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Environmental pollution liability insurance is becoming increasingly important for China to achieve its emission reduction targets. Insurance pricing is a crucial factor restricting the market share of environment pollution liability insurance, from the perspective of the Black–Scholes pricing model, which in turn has influenced the solvency of insurance companies in China. Firstly, this study analyzes the problems existing in compulsory liability insurance for environmental pollution in China. It proceeds with analyzing the price of compulsory environmental pollution liability insurance using the Black–Scholes pricing model, and derives a high premium insurance rate of 2.44%. Moreover, it performs a multivariate regression analysis using the asset and liability data, taken from the annual report, to identify three key factors affecting the solvency adequacy ratio, namely, capital debt ratio, reflecting the company asset structure; net interest rate on assets, reflecting the asset scale with actual solvency; and claim ratio, reflecting the business quality. Based on the results of regression analysis and robustness test for the China Insurance Clauses (CIC) company, People’s Insurance Company of China (PICC), and Asia-Pacific Property & Casualty Insurance (API) company, it is shown that the effect of total asset, total debt, capital debt ratio, claim ratio, and net interest rate on assets on the solvency adequacy ratio is significant, with respect to the size of the coefficients. Based on the Black–Scholes pricing model found in the previous cycle of liability insurance, and keeping in view the existing problems of environmental pollution liability insurance expenditure, this paper presents suggestions that are conducive to improving the solvency of insurance companies in China.

In the light of The Paris Agreement (COP 21), global leaders reached a consensus to curtail the increase in global temperature up to 2.0 °C ideally 1.5 °C pre-industrial level. Likewise, it has become a pathway to accomplish long-term goals of achieving carbon neutrality. In this regard, this paper aims to explore the role of green process innovation and environmental orientation toward environmental performance for achieving the long-term goal of carbon neutrality. In addition, this paper also discovers a mediating role of green competitive advantage in said context. Using data from a survey among managers of equipment manufacturing sector, the study employed structural equation modeling technique and found that green process innovation, environmental orientation and green competitive advantage significantly influence environmental performance. Subsequently, mediation analysis indicated that green competitive advantage partially mediates the relation from green process innovation and environmental orientation to environmental performance. In light of the carbon neutrality targets, the study highlight that improving environmental performance through green process innovation and environmental orientation can be a way-forward for manufacturing sector to play its role to achieve carbon neutrality. The study concludes with theoretical and practical implications.

In order to take full advantage of distributed generators, an evolution of the classical power system organization and management is also necessary. An aggregator of a residential urban electrical network can be considered by the distribution system operator as a stakeholder, which is able to control a cluster of local generators and loads with technical constraints for the connection with the remaining distribution grid and commercial contracts with outer electrical producers. This paper is focused on the design of the microgrid central energy management system which relies on a day-ahead operational planning and an online adjustment procedure during the operation. A dynamic programming-based algorithm is derived to solve the unit commitment problem with a multiobjective function in order to reduce the economic cost and CO 2 equivalent emissions. The proposed energy management system is implemented into a supervisory control and data acquisition (SCADA) and tested by using a hardware-in-the-loop simulation of the urban network. Economic and environmental gains are evaluated.

The paper proposes a methodology for energy planning of electricity consumption in electric power supply systems of urban electric public transport. Represented is the practical application of the methodology for a definite trolleybus line route in Varna, Bulgaria.

Accurate forecasting is required to measure future national energy performance levels in order to establish clear policies for both monitoring and reducing Nitrous Oxide and other harmful emissions. Using the well-established and accepted measures, we predict the Nitrous Oxide emissions for the year 2030 based on actual data from the years 2000 to 2016 for six countries responsible for 61% of global emissions (China, Indonesia, India, Japan, Russia and the USA). Three advanced mathematical grey predictions models were employed, namely the Even Grey Model (1, 1), the Discrete Grey Model (1, 1) and the Non-homogeneous Discrete Grey Model, which is capable of working with poor or limited data. Results showed that the Non-homogeneous Discrete Grey Model was a better fit and proved more effective in forecasting Nitrous Oxide emissions because it produced the lowest mean absolute percentage error for all countries when compared to the Even Grey Model (1, 1) and the Discrete Grey Model (1, 1). The mean absolute percentage error of the Even Grey Model (1, 1) was 2.4%, that of the Discrete Grey Model (1, 1) was 2.16%, and that of the Non-homogeneous Discrete Grey Model was 1.9%. Furthermore, the results show that China has the highest Nitrous Oxide emissions during the years studied (China 20,578,144, Russia 1,705,110, India 7,806,137, Indonesia 3,405,389, USA 8,891,219 and Japan 780,118). This study also suggests some implications for both academicians and practitioners in respect of reducing Nitrous Oxide emission levels.

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region.

The use of bio-plastic-based packaging as an alternative to conventional plastic packaging is increasing. Among the plethora of different bio-based plastics, the most relevant ones are those that, at the end of their life, can be treated with the organic fraction of municipal solid waste. Even in these cases, their impact on the waste processing and recycling is not always positive. This study aim to assess on a laboratory scale the influence on combined anaerobic digestion and composting industrial processes of a bio-based plastic film, namely cellulose acetate (CA), in pure and modified (additions of additive) forms. CA films were mixed with organic waste and subjected to: (i) anaerobic digestion; (ii) active composting and (iii) two stages of curing composting. Anaerobic digestion and composting were monitored through methane yield and oxygen uptake respectively; additionally, the bio-plastics degree of disintegration was assessed during all the processes. The final disintegration of pure and modified CA was 73.82% and 54.66%, respectively. Anaerobic digestion contributes to the disintegration of the material, while aerobic treatment appears to be nearly ineffective, especially for modified CA. The presence of cellulose acetate during anaerobic digestion of food waste increased the methane yield by about 4.5%. Bioassay confirmed the absence of possible toxic effects on the final compost from the bio-plastic treatment. Although bio-based materials are not the only solution to plastic pollution, the findings confirm the need to upgrade the organic waste treatment plants and the necessity to revise the requirements for the use of compost in agriculture.

Equipment manufacturing industry is one of the major industries of the Chinese economy. Previous researches have revealed that the industry has dilemmas of unreasonable industrial structure and high pollution. Using the data of 30 provinces in 2006-2015 in China, this study calculated a comprehensive pollution indicator when estimating the possible pollution reduction brought by the optimization of industrial structure and then evaluated the reasonable level of capital allocation of provinces and industries by using the methods of nonlinear programming and stochastic frontier method. Under the target of collaborative emission reduction, the results show that the optimized output of China's equipment manufacturing industry could be increased by 5.42%, the energy intensity could be reduced by about 10.4%, and the comprehensive emission intensity could be reduced by about 7.47%. Due to the industry heterogeneity and regional heterogeneity, industrial capacity should be transferred between industries and regions. Since the capital investment in the equipment manufacturing industry is significantly mismatched between industries and regions, the capital allocation of provincial industries in China needs to be adjusted properly. This study provides theoretically and practically reference for collaborative pollution reduction, industry restructure, spatial layout and capital investment, which contributes to achieving the stereoscopic optimization of equipment manufacturing industry.