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In this paper, we use a stochastic dynamic programming model to evaluate the impacts of uncertainties on the abatement planning process. By involving the endogenous emission path, we differentiate two types of uncertainties during the planning process, which come from the volatility of the abatement cost and the ambiguity of emission rate. Results suggest that the considered uncertainties influence the decision-making process in several aspects by shaping the abatement path as well as emission path. (1) The impacts vary with the expected value and the level of variance of the uncertainty effects. Uncertainties caused by abatement costs from 0.02 to 0.06 and emission factors from 0.01 to 0.03 increase the total abatement costs around 7% and 5% respectively. (2) Both of these uncertainties can generate precautionary abatement in short-term. Especially during the early stages, the abatement task will be increased by 1% around in each period due to the uncertainties. However such an action will be diminishing as the duration elapses. (3) Both of these uncertainties influence the long-term abatement performances, however, with different forms and mechanisms. With small volatility, the emission rate changes the priority sequence of abatement actions more substantially in the short-term than the emission rate does. (4) The combined uncertainties can behave in a compound way to improve the uncertainty performance in the model. The difference between the emission peaks of conservative and extreme cases is significant with the gap being about 5 million metric ton. These results have potentially important policy implications and can provide a rationale for abatement actions. • We use a stochastic dynamic programming model to evaluate the impacts of uncertainty on the abatement strategy. • We differentiate two types of uncertainties during the planning process. • For both types of the uncertainty precautionary abatement is required at early stage to hedge the future risk. • The level of synergy effect and the hedging effect between the uncertainties depends on how they are combined. [ABSTRACT FROM AUTHOR]

Thermal management of fuel cell vehicles is posing a constant challenge due to the growing installed power of the fuel cell stack and consequent heat-overloaded situations of the radiator. Based on the "time-dependent" idea that considers the fluctuated heat generation and the intermittent heat peak during the journey, a novel thermal management system that employs a heat-peak regulator is proposed in the present work. The heat-peak regulator is a thermal accumulator filled with phase-change material that separately exchanges heat with the fuel cell coolant and the air conditioner refrigerant. It temporarily receives the excess heat that cannot be released out by the radiator when the heat peak occurs; later on, when the heat peak is gone, the heat is transferred to the refrigerant to remove it from the condenser. The system simulations based on the developed thermal models demonstrate that, the thermal runaway of the fuel cell stack can be eliminated or effectively weakened by this novel thermal management system, relying on the amount of the phase-change material filling the heat-peak regulator. In the present study, the 135 s and 250 s thermal runaway durations can be shortened to 0 s and 105 s, respectively, in the standardized New European Driving Cycle and World-wide Harmonized Light Duty Test Cycle under 38 °C summer weather, and the maximum temperature for the latter can be reduced from 89 °C to 83 °C. This work can make a significant contribution towards the solution of thermal management problems of the fuel cell vehicles. • Proposing the "time-dependent" thermal management methodology. • The thermal management system contains a heat-peak regulator. • The heat-overload problem for the radiator of fuel cell stack is properly handled. • The proposed system is analyzed based on a self-developed thermal model. [ABSTRACT FROM AUTHOR]

We specify a general model of the dynamic abatement technology adoption process and show how the accumulated experience among them can alter the adoption timing. Learning-by-doing (LBD) effect is considered to describe the reduction of the costly price to the adoption. Moreover, spillovers effect which reflects how accumulated experience can be shared among different technologies, is integrated into the learning process. To ensure the specification convincing, some reasonable assumptions are considered such as one abatement technology must conduct its own experience in order to realize the spillover. Besides, to investigate how the abatement process is influenced by the technology allocation under the spillovers effect, an endogenous emission path is considered to necessitate a meaningful optimization problem. Finally, apart from obtaining some basic properties of the model, we implement an empirical study to verify the effectiveness of the model. Data from the iron industry in China is used to specify the process, which can highlight the spillovers effect among different technologies from a sector angle. In the results, we find that intrinsic mechanism in the model results in the interplay among abatement coefficient, spillover factor and the growing rate of the emission etc. Collaborative relationships between variables are discussed as well. [ABSTRACT FROM AUTHOR]

As CO 2 emissions are quantified by allowances and traded in markets, wise trading strategies will bring emitting companies higher profits or lower costs. Based on the big data of Community Independent Transaction Log (CITL), this article hereby presents a micro study on the emitting companies' efforts in increasing profits and saving costs during the allowances trading in the first two phases of the European Union Emission Trading Scheme (EU ETS). The efforts are measured by an after-action factor of trading performance, which is built on a series of behaviour and monetary variables. By comparison, demanders of the emitting companies are more inclined to reach a higher trading performance, while that inclination is heterogeneous among the suppliers. In addition, emitting companies with lower emission levels had a better trading performance. With a higher proportion of low-emitting companies, the manufacturing sector had a better trading performance than the energy sector. The effect of the trading requirement on trading performance are investigated via a quantile regression mode. Results suggest that: (1) the selling requirement of suppliers has a positive effect on their trading performance, while the effect becomes weaker when the selling requirement increases; (2) the buying requirement has a positive effect on the demanders' trading performance only when the requirement is high, and the effect becomes stronger as the requirement increases; and (3) when the buying requirement is at a lower level, demanders' trading performance becomes worse as the requirement grows. The conclusion is that the emission level, industrial sector and trading requirement do have influences on the trading performance of emitting companies in emission trading. [ABSTRACT FROM AUTHOR]

How to minimize the emission of air pollutants while making decisions about mitigating climate change has become a hot research topic. Considering the homologous homology of the two types of emissions, it is completely feasible to achieve synergistic control for different pollutants through a rational allocation of technology investment. To attain this goal, in this paper, I propose a pollutant synergistic control model. Carbon and sulfur emissions are chose as the major control object. Under a weak condition, I obtained analytical expressions for two types of cleaner technology investment paths, technology capital accumulation paths, and emission reduction paths. Further, characteristics of the respective paths, and the conditions for meeting the corresponding theoretical inflection points or peaks, were analyzed, which lays a foundation for applications of the theory. In particular, this study draws the necessary and sufficient conditions for an internal solution to the choice of cleaner technology, which is a powerful and practical evaluation index. The theoretical predictions were verified with numerical simulation. The investment of the two types of cleaner technologies peaked during the 10th period, and the capital accumulation peaked during 15th period after a slight delay. The actual measured investment and subsidy feedback gains were around the 0.04 and 0.02 levels. In addition, different combinations of targets for sulfur and carbon reduction also simulated scenarios in which the model had different internal solutions. The relevant theoretical exploration work in this paper has deep implications for policy decisions in the real world. • We propose a cleaner technology investment model to control greenhouse gas and air pollution simultaneously. • We differentiate two types of cleaner technology between the production process and end-tail process. • We obtained analytical expressions for investment paths, capital accumulation paths and abatement paths. • Necessary and sufficient conditions for an internal solution to the choice of cleaning technology is established. • Closed-loop strategy of the investment and optimal subsidy are given as well. [ABSTRACT FROM AUTHOR]

While prior studies of consumer preferences on electric vehicles (EVs) utilized stated preference (SP) or revealed preference (RP) data, this study examined consumer preferences for EVs through their online behaviour. Using big data and text mining technologies to explore large quantities of Chinese consumers' online behaviour pertaining to EV selection, comparison, purchase inquiry, and comments, we found that EV prices, car classification and powertrain types were the most important factors influencing consumer response. In addition to general interests, we also noted application specific interests. For instance, we observed a preference for small battery electric vehicle (BEV) models with fast-charging batteries, which are well-suited to work commutes and daily life. By contrast, when examining plug-in hybrid electric vehicles (PHEVs), we noticed that consumers expressed a preference for sport utility vehicle (SUV) models. Finally, we found that in addition to price and technical specifications, EV aesthetics play a significant role in consumer choice. These conclusions are valuable for government agencies and EV manufacturers looking to promote EVs and address environmental pollution by appealing to consume interests. [ABSTRACT FROM AUTHOR]

Changed land use patterns are the most direct manifestation of ecological engineering implemented by humans, being among the driving factors for changes in ecosystem services (ESs). However, different ecological engineering governance measures can lead to different changes in land use, resulting in corresponding changes to ESs and ecosystem service value (ESV), yet these changes is the content that needs to be clarified. In this study, four kinds of ESs, namely water yield, soil conservation, carbon storage and water purification, were quantified in part region of the Beijing-Tianjin Sandstorm Source Control Project (BTSSCP). In addition, eight scenarios were simulated according to the land use change caused by engineering management measures, and the ESs and ESV ensuing under these differing scenarios quantified accordingly. The results show that the main land use types in the study area from 2000 to 2020 were cropland and forestland, while during this period land use change mainly consisted of the expansion of grassland (+466.79 km2) and loss of cropland (−2095.27 km2). For different engineering management measures, protective governance will play a greater role in improving ESs than restoration governance. Protecting the existing governance outcomes from degrading can maintain a water yield of 36.85 × 108 m3, soil conservation of 73.72 × 106 t, carbon storage of 89.01 × 106 t, the purified water nitrogen content of 1.21 × 106 t, with the total ESV maintained at 54.237 billion yuan. Moreover, the high-investment counties clearly engage in ecological restoration that provides high ESV. Yet, there are still some counties with high project investment where the gains in ESV are low, such as Luanping county and Pingquan county. Therefore, it should be reasonably distributed based on the existing governance results in the next round of project investment allocation. In the past 20 years, engineering governance measures have significantly improved the ESs in the region. Nevertheless, looking ahead, it remains necessary to strengthen the emphasis on protecting governance outcomes. [Display omitted] • Scenario simulations were run under governance measures for ecological engineering. • Ecosystem service value (ESV) under differing governance measures were simulated. • ESV is higher when ecological protection rather than restoration measures are used. • Project investment vis-à-vis ESV under the different scenarios were analyzed. [ABSTRACT FROM AUTHOR]

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning. • A third-party damage comprehensive disaster bearing capacity framework for evaluating natural gas pipeline. • Using clustering analysis to eliminate the influence of subjective evaluation. • Evaluation of disaster bearing capacity based on probabilistic neural network. • PSO-PNN optimization model effectively improves the accuracy of evaluation. [ABSTRACT FROM AUTHOR]

Urban spatial structure has important impact on environmental pollutant emissions, but few studies have investigated the possibility of a bidirectional influence mechanism between the two. This study used panel data of 108 prefecture-level cities in the Yangtze River Economic Belt (China) from 2009 to 2018. The objective was to test empirically the dynamic emission reduction effect of polycentric spatial structure on environmental pollution using the STIRPAT model as a framework to decipher the bidirectional causality effect. System generalized method of moments and the Sobel Test were applied to construct a panel intermediary effect model to verify the mechanism of urban polycentric development mode on urban haze pollution in China. Results showed that the urban polycentric structure in the Yangtze River Economic Belt has produced substantial pollution reduction effects, and that there are heterogenous regional, size, and functional differences in the impact of urban spatial structure of the Yangtze River Economic Belt on the environment. Moreover, the results of the intermediary effect test showed that market integration and industrial structure optimization are important ways via which to improve environmental pollutant emissions using the polycentric spatial development model. On this basis, suggestions were proposed for optimizing urban spatial structure to promote high-quality development of the Yangtze River Economic Belt. [Display omitted] • Urban polycentric spatial structure has a dynamic abatement effect on environmental pollution. • There are heterogeneous regional, size, and functional differences in the influence mechanism. • Market integration and industrial structure optimization play an intermediary role. • Two intermediary variables can more effectively release the positive externality of polycentric spatial structure. • Cities should strengthen polycentric spatial structure to reduce PM 2.5 pollution. [ABSTRACT FROM AUTHOR]