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Fuzhou, China

Minjiang University is a public university located in Minhou County, Fuzhou, Fujian, China. The university is a comprehensive, multi-disciplinary university accredited by the Chinese Education Ministry the right to confer undergraduate degrees to students in China. In 2003, it has been granted the honor title ”Fujian Civilization University.” Wikipedia.

Lin B.,Minjiang University | Liu X.,Xiamen University
Energy | Year: 2012

Promoting technological development to improve energy efficiency has been the primary method of energy conservation in China. However, the existence of energy rebound effect will impose negative effects on the final result of energy saving. In this article, we adopt the Malmquist index approach to estimate the contribution of technological progress to economic growth. We also employ Logarithmic mean weight Divisia index (LMDI) to measure the impact of technological improvement on the energy intensity. Based on the above, we set up a model to estimate the technology-based energy rebound effect in China. The results show that, over 1981-2009, energy rebound effect amounts averagely to 53.2%, implying that China cannot simply rely on technical means to reduce energy consumption and emission. Economic instruments should also be applied as supplements to ensure results of energy conservation and emission reduction. © 2012.

Ouyang X.,East China Normal University | Lin B.,Minjiang University | Lin B.,Xiamen University
Renewable and Sustainable Energy Reviews | Year: 2014

Subsidies to fossil-fuel consumption have made Chinas energy system fragile and unsustainable. It is necessary for China to reform fossil-fuel subsidies and reflect the resource cost and environmental cost in energy prices. Considering the life-cycle external costs, this paper estimates the scale of fossil-fuel subsidy and the true cost of renewable energy in 2010 and evaluates impacts of increasing renewable energy subsidies and phasing out fossil fuel subsidies on macro-economy and energy system in China based on scenario analysis. Simulation results show that the negative impacts on economic growth can be reduced from 4.460% to 0.432%, if only 10% of fossil fuel subsidies were removed. Increasing subsidies for renewable energy has positive impacts on macroeconomic variables. Although the economic benefits per unit of subsidies for renewable energy are lower than those for fossil fuels by 0.06-0.19 CNY, the revenue gap can be narrowed by shifting more subsidies from fossil fuels to renewables. Increasing subsidies for renewable energy helps optimize Chinas energy system in three ways: the first is making energy consumption structure cleaner; the second is improving energy efficiency; and the third is addressing the problem of imbalanced distribution and consumption of energy. © 2014 Elsevier Ltd.

Lin B.,Minjiang University | Lin B.,Xiamen University | Moubarak M.,Xiamen University
Applied Energy | Year: 2014

We estimated the reduction potential of carbon dioxide emissions in the Chinese textile industry by forecasting the carbon intensity (CO2 emissions/industrial value added) in different scenarios. The Johansen co-integration technique was employed in order to establish the long term equilibrium equation. Three scenarios (Business As Usual (BAU), medium and optimum) were designed to estimate the future trend of carbon intensity in the Chinese textile industry. The results showed that energy price, energy substitution, labor productivity and technology have significant impact on the carbon intensity. Estimated to 1.49t CO2/10,000 yuan in 2010, we found that for the BAU scenario, the carbon intensity will decrease to 0.5 and 0.29t CO2/10,000 yuan by 2020 and 2025 respectively. For the medium scenario, carbon intensity will decline to 0.12t CO2/10,000 yuan. Yet by the optimum scenario, the intensity is expected to considerably decrease to 0.05t CO2/10,000 yuan by 2025. Using the BAU forecast as baseline, the quantity of reduction potential in carbon dioxide emissions is estimated to be 44.8milliontons CO2 by 2025. Considering this huge potential, we provided policy suggestions to reduce the level of CO2 emissions in the Chinese textile industry. © 2013 Elsevier Ltd.

Lin B.,Minjiang University | Lin B.,Xiamen University | Ouyang X.,Xiamen University
Energy Conversion and Management | Year: 2014

China's energy demand has shown characteristics of rigid growth in the current urbanization stage. This paper applied the panel data model and the cointegration model to examine the determinants of energy demand in China, and then forecasts China's energy demand based on the scenario analysis. Results demonstrate an inverted U-shaped relationship between energy demand and economic growth in the long term. In business as usual scenario, China's energy consumption will reach 6493.07 million tons of coal equivalent in 2030. The conclusions can be drawn on the basis of the comparison of characteristics between the US and China. First, energy demand has rigid growth characteristics in the rapid urbanization stage. Second, coal-dominated energy structure of China will lead to the severe problems of CO2 emissions. Third, rapid economic growth requires that energy prices should not rise substantially, so that energy conservation will be the major strategy for China's low-carbon transition. Major policy implications are: first, urbanization can be used as an opportunity for low-carbon development; second, energy price reform is crucial for China's energy sustainability. © 2013 Elsevier Ltd. All rights reserved.

Lin B.,Minjiang University | Lin B.,Xiamen University | Du K.,Xiamen University
Energy Economics | Year: 2013

This paper analyzes the energy efficiency of China's 30 administrative regions during the period from 1997 to 2010. Most existing studies ignored the variation of production technologies among regions in China. Taking this factor into account, we introduce a parametric metafrontier approach based on the Shephard energy distance function. For further analysis, regions in China are divided into three groups using cluster analysis. We find that the regions in group 1 (mainly the regions in the east area of China) not only have the highest energy efficiency score, but also take the lead in terms of technology gap ratio. Meanwhile, due to their backward technology levels, the average energy efficiency score of the regions in group 3 (mainly the regions in the west area of China) is particularly low. Moreover, the pooled estimation, which ignores the technology gap among the groups, tends to underestimate the energy efficiency. © 2013 Elsevier B.V.

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