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In order to analyze the impact of policy effect on carbon efficiency in the Yangtze River Economic Belt, an experimental group, including 11 regions possessing the features of the Yangtze River Economic Belt, was constructed using the synthetic control method for project evaluation with the single-factor carbon efficiency value as the core study variable in this paper. Several regions were selected from the control group as synthetic objects through a data-driven approach to construct synthetic regions that meet basic requirements before the implementation of the policy by weight ratio and evaluate the policy's impact on the implementation of carbon efficiency in the Yangtze River Economic Belt. The study shows that the single-factor carbon efficiency is relatively robust. But since 2013, the tail of the Yangtze River Economic Belt has seen an improvement. Sichuan, Hunan, Chongqing and Hubei have exceeded other provinces and cities in the growth of carbon efficiency and Shanghai has also witnessed a significant increase. Upon the inception of the synthetic Yangtze River Economic Belt, there's a discernible divergence between the curves of the real and the synthetic Yangtze River Economic Belt. Specifically, policy interventions in the Yangtze River Economic Belt didn't work well in earlier years and even restrained the economic growth of the whole region. [ABSTRACT FROM AUTHOR]

Firstly, through introducing the provincial product trading data into the input–output table, this paper constructs the provincial input–output table and then deduces the provincial MRIO (Multi-Regional Input–Output) model. Secondly, based on the empirical data from 30 provinces in mainland China, the provincial MRIO model was used to analyze the provincial carbon transfer caused by provincial trading and calculate the provincial direct carbon emission and complete carbon emission. Results show that: (1) The provincial MRIO model can separate the provincial self-demand carbon emissions, the net carbon emissions of import and export trading transfer and the net carbon emissions of provincial trading transfer, and can accurately calculate the source and destination of provincial carbon emissions. (2) There is a carbon transfer relationship between any two of China's 30 mainland provinces, and the carbon emissions transferred between provinces are quite different. The five provinces with the largest net provincial trading carbon transfer are Beijing (144.04 million tons), Shanghai (160.96 million tons), Jiangsu (133.85 million tons), Zhejiang (134.12 million tons) and Guangdong (268.32 million tons). (3) Most of the central and western regions dominated by economically underdeveloped provinces in China bear the carbon transfer of economically developed provinces. (4) When considering the dual carbon transfer of import and export trading and provincial product trading, there are only 5 provinces as Beijing (130.97 million tons), Shanghai (59.20 million tons), Jiangxi (3.36 million tons), Chongqing (11.75 million tons) and Qinghai (0.02 million tons) that have the positive difference among the 30 mainland provinces,, realize carbon transfer out, and the remaining 26 provinces are carbon transfer in. [ABSTRACT FROM AUTHOR]

To investigate impact factors and source area of secondary organic aerosols in the Yangtze River Delta (YRD) region, a world-class urban agglomeration in China, long-term measurements of organic carbon (OC) and elementary carbon (EC) in particulate matter of less than 2.5 µ m (PM 2.5) with hourly time resolution were conducted at a regional site in Shanghai from 2016 to 2020. Based on the 5-year measurements, the interannual, monthly, seasonal, and diurnal variations in OC and EC, as well as OC subtypes, i.e. secondary OC (SOC) and primary OC (POC), apportioned by the novel statistical model of the minimum R2 method, and the formation pathways of SOC, are presented. By examining the relationship between SOC and temperature, as well as relative humidity (RH), we show that SOC formation is greatly enhanced at high temperatures (> 30 ∘ C), whereas it correlates inversely with RH. In particular, we show that the photochemical formation of SOC is the major formation pathway even in winter, when solar radiation was supposedly less intense than in summer, which is different from that in the north China plain, where aqueous phase chemistry is found to be an important SOC formation pathway. Moreover, increased SOC concentrations are also found to be associated with high wind speed (> 5 m s -1) in winter, which is increased by 29.1 % (2.62 µ g m -3) when compared with that during lower winds, suggesting regional sources of SOC in winter. By analysing the potential source regions using the concentration-weighted trajectory (CWT), the geographic regions of SOC are found to be mainly associated with transport from outside Shanghai (SOC > 3.5 µ g m -3) including central and southern Anhui, Zhejiang, and Fujian. The results from this study provide critical information about the long-term trend of carbonaceous aerosol, in particular, SOC, in one of the largest megacities in the world, and are helpful for developing pollution control measures from a long-term planning perspective. [ABSTRACT FROM AUTHOR]

Improving our understanding of the patterns and drivers of regional carbon budgets is critical to mitigating climate change regionally and globally. Different from previous research, our study attempts to reveal the comprehensive impact of climate change and human activities factors on the carbon budget. Based on the Carnegie–Ames–Stanford approach (CASA) model, the IPCC inventory method, the ordinary least squares (OLS) regression model, the Geodetector model, and the geographically weighted regression (GWR) method, we investigated the spatiotemporal patterns of the carbon budget in the Yangtze River Delta (YRD) region from 2000 to 2015 and analyzed the effects of climate change and human activities on the carbon budget. The results showed that the carbon budget in the YRD region changed from 271.33 million tons in 2000 to −1193.76 million tons in 2015. During this period, the changes in the carbon budget per unit area in the four provinces all showed a decreasing trend, among which Shanghai decreased the most, followed by Jiangsu, Zhejiang and Anhui. In terms of spatial pattern, the carbon budget of the YRD region has a "core-edge" structural feature. The closer it is to Shanghai, the core area, the more severe the carbon budget deficit; the farther from it, the greater the carbon budget surplus. Overall, we found that human activities have a greater impact on the carbon budget than climate change. The top three drivers were, in order, changes in population density, GDP per capita, and unused land, with q values of 0.3317, 0.1202, and 0.0998, respectively. Locally, the impact of the drivers on the carbon budget shows obvious spatial heterogeneity. In particular, the population density was negatively correlated with carbon budget changes in the entire study area, and the coefficients of GDP per capita and unused land were negative in most counties. Based on the results, we put forward suggestions for restricting population flow among the core area and the peripheral area, promoting industrial innovation in the core area and ecological protection in the peripheral area, as well as implementing three-dimensional space development in the core area and controlling the expansion of construction land in the peripheral area. Our study can provide a scientific basis for low-carbon development in the YRD region. The methodology and findings of this study can provide references for similar studies in other urbanized regions around the world. [ABSTRACT FROM AUTHOR]

China's economic growth has entered "new normal," and the task of reducing carbon emissions has become more onerous. Hence, this study aimed to explore whether China's carbon emissions trading pilot policy stimulated corporate green innovation capabilities. The data pertained to the green patent data of the listed companies in Shanghai and Shenzhen stock exchanges during 2008-2018. Using a difference-in-difference-in-differences (DDD) method, the study took advantage of the variations across regions, across enterprises, and across years and obtained several novel findings. First, the pilot carbon emissions trading policy significantly stimulated the green innovation capabilities of emission control companies in the pilot areas compared with enterprises in nonpilot areas and the nonemission control list. Second, the effect of the policy on the improvement in corporate green innovation capabilities might be driven by the improvement in corporate input factor allocation efficiency and the additional benefits that could be obtained from the carbon trading market. Third, the positive effect of the policy on the green innovation capabilities of state-owned enterprises was more significant. Therefore, the establishment and promotion of a unified national carbon emissions trading market and supporting mechanisms should be accelerated to achieve the balance of stable economic growth and carbon emission task. [ABSTRACT FROM AUTHOR]

• The first to examine the herding behavior in China's eight carbon markets. • Beijing, Shanghai, Chongqing, Fujian four carbon markets exist herding behavior. • There is no herding behavior in China's overall carbon market. • Herding intensity is positively correlated with carbon market volatility. This paper introduces a modified cross-sectional absolute deviation approach to measure the existence of herding behavior and herding asymmetry, and uses the runs test to measure the herding intensity in China's carbon markets. It is the first study to examine the existence of herding behavior in China's carbon markets. The empirical results show that no herding behavior exists in China's overall carbon market. As for herding asymmetry, no herding behavior exists in both up and down markets and in the markets with high and low trading volumes, as well as in the markets with high volatility. Among China's eight carbon markets, herding behavior exists in Beijing, Shanghai, Chongqing, and Fujian, in which Chongqing has the highest herding intensity, while Guangdong, Hubei, Shenzhen, and Tianjin have no herding behavior. The herding behavior is prone to the occurrence when carbon price continuously rises or falls with high volatility. When the carbon market is highly volatile with a large transaction volume, the herding intensity is high. Herding intensity is positively correlated with market volatility. Finally, we propose policy implications to mitigate herding behavior. [ABSTRACT FROM AUTHOR]