Search

Utilizing an extensive dataset pertinent to the low-carbon economy and water resources extracted from 30 provinces across China, encompassing the period from 2004 to 2019, we have structured a synergistic model that explores the integrative interplay between the low-carbon economic framework and the water resource system. To assess the composite synergy effects, we employed Kernel density estimation in conjunction with Markov chain analysis, aiming to elucidate the temporal distribution dynamics and the spatial evolutionary patterns of provincial-level synergism within China's low-carbon economy-water resource nexus. Our findings reveal that, over the span from 2005 to 2019, the synergy effect within China's hybridized low-carbon economy-water resource system predominantly occupied a stage of moderate amalgamation, demonstrating a consistent upward trajectory since 2011. Notably, multilevel differentiation of composite system synergy is evident nationally, with pronounced trends of divergence manifesting in the eastern, western, and northeastern provinces. In contrast, the synergy effect exhibited in the central region is characterized by regional heterogeneity, yet the absolute disparities in synergy levels have diminished over time. Moreover, analysis of the synergy degree's rank order status within the composite system indicates a relative stability, punctuated by a 'club convergence' phenomenon. Against the backdrop of global climate change, the interactions between low-carbon economies and water resources have garnered significant attention within the realm of global sustainable development research. Exploring these synergistic relationships offers a potential avenue for identifying effective strategies to manage and harness resources, thereby facilitating economic growth and social advancement while mitigating the pace of global climate change. This paper employs data related to the low-carbon economy and water resources in 30 provinces of China from 2004 to 2019. It constructs a composite system model and an evaluation index system to investigate the synergistic effect between these two factors. The study employs the kernel density estimation method and Markov chain to analyze the distribution dynamics and spatial evolution of the synergy level in the composite system of the low-carbon economy and water resources across each province in China. The results indicate the following: [1] Between 2005 and 2019, the synergy effect of China's low-carbon economy-water resources complex system exhibited a mild level of synergy. Since 2011, the degree of synergy has steadily increased, with the synergy degrees of the low-carbon economy system, water resources system, and their complex systems reaching 0.256, 0.487, and 0.277, respectively, by 2019. [2] The absolute difference in the level of synergy between the low-carbon economy and water resources complex systems in the east, west, and northeast regions demonstrates a significant expansion. However, in the central region, regional differences in the synergy effect exist, although the absolute difference exhibits a narrowing trend. [3] The ranking status of the synergy in the low-carbon economy-water resources composite system is relatively stable, displaying the phenomenon of "club convergence." Nonetheless, when spatial effects are considered, the probability of "rank locking" decreases in the lowcarbon economy-water resources composite system. The findings of this paper contribute to a deeper understanding of the synergistic relationship between China's low-carbon economy and water resources, providing a basis for decision-making aimed at achieving China's sustainable development. [ABSTRACT FROM AUTHOR]

In order to cope with severe environmental crises such as pollution and climate, the Chinese government has put forward the strategic goal of "double carbon" and vigorously developed green emerging industries such as cultural industry. Based on the super efficiency model and the input oriented Malmquist index model, this paper comprehensively measures the innovation efficiency of cultural industry, and selects the panel data of the Yangtze River economic belt from 2015 to 2021 for empirical analysis. In addition, the spatial econometric model is introduced to explore the driving factors affecting the innovation efficiency of cultural industry from the perspective of time and space. The results show that: (1) the overall innovation efficiency of the cultural industry in the Yangtze River economic belt is high, but there is significant spatial heterogeneity. (2) The average Malmquist index of the innovation efficiency of the cultural industry in the Yangtze River economic belt is 1.123, which reflects the continuous improvement of the level of industrial innovation. Environmental factors such as sulfur dioxide emissions in waste gas, investment in industrial pollution control, and per capita GDP have a significant impact on the innovation efficiency of cultural industry in the Yangtze River economic belt. Finally, the paper puts forward policy suggestions to promote the high-quality development of cultural industry from the perspective of technology, policy and environment. [ABSTRACT FROM AUTHOR]

Reducing food waste is an important initiative to ensure food security and mitigate climate change. Previous studies have largely ignored the influence of reference points on food waste behavior and the role of human-to-human relational interactions, failing to capture their role in guiding residents' food waste behavior (RFWBs). To address this gap, this study proposes a model that examines the role of reference dependence, normative internalization, and symbolic expectations in RFWBs, while introducing network embeddedness as a moderating variable. Using data collected from 981 respondents online, ordinary least squares were used to test the hypotheses. The results showed that reference dependence and normative internalization had a positive effect on reducing RFWBs with regression coefficients of 0.178 and 0.293 respectively, while symbolic expectations also had a positive effect on RFWBs with a regression coefficient of 0.227. Additionally, Network embeddedness played a significant moderating role in reference dependence, normative internalization, and symbolic expectations with RFWBs. This paper is theoretically innovative and the relevant findings fill the current research gap in the field of food waste, providing guidance and reference for government departments to formulate policies and promote residents to implement food waste reduction behaviors. [ABSTRACT FROM AUTHOR]