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A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

The Yangtze River Major Conservation (YRMC) Action has been strategically crafted to catalyze industrial evolution within the Yangtze River basin, aligning with China's goal of achieving carbon neutrality by 2060, particularly from the corporate perspective. Despite its significance, the potential impact of this initiative on corporate environmental performance (CEP) remains understudied. To address this gap, we utilize panel data from Chinese firms listed on the Shanghai and Shenzhen A-shares (2012–2020) and employ the difference-in-differences (DID) method to investigate the causal relationship between the YRMC Action and the CEP. The main findings are threefold: (i) Firms located within a 100 km radius of the Yangtze River's main stream have exhibited an average increase of 0.1497 units in CEP due to the implementation of the YRMC Action, which are consistently validated across robustness tests; (ii) Mechanisms driving this enhancement include incentivizing firms to undertake green technological R&D programs, securing governmental green R&D subsidies, and credit incentives; and (iii) Heterogeneity analyses show that firms closer to the main stream of the Yangtze River, particularly those upstream and led by senior executives with awareness of green initiatives, have contributed to more pronounced improvements in CEP. This paper highlights policy implications for optimizing the incentive effects of the YRMC Action and strengthening the environmental regulatory governance to ensure enduring enhancements in CEP. [ABSTRACT FROM AUTHOR]

Understanding how municipal solid waste (MSW) classification influences the physiochemical properties and incineration emission characteristics of MSW is imperative for sustainable waste management. To investigate how long-term changes in the composition and physical properties of MSW resulting from MSW classification affect the emissions of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and multiple flue gas pollutants, a five-year experiment (2018–2022) was conducted in a full-scale MSW incinerator in Shenzhen City, China. After MSW classification, the food waste in MSW was substantially reduced while the paper, textile, and wood components were notably increased. MSW classification also remarkably decreased the water and chlorine contents of MSW while concomitantly increasing the wet-based calorific value, sulfur content, and nitrogen content. Consequently, the PCDD/Fs emissions declined from 106.7 ng I-TEQ/t MSW in 2018 to 26.0 ng I-TEQ/t MSW in 2022. Moreover, a process-tracing experiment demonstrated noticeable decreases in PCDD/F synthesis in the post-combustion area, with mass-reduction efficiencies of 23.6 %–95.4 %. In a correlation analysis, the PCDD/F emissions were significantly positively correlated with food waste, water content, and chlorine and negatively correlated with the wet-based calorific value and nitrogen content. Furthermore, MSW classification reduced the emissions of conventional flue gas pollutants (SO 2 , CO, and HCl) and multiple heavy metals (total sum of Sb, As, Pb, Cr, Co, Cu, Mn, and Ni). In summary, the long-term study demonstrated that MSW classification can feasibly separate incombustible waste, improve combustion quality, and reduce pollutant emissions from sources. These findings offer practical guidance for promoting MSW classification. [Display omitted] • MSW classification remarkably changes composition and physical properties of MSW. • MSW classification mitigates PCDD/F emission from 106.7 to 26.0 ng I-TEQ/t MSW. • MSW classification effectively reduces the emission of various flue gas pollutants. • The long-term experiment demonstrated the feasibility of MSW classification. [ABSTRACT FROM AUTHOR]