This study selects the entropy method to measure the comprehensive index of green finance and 2011–2020 panel data covering 283 cities in China; describes spatial and temporal evolution, the migration of the centre of gravity, and discrete trends in green finance and urban haze pollution; and empirically examines the effect of green finance on urban haze pollution using static and dynamic spatial Durbin models. The study revealed that Chinese urban haze pollution significantly decreased during the sample period and that the hotspot emission area shifted to the northeast. Green finance experienced significant advancement, transitioning from a lower stage to a higher stage and becoming more geographically focused. Green finance mainly suppresses urban haze pollution through spatial spillover effects, and such spatial spillover effects change from positive to negative over time. Regarding regional heterogeneity, the effect on the western region is positive, the effects on the central and eastern regions are negative, and there is a spatial spillover effect “from promotion to inhibition” in the eastern region. Due to the heterogeneity in resource endowments, green finance in nonresource-based cities has a greater impact on haze pollution than does green finance in other cities, and in the long term, it has a significant inhibitory effect on haze pollution. This study reveals the effect of green finance on urban haze pollution from a dynamic perspective and, in doing so, it not only provides a new path for joint governance of haze pollution between cities but also provides more accurate guidance for the government to formulate policies for different regions and regions with different resource endowments.

Despite global efforts to address climate change, carbon dioxide (CO₂) emissions are still on the rise. While carbon dioxide is essential for life on Earth, its increasing concentration due to human activities poses severe environmental and health risks. Therefore, accurately and efficiently predicting CO₂ emissions is essential. Hence, this research delves deeply into the prediction of CO₂ emissions by examining various deep learning models utilizing time series data to identify carbon dioxide levels in Oman. First, four important production materials of Oman (oil, gas, cement, and flaring), which have a great impact on CO₂ emissions, were selected. Then, the time series related to the release of CO₂ was collected from 1964 to 2022. After data collection, preprocessing was performed, in which outliers were removed and corrected, and data that had not been measured were completed using interpolation. Then, by dividing the data into two sections, education (1946–2004) and test (2022–2005) and creating scenarios, predictions were made. By creating four scenarios and modeling with two independent GRU and LSTM models and a hybrid LSTM-GRU model, annual carbon was predicted for Oman. The results were evaluated with three criteria: root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (r). The evaluations showed that the hybrid LSTM-GRU model with an error of 2.104 tons has the best performance compared to the rest of the models. By identifying key contributors to carbon footprints, these models can guide targeted interventions to reduce emissions. They can highlight the impact of industrial activities on per capita emissions, enabling policymakers to design more effective strategies. Therefore, in order to reduce pollution and increase the productivity of factories, using an advanced hybrid model, it is possible to identify the carbon footprint and make accurate predictions for different countries.

This paper examines how China’s Ecological and Environmental Code reconstructs the legal status of animals in the context of contemporary environmental and animal law. Guided by the principles of Ecological Civilisation and a Community of Life between Humans and Nature, the Code departs from traditional instrumental and utilitarian approaches. Through textual and comparative analysis, the study concludes that the Code promotes a threefold reimagining of the legal status of animals — covering wildlife, farmed animals and ecologically connected animals — by presenting them as functional elements within an ecological relational network rather than as mere legal objects. However, there are still institutional limitations, including the absence of explicit animal welfare legislation, unclear liability mechanisms and weak enforcement coordination. Unlike Western rights-based models, China adopts an integrative, ecologically holistic approach that revitalises indigenous ethical resources and offers an alternative framework for global animal governance.