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Marine industry continues to grow rapidly in China, while the contribution of the total factor productivity (TFP) to its gross output still remains very limited. Facing this issue, it is urgent to promote TFP by innovation, and Chinese government provides persistent subsidies to stimulate the innovation of relative enterprises. Taking listed companies of marine industry in Shanghai and Shenzhen Stock Markets (2007–2019) as samples, this study performed empirical tests by multiple regressions to check the effects of such subsidies on the TFP of Chinese marine industry. It was observed that, as a whole, government subsidies present positive effects on the TFP of associated companies, and subsidies beforehand yield higher promotion than subsidies afterwards. The subsidies work mainly via easing financing constraints and encouraging R&D investment of relative firms. Our results are highlighted by revealing the differential effects of government subsidies on the TFP of Chinese marine industry and their functional mechanism. It implies that, besides government subsidies, the optimization of financial market may also be helpful in promoting TFP by innovation. [ABSTRACT FROM AUTHOR]

Urban areas are the key drivers of energy usage and the associated carbon emissions. Due to the rapid economic development and urbanization process, China has faced increasing pressure on energy supply and carbon emission issues. It is urgent for China to change from the current coal-based high carbon intensity power supply structure. In this context, China has planned to utilize an undeveloped area in Shenzhen to become a low-carbon development demonstration project. The purpose of this paper is to propose the best combination of technology for electricity generation, involving a mix of renewable energy resources, to satisfy electrical needs and, at the same time, minimize carbon emissions from power generation in the low carbon demonstration area. The optimal renewable energy system configuration was determined based on the Hybrid Optimization Model for Electric Renewable model. The simulation results show that costs of energy for the three scenarios considered are $0.187/kW h, $0.128/kW h, and $0.150/kW h, respectively; annual carbon emissions for the three scenarios are 96 702 tons, 89 836 tons, and 78 065 tons, respectively. Given the economic advantage, scenario 2 is a suitable choice for the first phase of the international low carbon city (ILCC). With reinforced carbon emissions controls and a rising carbon allowance price, scenario 3 can be adopted in the second and third phase of the ILCC construction. [ABSTRACT FROM AUTHOR]

There is an increasing need to establish wastewater treatment facilities for improving water quality of heavily polluted rivers in rapidly urbanizing areas. Optimization models are widely used to determine the pollutant removal levels at different pollution sources, with the aim of minimizing the wastewater treatment cost and satisfying certain water quality criteria. Water quality is usually evaluated in a prescribed space or time point. Thus it cannot reflect the overall status of a tidal river that has significant spatio-temporal variations. In this paper, new spatio-temporal water quality criteria, which consider the water quality violation against specified water quality standards during the whole simulation period of time for the entire river simulated, are proposed and then applied to optimization of a wastewater treatment system in Shenzhen, China. The results indicate that the optimization based on the proposed criteria facilitates an improved performance of wastewater treatment systems in terms of water quality along the whole river during a long time period, instead of just in a prescribed space or time point. Furthermore, use of the new criteria derives a better Pareto front of cost and water quality in terms of convergence and coverage compared with the conventional criteria and thus they are recommended as the water quality criteria to measure spatial and temporal variation in a tidal river for wastewater treatment system planning. [ABSTRACT FROM AUTHOR]