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In the process of development, global economies are prioritizing environmental protection and firms are also recognizing the importance of minimizing environmental impact during production along with maximization of profits through green investments. It is vivid that green investments are vital for environmental preservation. So this paper contributes to literature by investigating the role of internal and external factors affecting the decision making of Chinese firms regarding adoption of green investments and impact of green investments on environmental, social, and economic performance of firms. The data is collected from directors/senior managers of the firms. We received 463 valid responses from listed companies with Shenzhen, Beijing, and Shanghai Stock Exchange. The "structural equation modeling" with "maximum likelihood estimation" is employed for empirical analysis. The empirical findings reveal that adaptation to climate change and its mitigation is the most important driver of green investment. Moreover, green investment positively contributes to enhancing the social, economic, and environmental performances of Chinese firms. Based on the findings of the study, green investment should be adopted as a corporate strategy by firms for profit maximization, competitive advantage, and improvement in social well-being without compromising the environment. Policy makers can promote green investment by offering policy instruments such as tax incentives, guaranteed credits, grants, and investor education. Training courses may be offered to raise environmental awareness among firms and the general public. [ABSTRACT FROM AUTHOR]

Clarifying the pattern of the urban greenhouse gas (GHG) budget and its influencing factors is the basis of promoting urban low-carbon development. This paper takes Beijing and Shenzhen—the capital city and the most rapidly developing city in China, respectively—as case studies, comprehensively accounts their GHG budgets from 2005 to 2020, and investigates and compares the factors affecting their GHG budgets. The total GHG emissions in Beijing were lowest in 2005 (160.3 TgCO2 equivalents) and peaked at 227.7 TgCO2 equivalents in 2019, and then decreased to 209.1 TgCO2 equivalents in 2020. Meanwhile, the total GHG emissions in Shenzhen gradually increased from 36.0 TgCO2 equivalents in 2005 to 121.4 TgCO2 equivalents in 2019, and then decreased to 119.1 TgCO2 equivalents in 2020. The energy activity sector was the greatest contributor to GHG emissions in this period, accounting for 82.5% and 76.0% of the total GHG emissions in Beijing and Shenzhen, respectively. The carbon sink of the ecosystems of these two cities could absorb only small parts of their emissions, and the neutralization rates of sinks ranged from 1.7% to 2.3% in Beijing and from 0.3% to 1.5% in Shenzhen. The enhancement of population, economic product, and consumption increased the greenhouse gas emissions in both cities. A 1% increase in population size, per capita GD (gross domestic product), and residential consumption level would increase total GHG emissions by 0.181%, 0.019%, and 0.030% in Beijing, respectively. The corresponding increases in Shenzhen would be 0.180%, 0.243%, and 0.172%, respectively. The household size had opposite effects on the two cities, i.e., a 1% increase in household size would increase GHG emissions by 0.487% in Shenzhen but reduce them by 2.083% in Beijing. Each 1% increase in secondary industry and energy intensity would reduce GHG emissions by 0.553% and 0.110% in Shenzhen, respectively, which are more significant reductions than those in Beijing. [ABSTRACT FROM AUTHOR]

The underlying complexity of urban space can be manifested by its fractal forms and scaling statistics. This paper examines these characteristics at the intra-urban scale through the lens of clustered street junctions (including road ends) in two Chinese metropolitan areas: Beijing and Shenzhen. We derived the cluster sets with Euclidean distance thresholds starting at 100 meters (m) and ending at 1000 m, and outlined each cluster using a concave-hull method to maintain their original irregular shapes. Within each delimited cluster, we examined four urban attributes: gross domestic product, number of street nodes, polygon area, and population. Our analysis revealed that power law distribution applied to almost every cluster set in terms of the four attributes, but varied from one attribute to another or from city to city, represented primarily by fluctuated power law exponents and ht-index values whose profiles along with the cluster growth can effectively characterize the urban structure. Additionally, we computed the spectrum of intra-urban scaling exponents with cluster size increments, contributing new insights into the allometric relationships between urban configuration and function. [ABSTRACT FROM AUTHOR]

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 addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

Abstract: This paper is the second in a series of three papers aimed at understanding the emissions of vehicles in China by conducting on-board emission measurements. This paper focuses on light-duty gasoline vehicles. In this study, we measured 57 light-duty gasoline vehicles (LDGVs) in three Chinese mega-cites (Beijing, Guangzhou, and Shenzhen), covering Euro 0 through Euro IV technologies, and generated CO, HC, and NOx emission factors and deterioration rates for each vehicle technology. The results show that the vehicle emission standards have played a significant role in reducing vehicle emission levels in China. The vehicle emission factors are reduced by 47–81%, 53–64%, 46–71%, and 78–82% for each phase from Euro I to Euro IV. Euro 0 vehicles have a considerably high emission level, which is hundreds of times larger than that of Euro IV vehicles. Three old taxis and four other Euro I and Euro II LDGVs are also identified as super emitters with equivalent emission levels to Euro 0 vehicles. Of the measured fleet, 23% super emitters were estimated to contribute 50–80% to total emissions. Besides vehicle emission standards, measures for restricting super emitters are equally important to reduce vehicle emissions. This study is intended to improve the understanding of the vehicle emission levels in China, but some key issues such as emission deterioration rates are yet to be addressed with the presence of a sufficient amount of vehicle emission measurements. [Copyright &y& Elsevier]

Previous studies have shown that climatic and anthropogenic factors influence the variations in the stable isotope signature of tree materials in an urban ecosystem. However, the responses of urban trees to various anthropogenic and precipitation interventions are unclear. To fill the research gap, the isotopic composition (δ15N and δ18O) and total nitrogen content of the soil-plant system of urban forests were measured, focusing on typical urban tree species (Populus tomentosa and Ficus virens). The research was done under various land uses in China's Beijing and Shenzhen megacities (with different humidity conditions). The linear mixed-effect model revealed that the δ15N values in the leaves of roadside Ficus virens were significantly higher than those in urban park sites, away from the road (δ15N difference = 7.2‰). In contrast, Populus tomentosa leaves exhibited a non-significant difference (δ15N difference = −1.8‰). Further regression analysis revealed that the variations in δ15N in tree leaves could be attributed to the proximity to highways, suggesting the influence of vehicle exhaust on δ15N values in roadside trees. The linear mixed-effect model also revealed δ18O values significantly higher in Populus tomentosa leaves than in Ficus virens leaves. To assess the influence of humidity conditions on δ18O values in the leaves, the ratio of the atmospheric and intercellular vapor pressures (ea/ei) of tree canopies was estimated. The result revealed that the ea/ei of Ficus virens was significantly higher than for Populus tomentosa, ascribed to the variation in the humidity conditions of the two megacities. These results highlight that urban trees can potentially serve as bioindicators for atmospheric pollution and humidity. Our findings highlight the ecophysiological responses of urban forests related to N and O as they vary according to traffic exhaust and relative humidity. Consequently, they are potentially valuable indicators of urban atmospheric contamination, forming a nature-based solution for citizen welfare improvement. [ABSTRACT FROM AUTHOR]

With the advantage of integrating distributed energy, storage and DC load with high efficiency, the DC distribution network recently attracted wide attention in the field of nearly zero energy buildings. Considering the large number of buildings and the enormous energy-saving potential, the distribution form and the revenue distribution are key factors affecting energy efficiency and operational economy. Aiming at the characteristics of AC and DC distribution topologies in nearly zero energy buildings, an energy efficiency evaluation method is proposed based on time-sequential power flow simulation, where the piecewise linear efficiency function of converters and dynamic line power loss model are established. Based on a DC distribution demonstration project, an AC power distribution system with the same boundary conditions is designed. The results show that the efficiency of DC distribution topology is about 6% higher than the AC system, while the efficiency advantage in office, hotel, commercial, educational and residential buildings in Beijing, Shenzhen and Shanghai varies from 0.6–4.96%, which shows a high correlation with the proportion of a high-power load, with Pearson correlation coefficient of 0.794. With the increase of DC load ratio and distributed power supply access capacity, the efficiency of the system will be improved accordingly. On the basis of this, a method to equalize DC distribution income in the nearly zero energy buildings power market is proposed. In addition, 15 typical scenarios of different cities were selected to evaluate efficiency and influencing factors. Finally, assessing the multi-stakeholder impact of DC retrofitting to balance the revenue generated by the DC distribution model suggests that transport costs should be raised by 8.2–13.1% to balance revenue with the equipment prices decreased by about 0.1 yuan/W. [ABSTRACT FROM AUTHOR]

Carbon prices are low and fluctuate greatly since Chinese carbon emissions trading scheme pilots were operated. This paper discusses the price drivers in the pilots, using structural breaks test and autoregressive distributed lag model. The results indicate that oversupply of allowances, low auction prices and use of China certified emission reductions will cause remarkable decline on carbon prices; the expansion of carbon market and centralized trading will rise carbon prices. Oil prices are positively correlated with carbon prices, and coal prices are negatively related with carbon prices. Carbon-intensive product prices also affect carbon prices. Chemical prices are positively linked with carbon prices in Shenzhen, while negatively related with those in Beijing and Shanghai, for differences in sectoral coverage. Nonferrous prices have positive impacts on carbon prices in Beijing but negative impacts on those in Shenzhen, for differences in industrial structure. Financial markets and weather have limited impacts on carbon prices. Our results provide policy implications for the development of pilots and the national carbon market. [ABSTRACT FROM AUTHOR]