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This paper investigates the neglected potential of carbon dioxide (CO2) emissions reduction from highway maintenance projects. Conventional assessment (CA) on fuel combustion of off-road equipment during maintenance, and a comprehensive life cycle assessment (LCA) on the materials production, fuel production, traffic delay and maintenance equipment were conducted for four typical maintenance schemes in a real highway project in China, through Microsoft spreadsheet models, VISSIM and CMEM model. The focus of the case study was to understand the magnitude and source of the gap of CO2 emissions assessment results between LCA and CA. In this study, we also checked scenarios that apply a few mitigation options from several industrial sectors in four typical maintenance schemes to investigate how significant potential for decarbonization of the highway maintenance might be neglected without using LCA. Results suggest that there exists an obvious gap between the total CO2 emissions per lane per kilometer of CA and that of LCA, and the gap is even up to 22 times of the CA results in this specific case study. Traffic delay caused by the lane closure has the largest contribution to the gap, while the shares of materials production and fuel production match. The scenarios analysis of mitigation strategies associated with the transport sector and other industrial sectors indicates that the LCA model could effectively identify the dominant inputs and CO2 emissions outputs of individual maintenance process in the complicated transport system, while CA may neglect or underestimate the potential for adopting some mitigation strategies, especially for mitigation options from other industrial sectors.

The fact that global warming will bring impact on immigration, agriculture and also generate human conflicts is becoming a focus in climate change topic and the forecasting of carbon dioxide emission has been attracting much attention. In this paper, we proposed an improved Gaussian processes regression method for carbon dioxide emission forecasting based on a modified PSO algorithm which can efficiently optimize the hyper parameters of covariance function in the Gaussian processes regression. Also we tested our improved PSO-GPR method with the total carbon dioxide emissions data of U.S., China and Japan in 1980–2012, and compared the prediction precision of our method with original GPR and BP Neural Networks by the data of U.S., China and Japan. The performance of our improved Gaussian processes regression method enhanced the prediction accuracy of original GPR method and is superior to other traditional forecasting method like BP Neural Networks. Furthermore, we applied PSO-GPR method in generating a prediction total carbon dioxide emissions for 2013 to 2020 and found out that China's total carbon dioxide emission will still increasing but finally at a decreasing rate and U.S. and Japan will have a good control on their amount of carbon emission in the near future. Finally, policy implications about carbon dioxide emission reduction were proposed.

Under the policy of energy saving and emission reduction, technological innovation has become the key power-driver to improve energy utilization efficiency. By using the panel data of 21 industrial segments between 2000 and 2015, this paper investigates the driving effect of technological innovation on energy-utilization efficiency in strategic emerging industries and analyzes the critical factors influencing the driving effect of technological innovation by the stochastic frontier analysis method. The empirical result shows that technological innovation between 2000 and 2015 has had a significant positive driving effect on the energy utilization efficiency of strategic emerging industries and exhibits a development trend of year-by-year steady-state growth on the whole. The greater the value of the development expenditure of new green products and the investment in environmental pollution treatment, the greater the technological efficiency; the greater the value of the transformation expenditure of energy-saving technology, the greater the technological inefficiency. Finally, the paper proposes the countermeasures and suggestions for the driving effect of technological innovation on energy utilization efficiency in strategic emerging industries. An empirical study is made to analyze the driving effect of technological innovation on energy utilization efficiency, the positive and negative factors of technological innovation are forgotten. Strategic emerging industries can select optimal innovation behavior to improve quality and efficiency of energy utilization based on the results.

During technology adoption, assessment of its sustainable character is a difficult task due to limited insight and the various dimensions of sustainability, as well as its complicated application at the operational level. A proper decision-making framework and method may be conducive to bridging generic and macro-level sustainability with local-site and task-oriented technology selection, improving the application of sustainable technology. For this reason, a framework is explored to highlight the sustainable nature of relevant components during an operational decision-making process within the supply chain. With an eye to the triple bottom of sustainability, the product chain, value-added activities of supply chain and stakeholders are analyzed and embedded into the technology selection method. The dynamic relationships among these components (product chain, value-added activities and stakeholders) as carriers of technology are also discussed so as to investigate their sustainable features. Furthermore, to figure out the whole technology decision-making logic map, a modified strategic balanced scorecard is established and applied to evaluate technology candidates in terms of their features of sustainability. Next, a computing method is designed to produce a sustainable technology choice. The multilateral mechanisms among the three groups, as well as within each group, during the technology selection process are identified and elaborated completely. The framework of analysis and method presented in the paper add insight to sustainability theoretically, and guide its application in technology adoption. Managerial implications, as well as limitations of this work, are concluded at the end of this paper.

Current semi-coking wastewater treatment process has low performance of pollutants removal and consumes a large amount of high-grade steams. It far exceeds the semi-coke plants’ utilities supply capacity and easily lead to the breakdown of whole treatment system. This paper identified the bottleneck of current process as followed: low removal efficiency of oil and dust; poor extraction conditions caused by the high pH value and the fouling and plugging of internals; high-grade steam requirement and investment cost. To improve the deficiencies of current process, a novel hybrid process is proposed based on the benchmark treatment capacity of 240 m3/d. The effects of acidification and extraction conditions on ζ potential and extraction efficiency have been analyzed following industrial semi-coking wastewater experiments. The results show that the concentrations of oil and dust are reduced less than 50 mg/L when pH value is lower than 4.8. The removal efficiencies of COD and total phenols after extracted are increasing as pH value decreases. The conceptual design is accomplished based on the experimental and simulative analysis. The concentrations of sour gas, total phenols, ammonia and COD decreases to 10 mg/L, 270 mg/L, 50 mg/L and 3050 mg/L, respectively. It also eliminates 2.5 MPag and 1.0 MPag steam demand at the expense of 24.1 kW more electricity uses and 70.84 kg/h reagents consumption, as suggested in simulation results. The estimated investment cost saving are 0.77 million US$, and the net cost reduction is 0.63 US$/t water.