Trade-Offs between Direct Emission Reduction and Intersectoral Additional Emissions: Evidence from the Electrification Transition in China's Transport Sector.

Electrifying the transport sector is crucial for reducing CO emissions and achieving Paris Agreement targets. This largely depends on rapid decarbonization in power plants; however, we often overlook the trade-offs between reduced transportation emissions and additional energy-supply sector emissions induced by electrification. Here, we developed a framework for China's transport sector, including analyzing driving factors of historical CO emissions, collecting energy-related parameters of numerous vehicles based on the field- investigation, and assessing the energy-environment impacts of electrification policies with national heterogeneity.

Additional strains acting as key microbes promoted composting process.

Microbial inoculums (MIs) were the widely used biofortification strategy in composting. However, lack of efficient MIs and unclear strengthening mechanisms might impaired the efficiency of MIs. Here, three experimental group (precise strains, commercial MI, Inoculum HJ) and one control group (untreated) were investigated to close these gaps.

An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China.

Water and energy consumptions in the residential sector are highly correlated. A better understanding of the correlation would help save both water and energy, for example, through technological innovations, management and policies. Recently, there is an increasing need for a higher spatiotemporal resolution in the analysis and modelling of water-energy demand, as the results would be more useful for policy analysis and infrastructure planning in both water and energy systems.

Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China.

Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation.