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Mitigating greenhouse gas emissions is a significant global challenge, and precise prediction of carbon emissions holds the utmost importance for China to attain its dual carbon goal. To overcome the limitations of existing studies using a single forecasting method, based on quadratic exponential smoothing, multiple linear regression, and Gaussian process regression models, this paper constructs a carbon emission combination forecasting model with the generalized induced ordered weighted average (GIOWA) operator and analyzes carbon emission reduction performance. Empirical testing utilizing China's carbon emission data from 1980 to 2020 reveals the following findings: (1) The GIOWA combination forecasting model significantly enhances the accuracy of carbon emission forecasts, with an average accuracy exceeding 99.5% over the sample period, surpassing various single forecasting methods. (2) The carbon emission reduction target can be achieved under three different scenarios of low, average, and high GDP growth rates. Specifically, under the low growth rate scenario, China can achieve a 60% reduction in carbon intensity by 2025 and a 65% reduction by 2030. This study offers valuable decision support for the development of effective carbon reduction policies. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO 2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO 2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US. • Integrating engine characteristics with the MOVES model can improve model performance. • Integrating machine learning with MOVES performs well in prediction of vehicle CO 2 and NO emission. • The proposed emission model provides a possibility for calculation rather than costly an experiment in countries outside the USA. [ABSTRACT FROM AUTHOR]

The issue of climate change has become an increasing concern for the international community. At the same time, climate change and, particularly, global warming affects the amount of energy required in China and the structure of this demand, thereby influencing the amount of CO 2 (carbon dioxide) emissions produced. To investigate the possible influence of climate change on China's CO 2 emissions, this paper utilizes the degree-day method to calculate the heating degree days (HDD) and cooling degree days (CDD) in 29 provinces in China from 1995 to 2011. A series of appropriate econometric models are subsequently estimated to measure the impact of climate change on CO 2 emissions per capita. To enhance the explanatory power of the estimation results, several indicators, such as income per capita, industry structure, urbanization rate and population density, are incorporated in the estimation equations as control variables. The estimation results indicate that the feedback effect of climate change on China's carbon emissions is statistically significant but not large in magnitude. During the sample period between 1995 and 2011, approximately 1.687% of China's increased total CO 2 emissions could be attributed to climate change. Moreover, the impacts of climate change on different regions of China are notably different. For example, the influence of HDD on CO 2 emissions per capita are most significant in the middle and southern regions, while the impacts of CDD are most significant in the eastern and northern regions. However, there is no obvious evidence that climate change has affected CO 2 emissions in the western region. [ABSTRACT FROM AUTHOR]

Understanding the causes of growing carbon transfers between developing countries is likely to be the first step in their mitigation. With this in mind, the current paper uses multi-regional input-output analysis to investigate CO 2 emissions and value-added embodied in China-Brazil trade. We propose the use of value-added exports, rather than gross exports, to more accurately estimate the economic benefits of trade. We find enormous increases in both CO 2 emissions and value-added embodied in exports for China and Brazil between 2000 and 2014. During this period, China's position as a net CO 2 emissions and net value-added exporter vis-à-vis Brazil deepens. Major sources of increasing embodied CO 2 exports for China were the basic materials and electricity industries and for Brazil were the basic materials, agriculture and mining industries. Estimates of net carbon intensities reveal advantages for China in electronics and for Brazil in agriculture and mining. Structural decomposition analysis shows that the changing consumption in China and Brazil as well as the changing structure of intermediate exports from China to Brazil were important sources of increasing embodied CO 2 emissions. This latter finding was possible due to the method proposed in this paper of decomposing the production structure into the domestic, export and import production structures. In line with the key findings, various implications and emission abatement policies are discussed. [ABSTRACT FROM AUTHOR]

Small and medium span bridges account for 86% of the bridges in China, and maintenance is necessary to ensure their safety and serviceability. The infrastructure sector causes substantial CO 2 emissions, and it is one of the most polluting sectors. Fly ash (FA) is often used in bridges to reduce CO 2 emissions. Moreover, the lack of temporal information is an important limitation of the life cycle CO 2 assessment of bridges. Taking the upper structure of small and medium span bridges as the research object, this paper proposes a Monte Carlo simulation-based life cycle multi-objective dynamic maintenance strategy optimization model to analyze the life-cycle carbon emissions, costs, and life cycle reliability of different preventive maintenance (PM) and essential maintenance (EM) methods of FA reinforced concrete (RC) beams. Considering the continuous effect of carbon emissions and the net present value of the cost, the time effect of carbon emissions and costs of maintenance and reinforcement at different times is analyzed. Through 50,000 simulations of the case beam, the parameter characteristics of different optimal reinforcement schemes and the relationship between the reliability, life cycle CO 2 emissions, and costs are studied. When introducing the discount rate, the essential maintenance with higher carbon emissions and costs has a marginal effect, i.e., the longer that future carbon emissions and costs occur, the smaller the net present value. The proposed method is demonstrated to be effective at supporting decision making for bridge managers. It would be helpful for identifying the effectiveness of PM and EM, selecting optimal maintenance time interval, and providing a rational estimate of the CO 2 emissions and costs of bridges. • A Multi-objective dynamic maintenance strategy optimization model is proposed. • The time effects of carbon emissions and costs are analyzed. • The parameter characteristics of the optimal maintenance of a T-beam are found. [ABSTRACT FROM AUTHOR]

The development of China's highway infrastructure has led to a rapid increase in carbon dioxide (CO 2) emissions while facilitating social and economic development. This paper introduces highway mileage and CO 2 emissions into the traditional production density model, using agglomeration as a mediating variable, to theoretically discuss the path through which highway infrastructure influences CO 2 emissions. We then use the spatial Durbin model (SDM) to verify the explanatory power of the expanded production density model based on panel data of China's 278 cities from 2003 to 2016. Empirical results indicate that (1) there is an inverted U-shaped relationship between highway infrastructure and CO 2 emissions in both local and peripheral cities; (2) highway infrastructure affects local CO 2 emissions through the agglomeration pathway but impacts CO 2 emissions in peripheral cities mainly through the spatial spillover effect of highway infrastructure; and (3) the U-shaped impact of highway infrastructure on CO 2 emissions in western China is diametrically opposed to the results for the east, the central region and the total sample. Therefore, China needs to fully consider the environmental effects of agglomeration to formulate a global emission reduction strategy when building highway infrastructure. Moreover, strategies based on the different stages of highway infrastructure development should be enacted and should be part of a spatially synergistic joint emission reduction mechanism while considering the characteristics of regional development. • The study extends the production density model and verifies the explanatory power. • The spatial correlation between variables is tested by the spatial Durbin model. • Regional heterogeneity is discussed, and the reasons for this are analysed. • The results are corrected using GS2SLS with slope as an instrumental variable. • Related policy recommendations are made for low carbon development in China. [ABSTRACT FROM AUTHOR]

An integrated approach is proposed in this paper to incorporate the group heterogeneity into the framework of natural disposability and managerial disposability, which allows evaluating the CO 2 emission performance under different mitigation strategies (scale-oriented strategy and innovation-oriented strategy). It is able to deal with the intertemporal incomparability when adopting a global technology. In the empirical study, we categorized China's 27 manufacturing sectors into five groups and evaluated their emission performance under different mitigation strategies during 2003–2016. According to the empirical results, we found that there is still much potential (about 70%–80%) for improving the CO 2 emission performance considering the entire manufacturing industry. From the perspective of different groups, the global technologies/innovators group shows the highest emission efficiency, while the energy-/resource-intensive commodities' group performed the worst. Besides, different sectors showed significant technology heterogeneity. Finally, the desirable CO 2 mitigation strategies varied with the different groups. For the labor-intensive tradables' group, we found that the desirable strategy is to decrease the input of energy and other production factors, i.e. scale-oriented strategy. For the regional processing group, the innovation-oriented strategy is more efficient. • Modified global meta-frontier non-radial DDF approach is proposed. • Group heterogeneity is incorporated in natural/managerial disposability framework. • CO 2 emissions performance for China's manufacturing sectors is measured. • Global technologies/innovators group performs best, while energy-/resource-intensive commodities group performs worst. • Desirable mitigation strategies varies with respect to technology heterogeneity of different groups. [ABSTRACT FROM AUTHOR]

In China's low-carbon development progress, industrial sectors play a crucial role in reducing CO 2 emissions and maintaining economic growth targets. Because the Chinese government is promoting industrial structure transformation for "high-quality economic development," the dominant CO 2 emitters have changed in recent years. It is important to identify the trend evolution of industrial CO 2 emitters and implement targeted measures and policies for CO 2 emission control at the sector level. In this paper, a hybrid input–output model is adopted to assess the situation and trend of sectoral CO 2 emissions from 2002 to 2017 in China. Demand-side drivers of sectorial embodied CO 2 emissions are discussed. Based on the embodied CO 2 emission decomposition of 27 sectors, a CO 2 emission evaluation index is defined for CO 2 emission ranking and key sectors for embodied CO 2 emissions are identified. For the key CO 2 emitters, we provide targeted policy suggestions for CO 2 emission mitigation from the perspective of trend evolution and index ranking. The results indicate the following. (1) 2015 is a turning point for China's CO 2 emission mitigation progress, because the previously increasing embodied CO 2 begins to decrease from 2015 to 2017. (2) Although the Construction sector stimulates a large amount of CO 2 emissions at present, the major driving forces of CO 2 emissions have gradually changed from infrastructure construction to the increasing demand of consumer goods, service industry, and export of technology-intensive products. (3) The CO 2 emission intensity of the Electric Power and Heat Supply sector has increased, and promotion of renewable energy consumption is urgently needed. (4) From the demand-driven aspect, tertiary industry has greater CO 2 emission potential than heavy industry. • A turning point of embodied carbon dioxide of China appears in 2015. • Most sectors have reached the peak of embodied carbon dioxide by 2015. • Construction, Other Service Industry and Manufacture of Machinery are the top three embodied carbon dioxide emitters. • Electric Power and Heat Supply sector should be concerned for high embodied carbon dioxide intensity. • Sectors with great carbon dioxide emission potential change towards tertiary and high-tech industries. [ABSTRACT FROM AUTHOR]