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Many calculation methods have been developed to forecast carbon emissions (CE). These methods can be classified into two categories: time series prediction and variables regression. However, time series prediction cannot fully use all CE‐related information and variables regression can only predict CE by knowing other variables in advance. To solve these problems, KLS is proposed in this paper which is a fusion of Kalman filter (KF), long short‐term memory (LSTM), and support vector machine (SVM). LSTM is used to predict CE as a time series, and 10 of 14 variables are selected by ridge regression (RR) to regress CE by SVM. Finally, KF is used to integrate the results of LSTM and SVM based on their variances. Overall, statistical filtering and deep learning algorithms are used in this paper, which achieved the fusion of time series prediction and variables regression. To confirm the effectiveness of KLS, we trained KLS model by CE from 1965 to 2005 and tested the model by the data from 2006 to 2014. The results showed KLS was more accurate than other four existing methods. Finally, we predicted the CE of China in next decade. [ABSTRACT FROM AUTHOR]

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure. [ABSTRACT FROM AUTHOR]

As the most effective way to manage the surge in municipal solid waste (MSW) and global energy issues, waste‐to‐energy (WTE) incineration plants have been actively promoted by the Chinese government, and the public‐private partnership (PPP) procurement model is widely used to improve management efficiency. However, risk events have occurred frequently in this industry due to a lack of practice experience and to poor risk management. To investigate the potential risk factors in PPP WTE incineration projects in China, this paper aims to identify critical risk factors by assessing their severity and occurrence probability. Opinions of industry experts were collected via an empirical questionnaire survey. The respondents were invited to rate a total of 18 risk factors that impact the success of PPP WTE projects as gleaned from previous research. The overall results ranked risk factors based on risk impact and showed that public opposition, government decision making, defective legal and regulatory systems, environmental pollution, lack of supporting infrastructure, and government credit are the top six risk factors affecting the sustainable development of PPP WTE incineration projects. In addition, the causes and consequences of each critical risk factor are comprehensively analyzed. Lastly, practical and managerial implications are analyzed in terms of their management of these critical risks. The originality of this research lies in thoroughly identifying potential implicit risk factors according to the current social background using an empirical questionnaire survey based on case study findings. The results of this study are expected to improve risk management and facilitate the sustainable development of the PPP WTE incineration industry in China. [ABSTRACT FROM AUTHOR]

China is transforming the growth model and speeding up industrial structural improvement and upgrading now and in the coming years, which means the slowdown of economic growth and more serious environmental costs in economic and social development would be the "New Normal." As the world's largest energy consumer and carbon‐emitting country, China's emissions reduction commitments are crucial to global greenhouse gas emissions mitigation, so there is a profusion of research focused on China's economy‐environment nexus. Among which, the dynamic relationship of carbon emission and economic growth and its evolution combined the national, regional, provincial data have not been discussed thoroughly. This paper analyses the elasticity of economic performance of carbon emissions based on 30 Chinese provincial data from 2000 to 2016; furthermore, a panel vector autoregressive model is constructed to discuss the interactions of variables within environmental economic system. It is concluded that China is experiencing a turning point from weak decoupling to strong decoupling in recent years, and its economic performance of carbon emissions has been gradually improved with larger disparities among provinces though. Regression result confirms the role of the energy mix on carbon emissions, and it also indicates that the impact of most factors influencing that have been discussed in previous studies may not be direct, prominent, and for long term. [ABSTRACT FROM AUTHOR]