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Controlling grain temperatures is the main approach employed in large commercial warehouses to maintain quality and prevent infestation by insects and fungi. Large commercial warehouses in northeast China have been utilizing inner cyclic cooling aeration (ICCA), a newly developed method for managing summertime temperatures, haphazardly and without tight standards. We utilized a homemade detector to track the temperature, humidity, and CO2 concentrations of maize that was held for four years under ICCA treatment. The monitoring occurred every week, specifically from July 1st to September 30th. In addition, we conducted weekly tests to determine the moisture content (MC) and free fatty acids (FFA) of the stored maize samples. Without the use of ICCA, the average temperature of the stored maize above a depth of 3.5 m ranged from 20°C to 28°C, and the relative humidity (RH) in the headspace was typically higher than 75%. However, when ICCA was applied, the average temperature could be maintained at 22°C, and the RH was below 65% for most of the time. In the first year, the MC of the stored maize at a depth of 1.0 m experienced a loss of approximately 0.5%. However, for one year, there was no significant loss in the MC of the maize stored at the same depth. Furthermore, the FFA in the stored maize at a depth of 1 m increased by 1–5 mg KOH/100 g. The use of ICCA effectively controls the temperature and RH in the stored maize, reducing the risk of insect and fungal reproduction. [ABSTRACT FROM AUTHOR]

High transition costs remain a major barrier to deeply decarbonizing sectors such as transport in many developing countries. Choice of mode and complementary policies are critical to shaping the costs of climate mitigation in the transport sector. This paper investigates the potentials offered by combined technological and behavioural changes stimulated by strategic infrastructure deployment that can facilitate decarbonization of transport in China. Our study is carried out using IMACLIM-R, a state-of-the-art integrated assessment model, which includes a detailed representation of transport dynamics by incorporating the behavioural determinants of mobility in a standard transport modelling framework. More specifically, this behavioural representation considers (i) the spatial organization of residential dwellings and industrial production, (ii) modal shift induced by transport infrastructure deployment and (iii) the intensity of freight transport production and distribution processes. It is found that supplementing carbon pricing with behavioural measures and a decoupling of economic activity from mobility needs can efficiently promote a modal shift towards low-carbon transport modes and reallocate the sectoral distribution of mitigation efforts. This in turn would significantly reduce the macro-economic impacts of deeply decarbonizing Chinese transport activities over the next decades. Complementary policies should focus on infrastructure, fiscal incentives, land-use, building regulations and other policies affecting the ways urban activities are distributed within the city's boundary, along with industrial policies and other regulations that affect where firms choose to locate. Key policy insights To decarbonize China's transport sector an integrated approach is needed due to strong inertia and distributional effects. Complementary policies have to focus on transport infrastructure, fiscal incentives, land-use and building regulations, as well as spatial organization. Transport-related climate policies interconnect with the ways in which urban activities are distributed within the city's boundary, but also with industrial policies and other regulations that affect where firms choose to locate. A suite of policy instruments is required, including larger institutional incentives and greater financial leverage supporting innovations in the transport sector. [ABSTRACT FROM AUTHOR]

Research on the emergency facility location-allocation problem has important theoretical and practical significance for responses to large-scale emergencies. During the actual planning of disaster supply warehouses (DSWs), reconstruction and expansion projects usually need to fully exploit different levels of designated facilities to achieve resource integration. This article presents a hierarchical supplement location-allocation (HSLA) optimization model for DSWs in both the preparedness and response phases to address the above concerns. The model minimizes the total number and cost of DSWs for the facility location problem and the total distance for delivering supplies at all levels for the supply allocation problem. A modified particle swarm optimization algorithm is applied to derive solutions to the HSLA model. The model is demonstrated for the Beijing–Tianjin–Hebei (BTH) region in China. The results show that twelve of the 180 counties, which account for 6.03% of the total area of the BTH region are not covered by county-, prefectural- and provincial-level DSWs. In addition, three sets of optimization schemes for the location-allocation problem for DSWs at the three levels and one final mixed optimal scheme (one county-level DSW, two prefectural-level DSWs, and one provincial-level DSW should be newly built or upgraded in four counties) are suggested based on the supplemental optimization site selection. This model is believed to remain more consistent with the knowledge of DSW planners and policy makers by incorporating the multi-level coverage and supplemental site selection concept into the location-allocation problem. The method can be easily duplicated to expand or build new emergency facilities at different levels in addition to DSWs. The results provide a scientific reference for DSW location-allocation planning in the BTH region. [ABSTRACT FROM AUTHOR]

Resolving inventory and location problems is the most important and fundamental tactical decision in the initial stages of developing a distribution network design. This is especially true for a logistics system dealing with perishable food. To minimise both the total inventory transportation cost of fresh agri-products and also waste in the supply chain, the evolution of the food supply chain structure is analysed and an inventory location allocation model is presented describing the real-life problem. The key issues are to describe food demand distribution and to determine the balance of transportation and inventory cost. To improve the quality of the solution, a local search is embedded in the two-grade delayed particle swarm optimisation. Experimental results demonstrate the algorithm can effectively resolve conflict between different costs and improve development decisions regarding Beijing's perishable food distribution centres. [ABSTRACT FROM AUTHOR]