance performance in Chinese low-carbon pilot cities. In other words, the driving paths of low-carbon city performance exhibit distinct pathways. CONTRIBUTION: The findings of this study can assist low-carbon pilot city managers in generating effective governance ideas, facilitating the successful implementation of low-carbon city pilot projects, and drawing valuable lessons from the experience of low-carbon city development in China.

Research on Motivational Mechanisms and Pathways for Promoting Public Participation in Environmental Protection Behavior.

Public participation in environmental protection is an essential component of evaluating the effectiveness of ecological and environmental protection. General awareness, social dynamics, and cognitive preferences frequently impact the protection's impact. The aim of this study is to investigate the correlation research on the confluence of mainstream awareness, social factors, and cognitive preferences by building a theoretical model. First, this work employs partial least squares structural equation modelling (PLS-SEM). Second, using the mediation model, the research describes and examines the factors that motivate public involvement in ecological and environmental conservation. Third, the research summarizes the suggested path countermeasures to offer practical advice and helpful ecological and environmental protection solutions. The findings demonstrate that mainstream policy leadership substantially impacts environmental conservation. Leadership in policy matters restricts the group's natural awareness of social factors. The subjective quality and competence basis in cognitive preferences are significantly influenced by policy leadership. Policy leadership significantly influences the effectiveness of environmental protection through the mediating factor of cognitive preferences. The ability base has a considerable mediating effect on cognitive preferences.

Deep Stereo Matching With Hysteresis Attention and Supervised Cost Volume Construction.

Stereo matching disparity prediction for rectified image pairs is of great importance to many vision tasks such as depth sensing and autonomous driving. Previous work on the end-to-end unary trained networks follows the pipeline of feature extraction, cost volume construction, matching cost aggregation, and disparity regression. In this paper, we propose a deep neural network architecture for stereo matching aiming at improving the first and second stages of the matching pipeline. Specifically, we show a network design inspired by hysteresis comparator in the circuit as our attention mechanism. Our attention module is multiple-block and generates an attentive feature directly from the input. The cost volume is constructed in a supervised way. We try to use data-driven to find a good balance between informativeness and compactness of extracted feature maps. The proposed approach is evaluated on several benchmark datasets. Experimental results demonstrate that our method outperforms previous methods on SceneFlow, KITTI 2012, and KITTI 2015 datasets.