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With the intensification of global environmental pollution and the overexploitation of resources and energy, low-carbon environmental economy (LCEE) has become the important means for countries in the world to achieve sustainable economic, resource, and environmental development. Therefore, the research on the coordinated development of logistics development and LCEE is very necessary in the development of our country's LCEE. Firstly, this paper discusses the development level of logistics industry in Hainan Province from five aspects: energy consumption, carbon emission, carbon productivity, economic benefit index, and carbon emission efficiency. Secondly, choose carbon emission intensity to represent the development level of low-carbon economy. Based on the efficiency coefficient method, coefficient of variation method, linear weighting method, spatial description method, and system evolution equation method, the coordination measurement method is adopted. Finally, the coordinated development of logistics development and low-carbon environmental economy in Hainan Province is analyzed. On this basis, this paper puts forward the related promotion strategies for the coordinated development of logistics development and logistics industry in Hainan. The research results show that the system coupling degree in Hainan Province is about 0.9, indicating that the logistics industry and the low-carbon economic subsystem have a relatively high degree of coupling development. Compared with other provinces, Hainan's level of coupled and coordinated development is at the upstream level. [ABSTRACT FROM AUTHOR]

With further urbanization, household consumption firmly plays a key role in China's national carbon emissions. However, current research concerning carbon issues has mainly focused on urban household consumption, and few studies have paid attention to herder households, leading to a research gap in the field of low-carbon shifting related to nomadic economies. In this study, we interviewed more than one-thousand herder households in the Sanjiangyuan region of the Qinghai–Tibet Plateau in China. The household carbon emissions and their influencing factors were investigated across the herder households of 15 counties. Our findings revealed the following: (1) There exist differences in the amounts of household carbon emissions and their compositions in the Sanjiangyuan region. From the perspective of spatial distribution, the emission hotspots are mainly concentrated in the eastern part of the Sanjiangyuan region. (2) At the prefecture level, average personal emissions were larger in the Hainan Prefecture (3.26 t ce/year), while they were approximately 1.36 times that of the Huangnan Prefecture (2.4 t ce/year), though with smaller personal emissions. The indirect carbon emissions of the four prefectures all occupied larger percentages of household carbon emissions that were mainly contributed by food consumption and housing. (3) Family type was the main diver influencing personal carbon emissions in the Huangnan Prefecture, Guoluo Prefecture, and Yushu Prefecture. The more people living in the household, the lower the per capita carbon emissions. However, the effect size of potential carbon reductions was weakened when the number of family members rose to over three. (4) We propose that grazing prohibitions and low-carbon dietary shifts would contribute to low-carbon herder livelihoods, especially for small-sized households that should be peer-to-peer targeted by regional government propaganda, which may help to strengthen the implementation of in-depth low-carbon promotions across the Sanjiangyuan region and even the overall Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions. [ABSTRACT FROM AUTHOR]

Promoting the use of zero-emission vehicles is an important measure for reducing pollutant and carbon dioxide emissions from medium- and heavy-duty trucks (MHDTs). This study took Hainan Island as an example. Based on big data such as industrial layout and traffic flow, it clarified that the main channels of freight transportation on Hainan Island are concentrated in the northern region, including the surrounding areas of Haikou; the important ports of Haikou, Yangpu, and Basuo; and Chengmai and Tunchang counties. Furthermore, pathways for the promotion of zero-emission MHDTs are proposed, which can reduce exhaust emissions by 1549 tons of NOx, 62 tons of particulate matter (PM), and 3.60 million tons of CO2 by 2030. Compared with the vehicle type categorization plan, the spatial layout plan can achieve higher emission reduction benefits in the medium term (2025). In addition, in conjunction with existing policies and planning requirements, this study also puts forward policy suggestions for the promotion of zero-emission MHDTs. [ABSTRACT FROM AUTHOR]

Environmental regulations are an important means for government managers to manage the environment. The motivation of this article is to investigate the influence mechanism of incentive and mandatory environmental regulations on energy efficiency and carbon dioxide (CO 2) emissions in the logistics industry. The quantile regression can estimate the comprehensive effect of explanatory variables on dependent variables, including maximum, minimum, and median. Based on the panel data of China's 30 provinces from 2005 to 2019, this paper adopts quantile regression to simulate the impact of environmental regulations on CO 2 emissions and energy efficiency. The empirical results obtained are as follows: (1) incentive environmental regulations make a greater contribution to CO 2 emission reduction in Ningxia, Qinghai, and Hainan provinces, due to their more aggressive levy of pollution fees. (2) Mandatory environmental regulations contribute the most to CO 2 emission reductions in the 25th-50th percentile provinces, since these provinces issue more environmental decrees. (3) Incentive environmental regulations produce a greater influence on the energy efficiency in the 50th-75th, 75th-90th and upper 90th percentile groups, due to their greater R&D investment. (4) Mandatory environmental regulations have a greater impact on energy efficiency in Xinjiang, Heilongjiang, and Yunnan provinces. The findings can provide empirical support for the government to formulate effective environmental policies. • Incentive environmental regulations have a greater impact on CO 2 emissions in Ningxia, Qinghai, and Hainan. • Mandatory environmental regulations exert a greater effect on CO 2 emissions in the 25th–50th quantile group. • Incentive environmental regulations produce a greater impact on energy efficiency in the high quantile groups. [ABSTRACT FROM AUTHOR]