Study on the coordinated development of urban competitiveness and energy-carbon emission reduction in China.

The urban competitiveness (UC) evaluation system is multidimensional and complex. This paper takes the simulated annealing (SA) model as the projection pursuit (PP) optimization to achieve a comprehensive assessment of competitiveness of 277 Chinese cities from 2011 to 2019, accompanied by energy saving and carbon-emission reduction (ESCER) as environmental measurements, to explore whether the two can meet the Porter hypothesis through coupling coordination degree (CCD). Further using spatiotemporal autocorrelation and obstacle degree model to uncover spatiotemporal features and interfering factors of coordinated development. Key findings include: (1) UC and ESCER show a slightly fluctuating upward trend during the research period, with apparent spatial variations. The eastern coastal region has a robust UC, while the less competitive central and western regions benefit from natural conditions, excelling in ESCER. (2) 87% of cities have achieved coordinated development between competitiveness and ESCER. Some coastal areas, often with a high CCD, are improving resource use efficiency and environmental benefits through economic agglomeration. From the perspective of the CCD collaboration network, the positive correlation accounts for about 85%, which reveals that most adjacent regions can cooperate on the road of coordinated development. (3) While differences exist in the coordinated development of UC-ESCER across various regions, social factors predominantly influence the obstacles affecting coordinated development. Specifically, a substantial barrier to the concordant progression of most cities is the number of patent applications, underscoring the pivotal role of innovation in aligning UC with ESCER.

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and ß-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition ß-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

A dual-core system dynamics approach for carbon emission spillover effects analysis and cross-regional policy simulation.

As carbon emission continue to rise and climate issues grow increasingly severe, countries worldwide have taken measures to reduce carbon emission. However, carbon dioxide is continuously flowing in the atmosphere and is easily influenced by neighboring cities' policies. Therefore, how to solve the problem of carbon emission spillover effect has become the key to improve policy efficiency. Cross-regional carbon governance provides a perspective on solving the carbon emission problem by regulating and guiding the cooperative behavior of cross-regional governance actors. Taking Chengdu-Chongqing area as an example, this study used the SDM to analyze the influencing factors and spatial spillover effects of emission. Then we used the system dynamics method to construct a dual-core carbon emission system, and simulated the spillover effect and emission reduction potential of Chengdu and Chongqing emission reduction policies under different policy schemes. The results reveal that the mobility of population and enterprises have a significant impact on carbon emission prediction. Carbon reduction policies exhibit the phenomena of "carbon transfer" and "free-riding." When Chengdu lowers its economic growth rate, it leads to the transfer of high energy-consuming enterprises to Chongqing, increasing carbon emission in Chongqing. The implementation of comprehensive carbon reduction policies in Chongqing has a positive effect on Chengdu. Emission reduction policies exhibit issues related to their temporal efficacy, as the effects of industrial structural policies in Chengdu yield opposite outcomes in the short and long term. Each city's unique circumstances necessitate tailored carbon reduction policies. In order to reduce carbon emissions, Chengdu and Chongqing require opposite population policies.

Dynamic simulation of carbon emission under different policy scenarios in Pearl River Delta urban agglomeration, China.

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR). Based on this, the system dynamics model was used to construct a CE system framework considering multi-dimensional driving factors, so as to combine the complex CE system with the emission reduction countermeasures considering spatial heterogeneity, and realize the dynamic simulation of CE reduction policies. The results showed that the urban agglomeration as a whole will reach carbon peak by 2025. Shenzhen, Zhuhai, and Dongguan have achieved carbon peak before 2020, while other cities will reach carbon peak by 2025-2030. The government policy constraints can effectively curb CE, but if government constraints were relaxed, CE will rise and individual cities will not reach carbon peak. Comprehensive CE reduction policies are better than a single CE reduction policy. The study found that this model framework provides a systematic analysis of carbon reduction strategies for urban agglomerations, offering decision-makers various combinations of economic development and green low-carbon objectives. This will further contribute to a multi-faceted mitigation of high emission in urban agglomeration and promote regional sustainable development.

[Simultaneous determination of 19 phthalate esters in cosmetics using gas chromatography-mass spectrometry].

A method was developed for the simultaneous determination of 19 phthalate esters (PAEs) at trace level in cosmetics by solid phase extraction (SPE) purification and gas chromatography-mass spectrometry (GC-MS) detection. The PAEs were extracted from cosmetic samples by dichloromethane with ultrasonic-assisted technique, purified by an SPE column packed with silica gel and neutral alumina (2: 3, m/m) with the elution of 20 mL of mixed solvent of ethyl acetate-hexane (8: 2, v/v). Qualitative and quantitative analysis were carried out by GC-MS in full scan and selected ion monitoring modes. The retention time of quantitative ions and the abundance ratio of characteristic ions were applied to rapidly and accurately identify each analyte so as to prevent the occurring of possible mistakes from complex matrix intervention. Under optimized conditions, the average recoveries for a shampoo sample spiked with the standards at 0.1, 0.5, 2.0 microg/g were in the range of 72.2% and 110.9%, and the relative standard deviations (RSDs) for the 19 PAEs were less than 10.3% (n = 6) at the spiked level of 0.1 microg/g. The limits of detection (LODs, as 3 times of standard deviation) were between 0.0065 microg/g (for diisopentyl phthalate) and 0.062 microg/g (for diisobutyl phthalate). The method was successfully applied to the determination of the PAEs in 6 types of cosmetics. It is expected to promote the determination of the PAEs in other cosmetics with different matrices.