The evaluation of China’s air pollution and the effectiveness of its governance policies is currently a topic of general concern in the academic community. We have improved the traditional evaluation method to construct a comprehensive air quality assessment model based on China’s major air pollutants. Using the daily air pollutant data of 2015–2018, we calculated and analyzed the monthly air quality of nine cities in the Pearl River Delta of China, and conducted a comparative study on the effect of the air pollution control policies of the cities in the Pearl River Delta. We found that the air quality control policies in those nine cities were not consistent. Specifically, the pollution control policies of Guangzhou and Foshan have achieved more than 20% improvement. The pollution control policies of Dongguan and Zhaoqing have also achieved more than 10% improvement. However, due to the relative lag of the formulation and implementation of air pollution control policies, the air quality of Jiangmen, Zhuhai and Zhongshan has declined. Based on the analysis of the air quality assessment results and the effects of governance policies in each city during the study period, we propose suggestions for further improvement of the effectiveness of air pollution control policies in the region.
With the rapid growth of China’s export trade and increasing pressure of domestic carbon emission reduction, the issue of carbon embodied in export trade has attracted increasing attention from academic circles. This paper has constructed a calculation model for embodied carbon in China’s export trade by using the multi-region input-output model and the international input-output data from the World Input-Output Database (WIOD) database in order to calculate the amount of embodied carbon. Our objective is to analyze the main source industry and specific sectors of embodied carbon in China’s export trade, and to provide a quantitative basis for emission reduction under the “carbon neutrality” strategy. The findings reveal that the embodied carbon in China’s export trade mainly comes from the secondary industry, which accounts for more than 90% of the total embodied carbon in export trade, while the proportions of embodied carbon in the primary industry and the tertiary industry are relatively low, about 1% and 5–7%, respectively. In terms of specific sectors, the crop and animal production and hunting sectors have the largest share (over 60%) of embodied carbon in the export trade of the primary industry; in the export trade of the secondary industry, the main sources of embodied carbon are the manufacturing sector and the power, gas, steam and air-conditioning supply sectors, respectively accounting for around 50% and 45% of the total embodied carbon in the export trade of the secondary industry; as for the tertiary industry, the transport and storage sectors have the largest share of embodied carbon in the export trade, which is around 70%. Based on the above research results, this paper has provided relevant policy recommendations, which are optimizing the export structure, improving the energy consumption structure and the carbon emissions trading system.
Since China’s reform and opening up, especially after its accession to the World Trade Organization, its foreign trade has achieved fruitful results. However, at the same time, the extensive foreign trade growth model with high energy consumption and high pollution has also caused a rapid increase in carbon emissions. There is a large amount of embodied carbon emissions in the export trade. In order to achieve the strategic goals of “Carbon Peak” and “Carbon Neutrality’, and at the same time build a green trading system to achieve coordinated development of trade and the environment, it is of great significance to study embodied carbon emissions and how to decouple them with China’s foreign trade. This paper uses the Logarithmic Mean Divisia Index method to decompose the influencing factors of the embodied carbon in China’s export trade in order to study the impact of three factors: export scale, export structure, and carbon emission intensity. The results show that the change in export scale is the most important factor affecting the embodied carbon of China’s export trade, and the expansion of export scale has caused the growth of trade embodied carbon. Carbon emission intensity is the second influential factor, and the decline in carbon intensity would slow down the growth of trade embodied carbon, while changes in the export structure have the smallest impact on trade embodied carbon. The high carbonization of the overall export structure will cause growth of trade embodied carbon, but the tertiary industry has seen some improvement in the export structure, which could facilitate the decline of trade embodied carbon.
ObjectiveThis article aims to identify factors that shape the knowledge, attitudes and behaviours of community residents in China's Heilongjiang province towards emergency preparedness. Findings of such a study may provide evidence to support the development of effective public risk communication strategies and education campaigns.DesignA cross-sectional household questionnaire survey was conducted in Heilongjiang province in 2014. A stratified cluster sampling strategy was employed to select study participants. The questionnaires were administered using face-to-face interviews. 2800 questionnaires were completed, among which 2686 (95.9%) were considered valid for data analyses. A multivariate logistic regression model was adopted to identify the extent to which the independent variables were associated with emergency preparedness.ResultsFewer than 5% respondents were well prepared for emergency. Over half (52%) of poorly prepared respondents did not know what to do in emergency; women (OR=1.691), higher household income (OR ranging from 1.666 to 2.117), previous experience with emergency (OR=1.552), higher levels of knowledge about emergency (OR=2.192), risk awareness (OR=1.531), self-efficacy (OR=1.796), as well as positive attitudes towards emergency preparedness (OR=2.265) were significant predictors for emergency preparedness. Neither educational attainment nor exposure to awareness-raising entered into the logic regression model as a significant predictor for emergency preparedness.ConclusionsThe level of emergency preparedness in Heilongjiang residents is very low, which is linked with poor knowledge and attitudes of the residents towards emergency preparedness. Future emergency awareness campaigns should be more focused and tailored to the needs of intended audience, taking into consideration of their usual source of information and knowledge in relation to emergency.
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.
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