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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]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

The transport sector accounts for a large proportion of energy demand and environmental emissions and is growing rapidly. The city is the hub of modern human life and the urban transport sector is developing fast. Specifically, in the Internet era, the development of public transport has brought new challenges and opportunities to urban passenger transport. Thus, research on the energy demand and environmental emissions of the urban transport sector is significant and timely. This paper takes Beijing’s public transport as an example and applies the Long-range Energy Alternatives Planning (LEAP) model to analyze the energy demand and the main greenhouse gas (GHG) emissions under different scenarios during the period 2016–2030. The results show that: 1) if the government fails to guide the development of passenger transport appropriately, its energy demand and GHG emissions will rise rapidly and under the baseline scenario, the energy demand and emissions in 2030 are about 1.8 times those oft 2015; 2) the scenarios of developing public transport and transport sharing have a significant effect of energy saving and emission reduction and in 2030 the energy demand and GHG emissions falls by 25%–30% compared with that of the baseline scenario, so the combination of public transport and transport sharing is a wise choice for the low carbon development of the passenger transport sector in Beijing; 3) scenarios of easing road congestion and new-energy and clean-energy vehicle development have huge potential for energy saving and emission reduction, particularly the internal adjustment of the energy structure in the scenario of developing new-energy and clean-energy vehicles, which can greatly reduce the demand for traditional energy sources and reduce GHG emissions. This paper illustrates the development trends of energy demand and GHG emissions in Beijing’s passenger transport sector under the scenarios of transport sharing development and traditional development in the Internet era, providing a basis for decision making. [ABSTRACT FROM AUTHOR]

A comprehensive GHG inventory provides an overview and the information of sources of emissions, and can serve as a sound scientific basis for mitigation actions. This study was the first initiative to develop a complete and transparent GHG inventory of Beijing, to document the current situation and to help governmental officials to clarify their priorities for reducing GHG emissions. The authors investigated and calculated the emission sources defined by the GPC, and assessed data accuracy. The contents of the detailed inventory were documented and are open for research verification and for methodological references for other cities. The results showed that three emission sources, i.e. CO 2 emissions from fossil fuel consumption, CO 2 emissions from industrial production and CH 4 emissions from landfills, contributed 97% of Beijing's GHGs. Considering the problem of missing data and low data quality, the authors suggested that instead of seeking to compile a complete inventory, Chinese cities should first report their GHG emissions from these sources, while improving the data quality for the long run. The authors anticipate that the findings reported in this paper will help to strengthen city-level GHG inventory research in China and throughout the world. [ABSTRACT FROM AUTHOR]

Sand and gravel aggregate (SGA) transportation accounts for over 30% of the freight volume in China, which is greater than that of any other cargo; low-carbon development can effectively promote the decarbonization of freight transportation. This study presents a four-step evaluation method to assess CO 2 emission reductions in freight transportation; the proposed method helps determine the relationship between the policy, CO 2 emission reductions, demand, and mode market share. Using Beijing as a case study, this paper estimates the potential of reducing CO 2 emissions from SGA transportation under various scenarios. The scenarios are set based on the avoid, shift, and improve strategies; the results of the Beijing case indicate that the CO 2 emission for SGA transportation is 920.86 kt, which is approximately 1% of the total CO 2 emissions in Beijing. The maximum reductions in CO 2 emissions that can be achieved using the avoid, shift, and improve strategies are 0.92 kt (0.1% of total), 560.52 kt (60.9% of total), and 78.59 kt (8.5% of total), respectively. The shift strategy is confirmed to be the most realistic and cost-effective approach for reducing CO 2 emissions from SGA transportation. In addition, the implementation of subsidy policies is discussed. Marginal abatement costs under the subsidy scenarios are higher than 224 CNY/t, which is higher than the price in the Chinese carbon trading market. • We evaluated CO 2 emission of aggregate transportation in Beijing at the OD level. • CO 2 emission reductions of the avoid, shift, and improve strategies were tested. • Shift strategy performed the best in reducing CO 2 emission. • Avoid and improve strategies can reduce CO 2 emission; but the effects were limited. • Subsidizing rail transport reduced more emission than subsidizing electric trucks. [ABSTRACT FROM AUTHOR]

In order to explore the establishment method of high-resolution vehicle emission inventory and analyze the temporal and spatial variation law of vehicle greenhouse gases (GHG) emissions. This paper proposes a bottom-up method based on road network information and the real-time average interval speed of road segments. A traffic speed-flow model is proposed to predict the hourly traffic flow and the localized Motor Vehicle Emission Simulator (MOVES) is used to simulate the CO 2 , N 2 O and CH 4 emission factors. A high temporal (1 h × 1 h) and spatial (1 km × 1 km) resolution GHG emission inventory of motor vehicles in Beijing in 2018 is developed by this means. The actual emissions of CO 2 , N 2 O and CH 4 are 19,864,590, 82 and 511 t, respectively. And the total GHG emission is 19,901,933 tCO 2e combined with global warming potential (GWP). The daily GHG emissions on weekday and weekend are 55206.30 and 52817.64 tCO 2e , respectively. There are three obvious peak emission periods on the weekday, namely, the morning peak (8:00–9:00), the noon peak (11:00–12:00) and the evening peak (18:00–19:00), which contribute 11.76%, 11.84% and 12.92% of the daily emission, respectively. The 1 h × 1 h emission grid shows the spatial distribution characteristics of emissions. The areas within the Fifth Ring Road (973 km2) are only 5.93% of the total area of the city (16,410 km2) but contribute 41.53% of the total vehicle GHG emissions. This study provides detailed data support for implementing vehicle GHG emission mitigation measures. • A 1 km × 1 km vehicle greenhouse gas emission inventory in Beijing was developed. • A bottom-up method based on road network and vehicle travel speed was proposed. • A modified speed-flow model was used to predict the hourly traffic flow on road. • The temporal and spatial variation laws of vehicle GHG emissions were analyzed. • The vehicle CO 2 emission in 2018 estimated in this study was 19,901,933 tCO 2e. [ABSTRACT FROM AUTHOR]