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In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China’s renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t.

This paper analyses the impact of postponing global mitigation action on abatement costs and energy systems changes in China and India. It compares energy-system changes and mitigation costs from a global and two national energy-system models under two global emission pathways with medium likelihood of meeting the 2 °C target: a least-cost pathway and a pathway that postpones ambitious mitigation action, starting from the Copenhagen Accord pledges. Both pathways have similar 2010–2050 cumulative greenhouse gas emissions. The analysis shows that postponing mitigation action increases the lock-in in less energy efficient technologies and results in much higher cumulative mitigation costs. The models agree that carbon capture and storage (CCS) and nuclear energy are important mitigation technologies, while the shares of biofuels and other renewables vary largely over the models. Differences between India and China with respect to the timing of emission reductions and the choice of mitigation measures relate to differences in projections of rapid economic change, capital stock turnover and technological development. Furthermore, depending on the way it is implemented, climate policy could increase indoor air pollution, but it is likely to provide synergies for energy security. These relations should be taken into account when designing national climate policies.

Study Region Mekong River Basin and surrounding areas. Study Focus: This study investigated the impacts of climate change on future meteorological and hydrological droughts in the Mekong River Basin and its surrounding areas. Our work is based on the output of five global climate models (GCMs) and simulations using the geomorphology-based hydrological model (GBHM) for the historical (1975–2004), near future (2010–2039), middle future (2040–2069), and far future (2070–2099) periods. The meteorological droughts in the study area were measured using SPI and SPEI, while the hydrological droughts were measured using SSI. New Hydrological Insights for the Region: The results suggest that droughts will generally reduce in the future over most of the study area, but will be more unevenly distributed with an eastward migration as compared to the historical period. Both meteorological and hydrological droughts will intensify in the near future, but will then reduce in intensity. Meteorological droughts will increase in the northeastern areas in the near future, followed by migration towards the south. Hydrological droughts showed similar aggravation followed by reduction, with upstream areas showing greater variability. In the general context of drought alleviation, southwestern China and the Mekong River estuary may suffer from a continuously increasing drought intensity in the future. This finding is based on 100-year extreme drought events.

When extreme weather events occur, people often turn to social media platforms to share information, opinions and experiences. One of the topics commonly discussed is the role climate change may or may not have played in influencing an event. Here, we examine Twitter posts that mentioned climate change in the context of three high-magnitude extreme weather events – Hurricane Irene, Hurricane Sandy and Snowstorm Jonas – in order to assess how the framing of the topic and the attention paid to it can vary between events. We also examine the role that contextual factors can play in shaping climate change coverage on the platform. We find that criticism of climate change denial dominated during Irene, while political and ideological struggle frames dominated during Sandy. Discourse during Jonas was, in contrast, more divided between posts about the scientific links between climate change and the events, and posts contesting climate science in general. The focus on political and ideological struggle frames during Sandy reflects the event's occurrence at a time when the Occupy movement was active and the 2012 US Presidential Election was nearing. These factors, we suggest, could also contribute to climate change being a more prominent discussion point during Sandy than during Irene or Jonas. The Jonas frames, meanwhile, hint at lesser public understanding of how climate change may influence cold weather events when compared with tropical storms. Overall, our findings demonstrate how event characteristics and short-term socio-political context can play a critical role in determining the lenses through which climate change is viewed.

Abstract To meet the needs of fuel security and combat the growing concerns of CO2 emissions, the automotive industry is seeking solutions through biofuels. Traditionally, when supplying biofuel blends to the combustion chamber, the blend is mixed externally prior to its injection in one location. This location occurs either before the cylinder (port-fuel injection, PFI), or directly into the cylinder (direct-injection, DI). However, the use of dual-injection allows the in-cylinder blending of two fuels at any blend ratio, when combining the two locations (PFI and DI). This injection strategy offers increased flexibility as the blend ratio can be changed instantaneously according to engine speed and load demand and fuel availability. Previous work by the authors has reported the improved combustion performance of dual-injection with 25% blends (in gasoline) of a new biofuel candidate: 2,5-dimethylfuran (DMF). This current investigation extends the analysis to include the gaseous emissions of various DMF blends (25%, 50% and 75%) from 3.5 bar to 8.5 bar IMEP and the particulate matter (PM) emissions of similar fraction ethanol blends at a selected condition of 5.5 bar IMEP. Compared to DI, dual-injection offers reduced CO and CO2 emissions and comparable HC emissions. The mean PM diameter is decreased and the accumulation mode particles are negligible compared to DI. However, the implication of the higher combustion pressures is an increase in NOx due to reduced charge-cooling.

Abstract Traditional life cycle assessment (LCA) methods are used to conduct building environmental impact assessment (EIA) with little consideration of influential factors that vary in time and of variation in occupancy behaviors. Because the life cycle of a building is quite long, such details have significant influence on the accuracy of evaluation results. To fill in this gap and extend the LCA system, this paper developed a dynamic assessment framework based on LCA principles after reviewing the research progress of DLCA (dynamic LCA). The new framework identified four dynamic building properties (i.e., technological progress, variation in occupancy behavior, dynamic characteristic factors, and dynamic weighting factors) and considered them in corresponding assessment steps to realize real-time EIA. In addition, residential occupancy profiles were described at personal level, family level, and social level; and three potential quantification methods were introduced to explore the relationship between occupancy profiles and household energy consumption. The DLCA framework expands the connotation of the LCA system from a dynamic perspective, making it possible to present time-varying EIs of buildings over their long life cycle and guide occupancy behavior in time. This framework has the potential to be base for developing a useful tool for conducting forecast evaluation and promoting sustainability.

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

Abstract Comparing with conventional power plants, wind power generations are highly dependent on the natural environment. Especially under extreme wind condition, individual wind turbine might automatically disconnect from the grid once the wind speed is near or over the cut-out speed. This results in the output power reduction of a wind farm, which raises the security concern due to the imbalance between generation and demand, particularly for power systems with high wind power penetration. Different from the continuous wind power fluctuation under normal weather condition, the power output of individual wind turbine drops suddenly under extreme weather. Thus, the power reduction of individual wind turbine requires to be simulated by a higher resolution tool in order to illustrate the sudden power drop under an extreme condition. Also, the wind turbines are dispersed geographically within a wind farm and a region, which has a large impact on the magnitude of power reduction of a wind farm and the region under extreme weather. Therefore under an extreme weather condition, the simulation of wind power reduction requires a model with higher resolution as well as considering “geography dispersion”, which cannot be satisfied properly by most of current existing models. This paper hence proposes to use a model in frequency domain to assess the power reduction of wind farms under extremely high wind speed condition. Though this model is originally designed for application under normal wind condition, the originality of this paper is the study of the applicability of this model under an extreme wind condition, because this model provides a second-by-second simulation resolution and uses the coherence matrix in frequency domain to describe the coherences of power reduction among wind turbines. Also, for a regional wind farm cluster, an additional advantage of this model is to provide a reasonable estimate of wind power reduction under extreme wind condition without using extensive history data of the whole region. This model is verified by both qualitative and quantitative analysis and then some statistics-based tools are further developed to assess the reserve requirement due to wind power reduction under extreme wind condition. A case study of Zhangjiakou (ZJK) power region shows the effectiveness of the proposed assessment methodology in an extended wind power system region, and then this model is demonstrated to be valuable for both power system planning and operation with high wind penetration under extreme wind condition.